library(rugarch)
library(rmgarch)
library("openxlsx")
library("writexl")
library(readxl)
library(tidyverse)
library(ggExtra)
library(ggplot2)
library("EnvStats")
library(zoo)
library(ConnectednessApproach)
library(VineCopula)
library(knitr)
library(dgof)
library(goftest)
library(nortest)DATA <- read_xlsx("C://Users//84896//Desktop//DATA//CN3-COPULA.xlsx", sheet="DATA")
SP500 <- DATA$y
VNI <- DATA$x1
MERVAL <- DATA$x2
CROBEX <- DATA$x3
MASI <- DATA$x4
MSM30 <- DATA$x5cor(cbind(SP500, VNI, MERVAL, CROBEX, MASI, MSM30), method="pearson")## SP500 VNI MERVAL CROBEX MASI MSM30
## SP500 1.0000000 0.2383986 0.3847411 0.3401699 0.1761739 0.1782534
## VNI 0.2383986 1.0000000 0.1299821 0.2093248 0.1066150 0.2090848
## MERVAL 0.3847411 0.1299821 1.0000000 0.2246492 0.1516347 0.1155129
## CROBEX 0.3401699 0.2093248 0.2246492 1.0000000 0.2382602 0.2149749
## MASI 0.1761739 0.1066150 0.1516347 0.2382602 1.0000000 0.1713024
## MSM30 0.1782534 0.2090848 0.1155129 0.2149749 0.1713024 1.0000000print("Mỹ")## [1] "Mỹ"autoarfima(SP500,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ar1 ar2 sigma
## 0.06240079 -0.07504879 -0.05225278 1.29792151print("Việt Nam")## [1] "Việt Nam"autoarfima(VNI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu sigma
## 0.05539759 1.48893168print("Argentina")## [1] "Argentina"autoarfima(MERVAL,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ma1 sigma
## 0.32222944 0.05246606 3.33261251print("Croatia")## [1] "Croatia"autoarfima(CROBEX,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ma1 ma2 sigma
## 0.75399736 -0.71736643 0.06903937 0.86816552print("Morocco")## [1] "Morocco"autoarfima(MASI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ma1 ma2 sigma
## -0.9900989 1.1840929 0.1977743 0.8721873print("Oman")## [1] "Oman"autoarfima(MSM30,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ar2 ma1 sigma
## -0.6428461 0.2488296 0.8440933 0.7926635print("Mỹ")## [1] "Mỹ"sp500.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
sp500.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
sp500.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
sp500.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
sp500.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
sp500.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
sp500.garch11n <-ugarchfit(data=SP500, spec= sp500.g11n ) #1
sp500.garch11s <-ugarchfit(data=SP500, spec= sp500.g11s )
sp500.garch11ss <-ugarchfit(data=SP500, spec= sp500.g11ss )
sp500.garch11g <-ugarchfit(data=SP500, spec= sp500.g11g )
sp500.garch11sg <-ugarchfit(data=SP500, spec= sp500.g11sg ) #5
sp500.garch12n <-ugarchfit(data=SP500, spec= sp500.g12n )
sp500.garch12s <-ugarchfit(data=SP500, spec= sp500.g12s )
sp500.garch12ss <-ugarchfit(data=SP500, spec= sp500.g12ss )
sp500.garch12g<-ugarchfit(data=SP500, spec= sp500.g12g )
sp500.garch12sg <-ugarchfit(data=SP500, spec= sp500.g12sg ) #10
sp500.garch21n <-ugarchfit(data=SP500, spec= sp500.g21n )
sp500.garch21s <-ugarchfit(data=SP500, spec= sp500.g21s )
sp500.garch21ss <-ugarchfit(data=SP500, spec= sp500.g21ss)
sp500.garch21g <-ugarchfit(data=SP500, spec= sp500.g21g )
sp500.garch21sg <-ugarchfit(data=SP500, spec= sp500.g21sg ) #15
sp500.garch22n <-ugarchfit(data=SP500, spec= sp500.g22n )
sp500.garch22s <-ugarchfit(data=SP500, spec= sp500.g22s )
sp500.garch22ss <-ugarchfit(data=SP500, spec= sp500.g22ss )
sp500.garch22g<-ugarchfit(data=SP500, spec= sp500.g22g )
sp500.garch22sg <-ugarchfit(data=SP500, spec= sp500.g22sg )
model.aic.list <- list(sp500.garch11n,sp500.garch11s,sp500.garch11ss,sp500.garch11g,sp500.garch11sg,sp500.garch12n,sp500.garch12s,sp500.garch12ss,sp500.garch12g,sp500.garch12sg,sp500.garch21n,sp500.garch21s,sp500.garch21ss,sp500.garch21g,sp500.garch21sg,sp500.garch22n,sp500.garch22s,sp500.garch22ss,sp500.garch22g,sp500.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 13sp500.garch21ss@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 6.787993e-02 0.01891923 3.587879e+00 3.333784e-04
## ar1 -8.941378e-02 0.02432172 -3.676293e+00 2.366474e-04
## ar2 -2.971677e-02 0.02519714 -1.179371e+00 2.382507e-01
## omega 4.301244e-02 0.01205471 3.568103e+00 3.595748e-04
## alpha1 1.428029e-07 0.13646673 1.046430e-06 9.999992e-01
## alpha2 4.220786e-02 0.11031127 3.826251e-01 7.019978e-01
## beta1 8.218984e-01 0.03118423 2.635622e+01 0.000000e+00
## gamma1 1.691936e-01 0.12889388 1.312658e+00 1.892982e-01
## gamma2 6.951413e-02 0.11653925 5.964868e-01 5.508500e-01
## skew 8.134953e-01 0.03113631 2.612690e+01 0.000000e+00
## shape 4.651639e+00 0.61000968 7.625516e+00 2.420286e-14print("Việt Nam")## [1] "Việt Nam"vni.g11n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
vni.g11s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
vni.g11ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g11g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
vni.g11sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
vni.g12n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
vni.g12s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
vni.g12ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g12g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
vni.g12sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
vni.g21n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
vni.g21s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
vni.g21ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g21g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
vni.g21sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
vni.g22n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
vni.g22s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
vni.g22ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g22g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
vni.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
vni.garch11n <-ugarchfit(data=VNI, spec= vni.g11n ) #1
vni.garch11s <-ugarchfit(data=VNI, spec= vni.g11s )
vni.garch11ss <-ugarchfit(data=VNI, spec= vni.g11ss )
vni.garch11g <-ugarchfit(data=VNI, spec= vni.g11g )
vni.garch11sg <-ugarchfit(data=VNI, spec= vni.g11sg ) #5
vni.garch12n <-ugarchfit(data=VNI, spec= vni.g12n )
vni.garch12s <-ugarchfit(data=VNI, spec= vni.g12s )
vni.garch12ss <-ugarchfit(data=VNI, spec= vni.g12ss )
vni.garch12g<-ugarchfit(data=VNI, spec= vni.g12g )
vni.garch12sg <-ugarchfit(data=VNI, spec= vni.g12sg ) #10
vni.garch21n <-ugarchfit(data=VNI, spec= vni.g21n )
vni.garch21s <-ugarchfit(data=VNI, spec= vni.g21s )
vni.garch21ss <-ugarchfit(data=VNI, spec= vni.g21ss)
vni.garch21g <-ugarchfit(data=VNI, spec= vni.g21g )
vni.garch21sg <-ugarchfit(data=VNI, spec= vni.g21sg ) #15
vni.garch22n <-ugarchfit(data=VNI, spec= vni.g22n )
vni.garch22s <-ugarchfit(data=VNI, spec= vni.g22s )
vni.garch22ss <-ugarchfit(data=VNI, spec= vni.g22ss )
vni.garch22g<-ugarchfit(data=VNI, spec= vni.g22g )
vni.garch22sg <-ugarchfit(data=VNI, spec= vni.g22sg )
model.aic.list <- list(vni.garch11n,vni.garch11s,vni.garch11ss,vni.garch11g,vni.garch11sg,vni.garch12n,vni.garch12s,vni.garch12ss,vni.garch12g,vni.garch12sg,vni.garch21n,vni.garch21s,vni.garch21ss,vni.garch21g,vni.garch21sg,vni.garch22n,vni.garch22s,vni.garch22ss,vni.garch22g,vni.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 15vni.garch21sg@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 7.073382e-02 0.01379763 5.126519e+00 2.951482e-07
## omega 1.625484e-01 0.05490831 2.960361e+00 3.072787e-03
## alpha1 7.055338e-08 0.02240546 3.148937e-06 9.999975e-01
## alpha2 7.018290e-02 0.01723121 4.073012e+00 4.640912e-05
## beta1 7.884684e-01 0.03867262 2.038829e+01 0.000000e+00
## gamma1 7.484212e-02 0.03121787 2.397413e+00 1.651132e-02
## gamma2 3.800238e-02 0.02742957 1.385453e+00 1.659139e-01
## skew 9.151130e-01 0.01464915 6.246869e+01 0.000000e+00
## shape 1.002875e+00 0.04568344 2.195270e+01 0.000000e+00print("Argentina")## [1] "Argentina"merval.g11n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
merval.g11s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
merval.g11ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g11g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
merval.g11sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
merval.g12n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
merval.g12s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
merval.g12ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g12g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
merval.g12sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
merval.g21n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
merval.g21s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
merval.g21ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g21g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
merval.g21sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
merval.g22n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
merval.g22s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
merval.g22ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g22g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
merval.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
merval.garch11n <-ugarchfit(data= MERVAL, spec= merval.g11n ) #1
merval.garch11s <-ugarchfit(data= MERVAL, spec= merval.g11s )
merval.garch11ss <-ugarchfit(data= MERVAL, spec= merval.g11ss )
merval.garch11g <-ugarchfit(data= MERVAL, spec= merval.g11g )
merval.garch11sg <-ugarchfit(data= MERVAL, spec= merval.g11sg ) #5
merval.garch12n <-ugarchfit(data= MERVAL, spec= merval.g12n )
merval.garch12s <-ugarchfit(data= MERVAL, spec= merval.g12s )
merval.garch12ss <-ugarchfit(data= MERVAL, spec= merval.g12ss )
merval.garch12g<-ugarchfit(data= MERVAL, spec= merval.g12g )
merval.garch12sg <-ugarchfit(data= MERVAL, spec= merval.g12sg ) #10
merval.garch21n <-ugarchfit(data= MERVAL, spec= merval.g21n )
merval.garch21s <-ugarchfit(data= MERVAL, spec= merval.g21s )
merval.garch21ss <-ugarchfit(data= MERVAL, spec= merval.g21ss)
merval.garch21g <-ugarchfit(data= MERVAL, spec= merval.g21g )
merval.garch21sg <-ugarchfit(data= MERVAL, spec= merval.g21sg ) #15
merval.garch22n <-ugarchfit(data= MERVAL, spec= merval.g22n )
merval.garch22s <-ugarchfit(data= MERVAL, spec= merval.g22s )
merval.garch22ss <-ugarchfit(data= MERVAL, spec= merval.g22ss )
merval.garch22g<-ugarchfit(data= MERVAL, spec= merval.g22g )
merval.garch22sg <-ugarchfit(data= MERVAL, spec= merval.g22sg )
model.aic.list <- list(merval.garch11n,merval.garch11s,merval.garch11ss,merval.garch11g,merval.garch11sg,merval.garch12n,merval.garch12s,merval.garch12ss,merval.garch12g,merval.garch12sg,merval.garch21n,merval.garch21s,merval.garch21ss,merval.garch21g,merval.garch21sg,merval.garch22n,merval.garch22s,merval.garch22ss,merval.garch22g,merval.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 12merval.garch21s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 3.201942e-01 0.057704212 5.548889e+00 2.874914e-08
## ma1 9.463703e-03 0.024641521 3.840552e-01 7.009376e-01
## omega 6.349843e-02 0.028190607 2.252468e+00 2.429274e-02
## alpha1 3.052161e-02 0.027295074 1.118209e+00 2.634776e-01
## alpha2 4.909700e-08 0.027745108 1.769573e-06 9.999986e-01
## beta1 9.600270e-01 0.004150225 2.313193e+02 0.000000e+00
## gamma1 3.294535e-01 0.054830013 6.008635e+00 1.870918e-09
## gamma2 -3.135793e-01 0.053197259 -5.894651e+00 3.754749e-09
## shape 3.774734e+00 0.351023838 1.075350e+01 0.000000e+00print("Crotia")## [1] "Crotia"crobex.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
crobex.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
crobex.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
crobex.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
crobex.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
crobex.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
crobex.garch11n <-ugarchfit(data= CROBEX, spec= crobex.g11n ) #1
crobex.garch11s <-ugarchfit(data= CROBEX, spec= crobex.g11s )
crobex.garch11ss <-ugarchfit(data= CROBEX, spec= crobex.g11ss )
crobex.garch11g <-ugarchfit(data= CROBEX, spec= crobex.g11g )
crobex.garch11sg <-ugarchfit(data= CROBEX, spec= crobex.g11sg ) #5
crobex.garch12n <-ugarchfit(data= CROBEX, spec= crobex.g12n )
crobex.garch12s <-ugarchfit(data= CROBEX, spec= crobex.g12s )
crobex.garch12ss <-ugarchfit(data= CROBEX, spec= crobex.g12ss )
crobex.garch12g<-ugarchfit(data= CROBEX, spec= crobex.g12g )
crobex.garch12sg <-ugarchfit(data= CROBEX, spec= crobex.g12sg ) #10
crobex.garch21n <-ugarchfit(data= CROBEX, spec= crobex.g21n )
crobex.garch21s <-ugarchfit(data= CROBEX, spec= crobex.g21s )
crobex.garch21ss <-ugarchfit(data= CROBEX, spec= crobex.g21ss)
crobex.garch21g <-ugarchfit(data= CROBEX, spec= crobex.g21g )
crobex.garch21sg <-ugarchfit(data= CROBEX, spec= crobex.g21sg ) #15
crobex.garch22n <-ugarchfit(data= CROBEX, spec= crobex.g22n )
crobex.garch22s <-ugarchfit(data= CROBEX, spec= crobex.g22s )
crobex.garch22ss <-ugarchfit(data= CROBEX, spec= crobex.g22ss )
crobex.garch22g<-ugarchfit(data= CROBEX, spec= crobex.g22g )
crobex.garch22sg <-ugarchfit(data= CROBEX, spec= crobex.g22sg )
model.aic.list <- list(crobex.garch11n,crobex.garch11s,crobex.garch11ss,crobex.garch11g,crobex.garch11sg,crobex.garch12n,crobex.garch12s,crobex.garch12ss,crobex.garch12g,crobex.garch12sg,crobex.garch21n,crobex.garch21s,crobex.garch21ss,crobex.garch21g,crobex.garch21sg,crobex.garch22n,crobex.garch22s,crobex.garch22ss,crobex.garch22g,crobex.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2crobex.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.051856197 1.740956e-02 2.978604e+00 2.895646e-03
## ar1 0.974169851 4.467384e-03 2.180627e+02 0.000000e+00
## ma1 -0.957271026 9.226903e-05 -1.037478e+04 0.000000e+00
## ma2 -0.007876803 1.035817e-03 -7.604434e+00 2.864375e-14
## omega 0.067321414 2.264945e-02 2.972320e+00 2.955585e-03
## alpha1 0.094403635 3.703972e-02 2.548713e+00 1.081211e-02
## beta1 0.817955925 4.465450e-02 1.831743e+01 0.000000e+00
## gamma1 0.035690885 4.317244e-02 8.267053e-01 4.084041e-01
## shape 2.864361319 2.336575e-01 1.225880e+01 0.000000e+00print("Morocco")## [1] "Morocco"masi.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
masi.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
masi.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
masi.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
masi.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
masi.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
masi.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
masi.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
masi.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
masi.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
masi.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
masi.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
masi.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
masi.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
masi.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
masi.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
masi.garch11n <-ugarchfit(data= MASI, spec= masi.g11n ) #1
masi.garch11s <-ugarchfit(data= MASI, spec= masi.g11s )
masi.garch11ss <-ugarchfit(data= MASI, spec= masi.g11ss )
masi.garch11g <-ugarchfit(data= MASI, spec= masi.g11g )
masi.garch11sg <-ugarchfit(data= MASI, spec= masi.g11sg ) #5
masi.garch12n <-ugarchfit(data= MASI, spec= masi.g12n )
masi.garch12s <-ugarchfit(data= MASI, spec= masi.g12s )
masi.garch12ss <-ugarchfit(data= MASI, spec= masi.g12ss )
masi.garch12g<-ugarchfit(data= MASI, spec= masi.g12g )
masi.garch12sg <-ugarchfit(data= MASI, spec= masi.g12sg ) #10
masi.garch21n <-ugarchfit(data= MASI, spec= masi.g21n )
masi.garch21s <-ugarchfit(data= MASI, spec= masi.g21s )
masi.garch21ss <-ugarchfit(data= MASI, spec= masi.g21ss)
masi.garch21g <-ugarchfit(data= MASI, spec= masi.g21g )
masi.garch21sg <-ugarchfit(data= MASI, spec= masi.g21sg ) #15
masi.garch22n <-ugarchfit(data= MASI, spec= masi.g22n )
masi.garch22s <-ugarchfit(data= MASI, spec= masi.g22s )
masi.garch22ss <-ugarchfit(data= MASI, spec= masi.g22ss )
masi.garch22g<-ugarchfit(data= MASI, spec= masi.g22g )
masi.garch22sg <-ugarchfit(data= MASI, spec= masi.g22sg )
model.aic.list <- list(masi.garch11n,masi.garch11s,masi.garch11ss,masi.garch11g,masi.garch11sg,masi.garch12n,masi.garch12s,masi.garch12ss,masi.garch12g,masi.garch12sg,masi.garch21n,masi.garch21s,masi.garch21ss,masi.garch21g,masi.garch21sg,masi.garch22n,masi.garch22s,masi.garch22ss,masi.garch22g,masi.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 7masi.garch12s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.03099072 1.468184e-02 2.110820 3.478776e-02
## ar1 -0.99489198 1.557667e-03 -638.706649 0.000000e+00
## ma1 1.10934723 1.535497e-06 722468.026694 0.000000e+00
## ma2 0.11678129 8.177005e-05 1428.167092 0.000000e+00
## omega 0.10543807 2.862063e-02 3.683988 2.296127e-04
## alpha1 0.14491197 5.072703e-02 2.856701 4.280687e-03
## beta1 0.18720996 1.257640e-01 1.488581 1.365978e-01
## beta2 0.47509380 1.216568e-01 3.905197 9.414851e-05
## gamma1 0.12555173 6.763410e-02 1.856338 6.340543e-02
## shape 3.15861450 2.589281e-01 12.198809 0.000000e+00print("Oman")## [1] "Oman"msm30.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
msm30.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
msm30.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
msm30.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
msm30.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
msm30.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
msm30.garch11n <-ugarchfit(data= MSM30, spec= msm30.g11n ) #1
msm30.garch11s <-ugarchfit(data= MSM30, spec= msm30.g11s )
msm30.garch11ss <-ugarchfit(data= MSM30, spec= msm30.g11ss )
msm30.garch11g <-ugarchfit(data= MSM30, spec= msm30.g11g )
msm30.garch11sg <-ugarchfit(data= MSM30, spec= msm30.g11sg ) #5
msm30.garch12n <-ugarchfit(data= MSM30, spec= msm30.g12n )
msm30.garch12s <-ugarchfit(data= MSM30, spec= msm30.g12s )
msm30.garch12ss <-ugarchfit(data= MSM30, spec= msm30.g12ss )
msm30.garch12g<-ugarchfit(data= MSM30, spec= msm30.g12g )
msm30.garch12sg <-ugarchfit(data= MSM30, spec= msm30.g12sg ) #10
msm30.garch21n <-ugarchfit(data= MSM30, spec= msm30.g21n )
msm30.garch21s <-ugarchfit(data= MSM30, spec= msm30.g21s )
msm30.garch21ss <-ugarchfit(data= MSM30, spec= msm30.g21ss)
msm30.garch21g <-ugarchfit(data= MSM30, spec= msm30.g21g )
msm30.garch21sg <-ugarchfit(data= MSM30, spec= msm30.g21sg ) #15
msm30.garch22n <-ugarchfit(data= MSM30, spec= msm30.g22n )
msm30.garch22s <-ugarchfit(data= MSM30, spec= msm30.g22s )
msm30.garch22ss <-ugarchfit(data= MSM30, spec= msm30.g22ss )
msm30.garch22g<-ugarchfit(data= MSM30, spec= msm30.g22g )
msm30.garch22sg <-ugarchfit(data= MSM30, spec= msm30.g22sg )
model.aic.list <- list(msm30.garch11n,msm30.garch11s,msm30.garch11ss,msm30.garch11g,msm30.garch11sg,msm30.garch12n,msm30.garch12s,msm30.garch12ss,msm30.garch12g,msm30.garch12sg,msm30.garch21n,msm30.garch21s,msm30.garch21ss,msm30.garch21g,msm30.garch21sg,msm30.garch22n,msm30.garch22s,msm30.garch22ss,msm30.garch22g,msm30.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 12msm30.garch21s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu -2.004075e-02 0.01574812 -1.272581e+00 2.031668e-01
## ar1 4.021751e-01 0.41030917 9.801757e-01 3.269994e-01
## ar2 2.506756e-02 0.08096771 3.095995e-01 7.568656e-01
## ma1 -2.422462e-01 0.41032344 -5.903787e-01 5.549368e-01
## omega 1.024715e-01 0.03030495 3.381344e+00 7.213206e-04
## alpha1 2.318084e-01 0.08353994 2.774821e+00 5.523204e-03
## alpha2 4.719300e-17 0.08371013 5.637670e-16 1.000000e+00
## beta1 6.066778e-01 0.08502737 7.135088e+00 9.672263e-13
## gamma1 -1.249729e-01 0.09325340 -1.340143e+00 1.801988e-01
## gamma2 1.696487e-01 0.09668055 1.754735e+00 7.930464e-02
## shape 3.144667e+00 0.28615571 1.098936e+01 0.000000e+00SP500_model <- sp500.garch21ss
VNI_model <- vni.garch21sg
MERVAL_model <- merval.garch21s
CROBEX_model <- crobex.garch11s
MASI_model <- masi.garch12s
MSM30_model <- msm30.garch21s
SP500.res <- residuals(SP500_model)/sigma(SP500_model)
VNI.res <- residuals(VNI_model)/sigma(VNI_model)
MERVAL.res <- residuals(MERVAL_model)/sigma(MERVAL_model)
CROBEX.res <- residuals(CROBEX_model)/sigma(CROBEX_model)
MASI.res <- residuals(MASI_model)/sigma(MASI_model)
MSM30.res <- residuals(MSM30_model)/sigma(MSM30_model)
fitdist(distribution = "sstd", SP500.res, control = list())$pars## mu sigma skew shape
## 0.0007246434 0.9977771753 0.8138744250 4.6751099026fitdist(distribution = "sged", VNI.res, control = list())$pars## mu sigma skew shape
## -0.008710607 1.003753061 0.909964089 1.002117618fitdist(distribution = "std", MERVAL.res, control = list())$pars## mu sigma shape
## 0.004008781 0.992147872 3.833880809fitdist(distribution = "std", CROBEX.res, control = list())$pars## mu sigma shape
## 0.002638961 0.986709272 2.903725585fitdist(distribution = "std", MASI.res, control = list())$pars## mu sigma shape
## 0.0000233141 1.0121271170 3.1097976463fitdist(distribution = "std", MSM30.res, control = list())$pars## mu sigma shape
## 0.007082759 1.014195295 3.087571193u <- pdist(distribution = "sstd", q = SP500.res, mu =0.0007246434 , sigma = 0.9977771753, skew= 0.8138744250,shape = 4.6751099026)
v1 <- pdist(distribution = "sged", q = VNI.res, mu =-0.008710607, sigma = 1.003753061, skew= 0.909964089,shape = 1.002117618)
v2 <- pdist(distribution = "std", q = MERVAL.res, mu = 1.002117618, sigma = 0.992147872, shape = 3.833880809)
v3 <- pdist(distribution = "std", q = CROBEX.res, mu = 0.002638961 , sigma = 0.986709272, shape = 2.903725585)
v4 <- pdist(distribution = "std", q = MASI.res, mu = 0.0000233141, sigma = 1.0121271170, shape = 3.1097976463)
v5 <- pdist(distribution = "std", q = MSM30.res, mu = 0.007082759, sigma = 1.014195295, shape = 3.087571193)
goftest::cvm.test(u, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## omega2 = 0.14925, p-value = 0.3919goftest::cvm.test(v1, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## omega2 = 0.17692, p-value = 0.317goftest::cvm.test(v2, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## omega2 = 192.22, p-value < 2.2e-16goftest::cvm.test(v3, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## omega2 = 0.049666, p-value = 0.8784goftest::cvm.test(v4, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## omega2 = 0.034962, p-value = 0.9572goftest::cvm.test(v5, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## omega2 = 0.041659, p-value = 0.9245goftest::ad.test(u, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## An = 0.80375, p-value = 0.4783goftest::ad.test(v1, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## An = 1.2228, p-value = 0.259goftest::ad.test(v2, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## An = 1106.2, p-value = 3.561e-07goftest::ad.test(v3, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## An = 0.29079, p-value = 0.9449goftest::ad.test(v4, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## An = 0.21061, p-value = 0.9872goftest::ad.test(v5, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## An = 0.29656, p-value = 0.9407ks.test(u, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: u
## D = 0.025757, p-value = 0.2136
## alternative hypothesis: two-sidedks.test(v1, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v1
## D = 0.027688, p-value = 0.151
## alternative hypothesis: two-sidedks.test(v2, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v2
## D = 0.48805, p-value < 2.2e-16
## alternative hypothesis: two-sidedks.test(v3, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v3
## D = 0.016146, p-value = 0.772
## alternative hypothesis: two-sidedks.test(v4, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v4
## D = 0.01217, p-value = 0.9642
## alternative hypothesis: two-sidedks.test(v5, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v5
## D = 0.012811, p-value = 0.945
## alternative hypothesis: two-sidedprint("Việt Nam")## [1] "Việt Nam"aa1 <- BiCopEst(u, v1, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 23.21
## AIC: -44.41
## BIC: -38.98aa2 <- BiCopEst(u, v1, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.13
## par2: 5.57
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 45.69
## AIC: -87.38
## BIC: -76.52aa3 <- BiCopEst(u, v1, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 28.45
## AIC: -54.89
## BIC: -49.46aa4 <- BiCopEst(u, v1, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.19
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.92
## AIC: -37.84
## BIC: -32.41aa5 <- BiCopEst(u, v1, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 27.81
## AIC: -53.62
## BIC: -48.19aa6 <- BiCopEst(u, v1, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 33.98
## AIC: -65.97
## BIC: -60.54aa7 <- BiCopEst(u, v1, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.77
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 12.94
## AIC: -23.88
## BIC: -18.45aa8 <- BiCopEst(u, v1, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 22.5
## AIC: -43
## BIC: -37.57aa9 <- BiCopEst(u, v1, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 30.51
## AIC: -59.02
## BIC: -53.59aa10 <- BiCopEst(u, v1, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.11
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.09
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 37.03
## AIC: -70.07
## BIC: -59.21aa11 <- BiCopEst(u, v1, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.09
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 38.13
## AIC: -72.26
## BIC: -61.4aa12 <- BiCopEst(u, v1, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.11
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 27.79
