Return and Volatility Spillovers Between FinTech, Blockchain and AI Firms
Estancia de Investigación
Avril Lobato Delgado
2024-11-01
Tecnológico de Monterrey
Doctor Teófilo Ozuna
Alumna:
Avril Lobato Delgado A00833113
EDA de base de datos
Librerias
library(ggplot2)
library(dplyr)
library(tidyr)
library(lubridate)
library(purrr)
library(plotly)
library(forecast)
library(readxl)
library(DataExplorer)
library(dplyr)
library(ggplot2)
library(tm)
library(cluster)
library(factoextra)
library(gridExtra)
library(purrr)
library(pROC)
library(rpart)
library(rpart.plot)
library(e1071)
library(ggpubr)
library(dlookr)
library(zoo)
library(caret)
library(stats)
library(tseries)
library(readr)
library(vars)
library(syuzhet)
library(kableExtra)
library(plotly)
library(scales)
library(knitr)
library(rmgarch)
library(devtools)
library(openxlsx)
library(relaimpo)
library(stargazer)
library(RColorBrewer)
library(PerformanceAnalytics)
library(ConnectednessApproach)
library(readr)
library(tseries)
library(forecast)
library(urca)
library(fGarch)
library(MTS)
library(MASS)
library(nortest)
library(outliers)
library(moments)
library(FinTS)
library(WeightedPortTest)Carga de base de datos
Tests y Estadísticos de bd original
# Function for summary statistics
summary_stats <- function(data) {
return(data.frame(
Mean = mean(data, na.rm = TRUE),
StdDev = sd(data, na.rm = TRUE),
Min = min(data, na.rm = TRUE),
P25 = quantile(data, 0.25, na.rm = TRUE),
P50 = median(data, na.rm = TRUE),
P75 = quantile(data, 0.75, na.rm = TRUE),
Max = max(data, na.rm = TRUE),
Skewness = skewness(data, na.rm = TRUE),
Kurtosis = kurtosis(data, na.rm = TRUE)
))
}
# Calculate statistics and tests for each index
results <- data.frame(
Test = c(
"Mean", "Standard Deviation", "Minimum", "25th Percentile", "Median",
"75th Percentile", "Maximum", "Skewness", "Kurtosis", "Jarque-Bera (p-value)",
"ADF Test (p-value)", "KPSS Test (p-value)", "PP Test (p-value)", "ERS Test",
"ARCH Test (p-value)", "Weighted Portmanteau Test (p-value)", "Q2(20) Test (p-value)",
"Outlier detection (Chen and Liu)","Correlation Matrix and R-squared"
),
AI_INDEX = NA, Fintech_Index = NA, Blockchain_Index = NA
)
# Populate the table with summary stats
stats_ai <- summary_stats(data_selected$AI_INDEX)
stats_fintech <- summary_stats(data_selected$Fintech_Index)
stats_blockchain <- summary_stats(data_selected$Blockchain_Index)
results$AI_INDEX[1:9] <- as.numeric(stats_ai)
results$Fintech_Index[1:9] <- as.numeric(stats_fintech)
results$Blockchain_Index[1:9] <- as.numeric(stats_blockchain)
# Normality and stationarity tests
results$AI_INDEX[10] <- jarque.bera.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[10] <- jarque.bera.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[10] <- jarque.bera.test(data_selected$Blockchain_Index)$p.value
results$AI_INDEX[11] <- adf.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[11] <- adf.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[11] <- adf.test(data_selected$Blockchain_Index)$p.value
results$AI_INDEX[12] <- kpss.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[12] <- kpss.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[12] <- kpss.test(data_selected$Blockchain_Index)$p.value
results$AI_INDEX[13] <- pp.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[13] <- pp.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[13] <- pp.test(data_selected$Blockchain_Index)$p.value
results$AI_INDEX[14] <- ur.ers(data_selected$AI_INDEX, type = "DF-GLS")@teststat
results$Fintech_Index[14] <- ur.ers(data_selected$Fintech_Index, type = "DF-GLS")@teststat
results$Blockchain_Index[14] <- ur.ers(data_selected$Blockchain_Index, type = "DF-GLS")@teststat
# ARCH test
results$AI_INDEX[15] <- ArchTest(data_selected$AI_INDEX)$p.value
results$Fintech_Index[15] <- ArchTest(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[15] <- ArchTest(data_selected$Blockchain_Index)$p.value
# Portmanteau test
results$portmanteau_ai <- Weighted.Box.test(data_selected$AI_INDEX)$p.value
results$portmanteau_fintech <- Weighted.Box.test(data_selected$Fintech_Index)$p.value
results$portmanteau_blockchain <- Weighted.Box.test(data_selected$Blockchain_Index)$p.value
# Q2(20) test
results$AI_INDEX[17] <- Box.test(data_selected$AI_INDEX^2, lag = 20, type = "Ljung-Box")$p.value
results$Fintech_Index[17] <- Box.test(data_selected$Fintech_Index^2, lag = 20, type = "Ljung-Box")$p.value
results$Blockchain_Index[17] <- Box.test(data_selected$Blockchain_Index^2, lag = 20, type = "Ljung-Box")$p.value
# Outlier detection (Chen and Liu)
results$outliers_ai <- sum(scores(data_selected$AI_INDEX, type = "chisq", prob = 0.05) != 0)
results$outliers_fintech <- sum(scores(data_selected$Fintech_Index, type = "chisq", prob = 0.05) != 0)
results$outliers_blockchain <- sum(scores(data_selected$Blockchain_Index, type = "chisq", prob = 0.05) != 0)
# Correlation Matrix and R-squared
cor_matrix <- cor(data_selected[, -1], use = "complete.obs")
r_squared <- cor_matrix^2
print(results)## Test AI_INDEX Fintech_Index
## 1 Mean 7.284978e-04 2.514532e-04
## 2 Standard Deviation 5.997773e-03 1.886246e-03
## 3 Minimum -1.803901e-02 -6.887190e-03
## 4 25th Percentile -2.658939e-03 -9.810085e-04
## 5 Median 1.024079e-03 2.999540e-04
## 6 75th Percentile 5.001330e-03 1.530050e-03
## 7 Maximum 1.653343e-02 5.406403e-03
## 8 Skewness -3.676255e-01 -6.215113e-02
## 9 Kurtosis 3.031592e+00 3.431835e+00
## 10 Jarque-Bera (p-value) 8.905365e-11 1.789472e-04
## 11 ADF Test (p-value) 1.000000e-02 1.000000e-02
## 12 KPSS Test (p-value) 1.000000e-01 1.000000e-01
## 13 PP Test (p-value) 1.000000e-02 1.000000e-02
## 14 ERS Test -8.226890e+00 -1.240032e+01
## 15 ARCH Test (p-value) 0.000000e+00 0.000000e+00
## 16 Weighted Portmanteau Test (p-value) NA NA
## 17 Q2(20) Test (p-value) 0.000000e+00 0.000000e+00
## 18 Outlier detection (Chen and Liu) NA NA
## 19 Correlation Matrix and R-squared NA NA
## Blockchain_Index portmanteau_ai portmanteau_fintech portmanteau_blockchain
## 1 4.414805e-04 2.791471e-321 0 0
## 2 5.269908e-03 2.791471e-321 0 0
## 3 -2.425996e-02 2.791471e-321 0 0
## 4 -2.095878e-03 2.791471e-321 0 0
## 5 7.566070e-04 2.791471e-321 0 0
## 6 3.584238e-03 2.791471e-321 0 0
## 7 2.091050e-02 2.791471e-321 0 0
## 8 -4.398032e-01 2.791471e-321 0 0
## 9 5.179150e+00 2.791471e-321 0 0
## 10 0.000000e+00 2.791471e-321 0 0
## 11 1.000000e-02 2.791471e-321 0 0
## 12 1.000000e-01 2.791471e-321 0 0
## 13 1.000000e-02 2.791471e-321 0 0
## 14 -1.321070e+01 2.791471e-321 0 0
## 15 0.000000e+00 2.791471e-321 0 0
## 16 NA 2.791471e-321 0 0
## 17 0.000000e+00 2.791471e-321 0 0
## 18 NA 2.791471e-321 0 0
## 19 NA 2.791471e-321 0 0
## outliers_ai outliers_fintech outliers_blockchain
## 1 1890 1974 1904
## 2 1890 1974 1904
## 3 1890 1974 1904
## 4 1890 1974 1904
## 5 1890 1974 1904
## 6 1890 1974 1904
## 7 1890 1974 1904
## 8 1890 1974 1904
## 9 1890 1974 1904
## 10 1890 1974 1904
## 11 1890 1974 1904
## 12 1890 1974 1904
## 13 1890 1974 1904
## 14 1890 1974 1904
## 15 1890 1974 1904
## 16 1890 1974 1904
## 17 1890 1974 1904
## 18 1890 1974 1904
## 19 1890 1974 1904# Convertir la columna `Date` a tipo Date si está en formato "dd/mm/yyyy"
index_alt$Date <- as.Date(index_alt$Date, format = "%d/%m/%Y")
# Mostrar estructura y resumen
str(index_alt)## spc_tbl_ [2,051 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Date : Date[1:2051], format: "2019-01-08" "2019-01-09" ...
## $ AI_INDEX : num [1:2051] 0.00865 0.00858 0.00851 0.00844 0.00836 ...
## $ Cumulative return...3: num [1:2051] 101 102 103 103 104 ...
## $ Fintech_Index : num [1:2051] -0.000675 -0.000675 -0.000676 -0.000676 -0.000677 ...
## $ Cumulative return...5: num [1:2051] 99.9 99.9 99.8 99.7 99.7 ...
## $ Blockchain_Index : num [1:2051] 0.00205 0.00204 0.00204 0.00203 0.00203 ...
## $ Cumulative return...7: num [1:2051] 100 100 101 101 101 ...
## $ Nasdaq Composite : num [1:2051] 1005 1010 1015 1020 1025 ...
## $ Dax 40 : num [1:2051] 1002 1003 1005 1006 1008 ...
## $ Dow Jones Industrial : num [1:2051] 1003 1007 1010 1014 1017 ...
## $ KFTX Index : num [1:2051] 1012 1023 1029 1028 1027 ...
## $ S&P Financials Index : num [1:2051] 1003 1005 1006 1008 1011 ...
## - attr(*, "spec")=
## .. cols(
## .. Date = col_character(),
## .. AI_INDEX = col_double(),
## .. `Cumulative return...3` = col_double(),
## .. Fintech_Index = col_double(),
## .. `Cumulative return...5` = col_double(),
## .. Blockchain_Index = col_double(),
## .. `Cumulative return...7` = col_double(),
## .. `Nasdaq Composite` = col_double(),
## .. `Dax 40` = col_double(),
## .. `Dow Jones Industrial` = col_double(),
## .. `KFTX Index` = col_double(),
## .. `S&P Financials Index` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>## Date AI_INDEX Cumulative return...3
## Min. :2019-01-08 Min. :-0.0180390 Min. :100.9
## 1st Qu.:2020-06-03 1st Qu.:-0.0026589 1st Qu.:206.3
## Median :2021-10-29 Median : 0.0010241 Median :275.6
## Mean :2021-10-29 Mean : 0.0007285 Mean :270.1
## 3rd Qu.:2023-03-25 3rd Qu.: 0.0050013 3rd Qu.:332.3
## Max. :2024-08-19 Max. : 0.0165334 Max. :439.8
## Fintech_Index Cumulative return...5 Blockchain_Index
## Min. :-0.0068872 Min. : 91.42 Min. :-0.0242600
## 1st Qu.:-0.0009810 1st Qu.:105.21 1st Qu.:-0.0020959
## Median : 0.0003000 Median :116.09 Median : 0.0007566
## Mean : 0.0002515 Mean :120.99 Mean : 0.0004415
## 3rd Qu.: 0.0015300 3rd Qu.:127.90 3rd Qu.: 0.0035842
## Max. : 0.0054064 Max. :173.73 Max. : 0.0209105
## Cumulative return...7 Nasdaq Composite Dax 40 Dow Jones Industrial
## Min. : 95.04 Min. :1005 Min. : 829.2 Min. : 818.2
## 1st Qu.:136.66 1st Qu.:1435 1st Qu.:1184.8 1st Qu.:1186.1
## Median :168.18 Median :1810 Median :1320.5 Median :1409.5
## Mean :176.21 Mean :1798 Mean :1329.6 Mean :1357.7
## 3rd Qu.:220.23 3rd Qu.:2098 3rd Qu.:1459.5 3rd Qu.:1482.5
## Max. :276.64 Max. :2723 Max. :1743.4 Max. :1757.1
## KFTX Index S&P Financials Index
## Min. : 881.8 Min. : 727.6
## 1st Qu.:1289.6 1st Qu.:1153.5
## Median :1404.2 Median :1371.1
## Mean :1466.7 Mean :1348.0
## 3rd Qu.:1680.7 3rd Qu.:1537.3
## Max. :1925.3 Max. :1811.6Chatziantoniou, Gabauer & Gupta (2021): Integration and risk transmission in the market for crude oil: A time-varying parameter frequency connectedness approach
# Convertir a objeto zoo
data_selected <- index_alt[, c("Date","AI_INDEX", "Fintech_Index", "Blockchain_Index")]
data_matrix <- as.matrix(data_selected)
data_zoo <- zoo(data_selected[, -1], order.by = data_selected$Date)
# Instalar y cargar el paquete ConnectednessApproach, si no está ya instalado
if (!requireNamespace("ConnectednessApproach", quietly = TRUE)) {
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("GabauerDavid/ConnectednessApproach")
}
library(ConnectednessApproach)
# Configuración de parámetros para TVPVAR
nlag <- 2 # Orden del rezago
lambda <- c(0.99, 0.99) # Valores de decaimiento para el filtro
# Crear la lista de configuración necesaria
config <- list(l = lambda, nlag = nlag)
# Estimar el modelo TVP-VAR con los parámetros especificados
tvpvar_model <- TVPVAR(data_zoo, configuration = config)Table 2: Averaged Connectedness Table
partition = c(pi+0.00001, pi/5, 0)
dca = ConnectednessApproach(data_zoo,
model="TVP-VAR",
connectedness="Frequency",
nlag=1,
nfore=100,
window.size=200,
VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="BayesPrior")),
Connectedness_config = list(
FrequencyConnectedness=list(partition=partition, generalized=TRUE, scenario="ABS")
))| AI_INDEX.Total | Fintech_Index.Total | Blockchain_Index.Total | FROM.Total | AI_INDEX.1-5 | Fintech_Index.1-5 | Blockchain_Index.1-5 | FROM.1-5 | AI_INDEX.5-Inf | Fintech_Index.5-Inf | Blockchain_Index.5-Inf | FROM.5-Inf | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AI_INDEX | 79.76 | 6.87 | 13.37 | 20.24 | 10.44 | 0.87 | 1.13 | 2.00 | 69.32 | 6.00 | 12.24 | 18.24 |
| Fintech_Index | 8.56 | 84.40 | 7.04 | 15.60 | 0.75 | 10.89 | 0.53 | 1.28 | 7.81 | 73.51 | 6.51 | 14.32 |
| Blockchain_Index | 15.46 | 5.78 | 78.76 | 21.24 | 1.24 | 0.69 | 10.50 | 1.93 | 14.22 | 5.08 | 68.26 | 19.31 |
| TO | 24.02 | 12.64 | 20.41 | 57.07 | 1.99 | 1.56 | 1.65 | 5.20 | 22.03 | 11.08 | 18.76 | 51.87 |
| Inc.Own | 103.79 | 97.04 | 99.17 | cTCI/TCI | 12.43 | 12.44 | 12.15 | cTCI/TCI | 91.35 | 84.60 | 87.02 | cTCI/TCI |
| Net | 3.79 | -2.96 | -0.83 | 28.54/19.02 | -0.01 | 0.28 | -0.27 | 2.60/1.73 | 3.79 | -3.24 | -0.55 | 25.94/17.29 |
| NPDC | 2.00 | 0.00 | 1.00 | 1.00 | 2.00 | 0.00 | 2.00 | 0.00 | 1.00 |
Gabauer and Gupta (2018): On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach
data_zoo2 <- zoo(data_selected[, c("AI_INDEX", "Fintech_Index", "Blockchain_Index")],
order.by = data_selected$Date)
# Configuración de parámetros para TVPVAR
nlag <- 2 # Orden del rezago
lambda <- c(0.99, 0.99) # Valores de decaimiento para el filtro
# Crear la lista de configuración necesaria
config <- list(l = lambda, nlag = nlag)
# Estimar el modelo TVP-VAR con los parámetros especificados
tvpvar_model <- TVPVAR(data_zoo2, configuration = config)dca = ConnectednessApproach(data_zoo2,
nlag=1,
nfore=10,
model="TVP-VAR",
connectedness="Time",
window.size=200,
VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="BayesPrior")))Table 1: Connectedness table
| AI_INDEX | Fintech_Index | Blockchain_Index | FROM | |
|---|---|---|---|---|