## AIC: -51.58
## BIC: -40.72aa13 <- BiCopEst(u, v1, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 33.97
## AIC: -63.94
## BIC: -53.08aa14 <- BiCopEst(u, v1, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 0.14
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 39.58
## AIC: -75.15
## BIC: -64.29aa15 <- BiCopEst(u, v1, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 0.13
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 40.23
## AIC: -76.45
## BIC: -65.59aa16 <- BiCopEst(u, v1, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.14
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 22.5
## AIC: -41
## BIC: -30.14aa17 <- BiCopEst(u, v1, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 30.51
## AIC: -57.02
## BIC: -46.16print("Argentina")## [1] "Argentina"ab1 <- BiCopEst(u, v2, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 45.96
## AIC: -89.93
## BIC: -84.5ab2 <- BiCopEst(u, v2, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.16
## par2: 17.04
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 51.46
## AIC: -98.93
## BIC: -88.07ab3 <- BiCopEst(u, v2, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 55
## AIC: -108
## BIC: -102.57ab4 <- BiCopEst(u, v2, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.2
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.25, p value < 0.01)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 22.95
## AIC: -43.9
## BIC: -38.47ab5 <- BiCopEst(u, v2, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.25, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 35.19
## AIC: -68.38
## BIC: -62.96ab6 <- BiCopEst(u, v2, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 53.61
## AIC: -105.22
## BIC: -99.79ab7 <- BiCopEst(u, v2, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.9
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 31.38
## AIC: -60.77
## BIC: -55.34ab8 <- BiCopEst(u, v2, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.25, p value < 0.01)
## Upper TD: 0.18
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 18.06
## AIC: -34.12
## BIC: -28.69ab9 <- BiCopEst(u, v2, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 50.62
## AIC: -99.25
## BIC: -93.82ab10 <- BiCopEst(u, v2, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.15
## par2: 1.02
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.25, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 55.41
## AIC: -106.81
## BIC: -95.95ab11 <- BiCopEst(u, v2, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 55.18
## AIC: -106.36
## BIC: -95.5ab12 <- BiCopEst(u, v2, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.13
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.25, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 35.12
## AIC: -66.25
## BIC: -55.39ab13 <- BiCopEst(u, v2, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 53.6
## AIC: -103.19
## BIC: -92.33ab14 <- BiCopEst(u, v2, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.03
## par2: 0.15
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.25, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 55.8
## AIC: -107.59
## BIC: -96.73ab15 <- BiCopEst(u, v2, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 55.82
## AIC: -107.65
## BIC: -96.79ab16 <- BiCopEst(u, v2, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.16
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 30.68
## AIC: -57.37
## BIC: -46.51ab17 <- BiCopEst(u, v2, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.25, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 50.62
## AIC: -97.25
## BIC: -86.39print("Croatia")## [1] "Croatia"ac1 <- BiCopEst(u, v3, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 27.32
## AIC: -52.64
## BIC: -47.21ac2 <- BiCopEst(u, v3, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.16
## par2: 5.24
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 53.86
## AIC: -103.73
## BIC: -92.87ac3 <- BiCopEst(u, v3, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.22
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 35.53
## AIC: -69.06
## BIC: -63.63ac4 <- BiCopEst(u, v3, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.19
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 20.58
## AIC: -39.16
## BIC: -33.73ac5 <- BiCopEst(u, v3, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 29.85
## AIC: -57.69
## BIC: -52.26ac6 <- BiCopEst(u, v3, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 43.77
## AIC: -85.53
## BIC: -80.1ac7 <- BiCopEst(u, v3, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.95
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.94
## AIC: -37.88
## BIC: -32.45ac8 <- BiCopEst(u, v3, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 21.68
## AIC: -41.37
## BIC: -35.94ac9 <- BiCopEst(u, v3, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.18
##
## Fit statistics
## --------------
## logLik: 40.17
## AIC: -78.34
## BIC: -72.91ac10 <- BiCopEst(u, v3, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.15
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 42.61
## AIC: -81.22
## BIC: -70.36ac11 <- BiCopEst(u, v3, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.07
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 45.89
## AIC: -87.79
## BIC: -76.93ac12 <- BiCopEst(u, v3, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 29.81
## AIC: -55.62
## BIC: -44.76ac13 <- BiCopEst(u, v3, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 43.76
## AIC: -83.52
## BIC: -72.66ac14 <- BiCopEst(u, v3, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.18
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 44.1
## AIC: -84.21
## BIC: -73.35ac15 <- BiCopEst(u, v3, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 0.12
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 47.92
## AIC: -91.84
## BIC: -80.98ac16 <- BiCopEst(u, v3, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.14
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 21.68
## AIC: -39.37
## BIC: -28.51ac17 <- BiCopEst(u, v3, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.16
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.18
##
## Fit statistics
## --------------
## logLik: 40.17
## AIC: -76.34
## BIC: -65.48print("Morocco")## [1] "Morocco"ad1 <- BiCopEst(u, v4, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.89
## AIC: -15.77
## BIC: -10.34ad2 <- BiCopEst(u, v4, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 18.13
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.44
## AIC: -18.87
## BIC: -8.01ad3 <- BiCopEst(u, v4, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.87
## AIC: -15.74
## BIC: -10.31ad4 <- BiCopEst(u, v4, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.61
## AIC: -11.21
## BIC: -5.78ad5 <- BiCopEst(u, v4, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.85
## AIC: -13.69
## BIC: -8.26ad6 <- BiCopEst(u, v4, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 10.09
## AIC: -18.18
## BIC: -12.75ad7 <- BiCopEst(u, v4, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.55
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.84
## AIC: -11.68
## BIC: -6.25ad8 <- BiCopEst(u, v4, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.44
## AIC: -8.89
## BIC: -3.46ad9 <- BiCopEst(u, v4, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 8.22
## AIC: -14.44
## BIC: -9.01ad10 <- BiCopEst(u, v4, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.07
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 10.81
## AIC: -17.62
## BIC: -6.76ad11 <- BiCopEst(u, v4, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.05
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 11.28
## AIC: -18.56
## BIC: -7.7ad12 <- BiCopEst(u, v4, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.82
## AIC: -11.64
## BIC: -0.78ad13 <- BiCopEst(u, v4, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 10.07
## AIC: -16.14
## BIC: -5.28ad14 <- BiCopEst(u, v4, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.09
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 10.91
## AIC: -17.82
## BIC: -6.96ad15 <- BiCopEst(u, v4, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 11.41
## AIC: -18.82
## BIC: -7.96ad16 <- BiCopEst(u, v4, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.18
## par2: 0.89
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.25
## AIC: -10.5
## BIC: 0.36ad17 <- BiCopEst(u, v4, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.94
## AIC: -13.87
## BIC: -3.02print("Oman")## [1] "Oman"ae1 <- BiCopEst(u, v5, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.97
## AIC: -5.95
## BIC: -0.52ae2 <- BiCopEst(u, v5, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 6.85
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0.03
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 20.07
## AIC: -36.14
## BIC: -25.28ae3 <- BiCopEst(u, v5, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.73
## AIC: -13.47
## BIC: -8.04ae4 <- BiCopEst(u, v5, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.07
## AIC: -6.14
## BIC: -0.71ae5 <- BiCopEst(u, v5, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6
## AIC: -10
## BIC: -4.57ae6 <- BiCopEst(u, v5, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 10.26
## AIC: -18.51
## BIC: -13.09ae7 <- BiCopEst(u, v5, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.37
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.98
## AIC: -3.97
## BIC: 1.46ae8 <- BiCopEst(u, v5, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.78
## AIC: -7.56
## BIC: -2.13ae9 <- BiCopEst(u, v5, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 9.6
## AIC: -17.2
## BIC: -11.77ae10 <- BiCopEst(u, v5, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.01)
## Upper TD: 0.04
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.16
## AIC: -14.32
## BIC: -3.46ae11 <- BiCopEst(u, v5, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.04
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 10.91
## AIC: -17.82
## BIC: -6.97ae12 <- BiCopEst(u, v5, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.05
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.99
## AIC: -7.98
## BIC: 2.88ae13 <- BiCopEst(u, v5, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.05
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 10.25
## AIC: -16.51
## BIC: -5.65ae14 <- BiCopEst(u, v5, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.01)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.77
## AIC: -15.54
## BIC: -4.68ae15 <- BiCopEst(u, v5, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.05
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 11.45
## AIC: -18.89
## BIC: -8.03ae16 <- BiCopEst(u, v5, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.97
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.23
## AIC: -6.45
## BIC: 4.41ae17 <- BiCopEst(u, v5, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.06
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.01)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 9.6
## AIC: -15.2
## BIC: -4.34rm(list=ls())
DATA <- read_xlsx("C://Users//84896//Desktop//DATA//CN3-COPULA.xlsx", sheet="Pre")
SP500 <- DATA$y
VNI <- DATA$x1
MERVAL <- DATA$x2
CROBEX <- DATA$x3
MASI <- DATA$x4
MSM30 <- DATA$x5cor(cbind(SP500, VNI, MERVAL, CROBEX, MASI, MSM30), method="pearson")## SP500 VNI MERVAL CROBEX MASI MSM30
## SP500 1.0000000 0.24921578 0.40675947 0.16042169 0.07534730 0.18158525
## VNI 0.2492158 1.00000000 0.11182635 0.06947806 0.10092491 0.25354097
## MERVAL 0.4067595 0.11182635 1.00000000 0.17113414 0.01255043 0.09547259
## CROBEX 0.1604217 0.06947806 0.17113414 1.00000000 0.04200376 0.14848855
## MASI 0.0753473 0.10092491 0.01255043 0.04200376 1.00000000 0.10079280
## MSM30 0.1815852 0.25354097 0.09547259 0.14848855 0.10079280 1.00000000print("Mỹ")## [1] "Mỹ"autoarfima(SP500,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ar1 ar2 ma1 ma2 sigma
## 0.05523651 -0.02468771 0.89433808 0.00000000 -0.94677710 1.01725671print("Việt Nam")## [1] "Việt Nam"autoarfima(VNI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ma1 ma2 sigma
## 0.06650518 0.05544425 0.05748963 1.27379362print("Argentina")## [1] "Argentina"autoarfima(MERVAL,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ar1 sigma
## 0.2106993 0.0906727 2.9979639print("Croatia")## [1] "Croatia"autoarfima(CROBEX,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu sigma
## 0.01139053 0.69464192print("Morocco")## [1] "Morocco"autoarfima(MASI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 sigma
## 0.1705336 0.6887281print("Oman")## [1] "Oman"autoarfima(MSM30,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ar1 ar2 ma1 sigma
## -0.05436165 -0.62044103 0.28407930 0.83714231 0.82768367print("Mỹ")## [1] "Mỹ"sp500.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
sp500.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
sp500.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
sp500.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
sp500.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
sp500.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
sp500.garch11n <-ugarchfit(data=SP500, spec= sp500.g11n ) #1
sp500.garch11s <-ugarchfit(data=SP500, spec= sp500.g11s )
sp500.garch11ss <-ugarchfit(data=SP500, spec= sp500.g11ss )
sp500.garch11g <-ugarchfit(data=SP500, spec= sp500.g11g )
sp500.garch11sg <-ugarchfit(data=SP500, spec= sp500.g11sg ) #5
sp500.garch12n <-ugarchfit(data=SP500, spec= sp500.g12n )
sp500.garch12s <-ugarchfit(data=SP500, spec= sp500.g12s )
sp500.garch12ss <-ugarchfit(data=SP500, spec= sp500.g12ss )
sp500.garch12g<-ugarchfit(data=SP500, spec= sp500.g12g )
sp500.garch12sg <-ugarchfit(data=SP500, spec= sp500.g12sg ) #10
sp500.garch21n <-ugarchfit(data=SP500, spec= sp500.g21n )
sp500.garch21s <-ugarchfit(data=SP500, spec= sp500.g21s )
sp500.garch21ss <-ugarchfit(data=SP500, spec= sp500.g21ss)
sp500.garch21g <-ugarchfit(data=SP500, spec= sp500.g21g )
sp500.garch21sg <-ugarchfit(data=SP500, spec= sp500.g21sg ) #15
sp500.garch22n <-ugarchfit(data=SP500, spec= sp500.g22n )
sp500.garch22s <-ugarchfit(data=SP500, spec= sp500.g22s )
sp500.garch22ss <-ugarchfit(data=SP500, spec= sp500.g22ss )
sp500.garch22g<-ugarchfit(data=SP500, spec= sp500.g22g )
sp500.garch22sg <-ugarchfit(data=SP500, spec= sp500.g22sg )
model.aic.list <- list(sp500.garch11n,sp500.garch11s,sp500.garch11ss,sp500.garch11g,sp500.garch11sg,sp500.garch12n,sp500.garch12s,sp500.garch12ss,sp500.garch12g,sp500.garch12sg,sp500.garch21n,sp500.garch21s,sp500.garch21ss,sp500.garch21g,sp500.garch21sg,sp500.garch22n,sp500.garch22s,sp500.garch22ss,sp500.garch22g,sp500.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 13sp500.garch21ss@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 5.450125e-02 2.185075e-02 2.494251e+00 1.262233e-02
## ar1 -1.084826e+00 2.920163e-02 -3.714951e+01 0.000000e+00
## ar2 -1.242021e-01 2.924620e-02 -4.246775e+00 2.168695e-05
## ma1 9.935403e-01 3.472634e-05 2.861056e+04 0.000000e+00
## ma2 1.773927e-02 5.536002e-04 3.204347e+01 0.000000e+00
## omega 9.024410e-02 2.505663e-02 3.601605e+00 3.162584e-04
## alpha1 7.440995e-11 1.476025e-01 5.041239e-10 1.000000e+00
## alpha2 2.495333e-08 9.495720e-02 2.627850e-07 9.999998e-01
## beta1 7.217150e-01 5.678117e-02 1.271046e+01 0.000000e+00
## gamma1 1.721859e-01 1.466764e-01 1.173917e+00 2.404281e-01
## gamma2 2.499680e-01 1.673322e-01 1.493843e+00 1.352167e-01
## skew 8.263522e-01 3.702602e-02 2.231815e+01 0.000000e+00
## shape 3.756452e+00 5.782007e-01 6.496797e+00 8.204837e-11print("Việt Nam")## [1] "Việt Nam"vni.g11n <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
vni.g11s <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
vni.g11ss <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g11g <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
vni.g11sg <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
vni.g12n <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
vni.g12s <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
vni.g12ss <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g12g <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
vni.g12sg <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
vni.g21n <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
vni.g21s <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
vni.g21ss <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g21g <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
vni.g21sg <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
vni.g22n <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
vni.g22s <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
vni.g22ss <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g22g <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
vni.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
vni.garch11n <-ugarchfit(data=VNI, spec= vni.g11n ) #1
vni.garch11s <-ugarchfit(data=VNI, spec= vni.g11s )
vni.garch11ss <-ugarchfit(data=VNI, spec= vni.g11ss )
vni.garch11g <-ugarchfit(data=VNI, spec= vni.g11g )
vni.garch11sg <-ugarchfit(data=VNI, spec= vni.g11sg ) #5
vni.garch12n <-ugarchfit(data=VNI, spec= vni.g12n )
vni.garch12s <-ugarchfit(data=VNI, spec= vni.g12s )
vni.garch12ss <-ugarchfit(data=VNI, spec= vni.g12ss )
vni.garch12g<-ugarchfit(data=VNI, spec= vni.g12g )
vni.garch12sg <-ugarchfit(data=VNI, spec= vni.g12sg ) #10
vni.garch21n <-ugarchfit(data=VNI, spec= vni.g21n )
vni.garch21s <-ugarchfit(data=VNI, spec= vni.g21s )
vni.garch21ss <-ugarchfit(data=VNI, spec= vni.g21ss)
vni.garch21g <-ugarchfit(data=VNI, spec= vni.g21g )
vni.garch21sg <-ugarchfit(data=VNI, spec= vni.g21sg ) #15
vni.garch22n <-ugarchfit(data=VNI, spec= vni.g22n )
vni.garch22s <-ugarchfit(data=VNI, spec= vni.g22s )
vni.garch22ss <-ugarchfit(data=VNI, spec= vni.g22ss )
vni.garch22g<-ugarchfit(data=VNI, spec= vni.g22g )
vni.garch22sg <-ugarchfit(data=VNI, spec= vni.g22sg )
model.aic.list <- list(vni.garch11n,vni.garch11s,vni.garch11ss,vni.garch11g,vni.garch11sg,vni.garch12n,vni.garch12s,vni.garch12ss,vni.garch12g,vni.garch12sg,vni.garch21n,vni.garch21s,vni.garch21ss,vni.garch21g,vni.garch21sg,vni.garch22n,vni.garch22s,vni.garch22ss,vni.garch22g,vni.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 13vni.garch21ss@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 7.562479e-02 0.03729637 2.027672e+00 4.259378e-02
## ma1 4.359318e-02 0.02896152 1.505211e+00 1.322700e-01
## ma2 3.802679e-02 0.03205796 1.186189e+00 2.355478e-01
## omega 1.168913e-01 0.05242204 2.229812e+00 2.575995e-02
## alpha1 1.416327e-08 0.04732323 2.992879e-07 9.999998e-01
## alpha2 7.257158e-02 0.05873903 1.235492e+00 2.166476e-01
## beta1 7.961581e-01 0.05175503 1.538320e+01 0.000000e+00
## gamma1 1.158869e-01 0.08437191 1.373525e+00 1.695892e-01
## gamma2 3.388434e-02 0.10144402 3.340200e-01 7.383644e-01
## skew 9.350630e-01 0.03982151 2.348135e+01 0.000000e+00
## shape 3.875781e+00 0.51858269 7.473795e+00 7.793766e-14print("Argentina")## [1] "Argentina"merval.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
merval.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
merval.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
merval.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
merval.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
merval.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
merval.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
merval.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
merval.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
merval.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
merval.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
merval.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
merval.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
merval.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
merval.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
merval.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
merval.garch11n <-ugarchfit(data= MERVAL, spec= merval.g11n ) #1
merval.garch11s <-ugarchfit(data= MERVAL, spec= merval.g11s )
merval.garch11ss <-ugarchfit(data= MERVAL, spec= merval.g11ss )
merval.garch11g <-ugarchfit(data= MERVAL, spec= merval.g11g )
merval.garch11sg <-ugarchfit(data= MERVAL, spec= merval.g11sg ) #5
merval.garch12n <-ugarchfit(data= MERVAL, spec= merval.g12n )
merval.garch12s <-ugarchfit(data= MERVAL, spec= merval.g12s )
merval.garch12ss <-ugarchfit(data= MERVAL, spec= merval.g12ss )
merval.garch12g<-ugarchfit(data= MERVAL, spec= merval.g12g )
merval.garch12sg <-ugarchfit(data= MERVAL, spec= merval.g12sg ) #10
merval.garch21n <-ugarchfit(data= MERVAL, spec= merval.g21n )
merval.garch21s <-ugarchfit(data= MERVAL, spec= merval.g21s )
merval.garch21ss <-ugarchfit(data= MERVAL, spec= merval.g21ss)
merval.garch21g <-ugarchfit(data= MERVAL, spec= merval.g21g )
merval.garch21sg <-ugarchfit(data= MERVAL, spec= merval.g21sg ) #15
merval.garch22n <-ugarchfit(data= MERVAL, spec= merval.g22n )
merval.garch22s <-ugarchfit(data= MERVAL, spec= merval.g22s )
merval.garch22ss <-ugarchfit(data= MERVAL, spec= merval.g22ss )
merval.garch22g<-ugarchfit(data= MERVAL, spec= merval.g22g )
merval.garch22sg <-ugarchfit(data= MERVAL, spec= merval.g22sg )
model.aic.list <- list(merval.garch11n,merval.garch11s,merval.garch11ss,merval.garch11g,merval.garch11sg,merval.garch12n,merval.garch12s,merval.garch12ss,merval.garch12g,merval.garch12sg,merval.garch21n,merval.garch21s,merval.garch21ss,merval.garch21g,merval.garch21sg,merval.garch22n,merval.garch22s,merval.garch22ss,merval.garch22g,merval.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 12merval.garch21s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 2.687279e-01 0.069025375 3.893176e+00 9.894036e-05
## ar1 1.381752e-02 0.033022059 4.184329e-01 6.756306e-01
## omega 4.115233e-02 0.034434716 1.195082e+00 2.320548e-01
## alpha1 1.363396e-02 0.056076380 2.431320e-01 8.079031e-01
## alpha2 4.054015e-07 0.057126284 7.096584e-06 9.999943e-01
## beta1 9.676682e-01 0.009708130 9.967607e+01 0.000000e+00
## gamma1 4.492547e-01 0.003754312 1.196637e+02 0.000000e+00
## gamma2 -4.164986e-01 0.004796747 -8.682939e+01 0.000000e+00
## shape 3.931523e+00 0.480813134 8.176822e+00 2.220446e-16print("Crotia")## [1] "Crotia"crobex.g11n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g11s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
crobex.g11ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g11g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g11sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g12n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g12s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
crobex.g12ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g12g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g12sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
crobex.g21n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g21s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
crobex.g21ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g21g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g21sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g22n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g22s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
crobex.g22ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g22g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
crobex.garch11n <-ugarchfit(data= CROBEX, spec= crobex.g11n ) #1
crobex.garch11s <-ugarchfit(data= CROBEX, spec= crobex.g11s )
crobex.garch11ss <-ugarchfit(data= CROBEX, spec= crobex.g11ss )
crobex.garch11g <-ugarchfit(data= CROBEX, spec= crobex.g11g )
crobex.garch11sg <-ugarchfit(data= CROBEX, spec= crobex.g11sg ) #5
crobex.garch12n <-ugarchfit(data= CROBEX, spec= crobex.g12n )
crobex.garch12s <-ugarchfit(data= CROBEX, spec= crobex.g12s )
crobex.garch12ss <-ugarchfit(data= CROBEX, spec= crobex.g12ss )
crobex.garch12g<-ugarchfit(data= CROBEX, spec= crobex.g12g )
crobex.garch12sg <-ugarchfit(data= CROBEX, spec= crobex.g12sg ) #10
crobex.garch21n <-ugarchfit(data= CROBEX, spec= crobex.g21n )
crobex.garch21s <-ugarchfit(data= CROBEX, spec= crobex.g21s )
crobex.garch21ss <-ugarchfit(data= CROBEX, spec= crobex.g21ss)
crobex.garch21g <-ugarchfit(data= CROBEX, spec= crobex.g21g )
crobex.garch21sg <-ugarchfit(data= CROBEX, spec= crobex.g21sg ) #15
crobex.garch22n <-ugarchfit(data= CROBEX, spec= crobex.g22n )
crobex.garch22s <-ugarchfit(data= CROBEX, spec= crobex.g22s )
crobex.garch22ss <-ugarchfit(data= CROBEX, spec= crobex.g22ss )
crobex.garch22g<-ugarchfit(data= CROBEX, spec= crobex.g22g )
crobex.garch22sg <-ugarchfit(data= CROBEX, spec= crobex.g22sg )
model.aic.list <- list(crobex.garch11n,crobex.garch11s,crobex.garch11ss,crobex.garch11g,crobex.garch11sg,crobex.garch12n,crobex.garch12s,crobex.garch12ss,crobex.garch12g,crobex.garch12sg,crobex.garch21n,crobex.garch21s,crobex.garch21ss,crobex.garch21g,crobex.garch21sg,crobex.garch22n,crobex.garch22s,crobex.garch22ss,crobex.garch22g,crobex.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2crobex.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.02060669 0.01631679 1.2629135 2.066203e-01
## omega 0.10382719 0.05341341 1.9438411 5.191461e-02
## alpha1 0.11446165 0.06789041 1.6859767 9.180029e-02
## beta1 0.71062757 0.11790363 6.0271899 1.668350e-09
## gamma1 0.04397669 0.07268089 0.6050654 5.451355e-01
## shape 3.02131079 0.34713157 8.7036475 0.000000e+00print("Morocco")## [1] "Morocco"masi.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
masi.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
masi.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
masi.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
masi.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
masi.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
masi.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