| AI_INDEX | 81.70 | 6.65 | 11.65 | 18.30 |
| Fintech_Index | 7.31 | 86.71 | 5.98 | 13.29 |
| Blockchain_Index | 11.98 | 5.17 | 82.86 | 17.14 |
| TO | 19.29 | 11.82 | 17.63 | 48.73 |
| Inc.Own | 100.99 | 98.53 | 100.48 | cTCI/TCI |
| NET | 0.99 | -1.47 | 0.48 | 24.37/16.24 |
| NPT | 2.00 | 0.00 | 1.00 |
# CONNECTEDNESS DECOMPOSITION
int = InternalConnectedness(dca, groups=list("Group1"=c(1), "Group2"=c(2), "Group3"=c(3)))
ext = ExternalConnectedness(dca, groups=list("Group1"=c(1), "Group2"=c(2), "Group3"=c(3)))
kable(int$TABLE)| AI_INDEX | Fintech_Index | Blockchain_Index | FROM | |
|---|---|---|---|---|
| AI_INDEX | 81.70 | 0.00 | 0.00 | 0.00 |
| Fintech_Index | 0.00 | 86.71 | 0.00 | 0.00 |
| Blockchain_Index | 0.00 | 0.00 | 82.86 | 0.00 |
| TO | 0.00 | 0.00 | 0.00 | 0.00 |
| Inc.Own | 81.70 | 86.71 | 82.86 | cTCI/TCI |
| NET | 0.00 | 0.00 | 0.00 | 0.00/0.00 |
| NPT | 0.00 | 0.00 | 0.00 |
| AI_INDEX | Fintech_Index | Blockchain_Index | FROM | |
|---|---|---|---|---|
| AI_INDEX | 0.00 | 6.65 | 11.65 | 18.30 |
| Fintech_Index | 7.31 | 0.00 | 5.98 | 13.29 |
| Blockchain_Index | 11.98 | 5.17 | 0.00 | 17.14 |
| TO | 19.29 | 11.82 | 17.63 | 48.73 |
| Inc.Own | 19.29 | 11.82 | 17.63 | cTCI/TCI |
| NET | 0.99 | -1.47 | 0.48 | 24.37/16.24 |
| NPT | 2.00 | 0.00 | 1.00 |
Cocca, Gabauer, and Pomberger (2024): Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures
# Convierte la columna 'Date' a formato Date
date <- as.Date(data_selected$Date, format="%d/%m/%Y")
# Crea la serie de tiempo (zoo) solo con las columnas seleccionadas
Y <- zoo(data_selected[, c("Fintech_Index", "AI_INDEX", "Blockchain_Index")], order.by = date)
# Asegúrate de que los datos sean numéricos y elimina valores NA
Y <- na.omit(zoo(data.frame(
Fintech_Index_diff = as.numeric(data_selected$Fintech_Index),
AI_INDEX_diff = as.numeric(data_selected$AI_INDEX),
Blockchain_Index_diff = as.numeric(data_selected$Blockchain_Index)
), order.by = date))
# Reemplaza los valores NA restantes por 0
Y[is.na(Y)] <- 0
# Establece 'dimnames' de Y con números como índices (como en Y2) sin cambiar el índice de fecha
dimnames(Y) <- list(as.character(1:nrow(Y)), colnames(Y))
NAMES = colnames(Y)
k = ncol(Y)
t = nrow(Y)
# Verifica la estructura de Y
str(Y)## 'zoo' series from 2019-01-08 to 2024-08-19
## Data: num [1:2051, 1:3] -0.000675 -0.000675 -0.000676 -0.000676 -0.000677 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:2051] "1" "2" "3" "4" ...
## ..$ : chr [1:3] "Fintech_Index_diff" "AI_INDEX_diff" "Blockchain_Index_diff"
## Index: Date[1:2051], format: "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" "2019-01-12" ...| Fintech_Index_diff | AI_INDEX_diff | Blockchain_Index_diff | |
|---|---|---|---|
| Mean | 0.000*** | 0.001*** | 0.000*** |
| (0.000) | (0.000) | (0.000) | |
| Variance | 0 | 0 | 0 |
| Skewness | -0.062 | -0.368*** | -0.440*** |
| (0.249) | (0.000) | (0.000) | |
| Ex.Kurtosis | 0.432*** | 0.032 | 2.179*** |
| (0.001) | (0.712) | (0.000) | |
| JB | 17.257*** | 46.284*** | 471.935*** |
| (0.000) | (0.000) | (0.000) | |
| ERS | -12.400*** | -8.227*** | -13.211*** |
| (0.000) | (0.000) | (0.000) | |
| Q(20) | 3870.587*** | 3338.261*** | 3939.808*** |
| (0.000) | (0.000) | (0.000) | |
| Q2(20) | 3413.861*** | 5253.401*** | 4241.131*** |
| (0.000) | (0.000) | (0.000) | |
| kendall | Fintech_Index_diff | AI_INDEX_diff | Blockchain_Index_diff |
| Fintech_Index_diff | 1.000*** | -0.062*** | 0.016 |
| AI_INDEX_diff | -0.062*** | 1.000*** | 0.144*** |
| Blockchain_Index_diff | 0.016 | 0.144*** | 1.000*** |
Figure 0: Percentage changes
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (i in 1:k) {
plot(date,Y[,i],type='l',las=1,xaxs='i',yaxs='i',ylim=c(-0.1,0.1),xlab='',ylab='',main=paste(NAMES[i]),tck=-.02,col='steelblue4')
grid(NA,NULL)
lines(date,Y[,i],col='steelblue4')
abline(h=0,lty=3)
box()
}
spec <- list()
# Itera sobre cada columna de Y
for (i in 1:k) {
# Intenta seleccionar el mejor modelo GARCH para cada serie
u <- tryCatch({
GARCHselection(Y[, i],
distributions = c("norm", "std", "sstd", "ged", "sged"),
models = c("sGARCH", "gjrGARCH", "eGARCH", "iGARCH", "AVGARCH", "TGARCH"))
}, error = function(e) {
warning(paste("Error al seleccionar modelo GARCH para la variable", i, ":", e$message))
NULL
})
# Si 'u' no es NULL y tiene un mejor modelo, agrega a 'spec'
if (!is.null(u) && !is.null(u$best_ugarch)) {
spec[[i]] <- u$best_ugarch
} else {
# Si no se seleccionó un modelo, asigna un modelo GARCH(1,1) por defecto
warning(paste("No se seleccionó ningún modelo GARCH para", NAMES[i], ". Usando modelo GARCH(1,1) con distribución normal"))
spec[[i]] <- ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),
mean.model = list(armaOrder = c(0, 0)),
distribution.model = "norm")
}
}
# Verifica que 'spec' tenga modelos para cada serie en Y
if (length(spec) != k) {
stop("spec no contiene modelos válidos para todas las series de Y")
}
# Ejecuta el modelo DCC-GARCH si spec es válida
fit <- BivariateDCCGARCH(Y, spec)## [1] "DCC-GARCH estimation based on Fintech_Index_diff and AI_INDEX_diff"
##
## *-------------------------------------------------*
## * Copula GARCH Fit *
## *-------------------------------------------------*
##
## Distribution : mvt
## DCC Order : 1 1
## Asymmetric : FALSE
## No. of Parameters : 19
## [VAR GARCH DCC UncQ]: [0+15+3+1]
## No. of Series : 2
## No. of Observations : 2051
## Log-Likelihood : 21146.98
## Av.Log-Likelihood : 10.311
##
## Optimal Parameters
## ---------------------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## [Fintech_Index_diff].mu 0.000779 NA NA NA
## [Fintech_Index_diff].omega 0.000001 NA NA NA
## [Fintech_Index_diff].alpha1 1.000000 NA NA NA
## [Fintech_Index_diff].beta1 0.169934 NA NA NA
## [Fintech_Index_diff].eta11 0.076762 NA NA NA
## [Fintech_Index_diff].skew 2.447990 NA NA NA
## [Fintech_Index_diff].shape 3.327699 NA NA NA
## [AI_INDEX_diff].mu -0.000120 NA NA NA
## [AI_INDEX_diff].omega 0.000000 NA NA NA
## [AI_INDEX_diff].alpha1 1.000000 NA NA NA
## [AI_INDEX_diff].beta1 0.000000 NA NA NA
## [AI_INDEX_diff].eta11 -0.122576 NA NA NA
## [AI_INDEX_diff].eta21 -0.000026 NA NA NA
## [AI_INDEX_diff].skew 0.379831 NA NA NA
## [AI_INDEX_diff].shape 4.157634 NA NA NA
## [Joint]dcca1 0.999994 NA NA NA
## [Joint]dccb1 0.000000 NA NA NA
## [Joint]mshape 4.003043 NA NA NA
##
## Information Criteria
## ---------------------
##
## Akaike -20.604
## Bayes -20.554
## Shibata -20.604
## Hannan-Quinn -20.585
##
##
## Elapsed time : 8.244778
##
## [1] "DCC-GARCH estimation based on Fintech_Index_diff and Blockchain_Index_diff"
##
## *-------------------------------------------------*
## * Copula GARCH Fit *
## *-------------------------------------------------*
##
## Distribution : mvt
## DCC Order : 1 1
## Asymmetric : FALSE
## No. of Parameters : 18
## [VAR GARCH DCC UncQ]: [0+14+3+1]
## No. of Series : 2
## No. of Observations : 2051
## Log-Likelihood : 20257.12
## Av.Log-Likelihood : 9.877
##
## Optimal Parameters
## ---------------------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## [Fintech_Index_diff].mu 0.000779 NA NA NA
## [Fintech_Index_diff].omega 0.000001 NA NA NA
## [Fintech_Index_diff].alpha1 1.000000 NA NA NA
## [Fintech_Index_diff].beta1 0.169934 NA NA NA
## [Fintech_Index_diff].eta11 0.076762 NA NA NA
## [Fintech_Index_diff].skew 2.447990 NA NA NA
## [Fintech_Index_diff].shape 3.327699 NA NA NA
## [Blockchain_Index_diff].mu 0.000320 NA NA NA
## [Blockchain_Index_diff].omega -2.080531 NA NA NA
## [Blockchain_Index_diff].alpha1 0.040246 NA NA NA
## [Blockchain_Index_diff].beta1 0.813364 NA NA NA
## [Blockchain_Index_diff].gamma1 1.329788 NA NA NA
## [Blockchain_Index_diff].skew 0.542386 NA NA NA
## [Blockchain_Index_diff].shape 3.532068 NA NA NA
## [Joint]dcca1 0.923126 NA NA NA
## [Joint]dccb1 0.000000 NA NA NA
## [Joint]mshape 7.755774 NA NA NA
##
## Information Criteria
## ---------------------
##
## Akaike -19.737
## Bayes -19.690
## Shibata -19.737
## Hannan-Quinn -19.720
##
##
## Elapsed time : 5.123329
##
## [1] "DCC-GARCH estimation based on AI_INDEX_diff and Blockchain_Index_diff"
##
## *-------------------------------------------------*
## * Copula GARCH Fit *
## *-------------------------------------------------*
##
## Distribution : mvt
## DCC Order : 1 1
## Asymmetric : FALSE
## No. of Parameters : 19
## [VAR GARCH DCC UncQ]: [0+15+3+1]
## No. of Series : 2
## No. of Observations : 2051
## Log-Likelihood : 17917.44
## Av.Log-Likelihood : 8.736
##
## Optimal Parameters
## ---------------------------------------------------
## Estimate Std. Error t value Pr(>|t|)
## [AI_INDEX_diff].mu -0.000120 NA NA NA
## [AI_INDEX_diff].omega 0.000000 NA NA NA
## [AI_INDEX_diff].alpha1 1.000000 NA NA NA
## [AI_INDEX_diff].beta1 0.000000 NA NA NA
## [AI_INDEX_diff].eta11 -0.122576 NA NA NA
## [AI_INDEX_diff].eta21 -0.000026 NA NA NA
## [AI_INDEX_diff].skew 0.379831 NA NA NA
## [AI_INDEX_diff].shape 4.157634 NA NA NA
## [Blockchain_Index_diff].mu 0.000320 NA NA NA
## [Blockchain_Index_diff].omega -2.080531 NA NA NA
## [Blockchain_Index_diff].alpha1 0.040246 NA NA NA
## [Blockchain_Index_diff].beta1 0.813364 NA NA NA
## [Blockchain_Index_diff].gamma1 1.329788 NA NA NA
## [Blockchain_Index_diff].skew 0.542386 NA NA NA
## [Blockchain_Index_diff].shape 3.532068 NA NA NA
## [Joint]dcca1 0.941613 NA NA NA
## [Joint]dccb1 0.000000 NA NA NA
## [Joint]mshape 7.839345 NA NA NA
##
## Information Criteria
## ---------------------
##
## Akaike -17.454
## Bayes -17.405
## Shibata -17.455
## Hannan-Quinn -17.436
##
##
## Elapsed time : 6.651648Table 4: Evaluation of univariate GARCH performance
H = fit$H_t
R = fit$R_t
uGARCH_table = NULL
for (i in 1:k) {
fit = ugarchfit(spec[[i]], Y[,i])
gt = GARCHtests(fit, lag=20)
uGARCH_table = rbind(uGARCH_table, gt$TABLE)
}
kable(uGARCH_table)| SignBias | WARCH(20) | VaR | CVaR | VaR Dur. | |
|---|---|---|---|---|---|
| statistics | 0.6230910 | 1.9437522 | 113.69976 | -93.6559 | 0.1666667 |
| pvalues | 0.5332942 | 0.9997909 | 0.00000 | 0.1700 | 0.1755157 |
| statistics | 0.8620658 | 1.1073277 | 76.58490 | -148.2286 | 0.2843137 |
| pvalues | 0.3887523 | 0.9999953 | 0.00000 | 0.0000 | 0.3464012 |
| statistics | 1.4127318 | 13.8866627 | 74.01928 | -152.5048 | 0.2941176 |
| pvalues | 0.1578867 | 0.1760766 | 0.00000 | 0.2160 | 0.1284943 |
Figure 3: Dynamic conditional correlations
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (j in 1:k) {
plot(date,R[j,j,],type='l',las=1,xaxs='i',yaxs='i',xlab='',ylab='',main=paste(NAMES[j]),tck=-.02,col=j,ylim=c(-1.1,1.1))
grid(NA,NULL)
for (i in 1:k) {
lines(date,R[j,i,],col=i)
abline(h=0,lty=3)
box()
}
legend("bottom",NAMES[-j],fill=c(1:k)[-j],bty="n",cex=0.75,ncol=k)
}
Table 5: Averaged connectedness measures
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 33% | |======================= | 34% | |======================== | 34% | |======================== | 35% | |========================= | 35% | |========================= | 36% | |========================== | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 39% | |============================ | 40% | |============================ | 41% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |============================== | 44% | |=============================== | 44% | |=============================== | 45% | |================================ | 45% | |================================ | 46% | |================================= | 46% | |================================= | 47% | |================================= | 48% | |================================== | 48% | |================================== | 49% | |=================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |===================================== | 54% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 59% | |========================================== | 60% | |========================================== | 61% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 62% | |============================================ | 63% | |============================================ | 64% | |============================================= | 64% | |============================================= | 65% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |================================================= | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 72% | |=================================================== | 73% | |=================================================== | 74% | |==================================================== | 74% | |==================================================== | 75% | |===================================================== | 75% | |===================================================== | 76% | |====================================================== | 76% | |====================================================== | 77% | |====================================================== | 78% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 79% | |======================================================== | 80% | |======================================================== | 81% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 82% | |========================================================== | 83% | |========================================================== | 84% | |=========================================================== | 84% | |=========================================================== | 85% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 86% | |============================================================= | 87% | |============================================================= | 88% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 89% | |=============================================================== | 90% | |=============================================================== | 91% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================= | 94% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%| Fintech_Index_diff | AI_INDEX_diff | Blockchain_Index_diff | FROM | |
|---|---|---|---|---|
| Fintech_Index_diff | 100.00 | 62.16 | 22.18 | 84.34 |
| AI_INDEX_diff | 62.45 | 100.00 | 20.89 | 83.33 |
| Blockchain_Index_diff | 24.17 | 22.58 | 100.00 | 46.75 |
| TO | 86.62 | 84.74 | 43.07 | 214.42 |
| Inc.Own | 186.62 | 184.74 | 143.07 | cTCI/TCI |
| NET | 2.28 | 1.40 | -3.68 | 107.21/71.47 |
| NPT | 2.00 | 1.00 | 0.00 |
Figure 4: Dynamic conditional R2 decomposed measures
r2c = apply(dca$CT,c(1,3),sum)-1
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (j in 1:k) {
r2dd = matrix(0,ncol=k,nrow=t)
for (i in 1:t) {
ded = rep(0,k)
ded[j] = 1
r2dd[i,] = cumsum(dca$CT[j,,i]-ded)
}
plot(date,100*r2c[j,]*NA,type="l",las=1,xlab="",ylab="",ylim=c(0,100),xaxs="i",tck=-0.01,yaxs="i",main=NAMES[j])
grid(NA,NULL)
for (i in k:1) {
polygon(c(date, rev(date)), c(c(rep(0, t)), rev(100*r2dd[,i])), col=i, border=i)
}
box()
legend("topleft",NAMES[-j],fill=c(1:k)[-j],bty="n",cex=0.75,ncol=1)
}
Figure 5: Dynamic total connectedness
par(mfcol = c(1,1), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
plot(date,dca$TCI*NA,type='l',las=1,xaxs='i',yaxs='i',xlab='',ylab='',main='',tck=-.01, ylim=c(0,110))#c(max(c(0,min(TCI)-10)),max(TCI)+10))
grid(NA,NULL)
polygon(c(date, rev(date)), c(c(rep(0, t)), rev(dca$TCI)), col=1, border=1)
box()
Figure 6: Net total directional connectedness
par(mfcol = c(ceiling(k/2),2), oma = c(0, 0, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (i in 1:k) {
plot(date,dca$NET[,i]*NA,type='l',las=1,xaxs='i',yaxs='i',ylim=c(-20,20),xlab='',ylab='',main=paste("NET",NAMES[i]),tck=-.025)
grid(NA,NULL)
polygon(c(date, rev(date)), c(c(rep(0, t)), rev(dca$NET[,i])), col=1, border=1)
abline(h=0,lty=3)
box()
}
Table 7: Hedge ratios
method = "cumsum"
statistics = "Fisher"
metric = "StdDev"
hr = HedgeRatio(Y/100, H, statistics=statistics, method=method, metric=metric, digit=3)
kable(hr$TABLE)| Mean | Std.Dev. | 5% | 95% | HE | p-value | Return | Std.Dev | SR | |
|---|---|---|---|---|---|---|---|---|---|
| Fintech_Index_diff/AI_INDEX_diff | -4.933 | 118.289 | -2.128 | 3.001 | -10177.249 | 0.000 | 0.013 | 0.030 | 0.444 |
| Fintech_Index_diff/Blockchain_Index_diff | 0.043 | 0.600 | -0.716 | 0.846 | 0.040 | 0.000 | 0.001 | 0.000 | 4.142 |
| AI_INDEX_diff/Fintech_Index_diff | 3.522 | 64.710 | -15.822 | 17.286 | -76.120 | 0.350 | -0.003 | 0.008 | -0.403 |
| AI_INDEX_diff/Blockchain_Index_diff | 0.145 | 1.356 | -1.659 | 2.154 | 0.277 | 0.000 | 0.001 | 0.001 | 1.398 |
| Blockchain_Index_diff/Fintech_Index_diff | -3.450 | 72.603 | -17.189 | 8.992 | -126.712 | 0.350 | 0.008 | 0.009 | 0.862 |
| Blockchain_Index_diff/AI_INDEX_diff | 2.433 | 393.141 | -3.044 | 5.405 | -47175.052 | 0.000 | -0.097 | 0.182 | -0.533 |
Table 8: Multivariate hedging portfolios
mhp = MultivariateHedgingPortfolio(Y/100, H, statistics=statistics, method=method, digit=3)
kable(mhp$TABLE)| Mean | Std.Dev. | 5% | 95% | HE | p-value | Return | Risk | SR | |
|---|---|---|---|---|---|---|---|---|---|
| Fintech_Index_diff/AI_INDEX_diff | -4.015 | 95.488 | -2.036 | 2.679 | -6472.673 | 0.000 | 0.011 | 0.024 | 0.456 |
| Fintech_Index_diff/Blockchain_Index_diff | 0.015 | 0.356 | -0.160 | 0.233 | -6472.673 | 0.000 | 0.011 | 0.024 | 0.456 |
| AI_INDEX_diff/Fintech_Index_diff | 3.093 | 58.466 | -15.276 | 16.505 | -63.903 | 0.000 | -0.003 | 0.008 | -0.431 |
| AI_INDEX_diff/Blockchain_Index_diff | 0.072 | 0.760 | -0.144 | 0.593 | -63.903 | 0.000 | -0.003 | 0.008 | -0.431 |
| Blockchain_Index_diff/Fintech_Index_diff | -9.665 | 448.013 | -19.850 | 13.359 | -35347.965 | 0.000 | 0.088 | 0.157 | 0.562 |
| Blockchain_Index_diff/AI_INDEX_diff | 12.806 | 207.019 | -1.457 | 7.665 | -35347.965 | 0.000 | 0.088 | 0.157 | 0.562 |
Figure 3: Dynamic conditional betas
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (j in 1:k) {
plot(date,mhp$Beta[j,j,]*NA,type='l',las=1,xaxs='i',yaxs='i',xlab='',ylab='',
main=paste(NAMES[j]),tck=-.02,col=j,ylim=c(-0.5,70))
grid(NA,NULL)
coefs = lm(Y[,j]~Y[,-j])$coefficients[-1]
for (i in 1:k) {
if (i!=j) {
lines(date,mhp$Beta[j,i,],col=i)
abline(h=0,lty=3)
box()
}
}
col = 1:k
col = col[-j]
abline(h=coefs,lty=3,col=col)
legend("bottom",NAMES[-j],fill=c(1:k)[-j],bty="n",cex=0.6,ncol=k)
}
Table 9: Optimal bivariate portfolio weights
bpw = BivariatePortfolio(Y/100, H, statistics=statistics, method=method, metric=metric, digit=3)
## The optimal bivariate portfolios are computed according to:
## Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. The Review of Financial Studies, 11(4), 817-844.
##
## Hedging effectiveness is calculated according to:
## Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
##
## Statistics of the hedging effectiveness measure are implemented according to:
## Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
kable(bpw$TABLE)| Mean | Std.Dev. | 5% | 95% | HE | p-value | SR | |
|---|---|---|---|---|---|---|---|
| Fintech_Index_diff/AI_INDEX_diff | 0.708 | 0.341 | 0.000 | 1.000 | 0.350 | 0.000 | 4.412 |
| Fintech_Index_diff/Blockchain_Index_diff | 0.789 | 0.279 | 0.101 | 1.000 | 0.408 | 0.000 | 4.959 |
| AI_INDEX_diff/Fintech_Index_diff | 0.292 | 0.341 | 0.000 | 1.000 | 0.936 | 0.000 | 4.412 |
| AI_INDEX_diff/Blockchain_Index_diff | 0.536 | 0.386 | 0.000 | 1.000 | 0.640 | 0.000 | 1.295 |
| Blockchain_Index_diff/Fintech_Index_diff | 0.211 | 0.279 | 0.000 | 0.899 | 0.924 | 0.000 | 4.959 |
| Blockchain_Index_diff/AI_INDEX_diff | 0.464 | 0.386 | 0.000 | 1.000 | 0.533 | 0.000 | 1.295 |
Table 10: Multivariate portfolio analysis
PCIc = dca$PCI
PCIg = dca$CT
for (l in 1:dim(dca$CT)[3]) {
for (i in 1:k) {
for (j in 1:k) {
PCIg[i,j,l] = (2*R[i,j,l]^2)/(1+R[i,j,l]^2)
}
}
}
mvp = MinimumConnectednessPortfolio(Y/100, H, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
## Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
##
## Hedging effectiveness is calculated according to:
## Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
##
## Statistics of the hedging effectiveness measure are implemented according to:
## Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mcp = MinimumConnectednessPortfolio(Y/100, R, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
## Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
##
## Hedging effectiveness is calculated according to:
## Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
##
## Statistics of the hedging effectiveness measure are implemented according to:
## Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mpc = MinimumConnectednessPortfolio(Y/100, PCIc, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
## Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
##
## Hedging effectiveness is calculated according to:
## Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
##
## Statistics of the hedging effectiveness measure are implemented according to:
## Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mpg = MinimumConnectednessPortfolio(Y/100, PCIg, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
## Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
##
## Hedging effectiveness is calculated according to:
## Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
##
## Statistics of the hedging effectiveness measure are implemented according to:
## Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mrp = MinimumConnectednessPortfolio(Y/100, dca$CT, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
## Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
##
## Hedging effectiveness is calculated according to:
## Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
##
## Statistics of the hedging effectiveness measure are implemented according to:
## Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
MVA = rbind(mvp$TABLE, mcp$TABLE, mpc$TABLE, mpg$TABLE, mrp$TABLE)
kable(MVA)| Mean | Std.Dev. | 5% | 95% | HE | p-value | SR | |
|---|---|---|---|---|---|---|---|
| Fintech_Index_diff | 0.668 | 0.328 | 0.000 | 1.000 | 0.376 | 0.000 | 4.722 |
| AI_INDEX_diff | 0.265 | 0.318 | 0.000 | 1.000 | 0.938 | 0.000 | 4.722 |
| Blockchain_Index_diff | 0.067 | 0.126 | 0.000 | 0.330 | 0.920 | 0.000 | 4.722 |
| Fintech_Index_diff | 0.418 | 0.131 | 0.141 | 0.518 | -1.512 | 0.000 | 2.477 |
| AI_INDEX_diff | 0.347 | 0.188 | 0.000 | 0.520 | 0.752 | 0.000 | 2.477 |
| Blockchain_Index_diff | 0.235 | 0.217 | 0.000 | 0.498 | 0.678 | 0.000 | 2.477 |
| Fintech_Index_diff | 0.271 | 0.098 | 0.148 | 0.475 | -1.901 | 0.000 | 2.427 |
| AI_INDEX_diff | 0.277 | 0.100 | 0.032 | 0.401 | 0.713 | 0.000 | 2.427 |
| Blockchain_Index_diff | 0.452 | 0.030 | 0.436 | 0.492 | 0.628 | 0.000 | 2.427 |
| Fintech_Index_diff | 0.237 | 0.189 | 0.000 | 0.538 | -2.503 | 0.000 | 2.483 |
| AI_INDEX_diff | 0.311 | 0.210 | 0.000 | 0.550 | 0.654 | 0.000 | 2.483 |
| Blockchain_Index_diff | 0.452 | 0.105 | 0.287 | 0.499 | 0.551 | 0.000 | 2.483 |
| Fintech_Index_diff | 0.286 | 0.021 | 0.257 | 0.329 | -1.791 | 0.000 | 2.407 |
| AI_INDEX_diff | 0.291 | 0.028 | 0.249 | 0.332 | 0.724 | 0.000 | 2.407 |
| Blockchain_Index_diff | 0.423 | 0.029 | 0.361 | 0.469 | 0.642 | 0.000 | 2.407 |
Table 11: Portfolio performance
library(PerformanceAnalytics)
library(xts)
port.mat = data.frame(
MVP=mvp$portfolio_return,
MCP=mcp$portfolio_return,
MPC=mpc$portfolio_return,
MPG=mpg$portfolio_return,
MRP=mrp$portfolio_return
)
port.xts = xts(port.mat, order.by=date)
Rb <- xts(Y[,1]/100, order.by = index(Y))
colnames(Rb) <- "Benchmark"
ir <- matrix(NA, nrow = 1, ncol = ncol(port.xts))
colnames(ir) <- colnames(port.xts)
rownames(ir) <- "GRE"
for (i in 1:ncol(port.xts)) {
ir[, i] <- InformationRatio(Ra = port.xts[, i], Rb = Rb)
}
k = ncol(port.xts)
table = matrix(NA, ncol=k, nrow=5)
for (i in 1:k) {
table[,i] = c(
# Retorno anualizado
Return.annualized(port.xts[,i]),
# Desviación estándar anualizada
StdDev.annualized(port.xts[,i]),
# Ratio de Sharpe anualizado
SharpeRatio.annualized(port.xts[,i]),
# Valor en Riesgo (VaR) con distribución normal y percentil del 95%
VaR(as.numeric(port.xts[,i]), p=0.95, method="gaussian"),
# Expectativa de Pérdida (ES) con distribución normal y percentil del 95%
ES(as.numeric(port.xts[,i]), p=0.95, method="gaussian")
)
}
colnames(table) = colnames(port.xts)
rownames(table) = c("Return","StdDev","Sharpe Ratio (StdDev)","Sharpe Ratio (VaR)","Sharpe Ratio (CVaR)")
kable(rbind(table, ir))| MVP | MCP | MPC | MPG | MRP | |
|---|---|---|---|---|---|
| Return | 0.0011168 | 0.0011754 | 0.0012379 | 0.0013915 | 0.0012041 |
| StdDev | 0.0002365 | 0.0004746 | 0.0005100 | 0.0005605 | 0.0005003 |
| Sharpe Ratio (StdDev) | 4.7215607 | 2.4767543 | 2.4269769 | 2.4828300 | 2.4068835 |
| Sharpe Ratio (VaR) | -0.0000236 | -0.0000497 | -0.0000513 | -0.0000555 | -0.0000495 |
| Sharpe Ratio (CVaR) | -0.0000026 | -0.0000015 | -0.0000016 | -0.0000016 | -0.0000016 |
| GRE | 1.6028835 | 1.0169457 | 1.0795859 | 1.2355510 | 1.0451553 |
Henriques(2025): Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification
pkgs <- c("readr","zoo","xts","ConnectednessApproach","PerformanceAnalytics",
"riskParityPortfolio","quadprog","rugarch","rmgarch","tseries",
"urca","FinTS","robustbase","corrplot","matrixStats","psych","vars")
install_if_missing <- function(p) if (!requireNamespace(p, quietly = TRUE)) install.packages(p)
invisible(lapply(pkgs, install_if_missing))
library(readr); library(zoo); library(xts); library(ConnectednessApproach)
library(PerformanceAnalytics); library(riskParityPortfolio); library(quadprog)## Warning: package 'riskParityPortfolio' was built under R version 4.5.1## corrplot 0.95 loaded# --- 1) Cargar datos --------------------------------------------------------
data_path <- "C:/Users/AVRIL/Desktop/Spillovers/all_daily_index_returns.csv"
index_alt <- read_csv(data_path, show_col_types = FALSE)## New names:
## • `Cumulative return` -> `Cumulative return...3`
## • `Cumulative return` -> `Cumulative return...5`
## • `Cumulative return` -> `Cumulative return...7`if(!("Date" %in% colnames(index_alt))) stop("No se encontró la columna 'Date'.")
index_alt$Date <- as.Date(index_alt$Date, format = "%d/%m/%Y")
if(any(is.na(index_alt$Date))) index_alt$Date <- as.Date(index_alt$Date, format = "%Y-%m-%d")
data_selected <- index_alt[, c("Date", "AI_INDEX", "Fintech_Index", "Blockchain_Index")]
colnames(data_selected) <- c("Date","AI_INDEX","Fintech","Blockchain")
data_selected <- data_selected[!is.na(data_selected$Date), ]Detectar si los valores son precios o retornos y preparar series
# Heurística: si hay valores mayores que 1.5 -> tratamos como precios, sino como retornos
is_price_like <- function(x) median(abs(x), na.rm=TRUE) > 1.5
mat <- as.data.frame(data_selected[, -1])