masi.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
masi.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
masi.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
masi.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
masi.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
masi.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
masi.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
masi.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
masi.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
masi.garch11n <-ugarchfit(data= MASI, spec= masi.g11n ) #1
masi.garch11s <-ugarchfit(data= MASI, spec= masi.g11s )
masi.garch11ss <-ugarchfit(data= MASI, spec= masi.g11ss )
masi.garch11g <-ugarchfit(data= MASI, spec= masi.g11g )
masi.garch11sg <-ugarchfit(data= MASI, spec= masi.g11sg ) #5
masi.garch12n <-ugarchfit(data= MASI, spec= masi.g12n )
masi.garch12s <-ugarchfit(data= MASI, spec= masi.g12s )
masi.garch12ss <-ugarchfit(data= MASI, spec= masi.g12ss )
masi.garch12g<-ugarchfit(data= MASI, spec= masi.g12g )
masi.garch12sg <-ugarchfit(data= MASI, spec= masi.g12sg ) #10
masi.garch21n <-ugarchfit(data= MASI, spec= masi.g21n )
masi.garch21s <-ugarchfit(data= MASI, spec= masi.g21s )
masi.garch21ss <-ugarchfit(data= MASI, spec= masi.g21ss)
masi.garch21g <-ugarchfit(data= MASI, spec= masi.g21g )
masi.garch21sg <-ugarchfit(data= MASI, spec= masi.g21sg ) #15
masi.garch22n <-ugarchfit(data= MASI, spec= masi.g22n )
masi.garch22s <-ugarchfit(data= MASI, spec= masi.g22s )
masi.garch22ss <-ugarchfit(data= MASI, spec= masi.g22ss )
masi.garch22g<-ugarchfit(data= MASI, spec= masi.g22g )
masi.garch22sg <-ugarchfit(data= MASI, spec= masi.g22sg )
model.aic.list <- list(masi.garch11n,masi.garch11s,masi.garch11ss,masi.garch11g,masi.garch11sg,masi.garch12n,masi.garch12s,masi.garch12ss,masi.garch12g,masi.garch12sg,masi.garch21n,masi.garch21s,masi.garch21ss,masi.garch21g,masi.garch21sg,masi.garch22n,masi.garch22s,masi.garch22ss,masi.garch22g,masi.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 7masi.garch12s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.01558995 0.01811724 0.8605037 0.3895114538
## ar1 0.07952356 0.03093955 2.5702883 0.0101613899
## omega 0.09258907 0.04544159 2.0375404 0.0415959194
## alpha1 0.12336222 0.05488902 2.2474843 0.0246090910
## beta1 0.03968683 0.11773667 0.3370813 0.7360555835
## beta2 0.65978488 0.17289608 3.8160777 0.0001355898
## gamma1 -0.02947728 0.05974620 -0.4933750 0.6217476145
## shape 3.54207096 0.41530528 8.5288367 0.0000000000print("Oman")## [1] "Oman"msm30.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
msm30.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
msm30.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
msm30.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
msm30.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
msm30.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
msm30.garch11n <-ugarchfit(data= MSM30, spec= msm30.g11n ) #1
msm30.garch11s <-ugarchfit(data= MSM30, spec= msm30.g11s )
msm30.garch11ss <-ugarchfit(data= MSM30, spec= msm30.g11ss )
msm30.garch11g <-ugarchfit(data= MSM30, spec= msm30.g11g )
msm30.garch11sg <-ugarchfit(data= MSM30, spec= msm30.g11sg ) #5
msm30.garch12n <-ugarchfit(data= MSM30, spec= msm30.g12n )
msm30.garch12s <-ugarchfit(data= MSM30, spec= msm30.g12s )
msm30.garch12ss <-ugarchfit(data= MSM30, spec= msm30.g12ss )
msm30.garch12g<-ugarchfit(data= MSM30, spec= msm30.g12g )
msm30.garch12sg <-ugarchfit(data= MSM30, spec= msm30.g12sg ) #10
msm30.garch21n <-ugarchfit(data= MSM30, spec= msm30.g21n )
msm30.garch21s <-ugarchfit(data= MSM30, spec= msm30.g21s )
msm30.garch21ss <-ugarchfit(data= MSM30, spec= msm30.g21ss)
msm30.garch21g <-ugarchfit(data= MSM30, spec= msm30.g21g )
msm30.garch21sg <-ugarchfit(data= MSM30, spec= msm30.g21sg ) #15
msm30.garch22n <-ugarchfit(data= MSM30, spec= msm30.g22n )
msm30.garch22s <-ugarchfit(data= MSM30, spec= msm30.g22s )
msm30.garch22ss <-ugarchfit(data= MSM30, spec= msm30.g22ss )
msm30.garch22g<-ugarchfit(data= MSM30, spec= msm30.g22g )
msm30.garch22sg <-ugarchfit(data= MSM30, spec= msm30.g22sg )
model.aic.list <- list(msm30.garch11n,msm30.garch11s,msm30.garch11ss,msm30.garch11g,msm30.garch11sg,msm30.garch12n,msm30.garch12s,msm30.garch12ss,msm30.garch12g,msm30.garch12sg,msm30.garch21n,msm30.garch21s,msm30.garch21ss,msm30.garch21g,msm30.garch21sg,msm30.garch22n,msm30.garch22s,msm30.garch22ss,msm30.garch22g,msm30.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2msm30.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu -0.04377966 0.01883115 -2.324853 2.007982e-02
## ar1 -0.70590199 0.10952184 -6.445308 1.153657e-10
## ar2 0.16820104 0.03143664 5.350477 8.772250e-08
## ma1 0.90253157 0.09829594 9.181779 0.000000e+00
## omega 0.07562644 0.02400486 3.150463 1.630118e-03
## alpha1 0.14071110 0.07206587 1.952534 5.087479e-02
## beta1 0.68584769 0.07068398 9.703015 0.000000e+00
## gamma1 0.12649653 0.08050912 1.571207 1.161345e-01
## shape 3.15773600 0.34661925 9.110100 0.000000e+00SP500_model <- sp500.garch21ss
VNI_model <- vni.garch21ss
MERVAL_model <- merval.garch21s
CROBEX_model <- crobex.garch11s
MASI_model <- masi.garch12s
MSM30_model <- msm30.garch11s
SP500.res <- residuals(SP500_model)/sigma(SP500_model)
VNI.res <- residuals(VNI_model)/sigma(VNI_model)
MERVAL.res <- residuals(MERVAL_model)/sigma(MERVAL_model)
CROBEX.res <- residuals(CROBEX_model)/sigma(CROBEX_model)
MASI.res <- residuals(MASI_model)/sigma(MASI_model)
MSM30.res <- residuals(MSM30_model)/sigma(MSM30_model)
fitdist(distribution = "sstd", SP500.res, control = list())$pars## mu sigma skew shape
## 0.006156548 0.994348111 0.829886828 3.781440152fitdist(distribution = "sstd", VNI.res, control = list())$pars## mu sigma skew shape
## -0.002286712 1.009614892 0.933761104 3.803141934fitdist(distribution = "std", MERVAL.res, control = list())$pars## mu sigma shape
## -0.0003312207 0.9925514382 3.9927352656fitdist(distribution = "std", CROBEX.res, control = list())$pars## mu sigma shape
## 1.735272e-05 9.919804e-01 3.051209e+00fitdist(distribution = "std", MASI.res, control = list())$pars## mu sigma shape
## -0.0001714908 1.0176530742 3.4412295356fitdist(distribution = "std", MSM30.res, control = list())$pars## mu sigma shape
## 0.008536164 1.020942508 3.073196942u <- pdist(distribution = "sstd", q = SP500.res, mu = 0.006156548, sigma = 0.994348111, skew= 0.829886828,shape = 3.781440152)
v1 <- pdist(distribution = "sstd", q = VNI.res, mu =-0.002286712, sigma = 1.009614892, skew= 0.933761104,shape = 3.803141934)
v2 <- pdist(distribution = "std", q = MERVAL.res, mu = -0.0003312207, sigma = 0.9925514382, shape = 3.9927352656)
v3 <- pdist(distribution = "std", q = CROBEX.res, mu = 1.735272e-05, sigma = 9.919804e-01, shape = 3.051209e+00)
v4 <- pdist(distribution = "std", q = MASI.res, mu = -0.0001714908, sigma = 1.0176530742, shape = 3.4412295356)
v5 <- pdist(distribution = "std", q = MSM30.res, mu = 0.008536164, sigma = 1.020942508, shape = 3.073196942)
goftest::cvm.test(u, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## omega2 = 0.077638, p-value = 0.7057goftest::cvm.test(v1, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## omega2 = 0.016792, p-value = 0.9991goftest::cvm.test(v2, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## omega2 = 0.028429, p-value = 0.9811goftest::cvm.test(v3, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## omega2 = 0.044337, p-value = 0.9097goftest::cvm.test(v4, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## omega2 = 0.033814, p-value = 0.9621goftest::cvm.test(v5, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## omega2 = 0.044501, p-value = 0.9088goftest::ad.test(u, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## An = 0.53617, p-value = 0.7102goftest::ad.test(v1, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## An = 0.15169, p-value = 0.9985goftest::ad.test(v2, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## An = 0.24843, p-value = 0.9712goftest::ad.test(v3, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## An = 0.28118, p-value = 0.9516goftest::ad.test(v4, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## An = 0.27138, p-value = 0.958goftest::ad.test(v5, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## An = 0.32708, p-value = 0.9165ks.test(u, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: u
## D = 0.020581, p-value = 0.8013
## alternative hypothesis: two-sidedks.test(v1, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v1
## D = 0.01326, p-value = 0.9953
## alternative hypothesis: two-sidedks.test(v2, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v2
## D = 0.015702, p-value = 0.9692
## alternative hypothesis: two-sidedks.test(v3, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v3
## D = 0.024761, p-value = 0.5857
## alternative hypothesis: two-sidedks.test(v4, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v4
## D = 0.017247, p-value = 0.9328
## alternative hypothesis: two-sidedks.test(v5, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v5
## D = 0.02149, p-value = 0.7566
## alternative hypothesis: two-sidedprint("Việt Nam")## [1] "Việt Nam"aa1 <- BiCopEst(u, v1, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.19
## AIC: -16.37
## BIC: -11.49aa2 <- BiCopEst(u, v1, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 5.59
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 20.79
## AIC: -37.58
## BIC: -27.81aa3 <- BiCopEst(u, v1, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 11.22
## AIC: -20.44
## BIC: -15.56aa4 <- BiCopEst(u, v1, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.37
## AIC: -16.73
## BIC: -11.85aa5 <- BiCopEst(u, v1, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.83
## AIC: -21.66
## BIC: -16.77aa6 <- BiCopEst(u, v1, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 14.21
## AIC: -26.41
## BIC: -21.53aa7 <- BiCopEst(u, v1, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.59
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.38
## AIC: -6.75
## BIC: -1.87aa8 <- BiCopEst(u, v1, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 10.34
## AIC: -18.67
## BIC: -13.79aa9 <- BiCopEst(u, v1, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 12.76
## AIC: -23.51
## BIC: -18.63aa10 <- BiCopEst(u, v1, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.09
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 15.32
## AIC: -26.64
## BIC: -16.87aa11 <- BiCopEst(u, v1, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.09
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 16.72
## AIC: -29.44
## BIC: -19.67aa12 <- BiCopEst(u, v1, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.83
## AIC: -19.65
## BIC: -9.88aa13 <- BiCopEst(u, v1, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 14.2
## AIC: -24.4
## BIC: -14.62aa14 <- BiCopEst(u, v1, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.11
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.8
## AIC: -29.61
## BIC: -19.84aa15 <- BiCopEst(u, v1, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 0.12
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 17.86
## AIC: -31.72
## BIC: -21.94aa16 <- BiCopEst(u, v1, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 10.34
## AIC: -16.67
## BIC: -6.9aa17 <- BiCopEst(u, v1, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 12.76
## AIC: -21.51
## BIC: -11.74aacopulalist <- list(summary(aa1)$AIC,summary(aa2)$AIC, summary(aa3)$AIC, summary(aa4)$AIC, summary(aa5)$AIC, summary(aa6)$AIC, summary(aa7)$AIC, summary(aa8)$AIC, summary(aa9)$AIC, summary(aa10)$AIC, summary(aa11)$AIC, summary(aa12)$AIC, summary(aa13)$AIC, summary(aa14)$AIC, summary(aa15)$AIC, summary(aa16)$AIC, summary(aa17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.19
## AIC: -16.37
## BIC: -11.49
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 5.59
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 20.79
## AIC: -37.58
## BIC: -27.81
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 11.22
## AIC: -20.44
## BIC: -15.56
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.37
## AIC: -16.73
## BIC: -11.85
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.83
## AIC: -21.66
## BIC: -16.77
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 14.21
## AIC: -26.41
## BIC: -21.53
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.59
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.38
## AIC: -6.75
## BIC: -1.87
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 10.34
## AIC: -18.67
## BIC: -13.79
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 12.76
## AIC: -23.51
## BIC: -18.63
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.09
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 15.32
## AIC: -26.64
## BIC: -16.87
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.09
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 16.72
## AIC: -29.44
## BIC: -19.67
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.83
## AIC: -19.65
## BIC: -9.88
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 14.2
## AIC: -24.4
## BIC: -14.62
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.11
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.8
## AIC: -29.61
## BIC: -19.84
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 0.12
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 17.86
## AIC: -31.72
## BIC: -21.94
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.06, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 10.34
## AIC: -16.67
## BIC: -6.9
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.06, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 12.76
## AIC: -21.51
## BIC: -11.74aacopulalist## [[1]]
## [1] -16.37366
##
## [[2]]
## [1] -37.58398
##
## [[3]]
## [1] -20.44157
##
## [[4]]
## [1] -16.73263
##
## [[5]]
## [1] -21.6611
##
## [[6]]
## [1] -26.41161
##
## [[7]]
## [1] -6.754245
##
## [[8]]
## [1] -18.67434
##
## [[9]]
## [1] -23.51377
##
## [[10]]
## [1] -26.6439
##
## [[11]]
## [1] -29.4412
##
## [[12]]
## [1] -19.65089
##
## [[13]]
## [1] -24.39754
##
## [[14]]
## [1] -29.60951
##
## [[15]]
## [1] -31.71538
##
## [[16]]
## [1] -16.67434
##
## [[17]]
## [1] -21.51377print("Argentina")## [1] "Argentina"ab1 <- BiCopEst(u, v2, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.39
##
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 82.14
## AIC: -162.28
## BIC: -157.39ab2 <- BiCopEst(u, v2, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.4
## par2: 7.19
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.1
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 92.2
## AIC: -180.4
## BIC: -170.63ab3 <- BiCopEst(u, v2, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.49
##
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.24
##
## Fit statistics
## --------------
## logLik: 69.88
## AIC: -137.76
## BIC: -132.87ab4 <- BiCopEst(u, v2, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.52
##
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 61.85
## AIC: -121.71
## BIC: -116.82ab5 <- BiCopEst(u, v2, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.33
##
## Dependence measures
## -------------------
## Kendall's tau: 0.25 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.32
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 79.38
## AIC: -156.76
## BIC: -151.88ab6 <- BiCopEst(u, v2, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.31
##
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.3
##
## Fit statistics
## --------------
## logLik: 80.55
## AIC: -159.09
## BIC: -154.21ab7 <- BiCopEst(u, v2, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 2.63
##
## Dependence measures
## -------------------
## Kendall's tau: 0.27 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 81.8
## AIC: -161.6
## BIC: -156.72ab8 <- BiCopEst(u, v2, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.41
##
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.37
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 56.56
## AIC: -111.13
## BIC: -106.24ab9 <- BiCopEst(u, v2, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.39
##
## Dependence measures
## -------------------
## Kendall's tau: 0.18 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.35
##
## Fit statistics
## --------------
## logLik: 62.09
## AIC: -122.18
## BIC: -117.29ab10 <- BiCopEst(u, v2, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.22
## par2: 1.21
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.23
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 89.42
## AIC: -174.84
## BIC: -165.07ab11 <- BiCopEst(u, v2, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.22
## par2: 1.21
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0.23
##
## Fit statistics
## --------------
## logLik: 87.03
## AIC: -170.07
## BIC: -160.29ab12 <- BiCopEst(u, v2, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.33
## Dependence measures
## -------------------
## Kendall's tau: 0.25 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.32
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 79.34
## AIC: -154.68
## BIC: -144.91ab13 <- BiCopEst(u, v2, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.31
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.3
##
## Fit statistics
## --------------
## logLik: 80.51
## AIC: -157.02
## BIC: -147.25ab14 <- BiCopEst(u, v2, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.26
## par2: 0.36
## Dependence measures
## -------------------
## Kendall's tau: 0.25 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 87.11
## AIC: -170.22
## BIC: -160.45ab15 <- BiCopEst(u, v2, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.25
## par2: 0.37
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.16
## Lower TD: 0.26
##
## Fit statistics
## --------------
## logLik: 84.22
## AIC: -164.44
## BIC: -154.67ab16 <- BiCopEst(u, v2, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.38
## Dependence measures
## -------------------
## Kendall's tau: 0.27 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 79.92
## AIC: -155.84
## BIC: -146.07ab17 <- BiCopEst(u, v2, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 3.15
## par2: 0.66
## Dependence measures
## -------------------
## Kendall's tau: 0.27 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 84.21
## AIC: -164.42
## BIC: -154.65abcopulalist <- list(summary(ab1)$AIC,summary(ab2)$AIC, summary(ab3)$AIC, summary(ab4)$AIC, summary(ab5)$AIC, summary(ab6)$AIC, summary(ab7)$AIC, summary(ab8)$AIC, summary(ab9)$AIC, summary(ab10)$AIC, summary(ab11)$AIC, summary(ab12)$AIC, summary(ab13)$AIC, summary(ab14)$AIC, summary(ab15)$AIC, summary(ab16)$AIC, summary(ab17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.39
##
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 82.14
## AIC: -162.28
## BIC: -157.39
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.4
## par2: 7.19
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.1
## Lower TD: 0.1
##
## Fit statistics
## --------------
## logLik: 92.2
## AIC: -180.4
## BIC: -170.63
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.49
##
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.24
##
## Fit statistics
## --------------
## logLik: 69.88
## AIC: -137.76
## BIC: -132.87
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.52
##
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 61.85
## AIC: -121.71
## BIC: -116.82
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.33
##
## Dependence measures
## -------------------
## Kendall's tau: 0.25 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.32
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 79.38
## AIC: -156.76
## BIC: -151.88
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.31
##
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.3
##
## Fit statistics
## --------------
## logLik: 80.55
## AIC: -159.09
## BIC: -154.21
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 2.63
##
## Dependence measures
## -------------------
## Kendall's tau: 0.27 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 81.8
## AIC: -161.6
## BIC: -156.72
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.41
##
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.37
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 56.56
## AIC: -111.13
## BIC: -106.24
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.39
##
## Dependence measures
## -------------------
## Kendall's tau: 0.18 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.35
##
## Fit statistics
## --------------
## logLik: 62.09
## AIC: -122.18
## BIC: -117.29
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.22
## par2: 1.21
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.23
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 89.42
## AIC: -174.84
## BIC: -165.07
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.22
## par2: 1.21
## Dependence measures
## -------------------
## Kendall's tau: 0.26 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0.23
##
## Fit statistics
## --------------
## logLik: 87.03
## AIC: -170.07
## BIC: -160.29
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.33
## Dependence measures
## -------------------
## Kendall's tau: 0.25 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.32
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 79.34
## AIC: -154.68
## BIC: -144.91
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.31
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.3
##
## Fit statistics
## --------------
## logLik: 80.51
## AIC: -157.02
## BIC: -147.25
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.26
## par2: 0.36
## Dependence measures
## -------------------
## Kendall's tau: 0.25 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 87.11
## AIC: -170.22
## BIC: -160.45
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.25
## par2: 0.37
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.27, p value < 0.01)
## Upper TD: 0.16
## Lower TD: 0.26
##
## Fit statistics
## --------------
## logLik: 84.22
## AIC: -164.44
## BIC: -154.67
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.38
## Dependence measures
## -------------------
## Kendall's tau: 0.27 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 79.92
## AIC: -155.84
## BIC: -146.07
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 3.15
## par2: 0.66
## Dependence measures
## -------------------
## Kendall's tau: 0.27 (empirical = 0.27, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 84.21
## AIC: -164.42
## BIC: -154.65abcopulalist## [[1]]
## [1] -162.2768
##
## [[2]]
## [1] -180.4039
##
## [[3]]
## [1] -137.7597
##
## [[4]]
## [1] -121.7091
##
## [[5]]
## [1] -156.7629
##
## [[6]]
## [1] -159.0946
##
## [[7]]
## [1] -161.6045
##
## [[8]]
## [1] -111.1257
##
## [[9]]
## [1] -122.1808
##
## [[10]]
## [1] -174.8399
##
## [[11]]
## [1] -170.0665
##
## [[12]]
## [1] -154.6836
##
## [[13]]
## [1] -157.0228
##
## [[14]]
## [1] -170.2191
##
## [[15]]
## [1] -164.4409
##
## [[16]]
## [1] -155.8395
##
## [[17]]
## [1] -164.42print("Croatia")## [1] "Croatia"ac1 <- BiCopEst(u, v3, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.66
## AIC: -15.32
## BIC: -10.43ac2 <- BiCopEst(u, v3, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.13
## par2: 5.46
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 23.69
## AIC: -43.39
## BIC: -33.61ac3 <- BiCopEst(u, v3, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 11.87
## AIC: -21.74
## BIC: -16.86ac4 <- BiCopEst(u, v3, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.83
## AIC: -13.66
## BIC: -8.77ac5 <- BiCopEst(u, v3, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.12
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 12.31
## AIC: -22.63
## BIC: -17.74ac6 <- BiCopEst(u, v3, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 16.28
## AIC: -30.56
## BIC: -25.68ac7 <- BiCopEst(u, v3, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.74
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.08
## AIC: -12.16
## BIC: -7.28ac8 <- BiCopEst(u, v3, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.85
## AIC: -17.69
## BIC: -12.8ac9 <- BiCopEst(u, v3, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 14.94
## AIC: -27.89
## BIC: -23ac10 <- BiCopEst(u, v3, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.03
## AIC: -28.05
## BIC: -18.28ac11 <- BiCopEst(u, v3, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 17.35
## AIC: -30.69
## BIC: -20.92ac12 <- BiCopEst(u, v3, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.12
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 12.3
## AIC: -20.6
## BIC: -10.83ac13 <- BiCopEst(u, v3, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 16.28
## AIC: -28.56
## BIC: -18.78ac14 <- BiCopEst(u, v3, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 0.13
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.1
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.85
## AIC: -29.7
## BIC: -19.92ac15 <- BiCopEst(u, v3, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 18.16
## AIC: -32.33
## BIC: -22.55ac16 <- BiCopEst(u, v3, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.85
## AIC: -15.69
## BIC: -5.92ac17 <- BiCopEst(u, v3, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 14.94
## AIC: -25.89
## BIC: -16.11accopulalist <- list(summary(ac1)$AIC,summary(ac2)$AIC, summary(ac3)$AIC, summary(ac4)$AIC, summary(ac5)$AIC, summary(ac6)$AIC, summary(ac7)$AIC, summary(ac8)$AIC, summary(ac9)$AIC, summary(ac10)$AIC, summary(ac11)$AIC, summary(ac12)$AIC, summary(ac13)$AIC, summary(ac14)$AIC, summary(ac15)$AIC, summary(ac16)$AIC, summary(ac17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.66
## AIC: -15.32
## BIC: -10.43
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.13
## par2: 5.46
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 23.69
## AIC: -43.39
## BIC: -33.61
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 11.87
## AIC: -21.74
## BIC: -16.86
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.83
## AIC: -13.66
## BIC: -8.77
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.12
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 12.31
## AIC: -22.63
## BIC: -17.74
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 16.28
## AIC: -30.56
## BIC: -25.68
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.74
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.08
## AIC: -12.16
## BIC: -7.28
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.85
## AIC: -17.69
## BIC: -12.8
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 14.94
## AIC: -27.89
## BIC: -23
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.03
## AIC: -28.05
## BIC: -18.28
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 17.35
## AIC: -30.69
## BIC: -20.92
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.12
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 12.3
## AIC: -20.6
## BIC: -10.83
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 16.28
## AIC: -28.56
## BIC: -18.78
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 0.13
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.1
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.85
## AIC: -29.7
## BIC: -19.92
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 18.16
## AIC: -32.33
## BIC: -22.55
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.85
## AIC: -15.69
## BIC: -5.92
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.08, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 14.94
## AIC: -25.89
## BIC: -16.11accopulalist## [[1]]
## [1] -15.32109
##
## [[2]]
## [1] -43.38548
##
## [[3]]
## [1] -21.74464
##
## [[4]]
## [1] -13.65832
##
## [[5]]
## [1] -22.62571
##
## [[6]]
## [1] -30.56317
##
## [[7]]
## [1] -12.16447
##
## [[8]]
## [1] -17.69052
##
## [[9]]
## [1] -27.88701
##
## [[10]]
## [1] -28.05213
##
## [[11]]
## [1] -30.6909
##
## [[12]]
## [1] -20.60009
##
## [[13]]
## [1] -28.55538
##
## [[14]]
## [1] -29.69746
##
## [[15]]
## [1] -32.32755
##
## [[16]]
## [1] -15.69052
##
## [[17]]
## [1] -25.88701print("Morocco")## [1] "Morocco"ad1 <- BiCopEst(u, v4, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.63
## AIC: -9.26
## BIC: -4.38ad2 <- BiCopEst(u, v4, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 22.01
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.55
## AIC: -9.09
## BIC: 0.68ad3 <- BiCopEst(u, v4, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.64
## AIC: -9.28
## BIC: -4.39ad4 <- BiCopEst(u, v4, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.75
## AIC: -5.5
## BIC: -0.62ad5 <- BiCopEst(u, v4, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.1
## AIC: -6.19
## BIC: -1.31ad6 <- BiCopEst(u, v4, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 5.22
## AIC: -8.44
## BIC: -3.55ad7 <- BiCopEst(u, v4, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.6
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.83
## AIC: -7.66
## BIC: -2.77ad8 <- BiCopEst(u, v4, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.43
## AIC: -2.86
## BIC: 2.03ad9 <- BiCopEst(u, v4, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 3.94
## AIC: -5.89
## BIC: -1ad10 <- BiCopEst(u, v4, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.08
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.32
## AIC: -8.65
## BIC: 1.13ad11 <- BiCopEst(u, v4, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.05
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 5.92
## AIC: -7.83
## BIC: 1.94ad12 <- BiCopEst(u, v4, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.08
## AIC: -4.15
## BIC: 5.62ad13 <- BiCopEst(u, v4, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 5.21
## AIC: -6.41
## BIC: 3.36ad14 <- BiCopEst(u, v4, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.03
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.28
## AIC: -8.55
## BIC: 1.22ad15 <- BiCopEst(u, v4, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 5.8
## AIC: -7.6
## BIC: 2.17ad16 <- BiCopEst(u, v4, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.11
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.83
## AIC: -5.66
## BIC: 4.11ad17 <- BiCopEst(u, v4, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.4
## par2: 0.71
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5
## AIC: -6
## BIC: 3.78adcopulalist <- list(summary(ad1)$AIC,summary(ad2)$AIC, summary(ad3)$AIC, summary(ad4)$AIC, summary(ad5)$AIC, summary(ad6)$AIC, summary(ad7)$AIC, summary(ad8)$AIC, summary(ad9)$AIC, summary(ad10)$AIC, summary(ad11)$AIC, summary(ad12)$AIC, summary(ad13)$AIC, summary(ad14)$AIC, summary(ad15)$AIC, summary(ad16)$AIC, summary(ad17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.63
## AIC: -9.26
## BIC: -4.38
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.1
## par2: 22.01
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.55
## AIC: -9.09
## BIC: 0.68
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.64
## AIC: -9.28
## BIC: -4.39
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.75
## AIC: -5.5
## BIC: -0.62
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.1
## AIC: -6.19
## BIC: -1.31
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 5.22
## AIC: -8.44
## BIC: -3.55
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.6
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.83
## AIC: -7.66
## BIC: -2.77
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.43
## AIC: -2.86
## BIC: 2.03
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 3.94
## AIC: -5.89
## BIC: -1
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.08
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.32
## AIC: -8.65
## BIC: 1.13
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.05
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 5.92
## AIC: -7.83
## BIC: 1.94
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.08
## AIC: -4.15
## BIC: 5.62
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.08
##
## Fit statistics
## --------------
## logLik: 5.21
## AIC: -6.41
## BIC: 3.36
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.03
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.28
## AIC: -8.55
## BIC: 1.22
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 5.8
## AIC: -7.6
## BIC: 2.17
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.11
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.83
## AIC: -5.66
## BIC: 4.11
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.4
## par2: 0.71
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5
## AIC: -6
## BIC: 3.78adcopulalist## [[1]]
## [1] -9.263285
##
## [[2]]
## [1] -9.09209
##
## [[3]]
## [1] -9.276542
##
## [[4]]
## [1] -5.502468
##
## [[5]]
## [1] -6.192606
##
## [[6]]
## [1] -8.440108
##
## [[7]]
## [1] -7.659406
##
## [[8]]
## [1] -2.860587
##
## [[9]]
## [1] -5.886816
##
## [[10]]
## [1] -8.646212
##
## [[11]]
## [1] -7.830763
##
## [[12]]
## [1] -4.152056
##
## [[13]]
## [1] -6.414459
##
## [[14]]
## [1] -8.55463
##
## [[15]]
## [1] -7.603758
##
## [[16]]
## [1] -5.659713
##
## [[17]]
## [1] -5.995473print("Oman")## [1] "Oman"ae1 <- BiCopEst(u, v5, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.69
## AIC: -3.38
## BIC: 1.51ae2 <- BiCopEst(u, v5, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 6.19
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.04
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 13.96
## AIC: -23.93
## BIC: -14.16ae3 <- BiCopEst(u, v5, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.89
## AIC: -7.78
## BIC: -2.89ae4 <- BiCopEst(u, v5, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.16
## AIC: -4.32
## BIC: 0.57ae5 <- BiCopEst(u, v5, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.17
## AIC: -8.34
## BIC: -3.45ae6 <- BiCopEst(u, v5, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 6.39
## AIC: -10.78
## BIC: -5.9ae7 <- BiCopEst(u, v5, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.38
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.82
## AIC: -1.65
## BIC: 3.24ae8 <- BiCopEst(u, v5, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.1
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.65
## AIC: -7.31
## BIC: -2.42ae9 <- BiCopEst(u, v5, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 5.84
## AIC: -9.68
## BIC: -4.79ae10 <- BiCopEst(u, v5, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.73
## AIC: -9.45
## BIC: 0.32ae11 <- BiCopEst(u, v5, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.05
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 7.22
## AIC: -10.43
## BIC: -0.66ae12 <- BiCopEst(u, v5, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.17
## AIC: -6.34
## BIC: 3.44ae13 <- BiCopEst(u, v5, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.05
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 6.39
## AIC: -8.77
## BIC: 1ae14 <- BiCopEst(u, v5, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.06
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.48
## AIC: -10.97
## BIC: -1.19ae15 <- BiCopEst(u, v5, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 7.53
## AIC: -11.05
## BIC: -1.28ae16 <- BiCopEst(u, v5, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.1
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.65
## AIC: -5.31
## BIC: 4.46ae17 <- BiCopEst(u, v5, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 5.84
## AIC: -7.68
## BIC: 2.09aecopulalist <- list(summary(ae1)$AIC,summary(ae2)$AIC, summary(ae3)$AIC, summary(ae4)$AIC, summary(ae5)$AIC, summary(ae6)$AIC, summary(ae7)$AIC, summary(ae8)$AIC, summary(ae9)$AIC, summary(ae10)$AIC, summary(ae11)$AIC, summary(ae12)$AIC, summary(ae13)$AIC, summary(ae14)$AIC, summary(ae15)$AIC, summary(ae16)$AIC, summary(ae17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.69
## AIC: -3.38
## BIC: 1.51
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 6.19
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.04
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 13.96
## AIC: -23.93
## BIC: -14.16
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.89
## AIC: -7.78
## BIC: -2.89
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.16
## AIC: -4.32
## BIC: 0.57
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.06
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.17
## AIC: -8.34
## BIC: -3.45
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 6.39
## AIC: -10.78
## BIC: -5.9
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.38
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.82
## AIC: -1.65
## BIC: 3.24
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.1
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.65
## AIC: -7.31
## BIC: -2.42
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 5.84
## AIC: -9.68
## BIC: -4.79
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.73
## AIC: -9.45
## BIC: 0.32
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.05
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 7.22
## AIC: -10.43
## BIC: -0.66
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.17
## AIC: -6.34
## BIC: 3.44
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.05
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 6.39
## AIC: -8.77
## BIC: 1
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.06
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.08
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.48
## AIC: -10.97
## BIC: -1.19
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 7.53
## AIC: -11.05
## BIC: -1.28
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.08
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.05)
## Upper TD: 0.1
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.65
## AIC: -5.31
## BIC: 4.46
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 5.84
## AIC: -7.68
## BIC: 2.09aecopulalist## [[1]]
## [1] -3.376281
##
## [[2]]
## [1] -23.92905
##
## [[3]]
## [1] -7.780564
##
## [[4]]
## [1] -4.320973
##
## [[5]]
## [1] -8.339344
##
## [[6]]
## [1] -10.78295
##
## [[7]]
## [1] -1.646143
##
## [[8]]
## [1] -7.308318
##
## [[9]]
## [1] -9.678173
##
## [[10]]
## [1] -9.451174
##
## [[11]]
## [1] -10.43163
##
## [[12]]
## [1] -6.336344
##
## [[13]]
## [1] -8.771937
##
## [[14]]
## [1] -10.96731
##
## [[15]]
## [1] -11.05218
##
## [[16]]
## [1] -5.308318
##
## [[17]]
## [1] -7.678174rm(list=ls())
DATA <- read_xlsx("C://Users//84896//Desktop//DATA//CN3-COPULA.xlsx", sheet="Dur")
SP500 <- DATA$y
VNI <- DATA$x1
MERVAL <- DATA$x2
CROBEX <- DATA$x3
MASI <- DATA$x4
MSM30 <- DATA$x5cor(cbind(SP500, VNI, MERVAL, CROBEX, MASI, MSM30), method="pearson")## SP500 VNI MERVAL CROBEX MASI MSM30
## SP500 1.0000000 0.2912909 0.4932638 0.5871144 0.3564173 0.3678267
## VNI 0.2912909 1.0000000 0.2130450 0.3929132 0.1283902 0.2407316
## MERVAL 0.4932638 0.2130450 1.0000000 0.4133827 0.4040066 0.3307059
## CROBEX 0.5871144 0.3929132 0.4133827 1.0000000 0.3919515 0.4520334
## MASI 0.3564173 0.1283902 0.4040066 0.3919515 1.0000000 0.3897878
## MSM30 0.3678267 0.2407316 0.3307059 0.4520334 0.3897878 1.0000000print("Mỹ")## [1] "Mỹ"autoarfima(SP500,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ar2 ma1 ma2 sigma
## 0.1813688 -0.8197414 -0.2956438 0.8285811 1.7283524print("Việt Nam")## [1] "Việt Nam"autoarfima(VNI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ar2 ma1 ma2 sigma
## 0.0000000 -0.5953088 0.0000000 0.7038250 1.7726662print("Argentina")## [1] "Argentina"autoarfima(MERVAL,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ar2 ma1 ma2 sigma
## -0.3581623 -0.9201646 0.2858461 0.9039866 3.5196747print("Croatia")## [1] "Croatia"autoarfima(CROBEX,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ma1 ma2 sigma
## 0.7471203 -0.6828856 0.1269224 1.2513865print("Morocco")## [1] "Morocco"autoarfima(MASI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ma1 sigma
## 0.200622 1.244185print("Oman")## [1] "Oman"autoarfima(MSM30,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 sigma
## 0.1533201 0.8080980print("Mỹ")## [1] "Mỹ"sp500.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
sp500.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
sp500.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
sp500.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
sp500.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
sp500.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
sp500.garch11n <-ugarchfit(data=SP500, spec= sp500.g11n ) #1
sp500.garch11s <-ugarchfit(data=SP500, spec= sp500.g11s )
sp500.garch11ss <-ugarchfit(data=SP500, spec= sp500.g11ss )
sp500.garch11g <-ugarchfit(data=SP500, spec= sp500.g11g )
sp500.garch11sg <-ugarchfit(data=SP500, spec= sp500.g11sg ) #5
sp500.garch12n <-ugarchfit(data=SP500, spec= sp500.g12n )
sp500.garch12s <-ugarchfit(data=SP500, spec= sp500.g12s )
sp500.garch12ss <-ugarchfit(data=SP500, spec= sp500.g12ss )
sp500.garch12g<-ugarchfit(data=SP500, spec= sp500.g12g )
sp500.garch12sg <-ugarchfit(data=SP500, spec= sp500.g12sg ) #10
sp500.garch21n <-ugarchfit(data=SP500, spec= sp500.g21n )
sp500.garch21s <-ugarchfit(data=SP500, spec= sp500.g21s )
sp500.garch21ss <-ugarchfit(data=SP500, spec= sp500.g21ss)
sp500.garch21g <-ugarchfit(data=SP500, spec= sp500.g21g )
sp500.garch21sg <-ugarchfit(data=SP500, spec= sp500.g21sg ) #15
sp500.garch22n <-ugarchfit(data=SP500, spec= sp500.g22n )
sp500.garch22s <-ugarchfit(data=SP500, spec= sp500.g22s )
sp500.garch22ss <-ugarchfit(data=SP500, spec= sp500.g22ss )
sp500.garch22g<-ugarchfit(data=SP500, spec= sp500.g22g )
sp500.garch22sg <-ugarchfit(data=SP500, spec= sp500.g22sg )
model.aic.list <- list(sp500.garch11n,sp500.garch11s,sp500.garch11ss,sp500.garch11g,sp500.garch11sg,sp500.garch12n,sp500.garch12s,sp500.garch12ss,sp500.garch12g,sp500.garch12sg,sp500.garch21n,sp500.garch21s,sp500.garch21ss,sp500.garch21g,sp500.garch21sg,sp500.garch22n,sp500.garch22s,sp500.garch22ss,sp500.garch22g,sp500.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 13sp500.garch21ss@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 9.860247e-02 5.761156e-03 1.711505e+01 0.000000e+00
## ar1 -9.842324e-01 2.722600e-04 -3.615046e+03 0.000000e+00
## ar2 -2.073474e-02 5.235782e-04 -3.960198e+01 0.000000e+00
## ma1 9.410352e-01 3.147266e-04 2.990009e+03 0.000000e+00
## ma2 -8.456789e-02 3.076081e-05 -2.749209e+03 0.000000e+00
## omega 5.676658e-02 3.433981e-02 1.653083e+00 9.831387e-02
## alpha1 1.862977e-08 1.277554e-01 1.458237e-07 9.999999e-01
## alpha2 9.289748e-03 9.323425e-02 9.963879e-02 9.206311e-01
## beta1 8.449075e-01 7.040908e-02 1.199998e+01 0.000000e+00
## gamma1 3.203533e-01 1.556843e-01 2.057711e+00 3.961792e-02
## gamma2 -2.550503e-02 1.726830e-01 -1.476986e-01 8.825807e-01
## skew 7.902836e-01 6.112467e-02 1.292904e+01 0.000000e+00
## shape 3.837577e+00 6.448322e-01 5.951280e+00 2.660528e-09print("Việt Nam")## [1] "Việt Nam"vni.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
vni.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
vni.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
vni.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
vni.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
vni.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
vni.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
vni.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
vni.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
vni.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
vni.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
vni.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
vni.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
vni.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
vni.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
vni.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
vni.garch11n <-ugarchfit(data=VNI, spec= vni.g11n ) #1
vni.garch11s <-ugarchfit(data=VNI, spec= vni.g11s )
vni.garch11ss <-ugarchfit(data=VNI, spec= vni.g11ss )
vni.garch11g <-ugarchfit(data=VNI, spec= vni.g11g )
vni.garch11sg <-ugarchfit(data=VNI, spec= vni.g11sg ) #5
vni.garch12n <-ugarchfit(data=VNI, spec= vni.g12n )
vni.garch12s <-ugarchfit(data=VNI, spec= vni.g12s )
vni.garch12ss <-ugarchfit(data=VNI, spec= vni.g12ss )
vni.garch12g<-ugarchfit(data=VNI, spec= vni.g12g )
vni.garch12sg <-ugarchfit(data=VNI, spec= vni.g12sg ) #10
vni.garch21n <-ugarchfit(data=VNI, spec= vni.g21n )
vni.garch21s <-ugarchfit(data=VNI, spec= vni.g21s )
vni.garch21ss <-ugarchfit(data=VNI, spec= vni.g21ss)
vni.garch21g <-ugarchfit(data=VNI, spec= vni.g21g )
vni.garch21sg <-ugarchfit(data=VNI, spec= vni.g21sg ) #15
vni.garch22n <-ugarchfit(data=VNI, spec= vni.g22n )
vni.garch22s <-ugarchfit(data=VNI, spec= vni.g22s )
vni.garch22ss <-ugarchfit(data=VNI, spec= vni.g22ss )
#vni.garch22g<-ugarchfit(data=VNI, spec= vni.g22g )
vni.garch22sg <-ugarchfit(data=VNI, spec= vni.g22sg ) #19
model.aic.list <- list(vni.garch11n,vni.garch11s,vni.garch11ss,vni.garch11g,vni.garch11sg,vni.garch12n,vni.garch12s,vni.garch12ss,vni.garch12g,vni.garch12sg,vni.garch21n,vni.garch21s,vni.garch21ss,vni.garch21g,vni.garch21sg,vni.garch22n,vni.garch22s,vni.garch22ss,vni.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2vni.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 2.786925e-01 0.053523709 5.206898e+00 1.920234e-07
## ar1 -1.416231e+00 0.009817769 -1.442518e+02 0.000000e+00
## ar2 -9.865528e-01 0.002909220 -3.391125e+02 0.000000e+00
## ma1 1.404897e+00 0.004205975 3.340241e+02 0.000000e+00
## ma2 9.938315e-01 0.001064361 9.337350e+02 0.000000e+00
## omega 1.849020e+00 1.855547148 9.964822e-01 3.190159e-01
## alpha1 5.945553e-09 0.317046294 1.875295e-08 1.000000e+00
## beta1 6.809370e-01 0.160645372 4.238759e+00 2.247587e-05
## gamma1 6.361185e-01 0.607815974 1.046564e+00 2.953006e-01
## shape 2.169652e+00 0.143058700 1.516617e+01 0.000000e+00print("Argentina")## [1] "Argentina"merval.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
merval.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
merval.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
merval.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
merval.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
merval.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
merval.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
merval.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
merval.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
merval.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
merval.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
merval.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
merval.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
merval.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
merval.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
merval.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
merval.garch11n <-ugarchfit(data= MERVAL, spec= merval.g11n ) #1
merval.garch11s <-ugarchfit(data= MERVAL, spec= merval.g11s )
merval.garch11ss <-ugarchfit(data= MERVAL, spec= merval.g11ss )
merval.garch11g <-ugarchfit(data= MERVAL, spec= merval.g11g )
merval.garch11sg <-ugarchfit(data= MERVAL, spec= merval.g11sg ) #5
merval.garch12n <-ugarchfit(data= MERVAL, spec= merval.g12n )
merval.garch12s <-ugarchfit(data= MERVAL, spec= merval.g12s )
merval.garch12ss <-ugarchfit(data= MERVAL, spec= merval.g12ss )
merval.garch12g<-ugarchfit(data= MERVAL, spec= merval.g12g )
merval.garch12sg <-ugarchfit(data= MERVAL, spec= merval.g12sg ) #10
merval.garch21n <-ugarchfit(data= MERVAL, spec= merval.g21n )
merval.garch21s <-ugarchfit(data= MERVAL, spec= merval.g21s )
merval.garch21ss <-ugarchfit(data= MERVAL, spec= merval.g21ss)
merval.garch21g <-ugarchfit(data= MERVAL, spec= merval.g21g )
merval.garch21sg <-ugarchfit(data= MERVAL, spec= merval.g21sg ) #15
merval.garch22n <-ugarchfit(data= MERVAL, spec= merval.g22n )
merval.garch22s <-ugarchfit(data= MERVAL, spec= merval.g22s )
merval.garch22ss <-ugarchfit(data= MERVAL, spec= merval.g22ss )
merval.garch22g<-ugarchfit(data= MERVAL, spec= merval.g22g )
merval.garch22sg <-ugarchfit(data= MERVAL, spec= merval.g22sg )
model.aic.list <- list(merval.garch11n,merval.garch11s,merval.garch11ss,merval.garch11g,merval.garch11sg,merval.garch12n,merval.garch12s,merval.garch12ss,merval.garch12g,merval.garch12sg,merval.garch21n,merval.garch21s,merval.garch21ss,merval.garch21g,merval.garch21sg,merval.garch22n,merval.garch22s,merval.garch22ss,merval.garch22g,merval.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 12merval.garch21s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 1.205281e-01 0.140473219 8.580145e-01 3.908845e-01
## ar1 -1.081190e+00 0.003412230 -3.168572e+02 0.000000e+00
## ar2 -9.994243e-01 0.011434770 -8.740222e+01 0.000000e+00
## ma1 1.099934e+00 0.002540584 4.329454e+02 0.000000e+00
## ma2 1.012195e+00 0.001259036 8.039441e+02 0.000000e+00
## omega 1.680896e-01 0.108415892 1.550414e+00 1.210421e-01
## alpha1 2.378083e-10 0.100686711 2.361864e-09 1.000000e+00
## alpha2 1.502850e-03 0.119299215 1.259732e-02 9.899491e-01
## beta1 9.540921e-01 0.024369436 3.915118e+01 0.000000e+00
## gamma1 5.451381e-01 0.061078657 8.925181e+00 0.000000e+00
## gamma2 -4.723075e-01 0.057668616 -8.190026e+00 2.220446e-16
## shape 3.970306e+00 0.958549062 4.141995e+00 3.442975e-05print("Crotia")## [1] "Crotia"crobex.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
crobex.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
crobex.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
crobex.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
crobex.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
crobex.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
crobex.garch11n <-ugarchfit(data= CROBEX, spec= crobex.g11n ) #1
crobex.garch11s <-ugarchfit(data= CROBEX, spec= crobex.g11s )
crobex.garch11ss <-ugarchfit(data= CROBEX, spec= crobex.g11ss )
crobex.garch11g <-ugarchfit(data= CROBEX, spec= crobex.g11g )
crobex.garch11sg <-ugarchfit(data= CROBEX, spec= crobex.g11sg ) #5
crobex.garch12n <-ugarchfit(data= CROBEX, spec= crobex.g12n )
crobex.garch12s <-ugarchfit(data= CROBEX, spec= crobex.g12s )
crobex.garch12ss <-ugarchfit(data= CROBEX, spec= crobex.g12ss )
crobex.garch12g<-ugarchfit(data= CROBEX, spec= crobex.g12g )
crobex.garch12sg <-ugarchfit(data= CROBEX, spec= crobex.g12sg ) #10
crobex.garch21n <-ugarchfit(data= CROBEX, spec= crobex.g21n )
crobex.garch21s <-ugarchfit(data= CROBEX, spec= crobex.g21s )
crobex.garch21ss <-ugarchfit(data= CROBEX, spec= crobex.g21ss)
crobex.garch21g <-ugarchfit(data= CROBEX, spec= crobex.g21g )
crobex.garch21sg <-ugarchfit(data= CROBEX, spec= crobex.g21sg ) #15
crobex.garch22n <-ugarchfit(data= CROBEX, spec= crobex.g22n )
crobex.garch22s <-ugarchfit(data= CROBEX, spec= crobex.g22s )
crobex.garch22ss <-ugarchfit(data= CROBEX, spec= crobex.g22ss )
crobex.garch22g<-ugarchfit(data= CROBEX, spec= crobex.g22g )
crobex.garch22sg <-ugarchfit(data= CROBEX, spec= crobex.g22sg )
model.aic.list <- list(crobex.garch11n,crobex.garch11s,crobex.garch11ss,crobex.garch11g,crobex.garch11sg,crobex.garch12n,crobex.garch12s,crobex.garch12ss,crobex.garch12g,crobex.garch12sg,crobex.garch21n,crobex.garch21s,crobex.garch21ss,crobex.garch21g,crobex.garch21sg,crobex.garch22n,crobex.garch22s,crobex.garch22ss,crobex.garch22g,crobex.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2crobex.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.07053809 0.02414297 2.9216825 0.003481462