if(any(sapply(mat, is_price_like))) {
message("Detectado dato tipo precios -> calculando retornos logarítmicos.")
prices_xts <- xts(mat, order.by = data_selected$Date)
y.r <- na.omit(diff(log(prices_xts)))
} else {
message("Detectado dato tipo retornos -> usaremos directamente los retornos.")
y.r <- xts(mat, order.by = data_selected$Date)
if(all(abs(coredata(y.r)) > 1)) {
message("Retornos parecen en porcentaje. Se convierten a decimales (/100).")
y.r <- y.r / 100
}
y.r <- na.omit(y.r)
}## Detectado dato tipo retornos -> usaremos directamente los retornos.symbols <- colnames(y.r)
cat("Series preparadas:", paste(symbols, collapse=", "), " (n=", nrow(y.r), ")\n")## Series preparadas: AI_INDEX, Fintech, Blockchain (n= 2051 )
Estadísticos y correlaciones
##
## --- Estadísticos descriptivos ---## AI_INDEX Fintech Blockchain
## vars 1.00000000 2.000000e+00 3.00000000
## n 2051.00000000 2.051000e+03 2051.00000000
## mean 0.07284978 2.514532e-02 0.04414805
## sd 0.59977731 1.886246e-01 0.52699084
## median 0.10240790 2.999540e-02 0.07566070
## trimmed 0.09878005 2.391826e-02 0.06405141
## mad 0.57087483 1.879322e-01 0.42218102
## min -1.80390090 -6.887190e-01 -2.42599600
## max 1.65334260 5.406403e-01 2.09105040
## range 3.45724350 1.229359e+00 4.51704640
## skew -0.36735670 -6.210568e-02 -0.43948156
## kurtosis 0.02863654 4.284896e-01 2.17410101
## se 0.01324363 4.165005e-03 0.01163644##
## --- Matriz de correlaciones ---## AI_INDEX Fintech Blockchain
## AI_INDEX 1.000 -0.088 0.26
## Fintech -0.088 1.000 0.03
## Blockchain 0.260 0.030 1.00
## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 8 8 8 8
##
## $criteria
## 1 2 3 4 5
## AIC(n) -3.733363e+01 -3.734514e+01 -3.736101e+01 -3.738291e+01 -3.741355e+01
## HQ(n) -3.732150e+01 -3.732393e+01 -3.733071e+01 -3.734352e+01 -3.736506e+01
## SC(n) -3.730058e+01 -3.728731e+01 -3.727839e+01 -3.727550e+01 -3.728135e+01
## FPE(n) 6.112400e-17 6.042404e-17 5.947280e-17 5.818446e-17 5.642912e-17
## 6 7 8 9 10
## AIC(n) -3.745772e+01 -3.752502e+01 -3.830092e+01 -3.829888e+01 -3.829762e+01
## HQ(n) -3.740014e+01 -3.745834e+01 -3.822515e+01 -3.821402e+01 -3.820367e+01
## SC(n) -3.730073e+01 -3.734325e+01 -3.809435e+01 -3.806754e+01 -3.804149e+01
## FPE(n) 5.399074e-17 5.047683e-17 2.323408e-17 2.328139e-17 2.331083e-17## Estimating model## Computing connectedness measures## The (generalized) VAR connectedness approach is implemented according to:
## Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting.## AI_INDEX Fintech Blockchain FROM
## AI_INDEX "84.13" " 5.16" "10.71" "15.87"
## Fintech " 4.46" "92.53" " 3.02" " 7.47"
## Blockchain "12.66" " 4.00" "83.33" "16.67"
## TO "17.12" " 9.17" "13.72" "40.01"
## Inc.Own "101.25" "101.69" " 97.06" "cTCI/TCI"
## NET " 1.25" " 1.69" "-2.94" "20.00/13.34"
## NPT "1.00" "2.00" "0.00" ""
# tvp-var
dca = ConnectednessApproach(y.r,
nlag=1,
nfore=20,
#window.size=200,
corrected = FALSE,
model="TVP-VAR")## Estimating model## Computing connectedness measures## The TVP-VAR connectedness approach is implemented according to:
## Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management.## AI_INDEX Fintech Blockchain FROM
## AI_INDEX "84.66" " 4.79" "10.55" "15.34"
## Fintech " 4.24" "92.18" " 3.58" " 7.82"
## Blockchain "12.72" " 3.43" "83.85" "16.15"
## TO "16.96" " 8.22" "14.13" "39.32"
## Inc.Own "101.62" "100.40" " 97.98" "cTCI/TCI"
## NET " 1.62" " 0.40" "-2.02" "19.66/13.11"
## NPT "1.00" "1.00" "1.00" ""
## List of 11
## $ TABLE : chr [1:7, 1:4] "84.66" " 4.24" "12.72" "16.96" ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:7] "AI_INDEX" "Fintech" "Blockchain" "TO" ...
## .. ..$ : chr [1:4] "AI_INDEX" "Fintech" "Blockchain" "FROM"
## $ CT : num [1:3, 1:3, 1:2051] 0.9723 0.0115 0.0944 0.0201 0.986 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## $ TCI : num [1:2051, 1] 4.6 15.6 15.8 15.7 15.6 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## .. ..$ : chr "TCI"
## $ TO : num [1:2051, 1:3] 10.6 35.5 35.8 35.5 34.7 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## $ FROM : num [1:2051, 1:3] 2.77 10.35 10.51 10.69 10.97 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## $ NET : num [1:2051, 1:3] 7.82 25.2 25.3 24.82 23.73 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## $ NPT : num [1:2051, 1:3] 1 2 2 2 2 2 2 2 1 1 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## $ NPDC : num [1:3, 1:3, 1:2051] 0 -0.854 8.673 0.854 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## $ PCI : num [1:3, 1:3, 1:2051] 100 3.17 10.32 3.17 100 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## $ INFLUENCE: num [1:3, 1:3, 1:2051] 0 27 85 27 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:3] "AI_INDEX" "Fintech" "Blockchain"
## .. ..$ : chr [1:2051] "2019-01-08" "2019-01-09" "2019-01-10" "2019-01-11" ...
## $ config :List of 4
## ..$ nfore : num 20
## ..$ approach : chr "Time"
## ..$ generalized: logi TRUE
## ..$ corrected : logi FALSE## AI_INDEX Fintech Blockchain FROM
## AI_INDEX "84.66" " 4.79" "10.55" "15.34"
## Fintech " 4.24" "92.18" " 3.58" " 7.82"
## Blockchain "12.72" " 3.43" "83.85" "16.15"
## TO "16.96" " 8.22" "14.13" "39.32"
## Inc.Own "101.62" "100.40" " 97.98" "cTCI/TCI"
## NET " 1.62" " 0.40" "-2.02" "19.66/13.11"
## NPT "1.00" "1.00" "1.00" ""## [1] 3 3 2051## $FEVD
## AI_INDEX Fintech Blockchain
## AI_INDEX 84.656676 4.789393 10.553931
## Fintech 4.240865 92.179328 3.579807
## Blockchain 12.724088 3.428141 83.847771
##
## $TCI
## [1] 13.10541
##
## $cTCI
## [1] 19.65811
##
## $PCI
## [,1] [,2] [,3]
## [1,] 100.000000 9.716942 24.275440
## [2,] 9.716942 100.000000 7.657494
## [3,] 24.275440 7.657494 100.000000
##
## $TO
## AI_INDEX Fintech Blockchain
## 16.964953 8.217535 14.133738
##
## $FROM
## AI_INDEX Fintech Blockchain
## 15.343324 7.820672 16.152229
##
## $NET
## AI_INDEX Fintech Blockchain
## 1.6216283 0.3968625 -2.0184907
##
## $NPDC
## AI_INDEX Fintech Blockchain
## AI_INDEX 0.0000000 0.5485286 -2.1701569
## Fintech -0.5485286 0.0000000 0.1516661
## Blockchain 2.1701569 -0.1516661 0.0000000
##
## $TABLE
## AI_INDEX Fintech Blockchain FROM
## AI_INDEX "84.66" " 4.79" "10.55" "15.34"
## Fintech " 4.24" "92.18" " 3.58" " 7.82"
## Blockchain "12.72" " 3.43" "83.85" "16.15"
## TO "16.96" " 8.22" "14.13" "39.32"
## Inc.Own "101.62" "100.40" " 97.98" "cTCI/TCI"
## NET " 1.62" " 0.40" "-2.02" "19.66/13.11"
## NPT "1.00" "1.00" "1.00" ""
##
## $NPT
## AI_INDEX Fintech Blockchain
## 1 1 1
##
## $INFLUENCE
## AI_INDEX Fintech Blockchain
## AI_INDEX 0.000000 6.074340 9.322773
## Fintech 6.074340 0.000000 2.164201
## Blockchain 9.322773 2.164201 0.000000## [,1] [,2] [,3]
## [1,] 100.000000 9.716942 24.275440
## [2,] 9.716942 100.000000 7.657494
## [3,] 24.275440 7.657494 100.000000# png("plots/plot_tci.png", width = 600, height = 480 , pointsize=14)
PlotTCI(dca, ylim=c(0,100))
View (dca$TCI)
max(dca$TCI)## [1] 32.41053## [1] 2.462622## [1] 13.10541
# portfolio weights
mcp = MinimumConnectednessPortfolio(as.zoo(y.r), dca$PCI, statistics="Fisher")
mcp$TABLE## Mean Std.Dev. 5% 95% HE p-value SR
## AI_INDEX " 0.31" " 0.03" " 0.27" " 0.36" " 0.78" " 0.00" " 2.67"
## Fintech " 0.37" " 0.04" " 0.31" " 0.46" "-1.20" " 0.00" " 2.67"
## Blockchain " 0.32" " 0.03" " 0.26" " 0.38" " 0.72" " 0.00" " 2.67"## AI_INDEX Fintech Blockchain
## 0.3127304 0.3663442 0.3209254## Mean Std.Dev. 5% 95% HE p-value SR
## AI_INDEX " 0.32" " 0.01" " 0.30" " 0.34" " 0.76" " 0.00" " 2.63"
## Fintech " 0.35" " 0.02" " 0.32" " 0.39" "-1.40" " 0.00" " 2.63"
## Blockchain " 0.33" " 0.01" " 0.30" " 0.35" " 0.69" " 0.00" " 2.63"## AI_INDEX Fintech Blockchain
## 0.3244395 0.3480463 0.3275142## AI_INDEX Fintech Blockchain
## [1,] 0.3265071 0.3423964 0.3310965
## [2,] 0.3097279 0.3328982 0.3573739
## [3,] 0.3095399 0.3329365 0.3575236
## [4,] 0.3096072 0.3330941 0.3572987
## [5,] 0.3099208 0.3333798 0.3566995
## [6,] 0.3105212 0.3337755 0.3557033## AI_INDEX Fintech Blockchain
## [2046,] 0.3346011 0.3464380 0.3189609
## [2047,] 0.3348527 0.3459465 0.3192008
## [2048,] 0.3350871 0.3454904 0.3194226
## [2049,] 0.3353060 0.3450661 0.3196278
## [2050,] 0.3355112 0.3446707 0.3198181
## [2051,] 0.3357040 0.3443012 0.3199947## [1] 2051 3## [1] 2051 3## [1] 2051 3## AI_INDEX Fintech Blockchain
## 2019-01-08 0.002825686 -0.0002310878 0.0006773096
## 2019-01-09 0.002657475 -0.0002248291 0.0007295717
## 2019-01-10 0.002633269 -0.0002250068 0.0007283904
## 2019-01-11 0.002611624 -0.0002252656 0.0007264518
## 2019-01-12 0.002592402 -0.0002256114 0.0007237621
## 2019-01-13 0.002575878 -0.0002260321 0.0007202793## AI_INDEX
## 2019-01-08 0.002825686## AI_INDEX AI_INDEX.1 Fintech Blockchain
## 2019-01-08 0.008654286 0.3186217 0.3521304 0.3292479
## 2019-01-09 0.008580032 0.2657578 0.3436517 0.3905905
## 2019-01-10 0.008507041 0.2649176 0.3439984 0.3910839
## 2019-01-11 0.008435282 0.2650959 0.3442598 0.3906443
## 2019-01-12 0.008364723 0.2662147 0.3444827 0.3893026
## 2019-01-13 0.008295335 0.2683736 0.3446199 0.3870065## AI_INDEX.1 Fintech Blockchain
## 2019-01-08 0.3186217 0.3521304 0.3292479
## 2019-01-09 0.2657578 0.3436517 0.3905905
## 2019-01-10 0.2649176 0.3439984 0.3910839
## 2019-01-11 0.2650959 0.3442598 0.3906443
## 2019-01-12 0.2662147 0.3444827 0.3893026
## 2019-01-13 0.2683736 0.3446199 0.3870065## AI_INDEX AI_INDEX.1 Fintech Blockchain
## 2019-01-08 0.008654286 0.3265071 0.3423964 0.3310965
## 2019-01-09 0.008580032 0.3097279 0.3328982 0.3573739
## 2019-01-10 0.008507041 0.3095399 0.3329365 0.3575236
## 2019-01-11 0.008435282 0.3096072 0.3330941 0.3572987
## 2019-01-12 0.008364723 0.3099208 0.3333798 0.3566995
## 2019-01-13 0.008295335 0.3105212 0.3337755 0.3557033## AI_INDEX.1 Fintech Blockchain
## 2019-01-08 0.3265071 0.3423964 0.3310965
## 2019-01-09 0.3097279 0.3328982 0.3573739
## 2019-01-10 0.3095399 0.3329365 0.3575236
## 2019-01-11 0.3096072 0.3330941 0.3572987
## 2019-01-12 0.3099208 0.3333798 0.3566995
## 2019-01-13 0.3105212 0.3337755 0.3557033################################################################
# portfolios
################################################################
# choice of portfolios; mcp, rpp, ew, rp
# https://cran.r-project.org/web/packages/riskParityPortfolio/vignettes/RiskParityPortfolio.html#a-pratical-example-using-faang-price-data
# for backtesting
# https://cran.r-project.org/web/packages/portfolioBacktest/vignettes/PortfolioBacktest.html#defining-portfolios
y.r_c <- exp(y.r)-1
w.eq <- rep(1/ncol(y.r_c), ncol(y.r_c))
w.rp <- riskParityPortfolio(cov(y.r_c))$w
# https://bookdown.org/compfinezbook/introcompfinr/Efficient-portfolios-of.html
max_sharpe_ratio <- function(dataset) {
prices <- dataset
# 1. Eliminar columnas o filas con NAs
prices <- na.omit(prices)
if (nrow(prices) < 2) return(rep(NA, ncol(prices)))
# 2. Calcular retornos logarítmicos de forma segura
log_returns <- suppressWarnings(diff(log(prices)))
log_returns <- na.omit(exp(log_returns) - 1)
if (nrow(log_returns) < 2) return(rep(NA, ncol(prices)))
# 3. Estadísticos básicos
Sigma <- cov(log_returns, use = "pairwise.complete.obs")
mu <- colMeans(log_returns, na.rm = TRUE)
# 4. Validación de inputs
if (any(is.na(mu)) || any(is.na(Sigma))) return(rep(NA, ncol(prices)))
if (all(mu <= 1e-8, na.rm = TRUE)) return(rep(0, ncol(prices)))
# 5. Optimización clásica de portafolio máximo Sharpe
Dmat <- 2 * Sigma
Amat <- cbind(mu, diag(ncol(prices)))
bvec <- c(1, rep(0, ncol(prices)))
dvec <- rep(0, ncol(prices))
res <- try(solve.QP(Dmat = Dmat, dvec = dvec, Amat = Amat, bvec = bvec, meq = 1), silent = TRUE)
if (inherits(res, "try-error")) {
return(rep(NA, ncol(prices)))
} else {
w <- res$solution
return(w / sum(w))
}
}
# rolling portfolio weights
wl <- 252
roll_rp = na.omit (rollapply(y.r_c, wl ,function(x) riskParityPortfolio(cov(x))$w, by.column=FALSE,align="right"))
head(roll_rp)## AI_INDEX Fintech Blockchain
## 2019-09-16 0.2297251 0.5320311 0.2382438
## 2019-09-17 0.2307445 0.5310811 0.2381744
## 2019-09-18 0.2317617 0.5301275 0.2381108
## 2019-09-19 0.2327769 0.5291704 0.2380527
## 2019-09-20 0.2337901 0.5282097 0.2380002
## 2019-09-21 0.2348015 0.5272453 0.2379531## AI_INDEX Fintech Blockchain
## 2024-08-14 0.1987962 0.5399731 0.2612307
## 2024-08-15 0.1992887 0.5411880 0.2595233
## 2024-08-16 0.1997582 0.5423454 0.2578964
## 2024-08-17 0.2002060 0.5434493 0.2563446
## 2024-08-18 0.2006332 0.5445035 0.2548632
## 2024-08-19 0.2010408 0.5455113 0.2534479roll_ms = na.omit (rollapply(y.r_c, wl ,function(x) max_sharpe_ratio(x), by.column=FALSE,align="right"))
colnames(roll_ms) <- colnames(roll_rp)
head(roll_ms)## AI_INDEX Fintech Blockchain
## 2019-09-16 0 1 -1.179115e-13
## 2019-09-17 0 1 -1.179115e-13
## 2019-09-18 0 1 -1.179115e-13
## 2019-09-19 0 1 -1.179115e-13
## 2019-09-20 0 1 -1.179115e-13
## 2019-09-21 0 1 -1.179115e-13## AI_INDEX Fintech Blockchain
## 2024-08-14 0 0 0
## 2024-08-15 0 0 0
## 2024-08-16 0 0 0
## 2024-08-17 0 0 0
## 2024-08-18 0 0 0
## 2024-08-19 0 0 0###########################3
# port_eqw <- Return.portfolio(y.r, weights=w.eq , verbose=TRUE, geometric = FALSE)
port_eqw <- Return.portfolio( y.r_c, verbose=TRUE, rebalance_on = "days")
head (port_eqw$BOP.Weight,22)## AI_INDEX Fintech Blockchain