## ar1 0.91959985 0.05173879 17.7738960 0.000000000
## ma1 -0.89007369 0.09686834 -9.1884891 0.000000000
## ma2 -0.04627771 0.07004109 -0.6607222 0.508790470
## omega 0.10135650 0.07569409 1.3390279 0.180561575
## alpha1 0.09797342 0.09956111 0.9840531 0.325089408
## beta1 0.85643004 0.06805267 12.5848126 0.000000000
## gamma1 0.08919305 0.14684975 0.6073762 0.543601257
## shape 2.34387546 0.27184081 8.6222355 0.000000000print("Morocco")## [1] "Morocco"masi.g11n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
masi.g11s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
masi.g11ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g11g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
masi.g11sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
masi.g12n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
masi.g12s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
masi.g12ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g12g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
masi.g12sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
masi.g21n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
masi.g21s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
masi.g21ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g21g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
masi.g21sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
masi.g22n <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
masi.g22s <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
masi.g22ss <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g22g <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
masi.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
masi.garch11n <-ugarchfit(data= MASI, spec= masi.g11n ) #1
masi.garch11s <-ugarchfit(data= MASI, spec= masi.g11s )
masi.garch11ss <-ugarchfit(data= MASI, spec= masi.g11ss )
masi.garch11g <-ugarchfit(data= MASI, spec= masi.g11g )
masi.garch11sg <-ugarchfit(data= MASI, spec= masi.g11sg ) #5
masi.garch12n <-ugarchfit(data= MASI, spec= masi.g12n )
masi.garch12s <-ugarchfit(data= MASI, spec= masi.g12s )
masi.garch12ss <-ugarchfit(data= MASI, spec= masi.g12ss )
masi.garch12g<-ugarchfit(data= MASI, spec= masi.g12g )
masi.garch12sg <-ugarchfit(data= MASI, spec= masi.g12sg ) #10
masi.garch21n <-ugarchfit(data= MASI, spec= masi.g21n )
masi.garch21s <-ugarchfit(data= MASI, spec= masi.g21s )
masi.garch21ss <-ugarchfit(data= MASI, spec= masi.g21ss)
masi.garch21g <-ugarchfit(data= MASI, spec= masi.g21g )
masi.garch21sg <-ugarchfit(data= MASI, spec= masi.g21sg ) #15
#masi.garch22n <-ugarchfit(data= MASI, spec= masi.g22n )
masi.garch22s <-ugarchfit(data= MASI, spec= masi.g22s ) #16
masi.garch22ss <-ugarchfit(data= MASI, spec= masi.g22ss )
masi.garch22g<-ugarchfit(data= MASI, spec= masi.g22g )
masi.garch22sg <-ugarchfit(data= MASI, spec= masi.g22sg )
model.aic.list <- list(masi.garch11n,masi.garch11s,masi.garch11ss,masi.garch11g,masi.garch11sg,masi.garch12n,masi.garch12s,masi.garch12ss,masi.garch12g,masi.garch12sg,masi.garch21n,masi.garch21s,masi.garch21ss,masi.garch21g,masi.garch21sg,masi.garch22s,masi.garch22ss,masi.garch22g,masi.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 7masi.garch12s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.07463277 0.03032963 2.4607212 1.386581e-02
## ma1 0.03267513 0.05082075 0.6429485 5.202575e-01
## omega 0.05855406 0.02915010 2.0087085 4.456805e-02
## alpha1 0.06903788 0.08838293 0.7811224 4.347305e-01
## beta1 0.12649672 0.16610113 0.7615645 4.463200e-01
## beta2 0.67130262 0.16467517 4.0765260 4.571354e-05
## gamma1 0.16364317 0.12686566 1.2898933 1.970877e-01
## shape 2.80451839 0.38379366 7.3073599 2.724487e-13print("Oman")## [1] "Oman"msm30.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
msm30.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
msm30.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
msm30.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
msm30.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
msm30.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
msm30.garch11n <-ugarchfit(data= MSM30, spec= msm30.g11n ) #1
msm30.garch11s <-ugarchfit(data= MSM30, spec= msm30.g11s )
msm30.garch11ss <-ugarchfit(data= MSM30, spec= msm30.g11ss )
msm30.garch11g <-ugarchfit(data= MSM30, spec= msm30.g11g )
msm30.garch11sg <-ugarchfit(data= MSM30, spec= msm30.g11sg ) #5
msm30.garch12n <-ugarchfit(data= MSM30, spec= msm30.g12n )
msm30.garch12s <-ugarchfit(data= MSM30, spec= msm30.g12s )
msm30.garch12ss <-ugarchfit(data= MSM30, spec= msm30.g12ss )
msm30.garch12g<-ugarchfit(data= MSM30, spec= msm30.g12g )
msm30.garch12sg <-ugarchfit(data= MSM30, spec= msm30.g12sg ) #10
msm30.garch21n <-ugarchfit(data= MSM30, spec= msm30.g21n )
msm30.garch21s <-ugarchfit(data= MSM30, spec= msm30.g21s )
msm30.garch21ss <-ugarchfit(data= MSM30, spec= msm30.g21ss)
msm30.garch21g <-ugarchfit(data= MSM30, spec= msm30.g21g )
msm30.garch21sg <-ugarchfit(data= MSM30, spec= msm30.g21sg ) #15
msm30.garch22n <-ugarchfit(data= MSM30, spec= msm30.g22n )
msm30.garch22s <-ugarchfit(data= MSM30, spec= msm30.g22s )
msm30.garch22ss <-ugarchfit(data= MSM30, spec= msm30.g22ss )
msm30.garch22g<-ugarchfit(data= MSM30, spec= msm30.g22g )
msm30.garch22sg <-ugarchfit(data= MSM30, spec= msm30.g22sg )
model.aic.list <- list(msm30.garch11n,msm30.garch11s,msm30.garch11ss,msm30.garch11g,msm30.garch11sg,msm30.garch12n,msm30.garch12s,msm30.garch12ss,msm30.garch12g,msm30.garch12sg,msm30.garch21n,msm30.garch21s,msm30.garch21ss,msm30.garch21g,msm30.garch21sg,msm30.garch22n,msm30.garch22s,msm30.garch22ss,msm30.garch22g,msm30.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2msm30.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.05042051 0.03201975 1.5746690 1.153329e-01
## ar1 0.19668882 0.05165551 3.8077027 1.402638e-04
## omega 0.04830432 0.03391793 1.4241529 1.544022e-01
## alpha1 0.27176477 0.22663275 1.1991417 2.304729e-01
## beta1 0.75889640 0.12944128 5.8628621 4.549560e-09
## gamma1 -0.18108863 0.19732545 -0.9177155 3.587678e-01
## shape 3.04139432 0.50615983 6.0087628 1.869444e-09SP500_model <- sp500.garch21ss
VNI_model <- vni.garch11s
MERVAL_model <- merval.garch21s
CROBEX_model <- crobex.garch11s
MASI_model <- masi.garch12s
MSM30_model <- msm30.garch11s
SP500.res <- residuals(SP500_model)/sigma(SP500_model)
VNI.res <- residuals(VNI_model)/sigma(VNI_model)
MERVAL.res <- residuals(MERVAL_model)/sigma(MERVAL_model)
CROBEX.res <- residuals(CROBEX_model)/sigma(CROBEX_model)
MASI.res <- residuals(MASI_model)/sigma(MASI_model)
MSM30.res <- residuals(MSM30_model)/sigma(MSM30_model)
fitdist(distribution = "sstd", SP500.res, control = list())$pars## mu sigma skew shape
## 0.02337271 0.96009808 0.79435776 4.20827267fitdist(distribution = "std", VNI.res, control = list())$pars## mu sigma shape
## 0.001670273 3.866657691 2.010000153fitdist(distribution = "std", MERVAL.res, control = list())$pars## mu sigma shape
## 0.01148837 0.98591184 4.10177017fitdist(distribution = "std", CROBEX.res, control = list())$pars## mu sigma shape
## -0.003792578 0.854192799 2.525174567fitdist(distribution = "std", MASI.res, control = list())$pars## mu sigma shape
## -0.005268347 1.139949045 2.544101339fitdist(distribution = "std", MSM30.res, control = list())$pars## mu sigma shape
## 0.003218684 1.064035384 2.847148419u <- pdist(distribution = "sstd", q = SP500.res, mu = 0.02337271, sigma = 0.96009808, skew= 0.79435776,shape = 4.20827267)
v1 <- pdist(distribution = "std", q = VNI.res, mu =0.001670273, sigma = 3.866657691, shape= 2.010000153)
v2 <- pdist(distribution = "std", q = MERVAL.res, mu = 0.01148837, sigma = 0.98591184, shape = 4.10177017)
v3 <- pdist(distribution = "std", q = CROBEX.res, mu = -0.003792578, sigma = 0.854192799, shape = 2.525174567)
v4 <- pdist(distribution = "std", q = MASI.res, mu = -0.005268347, sigma = 1.139949045, shape = 2.544101339)
v5 <- pdist(distribution = "std", q = MSM30.res, mu = 0.003218684, sigma = 1.064035384, shape = 2.847148419)
goftest::cvm.test(u, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## omega2 = 0.045759, p-value = 0.9018goftest::cvm.test(v1, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## omega2 = 0.10912, p-value = 0.5423goftest::cvm.test(v2, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## omega2 = 0.036352, p-value = 0.9512goftest::cvm.test(v3, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## omega2 = 0.039155, p-value = 0.9377goftest::cvm.test(v4, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## omega2 = 0.090574, p-value = 0.6334goftest::cvm.test(v5, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## omega2 = 0.028598, p-value = 0.9807goftest::ad.test(u, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## An = 0.31427, p-value = 0.927goftest::ad.test(v1, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## An = 0.89706, p-value = 0.416goftest::ad.test(v2, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## An = 0.25099, p-value = 0.9699goftest::ad.test(v3, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## An = 0.24115, p-value = 0.9749goftest::ad.test(v4, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## An = 0.5447, p-value = 0.7016goftest::ad.test(v5, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## An = 0.20914, p-value = 0.9877ks.test(u, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: u
## D = 0.029789, p-value = 0.9299
## alternative hypothesis: two-sidedks.test(v1, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v1
## D = 0.050378, p-value = 0.3685
## alternative hypothesis: two-sidedks.test(v2, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v2
## D = 0.03056, p-value = 0.9158
## alternative hypothesis: two-sidedks.test(v3, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v3
## D = 0.032452, p-value = 0.8756
## alternative hypothesis: two-sidedks.test(v4, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v4
## D = 0.041429, p-value = 0.619
## alternative hypothesis: two-sidedks.test(v5, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v5
## D = 0.028879, p-value = 0.9447
## alternative hypothesis: two-sidedprint("Việt Nam")## [1] "Việt Nam"aa1 <- BiCopEst(u, v1, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.33
## AIC: -8.66
## BIC: -4.86aa2 <- BiCopEst(u, v1, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.14
## par2: 5.08
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.07
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 10.12
## AIC: -16.25
## BIC: -8.64aa3 <- BiCopEst(u, v1, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.21
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 7.27
## AIC: -12.55
## BIC: -8.74aa4 <- BiCopEst(u, v1, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.19
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.74
## AIC: -5.48
## BIC: -1.67aa5 <- BiCopEst(u, v1, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.94
## AIC: -9.87
## BIC: -6.07aa6 <- BiCopEst(u, v1, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.13
##
## Fit statistics
## --------------
## logLik: 7.73
## AIC: -13.47
## BIC: -9.66aa7 <- BiCopEst(u, v1, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.8
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.74
## AIC: -3.48
## BIC: 0.32aa8 <- BiCopEst(u, v1, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.47
## AIC: -6.94
## BIC: -3.14aa9 <- BiCopEst(u, v1, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.17
##
## Fit statistics
## --------------
## logLik: 7.41
## AIC: -12.82
## BIC: -9.01aa10 <- BiCopEst(u, v1, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.14
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.08
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 8.6
## AIC: -13.2
## BIC: -5.59aa11 <- BiCopEst(u, v1, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.07
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 8.19
## AIC: -12.38
## BIC: -4.77aa12 <- BiCopEst(u, v1, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.93
## AIC: -7.86
## BIC: -0.25aa13 <- BiCopEst(u, v1, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.01
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 7.74
## AIC: -11.48
## BIC: -3.87aa14 <- BiCopEst(u, v1, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 0.17
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.12
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 9.2
## AIC: -14.4
## BIC: -6.79aa15 <- BiCopEst(u, v1, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 0.12
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 8.81
## AIC: -13.62
## BIC: -6.01aa16 <- BiCopEst(u, v1, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.15
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.47
## AIC: -4.94
## BIC: 2.67aa17 <- BiCopEst(u, v1, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.16
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.64
## AIC: -11.29
## BIC: -3.68aacopulalist <- list(summary(aa1)$AIC,summary(aa2)$AIC, summary(aa3)$AIC, summary(aa4)$AIC, summary(aa5)$AIC, summary(aa6)$AIC, summary(aa7)$AIC, summary(aa8)$AIC, summary(aa9)$AIC, summary(aa10)$AIC, summary(aa11)$AIC, summary(aa12)$AIC, summary(aa13)$AIC, summary(aa14)$AIC, summary(aa15)$AIC, summary(aa16)$AIC, summary(aa17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.33
## AIC: -8.66
## BIC: -4.86
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.14
## par2: 5.08
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.07
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 10.12
## AIC: -16.25
## BIC: -8.64
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.21
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 7.27
## AIC: -12.55
## BIC: -8.74
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.19
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.74
## AIC: -5.48
## BIC: -1.67
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.12
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.94
## AIC: -9.87
## BIC: -6.07
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.13
##
## Fit statistics
## --------------
## logLik: 7.73
## AIC: -13.47
## BIC: -9.66
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.8
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.74
## AIC: -3.48
## BIC: 0.32
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.47
## AIC: -6.94
## BIC: -3.14
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.17
##
## Fit statistics
## --------------
## logLik: 7.41
## AIC: -12.82
## BIC: -9.01
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.14
## par2: 1.06
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.08
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 8.6
## AIC: -13.2
## BIC: -5.59
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.07
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 8.19
## AIC: -12.38
## BIC: -4.77
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.15
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.93
## AIC: -7.86
## BIC: -0.25
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.01
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 7.74
## AIC: -11.48
## BIC: -3.87
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.1
## par2: 0.17
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.12
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 9.2
## AIC: -14.4
## BIC: -6.79
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 0.12
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 8.81
## AIC: -13.62
## BIC: -6.01
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.15
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0.17
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.47
## AIC: -4.94
## BIC: 2.67
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.16
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.08, p value = 0.03)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.64
## AIC: -11.29
## BIC: -3.68aacopulalist## [[1]]
## [1] -8.66413
##
## [[2]]
## [1] -16.24632
##
## [[3]]
## [1] -12.54736
##
## [[4]]
## [1] -5.47845
##
## [[5]]
## [1] -9.872412
##
## [[6]]
## [1] -13.46845
##
## [[7]]
## [1] -3.48361
##
## [[8]]
## [1] -6.941889
##
## [[9]]
## [1] -12.81946
##
## [[10]]
## [1] -13.20416
##
## [[11]]
## [1] -12.37688
##
## [[12]]
## [1] -7.860596
##
## [[13]]
## [1] -11.47567
##
## [[14]]
## [1] -14.40136
##
## [[15]]
## [1] -13.62406
##
## [[16]]
## [1] -4.941889
##
## [[17]]
## [1] -11.28786print("Argentina")## [1] "Argentina"ab1 <- BiCopEst(u, v2, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.37
##
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 24.08
## AIC: -46.17
## BIC: -42.36ab2 <- BiCopEst(u, v2, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.35
## par2: 10.57
## Dependence measures
## -------------------
## Kendall's tau: 0.23 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 25.15
## AIC: -46.31
## BIC: -38.7ab3 <- BiCopEst(u, v2, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.47
##
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.23
##
## Fit statistics
## --------------
## logLik: 22.29
## AIC: -42.58
## BIC: -38.77ab4 <- BiCopEst(u, v2, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.41
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.18
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.01
## AIC: -30.02
## BIC: -26.21ab5 <- BiCopEst(u, v2, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.26
##
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 20.38
## AIC: -38.76
## BIC: -34.95ab6 <- BiCopEst(u, v2, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.28
##
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 24.3
## AIC: -46.6
## BIC: -42.8ab7 <- BiCopEst(u, v2, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 2.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 18.2
## AIC: -34.4
## BIC: -30.6ab8 <- BiCopEst(u, v2, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.3
##
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.3
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 14.03
## AIC: -26.06
## BIC: -22.26ab9 <- BiCopEst(u, v2, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.36
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.34
##
## Fit statistics
## --------------
## logLik: 20.28
## AIC: -38.57
## BIC: -34.76ab10 <- BiCopEst(u, v2, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.29
## par2: 1.13
## Dependence measures
## -------------------
## Kendall's tau: 0.23 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 26.46
## AIC: -48.92
## BIC: -41.31ab11 <- BiCopEst(u, v2, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.15
## par2: 1.21
## Dependence measures
## -------------------
## Kendall's tau: 0.23 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0.22
##
## Fit statistics
## --------------
## logLik: 25.74
## AIC: -47.49
## BIC: -39.88ab12 <- BiCopEst(u, v2, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.26
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 20.36
## AIC: -36.73
## BIC: -29.12ab13 <- BiCopEst(u, v2, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.28
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 24.3
## AIC: -44.59
## BIC: -36.98ab14 <- BiCopEst(u, v2, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.17
## par2: 0.38
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.19
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 26.54
## AIC: -49.08
## BIC: -41.47ab15 <- BiCopEst(u, v2, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.26
## par2: 0.27
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0.27
##
## Fit statistics
## --------------
## logLik: 26.06
## AIC: -48.11
## BIC: -40.5ab16 <- BiCopEst(u, v2, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.32
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 17.99
## AIC: -31.97
## BIC: -24.36ab17 <- BiCopEst(u, v2, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.45
## par2: 0.99
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 21.91
## AIC: -39.81
## BIC: -32.2abcopulalist <- list(summary(ab1)$AIC,summary(ab2)$AIC, summary(ab3)$AIC, summary(ab4)$AIC, summary(ab5)$AIC, summary(ab6)$AIC, summary(ab7)$AIC, summary(ab8)$AIC, summary(ab9)$AIC, summary(ab10)$AIC, summary(ab11)$AIC, summary(ab12)$AIC, summary(ab13)$AIC, summary(ab14)$AIC, summary(ab15)$AIC, summary(ab16)$AIC, summary(ab17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.37
##
## Dependence measures
## -------------------
## Kendall's tau: 0.24 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 24.08
## AIC: -46.17
## BIC: -42.36
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.35
## par2: 10.57
## Dependence measures
## -------------------
## Kendall's tau: 0.23 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 25.15
## AIC: -46.31
## BIC: -38.7
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.47
##
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.23
##
## Fit statistics
## --------------
## logLik: 22.29
## AIC: -42.58
## BIC: -38.77
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.41
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.18
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.01
## AIC: -30.02
## BIC: -26.21
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.26
##
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 20.38
## AIC: -38.76
## BIC: -34.95
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.28
##
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 24.3
## AIC: -46.6
## BIC: -42.8
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 2.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 18.2
## AIC: -34.4
## BIC: -30.6
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.3
##
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.3
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 14.03
## AIC: -26.06
## BIC: -22.26
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.36
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.34
##
## Fit statistics
## --------------
## logLik: 20.28
## AIC: -38.57
## BIC: -34.76
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.29
## par2: 1.13
## Dependence measures
## -------------------
## Kendall's tau: 0.23 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 26.46
## AIC: -48.92
## BIC: -41.31
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.15
## par2: 1.21
## Dependence measures
## -------------------
## Kendall's tau: 0.23 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0.22
##
## Fit statistics
## --------------
## logLik: 25.74
## AIC: -47.49
## BIC: -39.88
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.26
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 20.36
## AIC: -36.73
## BIC: -29.12
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.28
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 24.3
## AIC: -44.59
## BIC: -36.98
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.17
## par2: 0.38
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.19
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 26.54
## AIC: -49.08
## BIC: -41.47
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.26
## par2: 0.27
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.08
## Lower TD: 0.27
##
## Fit statistics
## --------------
## logLik: 26.06
## AIC: -48.11
## BIC: -40.5
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.32
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 17.99
## AIC: -31.97
## BIC: -24.36
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.45
## par2: 0.99
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 21.91
## AIC: -39.81
## BIC: -32.2abcopulalist## [[1]]
## [1] -46.16557
##
## [[2]]
## [1] -46.3072
##
## [[3]]
## [1] -42.57501
##
## [[4]]
## [1] -30.01894
##
## [[5]]
## [1] -38.75674
##
## [[6]]
## [1] -46.60388
##
## [[7]]
## [1] -34.40065
##
## [[8]]
## [1] -26.06305
##
## [[9]]
## [1] -38.56506
##
## [[10]]
## [1] -48.91629
##
## [[11]]
## [1] -47.486
##
## [[12]]
## [1] -36.72633
##
## [[13]]
## [1] -44.59102
##
## [[14]]
## [1] -49.07606
##
## [[15]]
## [1] -48.11319
##
## [[16]]
## [1] -31.97269
##
## [[17]]
## [1] -39.81287print("Croatia")## [1] "Croatia"ac1 <- BiCopEst(u, v3, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.26
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.47
## AIC: -20.94
## BIC: -17.14ac2 <- BiCopEst(u, v3, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.22
## par2: 3.95
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 18.76
## AIC: -33.51
## BIC: -25.9ac3 <- BiCopEst(u, v3, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.37
##
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 17.27
## AIC: -32.54
## BIC: -28.73ac4 <- BiCopEst(u, v3, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.23
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.5
## AIC: -8.99
## BIC: -5.19ac5 <- BiCopEst(u, v3, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.19
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.31
## AIC: -16.63
## BIC: -12.82ac6 <- BiCopEst(u, v3, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.2
##
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.21
##
## Fit statistics
## --------------
## logLik: 18.75
## AIC: -35.5
## BIC: -31.7ac7 <- BiCopEst(u, v3, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 1.32
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.31
## AIC: -12.62
## BIC: -8.82ac8 <- BiCopEst(u, v3, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.2
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.23
## AIC: -8.46
## BIC: -4.65ac9 <- BiCopEst(u, v3, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.28
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 18.71
## AIC: -35.43
## BIC: -31.62ac10 <- BiCopEst(u, v3, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.32
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 17.74
## AIC: -31.47
## BIC: -23.86ac11 <- BiCopEst(u, v3, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.01
## par2: 1.19
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.21
##
## Fit statistics
## --------------
## logLik: 18.75
## AIC: -33.51
## BIC: -25.9ac12 <- BiCopEst(u, v3, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.17
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.19
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.3
## AIC: -14.59
## BIC: -6.98ac13 <- BiCopEst(u, v3, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.25
##
## Fit statistics
## --------------
## logLik: 19.02
## AIC: -34.04
## BIC: -26.43ac14 <- BiCopEst(u, v3, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.07
## par2: 0.34
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.09
## Lower TD: 0.13
##
## Fit statistics
## --------------
## logLik: 17.99
## AIC: -31.99
## BIC: -24.38ac15 <- BiCopEst(u, v3, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.25
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.26
##
## Fit statistics
## --------------
## logLik: 19.68
## AIC: -35.36
## BIC: -27.75ac16 <- BiCopEst(u, v3, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.22
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.18
## AIC: -10.36
## BIC: -2.75ac17 <- BiCopEst(u, v3, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.28
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 18.71
## AIC: -33.43
## BIC: -25.82accopulalist <- list(summary(ac1)$AIC,summary(ac2)$AIC, summary(ac3)$AIC, summary(ac4)$AIC, summary(ac5)$AIC, summary(ac6)$AIC, summary(ac7)$AIC, summary(ac8)$AIC, summary(ac9)$AIC, summary(ac10)$AIC, summary(ac11)$AIC, summary(ac12)$AIC, summary(ac13)$AIC, summary(ac14)$AIC, summary(ac15)$AIC, summary(ac16)$AIC, summary(ac17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.26
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.47
## AIC: -20.94
## BIC: -17.14
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.22
## par2: 3.95
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 18.76
## AIC: -33.51
## BIC: -25.9
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.37
##
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 17.27
## AIC: -32.54
## BIC: -28.73
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.23
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.5
## AIC: -8.99
## BIC: -5.19
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.19
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.31
## AIC: -16.63
## BIC: -12.82
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.2
##
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.21
##
## Fit statistics
## --------------
## logLik: 18.75
## AIC: -35.5
## BIC: -31.7
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 1.32
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.31
## AIC: -12.62
## BIC: -8.82
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.2
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.23
## AIC: -8.46
## BIC: -4.65
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.28
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 18.71
## AIC: -35.43
## BIC: -31.62
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.32
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.06
## Lower TD: 0.12
##
## Fit statistics
## --------------
## logLik: 17.74
## AIC: -31.47
## BIC: -23.86
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.01
## par2: 1.19
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.21
##
## Fit statistics
## --------------
## logLik: 18.75
## AIC: -33.51
## BIC: -25.9
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.17
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.19
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 9.3
## AIC: -14.59
## BIC: -6.98
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.15 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.25
##
## Fit statistics
## --------------
## logLik: 19.02
## AIC: -34.04
## BIC: -26.43
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.07
## par2: 0.34
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.14, p value < 0.01)
## Upper TD: 0.09
## Lower TD: 0.13
##
## Fit statistics
## --------------
## logLik: 17.99
## AIC: -31.99
## BIC: -24.38
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.25
## par2: 0.1
## Dependence measures
## -------------------
## Kendall's tau: 0.16 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.26
##
## Fit statistics
## --------------
## logLik: 19.68
## AIC: -35.36
## BIC: -27.75
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.22
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 7.18
## AIC: -10.36
## BIC: -2.75
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.28
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.14, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.28
##
## Fit statistics
## --------------
## logLik: 18.71
## AIC: -33.43
## BIC: -25.82accopulalist## [[1]]
## [1] -20.94196
##
## [[2]]
## [1] -33.51118
##
## [[3]]
## [1] -32.53708
##
## [[4]]
## [1] -8.993295
##
## [[5]]
## [1] -16.62503
##
## [[6]]
## [1] -35.50067
##
## [[7]]
## [1] -12.6242
##
## [[8]]
## [1] -8.459225
##
## [[9]]
## [1] -35.42668
##
## [[10]]
## [1] -31.47466
##
## [[11]]
## [1] -33.50722
##
## [[12]]
## [1] -14.59334
##
## [[13]]
## [1] -34.03967
##
## [[14]]
## [1] -31.98533
##
## [[15]]
## [1] -35.36422
##
## [[16]]
## [1] -10.3615
##
## [[17]]
## [1] -33.42668print("Morocco")## [1] "Morocco"ad1 <- BiCopEst(u, v4, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.69
## AIC: -3.38
## BIC: 0.43ad2 <- BiCopEst(u, v4, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.12
## par2: 19.1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.23
## AIC: -2.45
## BIC: 5.16ad3 <- BiCopEst(u, v4, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 4.49
## AIC: -6.98
## BIC: -3.17ad4 <- BiCopEst(u, v4, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.72
## AIC: 0.57
## BIC: 4.37ad5 <- BiCopEst(u, v4, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.94
## AIC: 0.13
## BIC: 3.93ad6 <- BiCopEst(u, v4, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 4.98
## AIC: -7.96
## BIC: -4.15ad7 <- BiCopEst(u, v4, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.61
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.7
## AIC: -1.4
## BIC: 2.4ad8 <- BiCopEst(u, v4, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.03
##
## Dependence measures
## -------------------
## Kendall's tau: 0.02 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.04
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.17
## AIC: 1.65
## BIC: 5.46ad9 <- BiCopEst(u, v4, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 5.25
## AIC: -8.5
## BIC: -4.7ad10 <- BiCopEst(u, v4, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.18
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 4.47
## AIC: -4.95
## BIC: 2.66ad11 <- BiCopEst(u, v4, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 4.97
## AIC: -5.94
## BIC: 1.67ad12 <- BiCopEst(u, v4, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.05
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.92
## AIC: 2.15
## BIC: 9.76ad13 <- BiCopEst(u, v4, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 1.01
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 5.25
## AIC: -6.51
## BIC: 1.1ad14 <- BiCopEst(u, v4, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1
## par2: 0.18
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 4.48
## AIC: -4.95
## BIC: 2.66ad15 <- BiCopEst(u, v4, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 0.01
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 5.26
## AIC: -6.51
## BIC: 1.1ad16 <- BiCopEst(u, v4, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.11
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.63
## AIC: 0.74
## BIC: 8.35ad17 <- BiCopEst(u, v4, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.16
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.59
## AIC: -7.19
## BIC: 0.42adcopulalist <- list(summary(ad1)$AIC,summary(ad2)$AIC, summary(ad3)$AIC, summary(ad4)$AIC, summary(ad5)$AIC, summary(ad6)$AIC, summary(ad7)$AIC, summary(ad8)$AIC, summary(ad9)$AIC, summary(ad10)$AIC, summary(ad11)$AIC, summary(ad12)$AIC, summary(ad13)$AIC, summary(ad14)$AIC, summary(ad15)$AIC, summary(ad16)$AIC, summary(ad17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.69
## AIC: -3.38
## BIC: 0.43
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.12
## par2: 19.1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.23
## AIC: -2.45
## BIC: 5.16
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 4.49
## AIC: -6.98
## BIC: -3.17
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.72
## AIC: 0.57
## BIC: 4.37
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.94
## AIC: 0.13
## BIC: 3.93
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 4.98
## AIC: -7.96
## BIC: -4.15
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.61
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.7
## AIC: -1.4
## BIC: 2.4
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.03
##
## Dependence measures
## -------------------
## Kendall's tau: 0.02 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.04
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.17
## AIC: 1.65
## BIC: 5.46
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 5.25
## AIC: -8.5
## BIC: -4.7
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.18
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 4.47
## AIC: -4.95
## BIC: 2.66
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0
## par2: 1.09
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 4.97
## AIC: -5.94
## BIC: 1.67
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.05
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.92
## AIC: 2.15
## BIC: 9.76
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 1.01
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 5.25
## AIC: -6.51
## BIC: 1.1
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1
## par2: 0.18
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 4.48
## AIC: -4.95
## BIC: 2.66
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.13
## par2: 0.01
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 5.26
## AIC: -6.51
## BIC: 1.1
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.11
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.63
## AIC: 0.74
## BIC: 8.35
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.16
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.59
## AIC: -7.19
## BIC: 0.42adcopulalist## [[1]]
## [1] -3.376105
##
## [[2]]
## [1] -2.450115
##
## [[3]]
## [1] -6.975688
##
## [[4]]
## [1] 0.5657957
##
## [[5]]
## [1] 0.1250453
##
## [[6]]
## [1] -7.958819
##
## [[7]]
## [1] -1.404553
##
## [[8]]
## [1] 1.654985
##
## [[9]]
## [1] -8.504154
##
## [[10]]
## [1] -4.949565
##
## [[11]]
## [1] -5.942356
##
## [[12]]
## [1] 2.152979
##
## [[13]]
## [1] -6.50652
##
## [[14]]
## [1] -4.95463
##
## [[15]]
## [1] -6.514762
##
## [[16]]
## [1] 0.7426967
##
## [[17]]
## [1] -7.188432print("Oman")## [1] "Oman"ae1 <- BiCopEst(u, v5, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.23
## AIC: -4.45
## BIC: -0.65ae2 <- BiCopEst(u, v5, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.12
## par2: 7.68
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.03
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 5.96
## AIC: -7.91
## BIC: -0.3ae3 <- BiCopEst(u, v5, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 4.68
## AIC: -7.36
## BIC: -3.55ae4 <- BiCopEst(u, v5, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.02
## AIC: -2.05
## BIC: 1.76ae5 <- BiCopEst(u, v5, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.73
## AIC: -3.46
## BIC: 0.34ae6 <- BiCopEst(u, v5, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 5.22
## AIC: -8.43
## BIC: -4.63ae7 <- BiCopEst(u, v5, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.7
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.13
## AIC: -2.26
## BIC: 1.54ae8 <- BiCopEst(u, v5, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.12
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.66
## AIC: -1.31
## BIC: 2.49ae9 <- BiCopEst(u, v5, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.13
##
## Fit statistics
## --------------
## logLik: 4.87
## AIC: -7.74
## BIC: -3.94ae10 <- BiCopEst(u, v5, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.13
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.04
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 5.02
## AIC: -6.05
## BIC: 1.56ae11 <- BiCopEst(u, v5, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.04
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 5.38
## AIC: -6.75
## BIC: 0.86ae12 <- BiCopEst(u, v5, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.72
## AIC: -1.45
## BIC: 6.16ae13 <- BiCopEst(u, v5, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 5.22
## AIC: -6.43
## BIC: 1.18ae14 <- BiCopEst(u, v5, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.14
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.06
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 5.11
## AIC: -6.21
## BIC: 1.4ae15 <- BiCopEst(u, v5, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 5.61
## AIC: -7.22
## BIC: 0.39ae16 <- BiCopEst(u, v5, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.46
## par2: 0.72
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.41
## AIC: -0.82
## BIC: 6.79ae17 <- BiCopEst(u, v5, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.13
## AIC: -6.26
## BIC: 1.35aecopulalist <- list(summary(ae1)$AIC,summary(ae2)$AIC, summary(ae3)$AIC, summary(ae4)$AIC, summary(ae5)$AIC, summary(ae6)$AIC, summary(ae7)$AIC, summary(ae8)$AIC, summary(ae9)$AIC, summary(ae10)$AIC, summary(ae11)$AIC, summary(ae12)$AIC, summary(ae13)$AIC, summary(ae14)$AIC, summary(ae15)$AIC, summary(ae16)$AIC, summary(ae17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.14
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.23
## AIC: -4.45
## BIC: -0.65
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.12
## par2: 7.68
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.03
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 5.96
## AIC: -7.91
## BIC: -0.3
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.16
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 4.68
## AIC: -7.36
## BIC: -3.55
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.02
## AIC: -2.05
## BIC: 1.76
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.73
## AIC: -3.46
## BIC: 0.34
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 5.22
## AIC: -8.43
## BIC: -4.63
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.7
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.13
## AIC: -2.26
## BIC: 1.54
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.09
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.12
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.66
## AIC: -1.31
## BIC: 2.49
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.13
##
## Fit statistics
## --------------
## logLik: 4.87
## AIC: -7.74
## BIC: -3.94
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.13
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.04
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 5.02
## AIC: -6.05
## BIC: 1.56
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.04
## par2: 1.07
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.09
##
## Fit statistics
## --------------
## logLik: 5.38
## AIC: -6.75
## BIC: 0.86
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.11
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.72
## AIC: -1.45
## BIC: 6.16
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 5.22
## AIC: -6.43
## BIC: 1.18
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.14
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.07, p value = 0.05)
## Upper TD: 0.06
## Lower TD: 0.01
##
## Fit statistics
## --------------
## logLik: 5.11
## AIC: -6.21
## BIC: 1.4
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.09
## par2: 0.08
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 5.61
## AIC: -7.22
## BIC: 0.39
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.46
## par2: 0.72
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 2.41
## AIC: -0.82
## BIC: 6.79
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.12
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.07, p value = 0.05)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.13
## AIC: -6.26
## BIC: 1.35aecopulalist## [[1]]
## [1] -4.454624
##
## [[2]]
## [1] -7.912229
##
## [[3]]
## [1] -7.357102
##
## [[4]]
## [1] -2.046475
##
## [[5]]
## [1] -3.464867
##
## [[6]]
## [1] -8.432478
##
## [[7]]
## [1] -2.260566
##
## [[8]]
## [1] -1.312784
##
## [[9]]
## [1] -7.743965
##
## [[10]]
## [1] -6.047013
##
## [[11]]
## [1] -6.752645
##
## [[12]]
## [1] -1.448704
##
## [[13]]
## [1] -6.431314
##
## [[14]]
## [1] -6.211898
##
## [[15]]
## [1] -7.224862
##
## [[16]]
## [1] -0.8238002
##
## [[17]]
## [1] -6.259093rm(list=ls())
DATA <- read_xlsx("C://Users//84896//Desktop//DATA//CN3-COPULA.xlsx", sheet="After")
SP500 <- DATA$y
VNI <- DATA$x1
MERVAL <- DATA$x2
CROBEX <- DATA$x3
MASI <- DATA$x4
MSM30 <- DATA$x5cor(cbind(SP500, VNI, MERVAL, CROBEX, MASI, MSM30), method="pearson")## SP500 VNI MERVAL CROBEX MASI MSM30
## SP500 1.00000000 0.16545790 0.26840964 0.21922323 0.05287079 -0.03441537
## VNI 0.16545790 1.00000000 0.09561572 0.19035928 0.08844928 0.09313346
## MERVAL 0.26840964 0.09561572 1.00000000 0.09317479 0.09742475 -0.04449680
## CROBEX 0.21922323 0.19035928 0.09317479 1.00000000 0.27555800 0.05990226
## MASI 0.05287079 0.08844928 0.09742475 0.27555800 1.00000000 0.07294105
## MSM30 -0.03441537 0.09313346 -0.04449680 0.05990226 0.07294105 1.00000000print("Mỹ")## [1] "Mỹ"autoarfima(SP500,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ar2 ma1 ma2 sigma
## 0.5854079 -0.9802957 -0.5853677 0.9531066 1.4531486print("Việt Nam")## [1] "Việt Nam"autoarfima(VNI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ar2 ma1 ma2 sigma
## 0.0000000 -0.6078440 0.0000000 0.5104772 1.6839870print("Argentina")## [1] "Argentina"autoarfima(MERVAL,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu ar1 ar2 ma1 ma2 sigma
## 0.7137153 -1.2170917 -0.9802956 1.2289849 0.9598844 3.7995838print("Croatia")## [1] "Croatia"autoarfima(CROBEX,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## mu sigma
## 0.08358039 0.85100899print("Morocco")## [1] "Morocco"autoarfima(MASI,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 sigma
## 0.2062769 0.9052712print("Oman")## [1] "Oman"autoarfima(MSM30,ar.max = 2, ma.max = 2, criterion = "AIC", method = "full")$fit@fit$coef## ar1 ma1 sigma
## 0.6832304 -0.5083889 0.6597366print("Mỹ")## [1] "Mỹ"sp500.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
sp500.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
sp500.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
sp500.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
sp500.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
sp500.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
sp500.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
sp500.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
sp500.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
sp500.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
sp500.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
sp500.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
sp500.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
sp500.garch11n <-ugarchfit(data=SP500, spec= sp500.g11n ) #1
sp500.garch11s <-ugarchfit(data=SP500, spec= sp500.g11s )
sp500.garch11ss <-ugarchfit(data=SP500, spec= sp500.g11ss )
sp500.garch11g <-ugarchfit(data=SP500, spec= sp500.g11g )
sp500.garch11sg <-ugarchfit(data=SP500, spec= sp500.g11sg ) #5
sp500.garch12n <-ugarchfit(data=SP500, spec= sp500.g12n )
sp500.garch12s <-ugarchfit(data=SP500, spec= sp500.g12s )
sp500.garch12ss <-ugarchfit(data=SP500, spec= sp500.g12ss )
sp500.garch12g<-ugarchfit(data=SP500, spec= sp500.g12g )
sp500.garch12sg <-ugarchfit(data=SP500, spec= sp500.g12sg ) #10
sp500.garch21n <-ugarchfit(data=SP500, spec= sp500.g21n )
sp500.garch21s <-ugarchfit(data=SP500, spec= sp500.g21s )
#sp500.garch21ss <-ugarchfit(data=SP500, spec= sp500.g21ss)
sp500.garch21g <-ugarchfit(data=SP500, spec= sp500.g21g ) #13
sp500.garch21sg <-ugarchfit(data=SP500, spec= sp500.g21sg )
#sp500.garch22n <-ugarchfit(data=SP500, spec= sp500.g22n )
sp500.garch22s <-ugarchfit(data=SP500, spec= sp500.g22s )
sp500.garch22ss <-ugarchfit(data=SP500, spec= sp500.g22ss )
sp500.garch22g<-ugarchfit(data=SP500, spec= sp500.g22g )
sp500.garch22sg <-ugarchfit(data=SP500, spec= sp500.g22sg )
model.aic.list <- list(sp500.garch11n,sp500.garch11s,sp500.garch11ss,sp500.garch11g,sp500.garch11sg,sp500.garch12n,sp500.garch12s,sp500.garch12ss,sp500.garch12g,sp500.garch12sg,sp500.garch21n,sp500.garch21s,sp500.garch21g,sp500.garch21sg,sp500.garch22s,sp500.garch22ss,sp500.garch22g,sp500.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 14sp500.garch21sg@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 6.544845e-02 2.719793e-04 240.637567 0.000000000
## ar1 6.202403e-01 1.753016e-03 353.813230 0.000000000
## ar2 -7.999321e-01 2.523666e-03 -316.972315 0.000000000
## ma1 -6.907720e-01 1.940306e-03 -356.011917 0.000000000
## ma2 7.946323e-01 2.544028e-03 312.352043 0.000000000
## omega 1.201965e-02 4.579063e-05 262.491586 0.000000000
## alpha1 1.267776e-05 4.315103e-06 2.937998 0.003303394
## alpha2 1.451193e-05 4.762291e-06 3.047258 0.002309393
## beta1 9.486685e-01 1.982632e-03 478.489514 0.000000000
## gamma1 -6.553624e-02 1.852118e-04 -353.844750 0.000000000
## gamma2 1.434942e-01 4.044316e-04 354.804704 0.000000000
## skew 8.297736e-01 6.911571e-02 12.005572 0.000000000
## shape 2.027612e+00 2.070136e-01 9.794586 0.000000000print("Việt Nam")## [1] "Việt Nam"vni.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
vni.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
vni.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
vni.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
vni.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
vni.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
vni.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
vni.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
vni.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
vni.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
vni.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
vni.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
vni.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
vni.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
vni.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
vni.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
vni.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
vni.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
vni.garch11n <-ugarchfit(data=VNI, spec= vni.g11n ) #1
vni.garch11s <-ugarchfit(data=VNI, spec= vni.g11s )
vni.garch11ss <-ugarchfit(data=VNI, spec= vni.g11ss )
vni.garch11g <-ugarchfit(data=VNI, spec= vni.g11g )
vni.garch11sg <-ugarchfit(data=VNI, spec= vni.g11sg ) #5
vni.garch12n <-ugarchfit(data=VNI, spec= vni.g12n )
vni.garch12s <-ugarchfit(data=VNI, spec= vni.g12s )
vni.garch12ss <-ugarchfit(data=VNI, spec= vni.g12ss )
vni.garch12g<-ugarchfit(data=VNI, spec= vni.g12g )
vni.garch12sg <-ugarchfit(data=VNI, spec= vni.g12sg ) #10
vni.garch21n <-ugarchfit(data=VNI, spec= vni.g21n )
vni.garch21s <-ugarchfit(data=VNI, spec= vni.g21s )
vni.garch21ss <-ugarchfit(data=VNI, spec= vni.g21ss)
vni.garch21g <-ugarchfit(data=VNI, spec= vni.g21g )
vni.garch21sg <-ugarchfit(data=VNI, spec= vni.g21sg ) #15
vni.garch22n <-ugarchfit(data=VNI, spec= vni.g22n )
vni.garch22s <-ugarchfit(data=VNI, spec= vni.g22s )
vni.garch22ss <-ugarchfit(data=VNI, spec= vni.g22ss )
vni.garch22g<-ugarchfit(data=VNI, spec= vni.g22g )
vni.garch22sg <-ugarchfit(data=VNI, spec= vni.g22sg )
model.aic.list <- list(vni.garch11n,vni.garch11s,vni.garch11ss,vni.garch11g,vni.garch11sg,vni.garch12n,vni.garch12s,vni.garch12ss,vni.garch12g,vni.garch12sg,vni.garch21n,vni.garch21s,vni.garch21ss,vni.garch21g,vni.garch21sg,vni.garch22n,vni.garch22s,vni.garch22ss,vni.garch22g,vni.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 5vni.garch11sg@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.02557194 0.007029523 3.637791 2.749866e-04
## ar1 0.39313316 0.004355433 90.262696 0.000000e+00
## ar2 -0.93192669 0.014611011 -63.782493 0.000000e+00
## ma1 -0.43253231 0.004118520 -105.021307 0.000000e+00
## ma2 0.90108336 0.024681736 36.508104 0.000000e+00
## omega 0.12136337 0.014179887 8.558839 0.000000e+00
## alpha1 0.02178571 0.005748416 3.789862 1.507307e-04
## beta1 0.85866417 0.012170349 70.553783 0.000000e+00
## gamma1 0.11388973 0.018234806 6.245733 4.218166e-10
## skew 0.77849135 0.021807563 35.698227 0.000000e+00
## shape 1.09701511 0.084706245 12.950817 0.000000e+00print("Argentina")## [1] "Argentina"merval.g11n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
merval.g11s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
merval.g11ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g11g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
merval.g11sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
merval.g12n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
merval.g12s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
merval.g12ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g12g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