## 2019-01-08 0.3333333 0.3333333 0.3333333
## 2019-01-09 0.3333333 0.3333333 0.3333333
## 2019-01-10 0.3333333 0.3333333 0.3333333
## 2019-01-11 0.3333333 0.3333333 0.3333333
## 2019-01-12 0.3333333 0.3333333 0.3333333
## 2019-01-13 0.3333333 0.3333333 0.3333333
## 2019-01-14 0.3333333 0.3333333 0.3333333
## 2019-01-15 0.3333333 0.3333333 0.3333333
## 2019-01-16 0.3333333 0.3333333 0.3333333
## 2019-01-17 0.3333333 0.3333333 0.3333333
## 2019-01-18 0.3333333 0.3333333 0.3333333
## 2019-01-19 0.3333333 0.3333333 0.3333333
## 2019-01-20 0.3333333 0.3333333 0.3333333
## 2019-01-21 0.3333333 0.3333333 0.3333333
## 2019-01-22 0.3333333 0.3333333 0.3333333
## 2019-01-23 0.3333333 0.3333333 0.3333333
## 2019-01-24 0.3333333 0.3333333 0.3333333
## 2019-01-25 0.3333333 0.3333333 0.3333333
## 2019-01-26 0.3333333 0.3333333 0.3333333
## 2019-01-27 0.3333333 0.3333333 0.3333333
## 2019-01-28 0.3333333 0.3333333 0.3333333
## 2019-01-29 0.3333333 0.3333333 0.3333333## AI_INDEX Fintech Blockchain
## 2019-01-08 0.3351063 0.3319946 0.3328991
## 2019-01-09 0.3350903 0.3320032 0.3329065
## 2019-01-10 0.3350746 0.3320117 0.3329137
## 2019-01-11 0.3350591 0.3320200 0.3329209
## 2019-01-12 0.3350439 0.3320283 0.3329279
## 2019-01-13 0.3350289 0.3320363 0.3329347
## 2019-01-14 0.3350142 0.3320443 0.3329415
## 2019-01-15 0.3336926 0.3320248 0.3342827
## 2019-01-16 0.3336931 0.3320291 0.3342778
## 2019-01-17 0.3336935 0.3320334 0.3342730
## 2019-01-18 0.3336940 0.3320377 0.3342683
## 2019-01-19 0.3336945 0.3320419 0.3342637
## 2019-01-20 0.3336949 0.3320460 0.3342590
## 2019-01-21 0.3336954 0.3320501 0.3342545
## 2019-01-22 0.3343278 0.3326513 0.3330208
## 2019-01-23 0.3343218 0.3326547 0.3330235
## 2019-01-24 0.3343158 0.3326580 0.3330262
## 2019-01-25 0.3343098 0.3326613 0.3330289
## 2019-01-26 0.3343040 0.3326645 0.3330315
## 2019-01-27 0.3342982 0.3326677 0.3330341
## 2019-01-28 0.3342924 0.3326709 0.3330366
## 2019-01-29 0.3344755 0.3329409 0.3325836# port_rp <- Return.portfolio(y.r, weights=w.rp,verbose=TRUE)
port_rp <- Return.portfolio( y.r_c, weights=roll_rp, verbose=TRUE)
port_mcp <- Return.portfolio( y.r_c, weights=w.mcp_temp,verbose=TRUE)
port_rpp <- Return.portfolio( y.r_c, weights=w.rpp_temp,verbose=TRUE)
port_ms <- Return.portfolio( y.r_c, weights=roll_ms, verbose=TRUE)## Warning in Return.portfolio.geometric(R = R, weights = weights, wealth.index =
## wealth.index, : The weights for one or more periods do not sum up to 1:
## assuming a return of 0 for the residual weights## List of 6
## $ returns :An xts object on 2019-01-08 / 2024-08-19 containing:
## Data: double [2051, 1]
## Columns: portfolio.returns
## Index: Date [2051] (TZ: "UTC")
## $ contribution:An xts object on 2019-01-08 / 2024-08-19 containing:
## Data: double [2051, 3]
## Columns: AI_INDEX, Fintech, Blockchain
## Index: Date [2051] (TZ: "UTC")
## $ BOP.Weight :An xts object on 2019-01-08 / 2024-08-19 containing:
## Data: double [2051, 3]
## Columns: AI_INDEX, Fintech, Blockchain
## Index: Date [2051] (TZ: "UTC")
## $ EOP.Weight :An xts object on 2019-01-08 / 2024-08-19 containing:
## Data: double [2051, 3]
## Columns: AI_INDEX, Fintech, Blockchain
## Index: Date [2051] (TZ: "UTC")
## $ BOP.Value :An xts object on 2019-01-08 / 2024-08-19 containing:
## Data: double [2051, 3]
## Columns: AI_INDEX, Fintech, Blockchain
## Index: Date [2051] (TZ: "UTC")
## $ EOP.Value :An xts object on 2019-01-08 / 2024-08-19 containing:
## Data: double [2051, 3]
## Columns: AI_INDEX, Fintech, Blockchain
## Index: Date [2051] (TZ: "UTC")################################################################
# some checks on the eqw
head(port_eqw$returns)## portfolio.returns
## 2019-01-08 0.003354969
## 2019-01-09 0.003328457
## 2019-01-10 0.003302377
## 2019-01-11 0.003276717
## 2019-01-12 0.003251469
## 2019-01-13 0.003226621## AI_INDEX Fintech Blockchain
## 2019-01-08 0.002884762 -0.0002249710 0.0006818853
## 2019-01-09 0.002860011 -0.0002251230 0.0006804933
## 2019-01-10 0.002835680 -0.0002252750 0.0006791070
## 2019-01-11 0.002811761 -0.0002254273 0.0006777260
## 2019-01-12 0.002788241 -0.0002255800 0.0006763510## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-12
## 0.003341676 0.003315381 0.003289512 0.003264059 0.003239012## portfolio.returns
## 2019-09-17 0.0007909495
## 2019-09-18 0.0007901827
## 2019-09-19 0.0007893914
## 2019-09-20 0.0007885755
## 2019-09-21 0.0007877354
## 2019-09-22 0.0007868719## portfolio.returns
## 2024-08-14 0.003434350
## 2024-08-15 0.003391413
## 2024-08-16 0.003350085
## 2024-08-17 0.003310266
## 2024-08-18 0.003271866
## 2024-08-19 0.003234802##
## Annualized Return 0.1287
## Annualized Std Dev 0.0480
## Annualized Sharpe (Rf=1%) 2.4488## Index AI_INDEX Fintech
## Min. :2019-01-08 Min. :-0.0178773 Min. :-0.0068635
## 1st Qu.:2020-06-03 1st Qu.:-0.0026554 1st Qu.:-0.0009805
## Median :2021-10-29 Median : 0.0010246 Median : 0.0003000
## Mean :2021-10-29 Mean : 0.0007467 Mean : 0.0002533
## 3rd Qu.:2023-03-25 3rd Qu.: 0.0050139 3rd Qu.: 0.0015312
## Max. :2024-08-19 Max. : 0.0166709 Max. : 0.0054210
## Blockchain
## Min. :-0.0239681
## 1st Qu.:-0.0020937
## Median : 0.0007569
## Mean : 0.0004555
## 3rd Qu.: 0.0035907
## Max. : 0.0211307## AI_INDEX Fintech Blockchain
## 2.489137e-04 8.442111e-05 1.518176e-04## [1] 0.1222584# 0.06297518
################################################################
port_ret <- cbind(port_eqw$returns, port_rp$returns, port_rpp$returns, port_ms$returns )
colnames(port_ret) <- c("EQW", "RP", "RPC", "MS")
# all portfolios start at same time period
port_ret <- na.omit( port_ret)
head(port_ret)## EQW RP RPC MS
## 2019-09-17 0.0008288824 0.0007909495 0.0008167320 0.0007733999
## 2019-09-18 0.0008239991 0.0007901827 0.0008228203 0.0007728015
## 2019-09-19 0.0008191387 0.0007893914 0.0008180661 0.0007722050
## 2019-09-20 0.0008143003 0.0007885755 0.0008133313 0.0007716095
## 2019-09-21 0.0008094841 0.0007877354 0.0008086159 0.0007710141
## 2019-09-22 0.0008046894 0.0007868719 0.0008039194 0.0007704206# portfolio summary statistics
# puedes fijar una fecha o usar el inicio automático de tus datos
start_date <- start(y.r_c)
cat("Fecha inicial de los datos:", start_date, "\n")## Fecha inicial de los datos: 17904# Filtrar desde start_date
roll_rp_sub <- roll_rp[paste0(start_date, "/")]
roll_ms_sub <- roll_ms[paste0(start_date, "/")]
w_rpp_sub <- w.rpp_temp[paste0(start_date, "/")]
# promedios de pesos
aw1 <- apply(roll_rp_sub, 2, mean, na.rm = TRUE)
aw2 <- apply(roll_ms_sub, 2, mean, na.rm = TRUE)
aw3 <- apply(w_rpp_sub, 2, mean, na.rm = TRUE)
# estadísticas resumen
summary(roll_rp_sub)## Index AI_INDEX Fintech Blockchain
## Min. :2019-09-16 Min. :0.1271 Min. :0.4993 Min. :0.1290
## 1st Qu.:2020-12-08 1st Qu.:0.1421 1st Qu.:0.5492 1st Qu.:0.1492
## Median :2022-03-03 Median :0.1848 Median :0.5963 Median :0.2121
## Mean :2022-03-03 Mean :0.1829 Mean :0.6190 Mean :0.1981
## 3rd Qu.:2023-05-27 3rd Qu.:0.2182 3rd Qu.:0.7092 3rd Qu.:0.2312
## Max. :2024-08-19 Max. :0.2663 Max. :0.7410 Max. :0.2943## Index AI_INDEX Fintech Blockchain
## Min. :2019-09-16 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:2020-12-08 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :2022-03-03 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :2022-03-03 Mean :0.1067 Mean :0.2150 Mean :0.2255
## 3rd Qu.:2023-05-27 3rd Qu.:0.0000 3rd Qu.:0.5079 3rd Qu.:0.4800
## Max. :2024-08-19 Max. :1.0000 Max. :1.0000 Max. :1.0000## Index AI_INDEX.1 Fintech Blockchain
## Min. :2019-01-08 Min. :0.3017 Min. :0.3102 Min. :0.2995
## 1st Qu.:2020-06-03 1st Qu.:0.3153 1st Qu.:0.3358 1st Qu.:0.3203
## Median :2021-10-29 Median :0.3263 Median :0.3437 Median :0.3280
## Mean :2021-10-29 Mean :0.3244 Mean :0.3480 Mean :0.3275
## 3rd Qu.:2023-03-25 3rd Qu.:0.3316 3rd Qu.:0.3531 3rd Qu.:0.3330
## Max. :2024-08-19 Max. :0.3702 Max. :0.3987 Max. :0.3654# gráficas de pesos
plot(roll_rp_sub, main = "Pesos Rolling Risk Parity (RP)", auto.legend = TRUE, legend.loc = "topleft")
plot(roll_ms_sub, main = "Pesos Rolling Max Sharpe (MS)", auto.legend = TRUE, legend.loc = "topleft")
plot(w_rpp_sub, main = "Pesos Rolling Risk Parity Connectedness (RPC)", auto.legend = TRUE, legend.loc = "topleft")
# medidas agregadas: media, sd, cv, min, max
aw1_2 <- rbind(
Mean = apply(roll_rp_sub, 2, mean, na.rm = TRUE),
SD = apply(roll_rp_sub, 2, sd, na.rm = TRUE),
CV = apply(roll_rp_sub, 2, sd, na.rm = TRUE) / apply(roll_rp_sub, 2, mean, na.rm = TRUE),
Min = apply(roll_rp_sub, 2, min, na.rm = TRUE),
Max = apply(roll_rp_sub, 2, max, na.rm = TRUE)
)
aw2_2 <- rbind(
Mean = apply(roll_ms_sub, 2, mean, na.rm = TRUE),
SD = apply(roll_ms_sub, 2, sd, na.rm = TRUE),
CV = apply(roll_ms_sub, 2, sd, na.rm = TRUE) / apply(roll_ms_sub, 2, mean, na.rm = TRUE),
Min = apply(roll_ms_sub, 2, min, na.rm = TRUE),
Max = apply(roll_ms_sub, 2, max, na.rm = TRUE)
)
aw3_2 <- rbind(
Mean = apply(w_rpp_sub, 2, mean, na.rm = TRUE),
SD = apply(w_rpp_sub, 2, sd, na.rm = TRUE),
CV = apply(w_rpp_sub, 2, sd, na.rm = TRUE) / apply(w_rpp_sub, 2, mean, na.rm = TRUE),
Min = apply(w_rpp_sub, 2, min, na.rm = TRUE),
Max = apply(w_rpp_sub, 2, max, na.rm = TRUE)
)
# combinar en un solo marco de datos
portfolio_weights_summary <- rbind(
cbind(Type = "RP", Stat = rownames(aw1_2), round(aw1_2, 4)),
cbind(Type = "MS", Stat = rownames(aw2_2), round(aw2_2, 4)),
cbind(Type = "RPC", Stat = rownames(aw3_2), round(aw3_2, 4))
)
print(portfolio_weights_summary)## Type Stat AI_INDEX Fintech Blockchain
## Mean "RP" "Mean" "0.1829" "0.619" "0.1981"
## SD "RP" "SD" "0.0375" "0.0769" "0.044"
## CV "RP" "CV" "0.2051" "0.1243" "0.2221"
## Min "RP" "Min" "0.1271" "0.4993" "0.129"
## Max "RP" "Max" "0.2663" "0.741" "0.2943"
## Mean "MS" "Mean" "0.1067" "0.215" "0.2255"
## SD "MS" "SD" "0.2527" "0.3701" "0.3509"
## CV "MS" "CV" "2.368" "1.7211" "1.5564"
## Min "MS" "Min" "0" "0" "0"
## Max "MS" "Max" "1" "1" "1"
## Mean "RPC" "Mean" "0.3244" "0.348" "0.3275"
## SD "RPC" "SD" "0.0117" "0.0187" "0.0129"
## CV "RPC" "CV" "0.0361" "0.0538" "0.0393"
## Min "RPC" "Min" "0.3017" "0.3102" "0.2995"
## Max "RPC" "Max" "0.3702" "0.3987" "0.3654"# opcional: guardar resultados
# write.csv(portfolio_weights_summary, "tables_portfolio_weights_summary.csv", row.names = FALSE)
# Curvas acumuladas
chart.CumReturns(port_ret, main = "Equity Curves (EQW, RP, RPC, MS)",
ylab = "", geometric = TRUE, wealth.index = TRUE,
legend.loc = "topleft")
# dev.off()
# experimenting with colors
#chart.CumReturns(port_ret, main = "Equity curves", ylab="", geometric = FALSE, wealth.index = TRUE,
# colorset=tim6equal, legend.loc = "topleft", )
#chart.CumReturns(port_ret, main = "Equity curves", ylab="", geometric = FALSE, wealth.index = TRUE,
# colorset=rich6equal, legend.loc = "topleft", )
# set risk free rate ; from yahoo finance ^IRX
(rfr <- (0.9556624/100)/252)## [1] 3.792311e-05ta1 <- table.AnnualizedReturns(port_ret, scale=252, geometric = TRUE, Rf=rfr)
ta2 <- table.DownsideRisk(port_ret, scale=252, Rf=rfr, MAR =.00, p=.95)
ta3 <- Omega(port_ret, Rf=rfr)
SortinoRatio(port_ret)## EQW RP RPC MS
## Sortino Ratio (MAR = 0%) 0.2158486 0.2879532 0.2165428 0.3092285## EQW RP RPC MS
## Annualized Return 0.120900 0.10050 0.11730 0.108900
## Annualized Std Dev 0.049500 0.03220 0.04780 0.037000
## Annualized Sharpe (Rf=0.96%) 2.225500 2.79650 2.22940 2.660300
## Semi Deviation 0.002300 0.00150 0.00230 0.001500
## Gain Deviation 0.001700 0.00120 0.00170 0.002100
## Loss Deviation 0.002200 0.00140 0.00210 0.001900
## Downside Deviation (MAR=0%) 0.002100 0.00130 0.00210 0.001300
## Downside Deviation (Rf=0.96%) 0.002100 0.00130 0.00210 0.001300
## Downside Deviation (0%) 0.002100 0.00130 0.00210 0.001300
## Maximum Drawdown 0.306100 0.14570 0.29840 0.111700
## Historical VaR (95%) -0.005100 -0.00330 -0.00500 -0.002600
## Historical ES (95%) -0.006900 -0.00430 -0.00670 -0.005000
## Modified VaR (95%) -0.005000 -0.00320 -0.00490 -0.003000
## Modified ES (95%) -0.007300 -0.00430 -0.00690 -0.003800
## Omega (L = 0%) 1.451807 1.60503 1.45131 1.877081#################################################################
# weekly re-balancing
#################################################################
rbp <- c("days")
# rbp <- c("weeks")
# rbp <- c("months")
port_eqw <- Return.portfolio( y.r_c, verbose=TRUE, rebalance_on = rbp)
head (port_eqw$BOP.Weight,22)## AI_INDEX Fintech Blockchain
## 2019-01-08 0.3333333 0.3333333 0.3333333
## 2019-01-09 0.3333333 0.3333333 0.3333333
## 2019-01-10 0.3333333 0.3333333 0.3333333
## 2019-01-11 0.3333333 0.3333333 0.3333333
## 2019-01-12 0.3333333 0.3333333 0.3333333
## 2019-01-13 0.3333333 0.3333333 0.3333333
## 2019-01-14 0.3333333 0.3333333 0.3333333
## 2019-01-15 0.3333333 0.3333333 0.3333333
## 2019-01-16 0.3333333 0.3333333 0.3333333
## 2019-01-17 0.3333333 0.3333333 0.3333333
## 2019-01-18 0.3333333 0.3333333 0.3333333
## 2019-01-19 0.3333333 0.3333333 0.3333333
## 2019-01-20 0.3333333 0.3333333 0.3333333
## 2019-01-21 0.3333333 0.3333333 0.3333333
## 2019-01-22 0.3333333 0.3333333 0.3333333
## 2019-01-23 0.3333333 0.3333333 0.3333333
## 2019-01-24 0.3333333 0.3333333 0.3333333
## 2019-01-25 0.3333333 0.3333333 0.3333333
## 2019-01-26 0.3333333 0.3333333 0.3333333
## 2019-01-27 0.3333333 0.3333333 0.3333333
## 2019-01-28 0.3333333 0.3333333 0.3333333
## 2019-01-29 0.3333333 0.3333333 0.3333333## AI_INDEX Fintech Blockchain
## 2019-01-08 0.3351063 0.3319946 0.3328991
## 2019-01-09 0.3350903 0.3320032 0.3329065
## 2019-01-10 0.3350746 0.3320117 0.3329137
## 2019-01-11 0.3350591 0.3320200 0.3329209
## 2019-01-12 0.3350439 0.3320283 0.3329279
## 2019-01-13 0.3350289 0.3320363 0.3329347
## 2019-01-14 0.3350142 0.3320443 0.3329415
## 2019-01-15 0.3336926 0.3320248 0.3342827
## 2019-01-16 0.3336931 0.3320291 0.3342778
## 2019-01-17 0.3336935 0.3320334 0.3342730
## 2019-01-18 0.3336940 0.3320377 0.3342683
## 2019-01-19 0.3336945 0.3320419 0.3342637
## 2019-01-20 0.3336949 0.3320460 0.3342590
## 2019-01-21 0.3336954 0.3320501 0.3342545
## 2019-01-22 0.3343278 0.3326513 0.3330208
## 2019-01-23 0.3343218 0.3326547 0.3330235
## 2019-01-24 0.3343158 0.3326580 0.3330262
## 2019-01-25 0.3343098 0.3326613 0.3330289
## 2019-01-26 0.3343040 0.3326645 0.3330315
## 2019-01-27 0.3342982 0.3326677 0.3330341
## 2019-01-28 0.3342924 0.3326709 0.3330366
## 2019-01-29 0.3344755 0.3329409 0.3325836## AI_INDEX Fintech Blockchain
## 2024-07-29 0.3333333 0.3333333 0.3333333
## 2024-07-30 0.3333333 0.3333333 0.3333333
## 2024-07-31 0.3333333 0.3333333 0.3333333
## 2024-08-01 0.3333333 0.3333333 0.3333333
## 2024-08-02 0.3333333 0.3333333 0.3333333
## 2024-08-03 0.3333333 0.3333333 0.3333333
## 2024-08-04 0.3333333 0.3333333 0.3333333
## 2024-08-05 0.3333333 0.3333333 0.3333333
## 2024-08-06 0.3333333 0.3333333 0.3333333
## 2024-08-07 0.3333333 0.3333333 0.3333333
## 2024-08-08 0.3333333 0.3333333 0.3333333
## 2024-08-09 0.3333333 0.3333333 0.3333333
## 2024-08-10 0.3333333 0.3333333 0.3333333
## 2024-08-11 0.3333333 0.3333333 0.3333333
## 2024-08-12 0.3333333 0.3333333 0.3333333
## 2024-08-13 0.3333333 0.3333333 0.3333333
## 2024-08-14 0.3333333 0.3333333 0.3333333
## 2024-08-15 0.3333333 0.3333333 0.3333333
## 2024-08-16 0.3333333 0.3333333 0.3333333
## 2024-08-17 0.3333333 0.3333333 0.3333333
## 2024-08-18 0.3333333 0.3333333 0.3333333
## 2024-08-19 0.3333333 0.3333333 0.3333333## AI_INDEX Fintech Blockchain
## 2024-07-29 0.3315546 0.3346011 0.3338443
## 2024-07-30 0.3372767 0.3333147 0.3294086
## 2024-07-31 0.3372732 0.3333457 0.3293812
## 2024-08-01 0.3372706 0.3333770 0.3293524
## 2024-08-02 0.3372691 0.3334086 0.3293223
## 2024-08-03 0.3372686 0.3334405 0.3292909
## 2024-08-04 0.3372692 0.3334729 0.3292579
## 2024-08-05 0.3372708 0.3335056 0.3292236
## 2024-08-06 0.3342365 0.3319810 0.3337825
## 2024-08-07 0.3342302 0.3319884 0.3337814
## 2024-08-08 0.3342240 0.3319957 0.3337803
## 2024-08-09 0.3342179 0.3320030 0.3337792
## 2024-08-10 0.3342118 0.3320102 0.3337780
## 2024-08-11 0.3342058 0.3320172 0.3337769
## 2024-08-12 0.3341999 0.3320242 0.3337758
## 2024-08-13 0.3325669 0.3321661 0.3352670
## 2024-08-14 0.3325773 0.3321775 0.3352452
## 2024-08-15 0.3325875 0.3321888 0.3352238
## 2024-08-16 0.3325974 0.3321998 0.3352028
## 2024-08-17 0.3326071 0.3322106 0.3351823
## 2024-08-18 0.3326167 0.3322212 0.3351622
## 2024-08-19 0.3326260 0.3322316 0.3351424# port_rp <- Return.portfolio(y.r, weights=w.rp,verbose=TRUE)
ep <- endpoints(roll_rp, on = rbp)
port_rp <- Return.portfolio( y.r_c, weights= roll_rp[ep,] , verbose=TRUE)
head(roll_rp[ep,],22)## AI_INDEX Fintech Blockchain
## 2019-09-16 0.2297251 0.5320311 0.2382438
## 2019-09-17 0.2307445 0.5310811 0.2381744
## 2019-09-18 0.2317617 0.5301275 0.2381108
## 2019-09-19 0.2327769 0.5291704 0.2380527
## 2019-09-20 0.2337901 0.5282097 0.2380002
## 2019-09-21 0.2348015 0.5272453 0.2379531
## 2019-09-22 0.2358112 0.5262774 0.2379115
## 2019-09-23 0.2368191 0.5253058 0.2378751
## 2019-09-24 0.2351257 0.5258355 0.2390388
## 2019-09-25 0.2334720 0.5263306 0.2401975
## 2019-09-26 0.2318561 0.5267919 0.2413520
## 2019-09-27 0.2302765 0.5272202 0.2425032
## 2019-09-28 0.2287318 0.5276162 0.2436520
## 2019-09-29 0.2272204 0.5279806 0.2447990
## 2019-09-30 0.2257410 0.5283140 0.2459450
## 2019-10-01 0.2262891 0.5280190 0.2456919
## 2019-10-02 0.2268315 0.5277291 0.2454394
## 2019-10-03 0.2273682 0.5274443 0.2451875
## 2019-10-04 0.2278993 0.5271645 0.2449362
## 2019-10-05 0.2284248 0.5268898 0.2446854
## 2019-10-06 0.2289448 0.5266200 0.2444352
## 2019-10-07 0.2294592 0.5263553 0.2441855## AI_INDEX Fintech Blockchain
## 2019-09-17 0.2297251 0.5320311 0.2382438
## 2019-09-18 0.2307445 0.5310811 0.2381744
## 2019-09-19 0.2317617 0.5301275 0.2381108
## 2019-09-20 0.2327769 0.5291704 0.2380527
## 2019-09-21 0.2337901 0.5282097 0.2380002
## 2019-09-22 0.2348015 0.5272453 0.2379531
## 2019-09-23 0.2358112 0.5262774 0.2379115
## 2019-09-24 0.2368191 0.5253058 0.2378751
## 2019-09-25 0.2351257 0.5258355 0.2390388
## 2019-09-26 0.2334720 0.5263306 0.2401975
## 2019-09-27 0.2318561 0.5267919 0.2413520
## 2019-09-28 0.2302765 0.5272202 0.2425032
## 2019-09-29 0.2287318 0.5276162 0.2436520
## 2019-09-30 0.2272204 0.5279806 0.2447990
## 2019-10-01 0.2257410 0.5283140 0.2459450
## 2019-10-02 0.2262891 0.5280190 0.2456919
## 2019-10-03 0.2268315 0.5277291 0.2454394
## 2019-10-04 0.2273682 0.5274443 0.2451875
## 2019-10-05 0.2278993 0.5271645 0.2449362
## 2019-10-06 0.2284248 0.5268898 0.2446854
## 2019-10-07 0.2289448 0.5266200 0.2444352
## 2019-10-08 0.2294592 0.5263553 0.2441855## AI_INDEX Fintech Blockchain
## 2019-09-17 0.2303167 0.5320218 0.2376615
## 2019-09-18 0.2313363 0.5310719 0.2375918
## 2019-09-19 0.2323537 0.5301184 0.2375279
## 2019-09-20 0.2333691 0.5291614 0.2374695
## 2019-09-21 0.2343825 0.5282008 0.2374166
## 2019-09-22 0.2353941 0.5272367 0.2373692
## 2019-09-23 0.2364039 0.5262689 0.2373272
## 2019-09-24 0.2357207 0.5262990 0.2379803
## 2019-09-25 0.2340244 0.5268312 0.2391444
## 2019-09-26 0.2323675 0.5273289 0.2403035
## 2019-09-27 0.2307485 0.5277929 0.2414586
## 2019-09-28 0.2291656 0.5282240 0.2426104
## 2019-09-29 0.2276175 0.5286228 0.2437597
## 2019-09-30 0.2261025 0.5289901 0.2449074
## 2019-10-01 0.2270032 0.5272784 0.2457184
## 2019-10-02 0.2275485 0.5269848 0.2454666
## 2019-10-03 0.2280882 0.5266963 0.2452155
## 2019-10-04 0.2286222 0.5264129 0.2449649
## 2019-10-05 0.2291506 0.5261344 0.2447150
## 2019-10-06 0.2296735 0.5258610 0.2444655
## 2019-10-07 0.2301908 0.5255925 0.2442166
## 2019-10-08 0.2295617 0.5257297 0.2447086## AI_INDEX Fintech Blockchain
## 2024-07-29 0.1845341 0.5211630 0.2943029
## 2024-07-30 0.1880571 0.5220880 0.2898549
## 2024-07-31 0.1914323 0.5229209 0.2856468
## 2024-08-01 0.1946748 0.5236746 0.2816506
## 2024-08-02 0.1977975 0.5243599 0.2778427
## 2024-08-03 0.2008115 0.5249860 0.2742026
## 2024-08-04 0.2037266 0.5255607 0.2707127
## 2024-08-05 0.2065516 0.5260909 0.2673576
## 2024-08-06 0.2052349 0.5277786 0.2669865
## 2024-08-07 0.2039387 0.5294381 0.2666231
## 2024-08-08 0.2026622 0.5310707 0.2662671
## 2024-08-09 0.2014045 0.5326774 0.2659180
## 2024-08-10 0.2001649 0.5342593 0.2655758
## 2024-08-11 0.1989426 0.5358173 0.2652400
## 2024-08-12 0.1977369 0.5373525 0.2649105
## 2024-08-13 0.1982794 0.5386962 0.2630243
## 2024-08-14 0.1987962 0.5399731 0.2612307
## 2024-08-15 0.1992887 0.5411880 0.2595233
## 2024-08-16 0.1997582 0.5423454 0.2578964
## 2024-08-17 0.2002060 0.5434493 0.2563446
## 2024-08-18 0.2006332 0.5445035 0.2548632
## 2024-08-19 0.2010408 0.5455113 0.2534479## AI_INDEX Fintech Blockchain
## 2024-07-29 0.1856793 0.5203533 0.2939673
## 2024-07-30 0.1845341 0.5211630 0.2943029
## 2024-07-31 0.1880571 0.5220880 0.2898549
## 2024-08-01 0.1914323 0.5229209 0.2856468
## 2024-08-02 0.1946748 0.5236746 0.2816506
## 2024-08-03 0.1977975 0.5243599 0.2778427
## 2024-08-04 0.2008115 0.5249860 0.2742026
## 2024-08-05 0.2037266 0.5255607 0.2707127
## 2024-08-06 0.2065516 0.5260909 0.2673576
## 2024-08-07 0.2052349 0.5277786 0.2669865
## 2024-08-08 0.2039387 0.5294381 0.2666231
## 2024-08-09 0.2026622 0.5310707 0.2662671
## 2024-08-10 0.2014045 0.5326774 0.2659180
## 2024-08-11 0.2001649 0.5342593 0.2655758
## 2024-08-12 0.1989426 0.5358173 0.2652400
## 2024-08-13 0.1977369 0.5373525 0.2649105
## 2024-08-14 0.1982794 0.5386962 0.2630243
## 2024-08-15 0.1987962 0.5399731 0.2612307
## 2024-08-16 0.1992887 0.5411880 0.2595233
## 2024-08-17 0.1997582 0.5423454 0.2578964
## 2024-08-18 0.2002060 0.5434493 0.2563446
## 2024-08-19 0.2006332 0.5445035 0.2548632## AI_INDEX Fintech Blockchain
## 2024-07-29 0.1844231 0.5215819 0.2939950
## 2024-07-30 0.1869623 0.5218181 0.2912196
## 2024-07-31 0.1905074 0.5227318 0.2867608
## 2024-08-01 0.1939033 0.5235557 0.2825410
## 2024-08-02 0.1971651 0.5243026 0.2785323
## 2024-08-03 0.2003061 0.5249830 0.2747110
## 2024-08-04 0.2033374 0.5256060 0.2710566
## 2024-08-05 0.2062692 0.5261794 0.2675514
## 2024-08-06 0.2073631 0.5245936 0.2680433
## 2024-08-07 0.2060381 0.5262903 0.2676715
## 2024-08-08 0.2047338 0.5279588 0.2673073
## 2024-08-09 0.2034493 0.5296003 0.2669504
## 2024-08-10 0.2021838 0.5312158 0.2666004
## 2024-08-11 0.2009364 0.5328063 0.2662572
## 2024-08-12 0.1997065 0.5343730 0.2659205
## 2024-08-13 0.1974402 0.5358993 0.2666605
## 2024-08-14 0.1979899 0.5372630 0.2647471
## 2024-08-15 0.1985136 0.5385592 0.2629272
## 2024-08-16 0.1990129 0.5397928 0.2611943
## 2024-08-17 0.1994889 0.5409684 0.2595427
## 2024-08-18 0.1999430 0.5420900 0.2579670
## 2024-08-19 0.2003762 0.5431613 0.2564625ep <- endpoints(w.mcp_temp, on = rbp)
port_mcp <- Return.portfolio( y.r_c, weights= w.mcp_temp[ep,] ,verbose=TRUE)
ep <- endpoints(w.rpp_temp, on = rbp)
port_rpp <- Return.portfolio( y.r_c, weights= w.rpp_temp[ep,],verbose=TRUE)
head(w.rpp_temp[ep,],22)## AI_INDEX.1 Fintech Blockchain
## 2019-01-08 0.3265071 0.3423964 0.3310965
## 2019-01-09 0.3097279 0.3328982 0.3573739
## 2019-01-10 0.3095399 0.3329365 0.3575236
## 2019-01-11 0.3096072 0.3330941 0.3572987
## 2019-01-12 0.3099208 0.3333798 0.3566995
## 2019-01-13 0.3105212 0.3337755 0.3557033
## 2019-01-14 0.3114297 0.3342215 0.3543487
## 2019-01-15 0.3123933 0.3337710 0.3538357
## 2019-01-16 0.3693964 0.3101953 0.3204083
## 2019-01-17 0.3696746 0.3101726 0.3201528
## 2019-01-18 0.3696835 0.3102277 0.3200888
## 2019-01-19 0.3694967 0.3103686 0.3201347
## 2019-01-20 0.3691072 0.3106073 0.3202855
## 2019-01-21 0.3685150 0.3109457 0.3205393
## 2019-01-22 0.3702358 0.3106228 0.3191414
## 2019-01-23 0.3258050 0.3354621 0.3387329
## 2019-01-24 0.3206552 0.3359615 0.3433833
## 2019-01-25 0.3188437 0.3360249 0.3451314
## 2019-01-26 0.3179339 0.3360335 0.3460325
## 2019-01-27 0.3173917 0.3360312 0.3465771
## 2019-01-28 0.3170341 0.3360267 0.3469392
## 2019-01-29 0.3152786 0.3348212 0.3499001## AI_INDEX Fintech Blockchain
## 2019-01-09 0.3265071 0.3423964 0.3310965
## 2019-01-10 0.3097279 0.3328982 0.3573739
## 2019-01-11 0.3095399 0.3329365 0.3575236
## 2019-01-12 0.3096072 0.3330941 0.3572987
## 2019-01-13 0.3099208 0.3333798 0.3566995
## 2019-01-14 0.3105212 0.3337755 0.3557033
## 2019-01-15 0.3114297 0.3342215 0.3543487
## 2019-01-16 0.3123933 0.3337710 0.3538357
## 2019-01-17 0.3693964 0.3101953 0.3204083
## 2019-01-18 0.3696746 0.3101726 0.3201528
## 2019-01-19 0.3696835 0.3102277 0.3200888
## 2019-01-20 0.3694967 0.3103686 0.3201347
## 2019-01-21 0.3691072 0.3106073 0.3202855
## 2019-01-22 0.3685150 0.3109457 0.3205393
## 2019-01-23 0.3702358 0.3106228 0.3191414
## 2019-01-24 0.3258050 0.3354621 0.3387329
## 2019-01-25 0.3206552 0.3359615 0.3433833
## 2019-01-26 0.3188437 0.3360249 0.3451314
## 2019-01-27 0.3179339 0.3360335 0.3460325
## 2019-01-28 0.3173917 0.3360312 0.3465771
## 2019-01-29 0.3170341 0.3360267 0.3469392
## 2019-01-30 0.3152786 0.3348212 0.3499001## AI_INDEX Fintech Blockchain
## 2019-01-09 0.3282508 0.3410537 0.3306954
## 2019-01-10 0.3113931 0.3316286 0.3569783
## 2019-01-11 0.3111896 0.3316750 0.3571354
## 2019-01-12 0.3112426 0.3318398 0.3569176
## 2019-01-13 0.3115432 0.3321315 0.3563254
## 2019-01-14 0.3121318 0.3325322 0.3553360
## 2019-01-15 0.3117551 0.3328986 0.3553463
## 2019-01-16 0.3127198 0.3324538 0.3548263
## 2019-01-17 0.3697613 0.3089570 0.3212818
## 2019-01-18 0.3700404 0.3089384 0.3210212
## 2019-01-19 0.3700500 0.3089974 0.3209526
## 2019-01-20 0.3698638 0.3091417 0.3209945
## 2019-01-21 0.3694747 0.3093837 0.3211416
## 2019-01-22 0.3695544 0.3102590 0.3201866
## 2019-01-23 0.3712710 0.3099381 0.3187909
## 2019-01-24 0.3267756 0.3347930 0.3384314
## 2019-01-25 0.3216112 0.3353015 0.3430874
## 2019-01-26 0.3197908 0.3353702 0.3448390
## 2019-01-27 0.3188738 0.3353831 0.3457431
## 2019-01-28 0.3183250 0.3353846 0.3462905
## 2019-01-29 0.3181490 0.3356612 0.3461898
## 2019-01-30 0.3163789 0.3344656 0.3491555ep <- endpoints(roll_ms, on = rbp)
port_ms <- Return.portfolio( y.r_c, weights= roll_ms[ep,], verbose=TRUE)## Warning in Return.portfolio.geometric(R = R, weights = weights, wealth.index =
## wealth.index, : The weights for one or more periods do not sum up to 1:
## assuming a return of 0 for the residual weightsport_ret <- cbind(port_eqw$returns, port_rp$returns, port_rpp$returns, port_ms$returns )
colnames(port_ret) <- c("EQW", "RP", "RPC", "MS")
# all portfolios start at same time period
port_ret <- na.omit( port_ret)
head(port_ret)## EQW RP RPC MS
## 2019-09-17 0.0008288824 0.0007909495 0.0008167320 0.0007733999
## 2019-09-18 0.0008239991 0.0007901827 0.0008228203 0.0007728015
## 2019-09-19 0.0008191387 0.0007893914 0.0008180661 0.0007722050
## 2019-09-20 0.0008143003 0.0007885755 0.0008133313 0.0007716095
## 2019-09-21 0.0008094841 0.0007877354 0.0008086159 0.0007710141
## 2019-09-22 0.0008046894 0.0007868719 0.0008039194 0.0007704206ta1 <- table.AnnualizedReturns(port_ret, scale=252, geometric = TRUE, Rf=rfr)
ta2 <- table.DownsideRisk(port_ret, scale=252, Rf=rfr, MAR =.00, p=.95)