merval.g12sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
merval.g21n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
merval.g21s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
merval.g21ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
merval.g21g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
merval.g21sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
merval.g22n <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
merval.g22s <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
merval.g22ss <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
merval.g22g <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
merval.g22sg <- ugarchspec(mean.model = list(armaOrder = c(2,2)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
merval.garch11n <-ugarchfit(data= MERVAL, spec= merval.g11n ) #1
merval.garch11s <-ugarchfit(data= MERVAL, spec= merval.g11s )
merval.garch11ss <-ugarchfit(data= MERVAL, spec= merval.g11ss )
merval.garch11g <-ugarchfit(data= MERVAL, spec= merval.g11g )
merval.garch11sg <-ugarchfit(data= MERVAL, spec= merval.g11sg ) #5
merval.garch12n <-ugarchfit(data= MERVAL, spec= merval.g12n )
merval.garch12s <-ugarchfit(data= MERVAL, spec= merval.g12s )
merval.garch12ss <-ugarchfit(data= MERVAL, spec= merval.g12ss )
merval.garch12g<-ugarchfit(data= MERVAL, spec= merval.g12g )
merval.garch12sg <-ugarchfit(data= MERVAL, spec= merval.g12sg ) #10
merval.garch21n <-ugarchfit(data= MERVAL, spec= merval.g21n )
merval.garch21s <-ugarchfit(data= MERVAL, spec= merval.g21s )
merval.garch21ss <-ugarchfit(data= MERVAL, spec= merval.g21ss)
merval.garch21g <-ugarchfit(data= MERVAL, spec= merval.g21g )
merval.garch21sg <-ugarchfit(data= MERVAL, spec= merval.g21sg ) #15
merval.garch22n <-ugarchfit(data= MERVAL, spec= merval.g22n )
merval.garch22s <-ugarchfit(data= MERVAL, spec= merval.g22s )
merval.garch22ss <-ugarchfit(data= MERVAL, spec= merval.g22ss )
merval.garch22g<-ugarchfit(data= MERVAL, spec= merval.g22g )
merval.garch22sg <-ugarchfit(data= MERVAL, spec= merval.g22sg )
model.aic.list <- list(merval.garch11n,merval.garch11s,merval.garch11ss,merval.garch11g,merval.garch11sg,merval.garch12n,merval.garch12s,merval.garch12ss,merval.garch12g,merval.garch12sg,merval.garch21n,merval.garch21s,merval.garch21ss,merval.garch21g,merval.garch21sg,merval.garch22n,merval.garch22s,merval.garch22ss,merval.garch22g,merval.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 7merval.garch12s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 6.250402e-01 2.987537e-04 2.092159e+03 0
## ar1 -1.223271e+00 1.001409e-03 -1.221549e+03 0
## ar2 -9.261366e-01 2.377172e-03 -3.895959e+02 0
## ma1 1.246778e+00 3.216875e-03 3.875742e+02 0
## ma2 9.048829e-01 1.632291e-03 5.543636e+02 0
## omega 1.173672e-01 2.198831e-04 5.337710e+02 0
## alpha1 5.135416e-15 1.548756e-05 3.315832e-10 1
## beta1 1.483451e-01 1.525930e-04 9.721620e+02 0
## beta2 8.889866e-01 4.907192e-04 1.811599e+03 0
## gamma1 -1.630424e-01 2.717208e-04 -6.000366e+02 0
## shape 2.403179e+00 8.460224e-02 2.840561e+01 0print("Crotia")## [1] "Crotia"crobex.g11n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g11s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
crobex.g11ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g11g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g11sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g12n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g12s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
crobex.g12ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g12g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g12sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
crobex.g21n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
crobex.g21s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
crobex.g21ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
crobex.g21g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
crobex.g21sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
crobex.g22n <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
crobex.g22s <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
crobex.g22ss <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
crobex.g22g <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
crobex.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
crobex.garch11n <-ugarchfit(data= CROBEX, spec= crobex.g11n ) #1
#crobex.garch11s <-ugarchfit(data= CROBEX, spec= crobex.g11s)
#crobex.garch11ss <-ugarchfit(data= CROBEX, spec= crobex.g11ss)
crobex.garch11g <-ugarchfit(data= CROBEX, spec= crobex.g11g )
crobex.garch11sg <-ugarchfit(data= CROBEX, spec= crobex.g11sg )
crobex.garch12n <-ugarchfit(data= CROBEX, spec= crobex.g12n )
crobex.garch12s <-ugarchfit(data= CROBEX, spec= crobex.g12s ) #5
crobex.garch12ss <-ugarchfit(data= CROBEX, spec= crobex.g12ss )
crobex.garch12g<-ugarchfit(data= CROBEX, spec= crobex.g12g )
crobex.garch12sg <-ugarchfit(data= CROBEX, spec= crobex.g12sg )
crobex.garch21n <-ugarchfit(data= CROBEX, spec= crobex.g21n )
crobex.garch21s <-ugarchfit(data= CROBEX, spec= crobex.g21s ) #10
crobex.garch21ss <-ugarchfit(data= CROBEX, spec= crobex.g21ss)
crobex.garch21g <-ugarchfit(data= CROBEX, spec= crobex.g21g )
crobex.garch21sg <-ugarchfit(data= CROBEX, spec= crobex.g21sg )
crobex.garch22n <-ugarchfit(data= CROBEX, spec= crobex.g22n )
crobex.garch22s <-ugarchfit(data= CROBEX, spec= crobex.g22s )
crobex.garch22ss <-ugarchfit(data= CROBEX, spec= crobex.g22ss )#15
crobex.garch22g<-ugarchfit(data= CROBEX, spec= crobex.g22g )
crobex.garch22sg <-ugarchfit(data= CROBEX, spec= crobex.g22sg )
model.aic.list <- list(crobex.garch11n,crobex.garch11g,crobex.garch11sg,crobex.garch12n,crobex.garch12s,crobex.garch12ss,crobex.garch12g,crobex.garch12sg,crobex.garch21n,crobex.garch21s,crobex.garch21ss,crobex.garch21g,crobex.garch21sg,crobex.garch22n,crobex.garch22s,crobex.garch22ss,crobex.garch22g,crobex.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 10crobex.garch21s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.11611545 0.03018555 3.8467226 1.197084e-04
## omega 0.15917317 0.13876393 1.1470789 2.513490e-01
## alpha1 0.08675628 0.10419214 0.8326567 4.050384e-01
## alpha2 0.14861470 0.18833852 0.7890829 4.300636e-01
## beta1 0.59702034 0.28139306 2.1216598 3.386632e-02
## gamma1 -0.07810279 0.11413626 -0.6842943 4.937894e-01
## gamma2 0.03558747 0.15268471 0.2330781 8.157007e-01
## shape 3.16776862 0.55984309 5.6583152 1.528662e-08print("Morocco")## [1] "Morocco"masi.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
masi.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
masi.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
masi.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
masi.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
masi.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
masi.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
masi.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
masi.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
masi.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
masi.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
masi.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
masi.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
masi.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
masi.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
masi.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
masi.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,0)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
masi.g22sg <- ugarchspec(mean.model = list(armaOrder = c(0,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
masi.garch11n <-ugarchfit(data= MASI, spec= masi.g11n ) #1
masi.garch11s <-ugarchfit(data= MASI, spec= masi.g11s )
masi.garch11ss <-ugarchfit(data= MASI, spec= masi.g11ss )
masi.garch11g <-ugarchfit(data= MASI, spec= masi.g11g )
masi.garch11sg <-ugarchfit(data= MASI, spec= masi.g11sg ) #5
masi.garch12n <-ugarchfit(data= MASI, spec= masi.g12n )
masi.garch12s <-ugarchfit(data= MASI, spec= masi.g12s )
masi.garch12ss <-ugarchfit(data= MASI, spec= masi.g12ss )
masi.garch12g<-ugarchfit(data= MASI, spec= masi.g12g )
masi.garch12sg <-ugarchfit(data= MASI, spec= masi.g12sg ) #10
masi.garch21n <-ugarchfit(data= MASI, spec= masi.g21n )
masi.garch21s <-ugarchfit(data= MASI, spec= masi.g21s )
masi.garch21ss <-ugarchfit(data= MASI, spec= masi.g21ss)
masi.garch21g <-ugarchfit(data= MASI, spec= masi.g21g )
masi.garch21sg <-ugarchfit(data= MASI, spec= masi.g21sg ) #15
masi.garch22n <-ugarchfit(data= MASI, spec= masi.g22n )
masi.garch22s <-ugarchfit(data= MASI, spec= masi.g22s )
masi.garch22ss <-ugarchfit(data= MASI, spec= masi.g22ss )
masi.garch22g<-ugarchfit(data= MASI, spec= masi.g22g )
masi.garch22sg <-ugarchfit(data= MASI, spec= masi.g22sg )
model.aic.list <- list(masi.garch11n,masi.garch11s,masi.garch11ss,masi.garch11g,masi.garch11sg,masi.garch12n,masi.garch12s,masi.garch12ss,masi.garch12g,masi.garch12sg,masi.garch21n,masi.garch21s,masi.garch21ss,masi.garch21g,masi.garch21sg,masi.garch22n,masi.garch22s,masi.garch22ss,masi.garch22g,masi.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 2masi.garch11s@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.02569142 0.04138066 0.6208558 5.346945e-01
## ar1 0.26137996 0.05038774 5.1873718 2.132826e-07
## omega 0.09042151 0.03820412 2.3668002 1.794262e-02
## alpha1 0.04502314 0.05445348 0.8268183 4.083400e-01
## beta1 0.70082106 0.07565258 9.2636771 0.000000e+00
## gamma1 0.33684312 0.14553418 2.3145292 2.063871e-02
## shape 3.44896604 0.65789479 5.2424280 1.584773e-07print("Oman")## [1] "Oman"msm30.g11n <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g11s <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "std")
msm30.g11ss <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g11g <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g11sg <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g12n <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g12s <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "std")
msm30.g12ss <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g12g <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g12sg <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(1,2), model="gjrGARCH"), distribution.model = "sged")
msm30.g21n <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "norm")
msm30.g21s <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "std")
msm30.g21ss <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sstd")
msm30.g21g <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "ged")
msm30.g21sg <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,1), model="gjrGARCH"), distribution.model = "sged")
msm30.g22n <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "norm")
msm30.g22s <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "std")
msm30.g22ss <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sstd")
msm30.g22g <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "ged")
msm30.g22sg <- ugarchspec(mean.model = list(armaOrder = c(1,1)),variance.model= list(garchOrder=c(2,2), model="gjrGARCH"), distribution.model = "sged")
msm30.garch11n <-ugarchfit(data= MSM30, spec= msm30.g11n ) #1
msm30.garch11s <-ugarchfit(data= MSM30, spec= msm30.g11s )
msm30.garch11ss <-ugarchfit(data= MSM30, spec= msm30.g11ss )
msm30.garch11g <-ugarchfit(data= MSM30, spec= msm30.g11g )
msm30.garch11sg <-ugarchfit(data= MSM30, spec= msm30.g11sg ) #5
msm30.garch12n <-ugarchfit(data= MSM30, spec= msm30.g12n )
msm30.garch12s <-ugarchfit(data= MSM30, spec= msm30.g12s )
msm30.garch12ss <-ugarchfit(data= MSM30, spec= msm30.g12ss )
msm30.garch12g<-ugarchfit(data= MSM30, spec= msm30.g12g )
msm30.garch12sg <-ugarchfit(data= MSM30, spec= msm30.g12sg ) #10
msm30.garch21n <-ugarchfit(data= MSM30, spec= msm30.g21n )
msm30.garch21s <-ugarchfit(data= MSM30, spec= msm30.g21s )
msm30.garch21ss <-ugarchfit(data= MSM30, spec= msm30.g21ss)
msm30.garch21g <-ugarchfit(data= MSM30, spec= msm30.g21g )
msm30.garch21sg <-ugarchfit(data= MSM30, spec= msm30.g21sg ) #15
#msm30.garch22n <-ugarchfit(data= MSM30, spec= msm30.g22n )
msm30.garch22s <-ugarchfit(data= MSM30, spec= msm30.g22s )
msm30.garch22ss <-ugarchfit(data= MSM30, spec= msm30.g22ss )
msm30.garch22g<-ugarchfit(data= MSM30, spec= msm30.g22g )
msm30.garch22sg <-ugarchfit(data= MSM30, spec= msm30.g22sg )
model.aic.list <- list(msm30.garch11n,msm30.garch11s,msm30.garch11ss,msm30.garch11g,msm30.garch11sg,msm30.garch12n,msm30.garch12s,msm30.garch12ss,msm30.garch12g,msm30.garch12sg,msm30.garch21n,msm30.garch21s,msm30.garch21ss,msm30.garch21g,msm30.garch21sg,msm30.garch22s,msm30.garch22ss,msm30.garch22g,msm30.garch22sg)
model.aic <- sapply(model.aic.list, infocriteria)[-4,][-3,][-2,]
min_pos <- which(model.aic == min(model.aic), arr.ind = TRUE)
min_pos## [1] 10msm30.garch12sg@fit$matcoef## Estimate Std. Error t value Pr(>|t|)
## mu 0.0148831283 1.218650e-05 1221.27998 0
## ar1 0.5925473356 9.607185e-04 616.77518 0
## ma1 -0.4510025103 4.185493e-04 -1077.53747 0
## omega 0.0009688363 1.567579e-07 6180.46117 0
## alpha1 0.0085355475 8.424453e-06 1013.18712 0
## beta1 0.5687619116 2.830409e-04 2009.46905 0
## beta2 0.4655024523 2.397888e-04 1941.30155 0
## gamma1 -0.1002469516 5.454863e-05 -1837.75363 0
## skew 1.1588288129 9.520839e-02 12.17150 0
## shape 1.1088737233 5.670656e-02 19.55459 0SP500_model <- sp500.garch21sg
VNI_model <- vni.garch11sg
MERVAL_model <- merval.garch12s
CROBEX_model <- crobex.garch21s
MASI_model <- masi.garch11s
MSM30_model <- msm30.garch12sg
SP500.res <- residuals(SP500_model)/sigma(SP500_model)
VNI.res <- residuals(VNI_model)/sigma(VNI_model)
MERVAL.res <- residuals(MERVAL_model)/sigma(MERVAL_model)
CROBEX.res <- residuals(CROBEX_model)/sigma(CROBEX_model)
MASI.res <- residuals(MASI_model)/sigma(MASI_model)
MSM30.res <- residuals(MSM30_model)/sigma(MSM30_model)
fitdist(distribution = "sged", SP500.res, control = list())$pars## mu sigma skew shape
## -0.01771415 0.99894277 0.82263208 2.01037172fitdist(distribution = "sged", VNI.res, control = list())$pars## mu sigma skew shape
## -0.02628145 1.01366810 0.76521380 1.09584922fitdist(distribution = "std", MERVAL.res, control = list())$pars## mu sigma shape
## 0.00241711 0.67024617 3.55925856fitdist(distribution = "std", CROBEX.res, control = list())$pars## mu sigma shape
## -0.002198296 1.024532825 3.071780820fitdist(distribution = "std", MASI.res, control = list())$pars## mu sigma shape
## 0.0002867923 1.0144706997 3.3698767024fitdist(distribution = "sged", MSM30.res, control = list())$pars## mu sigma skew shape
## 0.04475697 1.01334721 1.17624276 1.14041909u <- pdist(distribution = "sged", q = SP500.res, mu = -0.01771415, sigma = 0.99894277, skew= 0.82263208,shape = 2.01037172)
v1 <- pdist(distribution = "sged", q = VNI.res, mu =-0.02628145, sigma = 1.01366810, skew= 0.76521380,shape= 1.09584922)
v2 <- pdist(distribution = "std", q = MERVAL.res, mu = 0.00241711, sigma = 0.67024617, shape = 3.55925856)
v3 <- pdist(distribution = "std", q = CROBEX.res, mu =-0.002198296 , sigma = 1.024532825,shape= 3.071780820)
v4 <- pdist(distribution = "std", q = MASI.res, mu = 0.0002867923, sigma = 1.0144706997, shape = 3.3698767024)
v5 <- pdist(distribution = "sged", q = MSM30.res, mu = -0.01618695, sigma = 0.99615106, skew = 1.14173763, shape= 1.11874195)
goftest::cvm.test(u, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## omega2 = 0.040118, p-value = 0.9328goftest::cvm.test(v1, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## omega2 = 0.02302, p-value = 0.9933goftest::cvm.test(v2, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## omega2 = 0.031287, p-value = 0.9719goftest::cvm.test(v3, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## omega2 = 0.031761, p-value = 0.9702goftest::cvm.test(v4, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## omega2 = 0.060335, p-value = 0.8122goftest::cvm.test(v5, "punif")##
## Cramer-von Mises test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## omega2 = 0.0961, p-value = 0.6046goftest::ad.test(u, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: u
## An = 0.2904, p-value = 0.9452goftest::ad.test(v1, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v1
## An = 0.16143, p-value = 0.9976goftest::ad.test(v2, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v2
## An = 0.21681, p-value = 0.9851goftest::ad.test(v3, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v3
## An = 0.20256, p-value = 0.9897goftest::ad.test(v4, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v4
## An = 0.38452, p-value = 0.8639goftest::ad.test(v5, "punif")##
## Anderson-Darling test of goodness-of-fit
## Null hypothesis: uniform distribution
## Parameters assumed to be fixed
##
## data: v5
## An = 0.56359, p-value = 0.683ks.test(u, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: u
## D = 0.033684, p-value = 0.7898
## alternative hypothesis: two-sidedks.test(v1, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v1
## D = 0.025985, p-value = 0.9623
## alternative hypothesis: two-sidedks.test(v2, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v2
## D = 0.026465, p-value = 0.9559
## alternative hypothesis: two-sidedks.test(v3, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v3
## D = 0.02515, p-value = 0.972
## alternative hypothesis: two-sidedks.test(v4, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v4
## D = 0.029209, p-value = 0.9071
## alternative hypothesis: two-sidedks.test(v5, "punif")##
## One-sample Kolmogorov-Smirnov test
##
## data: v5
## D = 0.041653, p-value = 0.5352
## alternative hypothesis: two-sidedprint("Việt Nam")## [1] "Việt Nam"aa1 <- BiCopEst(u, v1, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.82
## AIC: -9.64
## BIC: -5.72aa2 <- BiCopEst(u, v1, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.17
## par2: 7.08
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 9.35
## AIC: -14.71
## BIC: -6.86aa3 <- BiCopEst(u, v1, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.24
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 8.26
## AIC: -14.51
## BIC: -10.59aa4 <- BiCopEst(u, v1, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.15
## AIC: -4.3
## BIC: -0.38aa5 <- BiCopEst(u, v1, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.17
## AIC: -8.33
## BIC: -4.41aa6 <- BiCopEst(u, v1, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 9.16
## AIC: -16.31
## BIC: -12.39aa7 <- BiCopEst(u, v1, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.94
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.31
## AIC: -6.61
## BIC: -2.69aa8 <- BiCopEst(u, v1, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.18
## AIC: -4.36
## BIC: -0.43aa9 <- BiCopEst(u, v1, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.2
##
## Fit statistics
## --------------
## logLik: 8.79
## AIC: -15.59
## BIC: -11.66aa10 <- BiCopEst(u, v1, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.19
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 9.05
## AIC: -14.09
## BIC: -6.25aa11 <- BiCopEst(u, v1, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.02
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 9.2
## AIC: -14.4
## BIC: -6.55aa12 <- BiCopEst(u, v1, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.15
## AIC: -6.31
## BIC: 1.54aa13 <- BiCopEst(u, v1, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.03
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 9.18
## AIC: -14.36
## BIC: -6.51aa14 <- BiCopEst(u, v1, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.21
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 9.18
## AIC: -14.35
## BIC: -6.5aa15 <- BiCopEst(u, v1, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.15
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.18
##
## Fit statistics
## --------------
## logLik: 9.55
## AIC: -15.09
## BIC: -7.24aa16 <- BiCopEst(u, v1, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.16
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.27
## AIC: -4.55
## BIC: 3.3aa17 <- BiCopEst(u, v1, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.19
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.92
## AIC: -13.85
## BIC: -6aacopulalist <- list(summary(aa1)$AIC,summary(aa2)$AIC, summary(aa3)$AIC, summary(aa4)$AIC, summary(aa5)$AIC, summary(aa6)$AIC, summary(aa7)$AIC, summary(aa8)$AIC, summary(aa9)$AIC, summary(aa10)$AIC, summary(aa11)$AIC, summary(aa12)$AIC, summary(aa13)$AIC, summary(aa14)$AIC, summary(aa15)$AIC, summary(aa16)$AIC, summary(aa17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.82
## AIC: -9.64
## BIC: -5.72
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.17
## par2: 7.08
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.04
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 9.35
## AIC: -14.71
## BIC: -6.86
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.24
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 8.26
## AIC: -14.51
## BIC: -10.59
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.15
##
## Dependence measures
## -------------------
## Kendall's tau: 0.07 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.15
## AIC: -4.3
## BIC: -0.38
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.17
## AIC: -8.33
## BIC: -4.41
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 9.16
## AIC: -16.31
## BIC: -12.39
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.94
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.31
## AIC: -6.61
## BIC: -2.69
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.18
## AIC: -4.36
## BIC: -0.43
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.18
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.2
##
## Fit statistics
## --------------
## logLik: 8.79
## AIC: -15.59
## BIC: -11.66
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.19
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 9.05
## AIC: -14.09
## BIC: -6.25
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.02
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 9.2
## AIC: -14.4
## BIC: -6.55
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.15
## AIC: -6.31
## BIC: 1.54
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1.03
## par2: 1.1
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.16
##
## Fit statistics
## --------------
## logLik: 9.18
## AIC: -14.36
## BIC: -6.51
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.21
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.1, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 9.18
## AIC: -14.35
## BIC: -6.5
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.15
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.18
##
## Fit statistics
## --------------
## logLik: 9.55
## AIC: -15.09
## BIC: -7.24
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.16
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.27
## AIC: -4.55
## BIC: 3.3
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.19
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.1, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.92
## AIC: -13.85
## BIC: -6aacopulalist## [[1]]
## [1] -9.644319
##
## [[2]]
## [1] -14.70661
##
## [[3]]
## [1] -14.51034
##
## [[4]]
## [1] -4.30086
##
## [[5]]
## [1] -8.334533
##
## [[6]]
## [1] -16.31217
##
## [[7]]
## [1] -6.611814
##
## [[8]]
## [1] -4.358673
##
## [[9]]
## [1] -15.58884
##
## [[10]]
## [1] -14.09445
##
## [[11]]
## [1] -14.40088
##
## [[12]]
## [1] -6.308431
##
## [[13]]
## [1] -14.35779
##
## [[14]]
## [1] -14.35213
##
## [[15]]
## [1] -15.09058
##
## [[16]]
## [1] -4.549135
##
## [[17]]
## [1] -13.8462print("Argentina")## [1] "Argentina"ab1 <- BiCopEst(u, v2, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.31
##
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.15
## AIC: -36.31
## BIC: -32.39ab2 <- BiCopEst(u, v2, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.32
## par2: 8.97
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 21.58
## AIC: -39.16
## BIC: -31.31ab3 <- BiCopEst(u, v2, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.4
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.17
##
## Fit statistics
## --------------
## logLik: 16.93
## AIC: -31.86
## BIC: -27.94ab4 <- BiCopEst(u, v2, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.34
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 13.28
## AIC: -24.55
## BIC: -20.63ab5 <- BiCopEst(u, v2, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.23
##
## Dependence measures
## -------------------
## Kendall's tau: 0.18 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.24
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.78
## AIC: -31.56
## BIC: -27.63ab6 <- BiCopEst(u, v2, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.24
##
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.25
##
## Fit statistics
## --------------
## logLik: 19.42
## AIC: -36.84
## BIC: -32.92ab7 <- BiCopEst(u, v2, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 2.02
##
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.31
## AIC: -36.62
## BIC: -32.7ab8 <- BiCopEst(u, v2, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.26
##
## Dependence measures
## -------------------
## Kendall's tau: 0.13 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.23
## AIC: -20.47
## BIC: -16.54ab9 <- BiCopEst(u, v2, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.3
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.29
##
## Fit statistics
## --------------
## logLik: 14.72
## AIC: -27.44
## BIC: -23.51ab10 <- BiCopEst(u, v2, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.23
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 20.37
## AIC: -36.73
## BIC: -28.89ab11 <- BiCopEst(u, v2, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.12
## par2: 1.18
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0.2