ta3 <- Omega(port_ret, Rf=rfr)
SortinoRatio(port_ret)## EQW RP RPC MS
## Sortino Ratio (MAR = 0%) 0.2158486 0.2879532 0.2165428 0.3092285## EQW RP RPC MS
## Annualized Return 0.120900 0.10050 0.11730 0.108900
## Annualized Std Dev 0.049500 0.03220 0.04780 0.037000
## Annualized Sharpe (Rf=0.96%) 2.225500 2.79650 2.22940 2.660300
## Semi Deviation 0.002300 0.00150 0.00230 0.001500
## Gain Deviation 0.001700 0.00120 0.00170 0.002100
## Loss Deviation 0.002200 0.00140 0.00210 0.001900
## Downside Deviation (MAR=0%) 0.002100 0.00130 0.00210 0.001300
## Downside Deviation (Rf=0.96%) 0.002100 0.00130 0.00210 0.001300
## Downside Deviation (0%) 0.002100 0.00130 0.00210 0.001300
## Maximum Drawdown 0.306100 0.14570 0.29840 0.111700
## Historical VaR (95%) -0.005100 -0.00330 -0.00500 -0.002600
## Historical ES (95%) -0.006900 -0.00430 -0.00670 -0.005000
## Modified VaR (95%) -0.005000 -0.00320 -0.00490 -0.003000
## Modified ES (95%) -0.007300 -0.00430 -0.00690 -0.003800
## Omega (L = 0%) 1.451807 1.60503 1.45131 1.877081#################################################################
# Portfolio summary statistics (rebalance monthly)
#################################################################
rbp <- c("months") # <<< cambio clave respecto al bloque semanal
# Portafolio Equal Weight (EQW)
port_eqw_m <- Return.portfolio(y.r_c, verbose = TRUE, rebalance_on = rbp)
head(port_eqw_m$BOP.Weight, 22)## AI_INDEX Fintech Blockchain
## 2019-01-08 0.3333333 0.3333333 0.3333333
## 2019-01-09 0.3351063 0.3319946 0.3328991
## 2019-01-10 0.3368675 0.3306648 0.3324677
## 2019-01-11 0.3386170 0.3293438 0.3320391
## 2019-01-12 0.3403549 0.3280316 0.3316135
## 2019-01-13 0.3420814 0.3267280 0.3311906
## 2019-01-14 0.3437965 0.3254331 0.3307705
## 2019-01-15 0.3455003 0.3241466 0.3303531
## 2019-01-16 0.3458586 0.3228609 0.3312805
## 2019-01-17 0.3462150 0.3215820 0.3322030
## 2019-01-18 0.3465695 0.3203098 0.3331207
## 2019-01-19 0.3469222 0.3190442 0.3340336
## 2019-01-20 0.3472730 0.3177852 0.3349417
## 2019-01-21 0.3476221 0.3165328 0.3358452
## 2019-01-22 0.3479693 0.3152868 0.3367439
## 2019-01-23 0.3489805 0.3146174 0.3364022
## 2019-01-24 0.3499867 0.3139512 0.3360621
## 2019-01-25 0.3509881 0.3132882 0.3357236
## 2019-01-26 0.3519847 0.3126285 0.3353868
## 2019-01-27 0.3529765 0.3119719 0.3350516
## 2019-01-28 0.3539635 0.3113185 0.3347180
## 2019-01-29 0.3549458 0.3106682 0.3343860## AI_INDEX Fintech Blockchain
## 2019-01-08 0.3351063 0.3319946 0.3328991
## 2019-01-09 0.3368675 0.3306648 0.3324677
## 2019-01-10 0.3386170 0.3293438 0.3320391
## 2019-01-11 0.3403549 0.3280316 0.3316135
## 2019-01-12 0.3420814 0.3267280 0.3311906
## 2019-01-13 0.3437965 0.3254331 0.3307705
## 2019-01-14 0.3455003 0.3241466 0.3303531
## 2019-01-15 0.3458586 0.3228609 0.3312805
## 2019-01-16 0.3462150 0.3215820 0.3322030
## 2019-01-17 0.3465695 0.3203098 0.3331207
## 2019-01-18 0.3469222 0.3190442 0.3340336
## 2019-01-19 0.3472730 0.3177852 0.3349417
## 2019-01-20 0.3476221 0.3165328 0.3358452
## 2019-01-21 0.3479693 0.3152868 0.3367439
## 2019-01-22 0.3489805 0.3146174 0.3364022
## 2019-01-23 0.3499867 0.3139512 0.3360621
## 2019-01-24 0.3509881 0.3132882 0.3357236
## 2019-01-25 0.3519847 0.3126285 0.3353868
## 2019-01-26 0.3529765 0.3119719 0.3350516
## 2019-01-27 0.3539635 0.3113185 0.3347180
## 2019-01-28 0.3549458 0.3106682 0.3343860
## 2019-01-29 0.3561270 0.3102719 0.3336011# Portafolio Risk Parity (RP)
ep_m <- endpoints(roll_rp, on = rbp)
port_rp_m <- Return.portfolio(y.r_c, weights = roll_rp[ep_m, ], verbose = TRUE)
head(roll_rp[ep_m, ], 22)## AI_INDEX Fintech Blockchain
## 2019-09-30 0.2257410 0.5283140 0.2459450
## 2019-10-31 0.2435732 0.5127518 0.2436751
## 2019-11-30 0.2428329 0.5015914 0.2555756
## 2019-12-31 0.2209365 0.5235653 0.2554982
## 2020-01-31 0.2253063 0.5120199 0.2626738
## 2020-02-29 0.2112113 0.5237991 0.2649895
## 2020-03-31 0.1624413 0.6914295 0.1461292
## 2020-04-30 0.1402904 0.7127473 0.1469623
## 2020-05-31 0.1364577 0.7164528 0.1470895
## 2020-06-30 0.1440367 0.7097323 0.1462311
## 2020-07-31 0.1350736 0.7251373 0.1397891
## 2020-08-31 0.1382792 0.7231423 0.1385785
## 2020-09-30 0.1297673 0.7322973 0.1379354
## 2020-10-31 0.1423560 0.7082562 0.1493878
## 2020-11-30 0.1580203 0.6554109 0.1865688
## 2020-12-31 0.1870742 0.5941062 0.2188196
## 2021-01-31 0.1993651 0.5799397 0.2206951
## 2021-02-28 0.1560658 0.6152398 0.2286944
## 2021-03-31 0.1961157 0.5912551 0.2126292
## 2021-04-30 0.1952150 0.5885829 0.2162021
## 2021-05-31 0.1902963 0.5971289 0.2125748
## 2021-06-30 0.1869404 0.5974275 0.2156321## AI_INDEX Fintech Blockchain
## 2019-10-01 0.2257410 0.5283140 0.2459450
## 2019-10-02 0.2270032 0.5272784 0.2457184
## 2019-10-03 0.2282654 0.5262429 0.2454917
## 2019-10-04 0.2295276 0.5252073 0.2452651
## 2019-10-05 0.2307897 0.5241717 0.2450385
## 2019-10-06 0.2320519 0.5231362 0.2448119
## 2019-10-07 0.2333141 0.5221006 0.2445853
## 2019-10-08 0.2345763 0.5210650 0.2443587
## 2019-10-09 0.2346790 0.5204410 0.2448800
## 2019-10-10 0.2347816 0.5198175 0.2454009
## 2019-10-11 0.2348840 0.5191946 0.2459214
## 2019-10-12 0.2349864 0.5185722 0.2464414
## 2019-10-13 0.2350887 0.5179503 0.2469610
## 2019-10-14 0.2351910 0.5173289 0.2474801
## 2019-10-15 0.2352931 0.5167081 0.2479988
## 2019-10-16 0.2335041 0.5176499 0.2488460
## 2019-10-17 0.2317134 0.5185927 0.2496939
## 2019-10-18 0.2299209 0.5195363 0.2505427
## 2019-10-19 0.2281267 0.5204809 0.2513924
## 2019-10-20 0.2263306 0.5214265 0.2522429
## 2019-10-21 0.2245329 0.5223729 0.2530942
## 2019-10-22 0.2227333 0.5233203 0.2539464## AI_INDEX Fintech Blockchain
## 2019-10-01 0.2270032 0.5272784 0.2457184
## 2019-10-02 0.2282654 0.5262429 0.2454917
## 2019-10-03 0.2295276 0.5252073 0.2452651
## 2019-10-04 0.2307897 0.5241717 0.2450385
## 2019-10-05 0.2320519 0.5231362 0.2448119
## 2019-10-06 0.2333141 0.5221006 0.2445853
## 2019-10-07 0.2345763 0.5210650 0.2443587
## 2019-10-08 0.2346790 0.5204410 0.2448800
## 2019-10-09 0.2347816 0.5198175 0.2454009
## 2019-10-10 0.2348840 0.5191946 0.2459214
## 2019-10-11 0.2349864 0.5185722 0.2464414
## 2019-10-12 0.2350887 0.5179503 0.2469610
## 2019-10-13 0.2351910 0.5173289 0.2474801
## 2019-10-14 0.2352931 0.5167081 0.2479988
## 2019-10-15 0.2335041 0.5176499 0.2488460
## 2019-10-16 0.2317134 0.5185927 0.2496939
## 2019-10-17 0.2299209 0.5195363 0.2505427
## 2019-10-18 0.2281267 0.5204809 0.2513924
## 2019-10-19 0.2263306 0.5214265 0.2522429
## 2019-10-20 0.2245329 0.5223729 0.2530942
## 2019-10-21 0.2227333 0.5233203 0.2539464
## 2019-10-22 0.2224410 0.5239432 0.2536158# Portafolio Minimum Connectedness (MCP)
ep_m <- endpoints(w.mcp_temp, on = rbp)
port_mcp_m <- Return.portfolio(y.r_c, weights = w.mcp_temp[ep_m, ], verbose = TRUE)
# Portafolio Risk Parity Connectedness (RPC)
ep_m <- endpoints(w.rpp_temp, on = rbp)
port_rpp_m <- Return.portfolio(y.r_c, weights = w.rpp_temp[ep_m, ], verbose = TRUE)
head(w.rpp_temp[ep_m, ], 22)## AI_INDEX.1 Fintech Blockchain
## 2019-01-31 0.3289811 0.3341430 0.3368759
## 2019-02-28 0.3363116 0.3492580 0.3144304
## 2019-03-31 0.3308702 0.3393929 0.3297369
## 2019-04-30 0.3449267 0.3272312 0.3278422
## 2019-05-31 0.3320275 0.3420041 0.3259684
## 2019-06-30 0.3168507 0.3506587 0.3324906
## 2019-07-31 0.3134723 0.3612394 0.3252882
## 2019-08-31 0.3181933 0.3617609 0.3200458
## 2019-09-30 0.3257037 0.3514502 0.3228461
## 2019-10-31 0.3371312 0.3367531 0.3261157
## 2019-11-30 0.3375601 0.3363697 0.3260702
## 2019-12-31 0.3315039 0.3364480 0.3320481
## 2020-01-31 0.3282430 0.3464434 0.3253136
## 2020-02-29 0.3136865 0.3723297 0.3139838
## 2020-03-31 0.3018515 0.3986544 0.2994941
## 2020-04-30 0.3043240 0.3937593 0.3019166
## 2020-05-31 0.3032735 0.3948967 0.3018298
## 2020-06-30 0.3065739 0.3916484 0.3017777
## 2020-07-31 0.3039566 0.3880528 0.3079906
## 2020-08-31 0.3121820 0.3738214 0.3139966
## 2020-09-30 0.3093644 0.3722504 0.3183853
## 2020-10-31 0.3078739 0.3777330 0.3143930## AI_INDEX Fintech Blockchain
## 2019-02-01 0.3289811 0.3341430 0.3368759
## 2019-02-02 0.3300801 0.3337725 0.3361474
## 2019-02-03 0.3311693 0.3334053 0.3354254
## 2019-02-04 0.3322487 0.3330414 0.3347099
## 2019-02-05 0.3333185 0.3326807 0.3340008
## 2019-02-06 0.3341147 0.3326916 0.3331937
## 2019-02-07 0.3349093 0.3327025 0.3323882
## 2019-02-08 0.3357023 0.3327134 0.3315843
## 2019-02-09 0.3364938 0.3327242 0.3307820
## 2019-02-10 0.3372836 0.3327350 0.3299813
## 2019-02-11 0.3380719 0.3327458 0.3291822
## 2019-02-12 0.3388587 0.3327566 0.3283847
## 2019-02-13 0.3397594 0.3312211 0.3290195
## 2019-02-14 0.3406556 0.3296932 0.3296512
## 2019-02-15 0.3415475 0.3281727 0.3302798
## 2019-02-16 0.3424349 0.3266598 0.3309053
## 2019-02-17 0.3433181 0.3251542 0.3315277
## 2019-02-18 0.3441969 0.3236559 0.3321472
## 2019-02-19 0.3450715 0.3221649 0.3327636
## 2019-02-20 0.3455115 0.3212615 0.3332270
## 2019-02-21 0.3459494 0.3203624 0.3336883
## 2019-02-22 0.3463852 0.3194674 0.3341474## AI_INDEX Fintech Blockchain
## 2019-02-01 0.3300801 0.3337725 0.3361474
## 2019-02-02 0.3311693 0.3334053 0.3354254
## 2019-02-03 0.3322487 0.3330414 0.3347099
## 2019-02-04 0.3333185 0.3326807 0.3340008
## 2019-02-05 0.3341147 0.3326916 0.3331937
## 2019-02-06 0.3349093 0.3327025 0.3323882
## 2019-02-07 0.3357023 0.3327134 0.3315843
## 2019-02-08 0.3364938 0.3327242 0.3307820
## 2019-02-09 0.3372836 0.3327350 0.3299813
## 2019-02-10 0.3380719 0.3327458 0.3291822
## 2019-02-11 0.3388587 0.3327566 0.3283847
## 2019-02-12 0.3397594 0.3312211 0.3290195
## 2019-02-13 0.3406556 0.3296932 0.3296512
## 2019-02-14 0.3415475 0.3281727 0.3302798
## 2019-02-15 0.3424349 0.3266598 0.3309053
## 2019-02-16 0.3433181 0.3251542 0.3315277
## 2019-02-17 0.3441969 0.3236559 0.3321472
## 2019-02-18 0.3450715 0.3221649 0.3327636
## 2019-02-19 0.3455115 0.3212615 0.3332270
## 2019-02-20 0.3459494 0.3203624 0.3336883
## 2019-02-21 0.3463852 0.3194674 0.3341474
## 2019-02-22 0.3468190 0.3185766 0.3346044# Portafolio Máximo Sharpe (MS)
ep_m <- endpoints(roll_ms, on = rbp)
port_ms_m <- Return.portfolio(y.r_c, weights = roll_ms[ep_m, ], verbose = TRUE)## Warning in Return.portfolio.geometric(R = R, weights = weights, wealth.index =
## wealth.index, : The weights for one or more periods do not sum up to 1:
## assuming a return of 0 for the residual weights# Combinar retornos de todos los portafolios
port_ret_m <- cbind(
port_eqw_m$returns,
port_rp_m$returns,
port_rpp_m$returns,
port_ms_m$returns
)
colnames(port_ret_m) <- c("EQW", "RP", "RPC", "MS")
port_ret_m <- na.omit(port_ret_m)
head(port_ret_m)## EQW RP RPC MS
## 2019-10-01 0.0009009216 -2.314959e-06 0.0008324144 -0.001962428
## 2019-10-02 0.0009001104 -2.315402e-06 0.0008317218 -0.001966287
## 2019-10-03 0.0008993010 -2.315164e-06 0.0008310307 -0.001970161
## 2019-10-04 0.0008984931 -2.315130e-06 0.0008303408 -0.001974050
## 2019-10-05 0.0008976862 -2.315594e-06 0.0008296515 -0.001977955
## 2019-10-06 0.0008968814 -2.315136e-06 0.0008289641 -0.001981874# Estadísticas anuales y de riesgo
ta1_m <- table.AnnualizedReturns(port_ret_m, scale = 252, geometric = TRUE, Rf = rfr)
ta2_m <- table.DownsideRisk(port_ret_m, scale = 252, Rf = rfr, MAR = 0.00, p = 0.95)
ta3_m <- Omega(port_ret_m, Rf = rfr)
sr_m <- SortinoRatio(port_ret_m)
# Consolidar tabla resumen mensual
summary_monthly <- rbind(ta1_m, ta2_m, ta3_m, sr_m)
print(summary_monthly)## EQW RP RPC MS
## Annualized Return 0.1330000 0.1101000 0.1290000 0.103200
## Annualized Std Dev 0.0490000 0.0331000 0.0475000 0.037000
## Annualized Sharpe (Rf=0.96%) 2.4926000 3.0119000 2.4924000 2.505600
## Semi Deviation 0.0023000 0.0016000 0.0023000 0.001600
## Gain Deviation 0.0017000 0.0012000 0.0016000 0.002100
## Loss Deviation 0.0021000 0.0014000 0.0021000 0.002100
## Downside Deviation (MAR=0%) 0.0021000 0.0013000 0.0020000 0.001400
## Downside Deviation (Rf=0.96%) 0.0021000 0.0014000 0.0020000 0.001400
## Downside Deviation (0%) 0.0021000 0.0013000 0.0020000 0.001400
## Maximum Drawdown 0.2986000 0.1486000 0.2923000 0.090000
## Historical VaR (95%) -0.0051000 -0.0033000 -0.0049000 -0.002600
## Historical ES (95%) -0.0067000 -0.0044000 -0.0065000 -0.005100
## Modified VaR (95%) -0.0049000 -0.0032000 -0.0048000 -0.003100
## Modified ES (95%) -0.0070000 -0.0046000 -0.0066000 -0.004500
## Omega (L = 0%) 1.5048524 1.6605752 1.5036860 1.836729
## Sortino Ratio (MAR = 0%) 0.2415943 0.3091108 0.2419175 0.282443# Curva de rendimiento acumulado mensual
chart.CumReturns(
port_ret_m,
main = "Equity Curves (Monthly Rebalance)",
ylab = "",
geometric = TRUE,
wealth.index = TRUE,
legend.loc = "topleft"
)
Connectedness: TVP-VAR
Extraer medidas clave (GFEVD, TCI, TO, FROM, NET, PCI)
Portafolios: EQW, RiskParity, MinimumConnectednessPortfolio, MS
Performance summary and relación con TCI
---
title: "Return and Volatility Spillovers Between FinTech, Blockchain and AI Firms"
subtitle: "Estancia de Investigación"
author: "Avril Lobato Delgado"
date: "2024-11-01"
output:
  html_document:
    toc: yes
    toc_float: yes
    code_download: yes
    theme: cerulean
    highlight: pygments
---