##
## Fit statistics
## --------------
## logLik: 20.56
## AIC: -37.12
## BIC: -29.27ab12 <- BiCopEst(u, v2, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.22
## Dependence measures
## -------------------
## Kendall's tau: 0.18 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.24
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.76
## AIC: -29.53
## BIC: -21.68ab13 <- BiCopEst(u, v2, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.24
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.25
##
## Fit statistics
## --------------
## logLik: 19.41
## AIC: -34.82
## BIC: -26.97ab14 <- BiCopEst(u, v2, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.14
## par2: 0.31
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.16
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 19.7
## AIC: -35.41
## BIC: -27.56ab15 <- BiCopEst(u, v2, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.2
## par2: 0.23
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.22
##
## Fit statistics
## --------------
## logLik: 19.66
## AIC: -35.32
## BIC: -27.47ab16 <- BiCopEst(u, v2, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 5.96
## par2: 0.32
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.4
## AIC: -34.8
## BIC: -26.95ab17 <- BiCopEst(u, v2, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.32
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.25
## AIC: -34.5
## BIC: -26.65abcopulalist <- list(summary(ab1)$AIC,summary(ab2)$AIC, summary(ab3)$AIC, summary(ab4)$AIC, summary(ab5)$AIC, summary(ab6)$AIC, summary(ab7)$AIC, summary(ab8)$AIC, summary(ab9)$AIC, summary(ab10)$AIC, summary(ab11)$AIC, summary(ab12)$AIC, summary(ab13)$AIC, summary(ab14)$AIC, summary(ab15)$AIC, summary(ab16)$AIC, summary(ab17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.31
##
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.15
## AIC: -36.31
## BIC: -32.39
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.32
## par2: 8.97
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 21.58
## AIC: -39.16
## BIC: -31.31
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.4
##
## Dependence measures
## -------------------
## Kendall's tau: 0.17 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.17
##
## Fit statistics
## --------------
## logLik: 16.93
## AIC: -31.86
## BIC: -27.94
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.34
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 13.28
## AIC: -24.55
## BIC: -20.63
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.23
##
## Dependence measures
## -------------------
## Kendall's tau: 0.18 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.24
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.78
## AIC: -31.56
## BIC: -27.63
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.24
##
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.25
##
## Fit statistics
## --------------
## logLik: 19.42
## AIC: -36.84
## BIC: -32.92
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 2.02
##
## Dependence measures
## -------------------
## Kendall's tau: 0.22 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.31
## AIC: -36.62
## BIC: -32.7
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.26
##
## Dependence measures
## -------------------
## Kendall's tau: 0.13 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.27
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 11.23
## AIC: -20.47
## BIC: -16.54
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.3
##
## Dependence measures
## -------------------
## Kendall's tau: 0.14 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.29
##
## Fit statistics
## --------------
## logLik: 14.72
## AIC: -27.44
## BIC: -23.51
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.23
## par2: 1.12
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.15
## Lower TD: 0.07
##
## Fit statistics
## --------------
## logLik: 20.37
## AIC: -36.73
## BIC: -28.89
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.12
## par2: 1.18
## Dependence measures
## -------------------
## Kendall's tau: 0.2 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.01
## Lower TD: 0.2
##
## Fit statistics
## --------------
## logLik: 20.56
## AIC: -37.12
## BIC: -29.27
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.22
## Dependence measures
## -------------------
## Kendall's tau: 0.18 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.24
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 16.76
## AIC: -29.53
## BIC: -21.68
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.24
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.25
##
## Fit statistics
## --------------
## logLik: 19.41
## AIC: -34.82
## BIC: -26.97
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.14
## par2: 0.31
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.16
## Lower TD: 0.11
##
## Fit statistics
## --------------
## logLik: 19.7
## AIC: -35.41
## BIC: -27.56
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.2
## par2: 0.23
## Dependence measures
## -------------------
## Kendall's tau: 0.19 (empirical = 0.22, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.22
##
## Fit statistics
## --------------
## logLik: 19.66
## AIC: -35.32
## BIC: -27.47
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 5.96
## par2: 0.32
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.4
## AIC: -34.8
## BIC: -26.95
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.32
## Dependence measures
## -------------------
## Kendall's tau: 0.21 (empirical = 0.22, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 19.25
## AIC: -34.5
## BIC: -26.65abcopulalist## [[1]]
## [1] -36.30977
##
## [[2]]
## [1] -39.16162
##
## [[3]]
## [1] -31.86368
##
## [[4]]
## [1] -24.55113
##
## [[5]]
## [1] -31.55797
##
## [[6]]
## [1] -36.84236
##
## [[7]]
## [1] -36.62467
##
## [[8]]
## [1] -20.46786
##
## [[9]]
## [1] -27.43611
##
## [[10]]
## [1] -36.73426
##
## [[11]]
## [1] -37.11788
##
## [[12]]
## [1] -29.52619
##
## [[13]]
## [1] -34.8207
##
## [[14]]
## [1] -35.40658
##
## [[15]]
## [1] -35.32096
##
## [[16]]
## [1] -34.80051
##
## [[17]]
## [1] -34.49915print("Croatia")## [1] "Croatia"ac1 <- BiCopEst(u, v3, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.19
##
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.98
## AIC: -11.95
## BIC: -8.03ac2 <- BiCopEst(u, v3, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.18
## par2: 9.35
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 8.98
## AIC: -13.97
## BIC: -6.12ac3 <- BiCopEst(u, v3, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.24
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 8.61
## AIC: -15.22
## BIC: -11.29ac4 <- BiCopEst(u, v3, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.81
## AIC: -5.61
## BIC: -1.69ac5 <- BiCopEst(u, v3, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.43
## AIC: -8.86
## BIC: -4.94ac6 <- BiCopEst(u, v3, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 9.01
## AIC: -16.02
## BIC: -12.1ac7 <- BiCopEst(u, v3, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.98
## AIC: -7.97
## BIC: -4.04ac8 <- BiCopEst(u, v3, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.21
## AIC: -4.42
## BIC: -0.5ac9 <- BiCopEst(u, v3, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.19
##
## Fit statistics
## --------------
## logLik: 8.29
## AIC: -14.58
## BIC: -10.65ac10 <- BiCopEst(u, v3, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.19
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 9.32
## AIC: -14.64
## BIC: -6.79ac11 <- BiCopEst(u, v3, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.04
## par2: 1.11
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 9.16
## AIC: -14.32
## BIC: -6.47ac12 <- BiCopEst(u, v3, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.11
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.42
## AIC: -6.83
## BIC: 1.02ac13 <- BiCopEst(u, v3, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.13
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 9.01
## AIC: -14.02
## BIC: -6.17ac14 <- BiCopEst(u, v3, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.21
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 9.43
## AIC: -14.87
## BIC: -7.02ac15 <- BiCopEst(u, v3, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.15
## par2: 0.09
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.17
##
## Fit statistics
## --------------
## logLik: 9.37
## AIC: -14.74
## BIC: -6.89ac16 <- BiCopEst(u, v3, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.17
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.88
## AIC: -5.75
## BIC: 2.09ac17 <- BiCopEst(u, v3, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.2
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.64
## AIC: -13.29
## BIC: -5.44accopulalist <- list(summary(ac1)$AIC,summary(ac2)$AIC, summary(ac3)$AIC, summary(ac4)$AIC, summary(ac5)$AIC, summary(ac6)$AIC, summary(ac7)$AIC, summary(ac8)$AIC, summary(ac9)$AIC, summary(ac10)$AIC, summary(ac11)$AIC, summary(ac12)$AIC, summary(ac13)$AIC, summary(ac14)$AIC, summary(ac15)$AIC, summary(ac16)$AIC, summary(ac17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.19
##
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 6.98
## AIC: -11.95
## BIC: -8.03
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.18
## par2: 9.35
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0.02
##
## Fit statistics
## --------------
## logLik: 8.98
## AIC: -13.97
## BIC: -6.12
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.24
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 8.61
## AIC: -15.22
## BIC: -11.29
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.08 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.02
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.81
## AIC: -5.61
## BIC: -1.69
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.43
## AIC: -8.86
## BIC: -4.94
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.13
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 9.01
## AIC: -16.02
## BIC: -12.1
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.98
## AIC: -7.97
## BIC: -4.04
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.11
##
## Dependence measures
## -------------------
## Kendall's tau: 0.06 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.14
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 3.21
## AIC: -4.42
## BIC: -0.5
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.17
##
## Dependence measures
## -------------------
## Kendall's tau: 0.09 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.19
##
## Fit statistics
## --------------
## logLik: 8.29
## AIC: -14.58
## BIC: -10.65
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.19
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.05
## Lower TD: 0.03
##
## Fit statistics
## --------------
## logLik: 9.32
## AIC: -14.64
## BIC: -6.79
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.04
## par2: 1.11
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.14
##
## Fit statistics
## --------------
## logLik: 9.16
## AIC: -14.32
## BIC: -6.47
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.11
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.13
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 5.42
## AIC: -6.83
## BIC: 1.02
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.13
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.15
##
## Fit statistics
## --------------
## logLik: 9.01
## AIC: -14.02
## BIC: -6.17
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.05
## par2: 0.21
## Dependence measures
## -------------------
## Kendall's tau: 0.12 (empirical = 0.11, p value < 0.01)
## Upper TD: 0.07
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 9.43
## AIC: -14.87
## BIC: -7.02
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.15
## par2: 0.09
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0.17
##
## Fit statistics
## --------------
## logLik: 9.37
## AIC: -14.74
## BIC: -6.89
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.17
## Dependence measures
## -------------------
## Kendall's tau: 0.11 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 4.88
## AIC: -5.75
## BIC: 2.09
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1.2
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0.1 (empirical = 0.11, p value < 0.01)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 8.64
## AIC: -13.29
## BIC: -5.44accopulalist## [[1]]
## [1] -11.95057
##
## [[2]]
## [1] -13.96529
##
## [[3]]
## [1] -15.21718
##
## [[4]]
## [1] -5.613569
##
## [[5]]
## [1] -8.859466
##
## [[6]]
## [1] -16.02217
##
## [[7]]
## [1] -7.966777
##
## [[8]]
## [1] -4.42437
##
## [[9]]
## [1] -14.57822
##
## [[10]]
## [1] -14.64226
##
## [[11]]
## [1] -14.32234
##
## [[12]]
## [1] -6.832088
##
## [[13]]
## [1] -14.02028
##
## [[14]]
## [1] -14.86626
##
## [[15]]
## [1] -14.74036
##
## [[16]]
## [1] -5.754781
##
## [[17]]
## [1] -13.28985print("Morocco")## [1] "Morocco"ad1 <- BiCopEst(u, v4, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1
## AIC: -0.01
## BIC: 3.92ad2 <- BiCopEst(u, v4, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.07
## par2: 19.88
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.63
## AIC: 0.74
## BIC: 8.59ad3 <- BiCopEst(u, v4, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.32
## AIC: -0.63
## BIC: 3.29ad4 <- BiCopEst(u, v4, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.66
## AIC: 0.67
## BIC: 4.6ad5 <- BiCopEst(u, v4, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.04
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.2
## AIC: -0.4
## BIC: 3.52ad6 <- BiCopEst(u, v4, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.04
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 1.18
## AIC: -0.35
## BIC: 3.57ad7 <- BiCopEst(u, v4, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.31
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.5
## AIC: 0.99
## BIC: 4.92ad8 <- BiCopEst(u, v4, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.96
## AIC: 0.08
## BIC: 4ad9 <- BiCopEst(u, v4, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 1.06
## AIC: -0.12
## BIC: 3.8ad10 <- BiCopEst(u, v4, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.7
## AIC: 0.6
## BIC: 8.45ad11 <- BiCopEst(u, v4, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.03
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 1.28
## AIC: 1.44
## BIC: 9.29ad12 <- BiCopEst(u, v4, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.2
## AIC: 1.6
## BIC: 9.45ad13 <- BiCopEst(u, v4, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 1.18
## AIC: 1.65
## BIC: 9.5ad14 <- BiCopEst(u, v4, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.8
## AIC: 0.4
## BIC: 8.25ad15 <- BiCopEst(u, v4, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.04
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 1.35
## AIC: 1.31
## BIC: 9.15ad16 <- BiCopEst(u, v4, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.07
## par2: 0.99
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.18
## AIC: 1.64
## BIC: 9.49ad17 <- BiCopEst(u, v4, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.06
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.5
## AIC: 3.01
## BIC: 10.86adcopulalist <- list(summary(ad1)$AIC,summary(ad2)$AIC, summary(ad3)$AIC, summary(ad4)$AIC, summary(ad5)$AIC, summary(ad6)$AIC, summary(ad7)$AIC, summary(ad8)$AIC, summary(ad9)$AIC, summary(ad10)$AIC, summary(ad11)$AIC, summary(ad12)$AIC, summary(ad13)$AIC, summary(ad14)$AIC, summary(ad15)$AIC, summary(ad16)$AIC, summary(ad17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1
## AIC: -0.01
## BIC: 3.92
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: 0.07
## par2: 19.88
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.63
## AIC: 0.74
## BIC: 8.59
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0.08
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.32
## AIC: -0.63
## BIC: 3.29
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0.07
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.66
## AIC: 0.67
## BIC: 4.6
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1.04
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.2
## AIC: -0.4
## BIC: 3.52
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1.04
##
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 1.18
## AIC: -0.35
## BIC: 3.57
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.31
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.5
## AIC: 0.99
## BIC: 4.92
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.07
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.96
## AIC: 0.08
## BIC: 4
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.06
##
## Fit statistics
## --------------
## logLik: 1.06
## AIC: -0.12
## BIC: 3.8
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0.06
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.03
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.7
## AIC: 0.6
## BIC: 8.45
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0.03
## par2: 1.03
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.04
##
## Fit statistics
## --------------
## logLik: 1.28
## AIC: 1.44
## BIC: 9.29
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.06
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.2
## AIC: 1.6
## BIC: 9.45
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1.04
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 1.18
## AIC: 1.65
## BIC: 9.5
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.07
## Dependence measures
## -------------------
## Kendall's tau: 0.05 (empirical = 0.04, p value = 0.25)
## Upper TD: 0.05
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.8
## AIC: 0.4
## BIC: 8.25
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1.04
## par2: 0.04
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0.05
##
## Fit statistics
## --------------
## logLik: 1.35
## AIC: 1.31
## BIC: 9.15
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1.07
## par2: 0.99
## Dependence measures
## -------------------
## Kendall's tau: 0.04 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 1.18
## AIC: 1.64
## BIC: 9.49
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 6
## par2: 0.06
## Dependence measures
## -------------------
## Kendall's tau: 0.03 (empirical = 0.04, p value = 0.25)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.5
## AIC: 3.01
## BIC: 10.86adcopulalist## [[1]]
## [1] -0.005363005
##
## [[2]]
## [1] 0.7428605
##
## [[3]]
## [1] -0.6332963
##
## [[4]]
## [1] 0.6729874
##
## [[5]]
## [1] -0.4031813
##
## [[6]]
## [1] -0.3547936
##
## [[7]]
## [1] 0.9942586
##
## [[8]]
## [1] 0.07693469
##
## [[9]]
## [1] -0.1220743
##
## [[10]]
## [1] 0.6037045
##
## [[11]]
## [1] 1.442982
##
## [[12]]
## [1] 1.602509
##
## [[13]]
## [1] 1.647167
##
## [[14]]
## [1] 0.3978546
##
## [[15]]
## [1] 1.306292
##
## [[16]]
## [1] 1.640302
##
## [[17]]
## [1] 3.008253print("Oman")## [1] "Oman"ae1 <- BiCopEst(u, v5, family = 1, method = "mle", se = F) %>% summary()## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: -0.02
##
## Dependence measures
## -------------------
## Kendall's tau: -0.01 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.04
## AIC: 1.91
## BIC: 5.84ae2 <- BiCopEst(u, v5, family = 2, method = "mle", se = F) %>% summary()## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: -0.01
## par2: 30
## Dependence measures
## -------------------
## Kendall's tau: -0.01 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.11
## AIC: 3.77
## BIC: 11.62ae3 <- BiCopEst(u, v5, family = 3, method = "mle", se = F) %>% summary()## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93ae4 <- BiCopEst(u, v5, family = 13, method = "mle", se = F) %>% summary()## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93ae5 <- BiCopEst(u, v5, family = 4, method = "mle", se = F) %>% summary()## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2.01
## BIC: 5.93ae6 <- BiCopEst(u, v5, family = 14, method = "mle", se = F) %>% summary()## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93ae7 <- BiCopEst(u, v5, family = 5, method = "mle", se = F) %>% summary()## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.01 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.01
## AIC: 1.97
## BIC: 5.9ae8 <- BiCopEst(u, v5, family = 6, method = "mle", se = F) %>% summary()## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2.01
## BIC: 5.93ae9 <- BiCopEst(u, v5, family = 16, method = "mle", se = F) %>% summary()## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93ae10 <- BiCopEst(u, v5, family = 7, method = "mle", se = F) %>% summary()## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.03
## AIC: 4.06
## BIC: 11.91ae11 <- BiCopEst(u, v5, family = 17, method = "mle", se = F) %>% summary()## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.02
## AIC: 4.03
## BIC: 11.88ae12 <- BiCopEst(u, v5, family = 8, method = "mle", se = F) %>% summary()## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.06
## AIC: 4.11
## BIC: 11.96ae13 <- BiCopEst(u, v5, family = 18, method = "mle", se = F) %>% summary()## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.01
## AIC: 4.03
## BIC: 11.88ae14 <- BiCopEst(u, v5, family = 9, method = "mle", se = F) %>% summary()## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.03
## AIC: 4.06
## BIC: 11.91ae15 <- BiCopEst(u, v5, family = 19, method = "mle", se = F) %>% summary()## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.02
## AIC: 4.03
## BIC: 11.88ae16 <- BiCopEst(u, v5, family = 10, method = "mle", se = F) %>% summary()## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 4
## BIC: 11.85ae17 <- BiCopEst(u, v5, family = 20, method = "mle", se = F) %>% summary()## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 4
## BIC: 11.85aecopulalist <- list(summary(ae1)$AIC,summary(ae2)$AIC, summary(ae3)$AIC, summary(ae4)$AIC, summary(ae5)$AIC, summary(ae6)$AIC, summary(ae7)$AIC, summary(ae8)$AIC, summary(ae9)$AIC, summary(ae10)$AIC, summary(ae11)$AIC, summary(ae12)$AIC, summary(ae13)$AIC, summary(ae14)$AIC, summary(ae15)$AIC, summary(ae16)$AIC, summary(ae17)$AIC)## Family
## ------
## No: 1
## Name: Gaussian
##
## Parameter(s)
## ------------
## par: -0.02
##
## Dependence measures
## -------------------
## Kendall's tau: -0.01 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.04
## AIC: 1.91
## BIC: 5.84
##
## Family
## ------
## No: 2
## Name: t
##
## Parameter(s)
## ------------
## par: -0.01
## par2: 30
## Dependence measures
## -------------------
## Kendall's tau: -0.01 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.11
## AIC: 3.77
## BIC: 11.62
##
## Family
## ------
## No: 3
## Name: Clayton
##
## Parameter(s)
## ------------
## par: 0
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93
##
## Family
## ------
## No: 13
## Name: Survival Clayton
##
## Parameter(s)
## ------------
## par: 0
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93
##
## Family
## ------
## No: 4
## Name: Gumbel
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2.01
## BIC: 5.93
##
## Family
## ------
## No: 14
## Name: Survival Gumbel
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93
##
## Family
## ------
## No: 5
## Name: Frank
##
## Parameter(s)
## ------------
## par: 0.05
##
## Dependence measures
## -------------------
## Kendall's tau: 0.01 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0.01
## AIC: 1.97
## BIC: 5.9
##
## Family
## ------
## No: 6
## Name: Joe
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2.01
## BIC: 5.93
##
## Family
## ------
## No: 16
## Name: Survival Joe
##
## Parameter(s)
## ------------
## par: 1
##
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 2
## BIC: 5.93
##
## Family
## ------
## No: 7
## Name: BB1
##
## Parameter(s)
## ------------
## par: 0
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.03
## AIC: 4.06
## BIC: 11.91
##
## Family
## ------
## No: 17
## Name: Survival BB1
##
## Parameter(s)
## ------------
## par: 0
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.02
## AIC: 4.03
## BIC: 11.88
##
## Family
## ------
## No: 8
## Name: BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.06
## AIC: 4.11
## BIC: 11.96
##
## Family
## ------
## No: 18
## Name: Survival BB6
##
## Parameter(s)
## ------------
## par: 1
## par2: 1
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.01
## AIC: 4.03
## BIC: 11.88
##
## Family
## ------
## No: 9
## Name: BB7
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.03
## AIC: 4.06
## BIC: 11.91
##
## Family
## ------
## No: 19
## Name: Survival BB7
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: -0.02
## AIC: 4.03
## BIC: 11.88
##
## Family
## ------
## No: 10
## Name: BB8
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 4
## BIC: 11.85
##
## Family
## ------
## No: 20
## Name: Survival BB8
##
## Parameter(s)
## ------------
## par: 1
## par2: 0
## Dependence measures
## -------------------
## Kendall's tau: 0 (empirical = 0.01, p value = 0.86)
## Upper TD: 0
## Lower TD: 0
##
## Fit statistics
## --------------
## logLik: 0
## AIC: 4
## BIC: 11.85aecopulalist## [[1]]
## [1] 1.91187
##
## [[2]]
## [1] 3.773945
##
## [[3]]
## [1] 2.000916
##
## [[4]]
## [1] 2.003167
##
## [[5]]
## [1] 2.008688
##
## [[6]]
## [1] 2.002018
##
## [[7]]
## [1] 1.971975
##
## [[8]]
## [1] 2.009212
##
## [[9]]
## [1] 2.002001
##
## [[10]]
## [1] 4.063418
##
## [[11]]
## [1] 4.034323
##
## [[12]]
## [1] 4.113751
##
## [[13]]
## [1] 4.027288
##
## [[14]]
## [1] 4.061948
##
## [[15]]
## [1] 4.032469
##
## [[16]]
## [1] 3.999946
##
## [[17]]
## [1] 3.999953