![](https://citris-uc.org/wp-content/uploads/2019/10/Tec-de-Monterrey-logo-horizontal-blue.png)

<div style="text-align: center">
  <p><strong>Tecnológico de Monterrey</strong></p>
  <p><strong>Doctor Teófilo Ozuna</strong></p>
</div>

<div style="text-align: center">
  <p><strong>Alumna:</strong></p>

  Avril Lobato Delgado A00833113
 
</div>


```{r setup, include=FALSE}
if (packageVersion("xfun") < "0.44") {
  install.packages("xfun")
}

# Carga el paquete actualizado
library(xfun)

# Configuración de los chunks de knitr
knitr::opts_chunk$set(
	echo = TRUE,
	message = TRUE,
	warning = TRUE
)
```

# EDA de base de datos

Librerias
```{r echo=TRUE, message=FALSE, warning=FALSE}
library(ggplot2)
library(dplyr)
library(tidyr)
library(lubridate)
library(purrr)
library(plotly)
library(forecast)
library(readxl)
library(DataExplorer)
library(dplyr)
library(ggplot2)
library(tm)
library(cluster)
library(factoextra) 
library(gridExtra)
library(purrr)
library(pROC)
library(rpart)
library(rpart.plot)
library(e1071)
library(ggpubr)
library(dlookr)
library(zoo)
library(caret)
library(stats)
library(tseries)
library(readr)
library(vars)
library(syuzhet)
library(kableExtra)
library(plotly)
library(scales)
library(knitr)
library(rmgarch)
library(devtools)
library(openxlsx)
library(relaimpo)
library(stargazer)
library(RColorBrewer)
library(PerformanceAnalytics)
library(ConnectednessApproach)
library(readr)
library(tseries)
library(forecast)
library(urca)
library(fGarch)
library(MTS)
library(MASS)
library(nortest)
library(outliers)
library(moments)
library(FinTS)
library(WeightedPortTest)
```

## Carga de base de datos
```{r message=FALSE, warning=FALSE}
# Lee el archivo CSV y selecciona las columnas relevantes
index_alt <- read_csv("C:/Users/AVRIL/Desktop/Spillovers/all_daily_index_returns.csv")
data_selected <- index_alt[, c("Date", "AI_INDEX", "Fintech_Index", "Blockchain_Index")]
```

## Tests y Estadísticos de bd original
```{r message=FALSE, warning=FALSE}
# Function for summary statistics
summary_stats <- function(data) {
  return(data.frame(
    Mean = mean(data, na.rm = TRUE),
    StdDev = sd(data, na.rm = TRUE),
    Min = min(data, na.rm = TRUE),
    P25 = quantile(data, 0.25, na.rm = TRUE),
    P50 = median(data, na.rm = TRUE),
    P75 = quantile(data, 0.75, na.rm = TRUE),
    Max = max(data, na.rm = TRUE),
    Skewness = skewness(data, na.rm = TRUE),
    Kurtosis = kurtosis(data, na.rm = TRUE)
  ))
}

# Calculate statistics and tests for each index
results <- data.frame(
  Test = c(
    "Mean", "Standard Deviation", "Minimum", "25th Percentile", "Median",
    "75th Percentile", "Maximum", "Skewness", "Kurtosis", "Jarque-Bera (p-value)",
    "ADF Test (p-value)", "KPSS Test (p-value)", "PP Test (p-value)", "ERS Test",
    "ARCH Test (p-value)", "Weighted Portmanteau Test (p-value)", "Q2(20) Test (p-value)",
    "Outlier detection (Chen and Liu)","Correlation Matrix and R-squared"
  ),
  AI_INDEX = NA, Fintech_Index = NA, Blockchain_Index = NA
)

# Populate the table with summary stats
stats_ai <- summary_stats(data_selected$AI_INDEX)
stats_fintech <- summary_stats(data_selected$Fintech_Index)
stats_blockchain <- summary_stats(data_selected$Blockchain_Index)
results$AI_INDEX[1:9] <- as.numeric(stats_ai)
results$Fintech_Index[1:9] <- as.numeric(stats_fintech)
results$Blockchain_Index[1:9] <- as.numeric(stats_blockchain)

# Normality and stationarity tests
results$AI_INDEX[10] <- jarque.bera.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[10] <- jarque.bera.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[10] <- jarque.bera.test(data_selected$Blockchain_Index)$p.value

results$AI_INDEX[11] <- adf.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[11] <- adf.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[11] <- adf.test(data_selected$Blockchain_Index)$p.value

results$AI_INDEX[12] <- kpss.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[12] <- kpss.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[12] <- kpss.test(data_selected$Blockchain_Index)$p.value

results$AI_INDEX[13] <- pp.test(data_selected$AI_INDEX)$p.value
results$Fintech_Index[13] <- pp.test(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[13] <- pp.test(data_selected$Blockchain_Index)$p.value

results$AI_INDEX[14] <- ur.ers(data_selected$AI_INDEX, type = "DF-GLS")@teststat
results$Fintech_Index[14] <- ur.ers(data_selected$Fintech_Index, type = "DF-GLS")@teststat
results$Blockchain_Index[14] <- ur.ers(data_selected$Blockchain_Index, type = "DF-GLS")@teststat

# ARCH test
results$AI_INDEX[15] <- ArchTest(data_selected$AI_INDEX)$p.value
results$Fintech_Index[15] <- ArchTest(data_selected$Fintech_Index)$p.value
results$Blockchain_Index[15] <- ArchTest(data_selected$Blockchain_Index)$p.value

# Portmanteau test
results$portmanteau_ai <- Weighted.Box.test(data_selected$AI_INDEX)$p.value
results$portmanteau_fintech <- Weighted.Box.test(data_selected$Fintech_Index)$p.value
results$portmanteau_blockchain <- Weighted.Box.test(data_selected$Blockchain_Index)$p.value

# Q2(20) test
results$AI_INDEX[17] <- Box.test(data_selected$AI_INDEX^2, lag = 20, type = "Ljung-Box")$p.value
results$Fintech_Index[17] <- Box.test(data_selected$Fintech_Index^2, lag = 20, type = "Ljung-Box")$p.value
results$Blockchain_Index[17] <- Box.test(data_selected$Blockchain_Index^2, lag = 20, type = "Ljung-Box")$p.value

# Outlier detection (Chen and Liu)
results$outliers_ai <- sum(scores(data_selected$AI_INDEX, type = "chisq", prob = 0.05) != 0)
results$outliers_fintech <- sum(scores(data_selected$Fintech_Index, type = "chisq", prob = 0.05) != 0)
results$outliers_blockchain <- sum(scores(data_selected$Blockchain_Index, type = "chisq", prob = 0.05) != 0)

# Correlation Matrix and R-squared
cor_matrix <- cor(data_selected[, -1], use = "complete.obs")
r_squared <- cor_matrix^2

print(results)
```

```{r}
# Convertir la columna `Date` a tipo Date si está en formato "dd/mm/yyyy"
index_alt$Date <- as.Date(index_alt$Date, format = "%d/%m/%Y")

# Mostrar estructura y resumen
str(index_alt)
summary(index_alt)
```

# **Chatziantoniou, Gabauer & Gupta (2021):** Integration and risk transmission in the market for crude oil: A time-varying parameter frequency connectedness approach
```{r message=FALSE, warning=FALSE}
# Convertir a objeto zoo
data_selected <- index_alt[, c("Date","AI_INDEX", "Fintech_Index", "Blockchain_Index")]

data_matrix <- as.matrix(data_selected)

data_zoo <- zoo(data_selected[, -1], order.by = data_selected$Date)

# Instalar y cargar el paquete ConnectednessApproach, si no está ya instalado
if (!requireNamespace("ConnectednessApproach", quietly = TRUE)) {
  if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
  }
  devtools::install_github("GabauerDavid/ConnectednessApproach")
}
library(ConnectednessApproach)

# Configuración de parámetros para TVPVAR
nlag <- 2       # Orden del rezago
lambda <- c(0.99, 0.99)  # Valores de decaimiento para el filtro

# Crear la lista de configuración necesaria
config <- list(l = lambda, nlag = nlag)

# Estimar el modelo TVP-VAR con los parámetros especificados
tvpvar_model <- TVPVAR(data_zoo, configuration = config)
```

## Table 2: Averaged Connectedness Table
```{r message=FALSE, warning=FALSE}
partition = c(pi+0.00001, pi/5, 0)
dca = ConnectednessApproach(data_zoo, 
                            model="TVP-VAR",
                            connectedness="Frequency",
                            nlag=1,
                            nfore=100,
                            window.size=200,
                            VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="BayesPrior")),
                            Connectedness_config = list(
                              FrequencyConnectedness=list(partition=partition, generalized=TRUE, scenario="ABS")
                            ))
```

```{r message=FALSE, warning=FALSE}
kable(dca$TABLE)
```

## Figure 4: Net Total Directional Connectedness
```{r message=FALSE, warning=FALSE}
PlotNET(dca, ylim=c(-60,60))
```



# **Gabauer and Gupta (2018): ** On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach

```{r message=FALSE, warning=FALSE}
data_zoo2 <- zoo(data_selected[, c("AI_INDEX", "Fintech_Index", "Blockchain_Index")], 
                 order.by = data_selected$Date)

# Configuración de parámetros para TVPVAR
nlag <- 2       # Orden del rezago
lambda <- c(0.99, 0.99)  # Valores de decaimiento para el filtro

# Crear la lista de configuración necesaria
config <- list(l = lambda, nlag = nlag)

# Estimar el modelo TVP-VAR con los parámetros especificados
tvpvar_model <- TVPVAR(data_zoo2, configuration = config)
```

```{r message=FALSE, warning=FALSE}
dca = ConnectednessApproach(data_zoo2, 
                            nlag=1,
                            nfore=10,
                            model="TVP-VAR",
                            connectedness="Time",
                            window.size=200,
                            VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="BayesPrior")))
```

## Table 1: Connectedness table
```{r message=FALSE, warning=FALSE}
kable(dca$TABLE)
# CONNECTEDNESS DECOMPOSITION
int = InternalConnectedness(dca, groups=list("Group1"=c(1), "Group2"=c(2), "Group3"=c(3)))
ext = ExternalConnectedness(dca, groups=list("Group1"=c(1), "Group2"=c(2), "Group3"=c(3)))
kable(int$TABLE)
kable(ext$TABLE)
```


```{r message=FALSE, warning=FALSE}
#Figure 3: Dynamic total connectedness
PlotTCI(dca, int, ylim=c(0, 40))

#Figure 4: Net total directional connectedness
PlotNET(dca, int, ylim=c(-10, 10))

#Figure 5: Total directional connectedness FROM others
PlotFROM(dca, int, ylim=c(0, 60))

#Figure 6: Total directional connectedness TO others
PlotTO(dca, int, ylim=c(0, 60))

#Figure 7: Internal net pairwise total directional connectedness
PlotNPDC(dca, int, ylim=c(-5, 5))
```

# **Cocca, Gabauer, and Pomberger (2024):** Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures
```{r message=FALSE, warning=FALSE}
# Convierte la columna 'Date' a formato Date
date <- as.Date(data_selected$Date, format="%d/%m/%Y")

# Crea la serie de tiempo (zoo) solo con las columnas seleccionadas
Y <- zoo(data_selected[, c("Fintech_Index", "AI_INDEX", "Blockchain_Index")], order.by = date)

# Asegúrate de que los datos sean numéricos y elimina valores NA
Y <- na.omit(zoo(data.frame(
  Fintech_Index_diff = as.numeric(data_selected$Fintech_Index),
  AI_INDEX_diff = as.numeric(data_selected$AI_INDEX),
  Blockchain_Index_diff = as.numeric(data_selected$Blockchain_Index)
), order.by = date))

# Reemplaza los valores NA restantes por 0
Y[is.na(Y)] <- 0

# Establece 'dimnames' de Y con números como índices (como en Y2) sin cambiar el índice de fecha
dimnames(Y) <- list(as.character(1:nrow(Y)), colnames(Y))

NAMES = colnames(Y)
k = ncol(Y)
t = nrow(Y)

# Verifica la estructura de Y
str(Y)

```

```{r message=FALSE, warning=FALSE}
kable(SummaryStatistics(Y, nlag=20))
```

## Figure 0: Percentage changes
```{r message=FALSE, warning=FALSE}
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (i in 1:k) {
  plot(date,Y[,i],type='l',las=1,xaxs='i',yaxs='i',ylim=c(-0.1,0.1),xlab='',ylab='',main=paste(NAMES[i]),tck=-.02,col='steelblue4')
  grid(NA,NULL)
  lines(date,Y[,i],col='steelblue4')
  abline(h=0,lty=3)
  box()
}
```

```{r message=FALSE, warning=FALSE}
spec <- list()

# Itera sobre cada columna de Y
for (i in 1:k) {
  # Intenta seleccionar el mejor modelo GARCH para cada serie
  u <- tryCatch({
    GARCHselection(Y[, i],
                   distributions = c("norm", "std", "sstd", "ged", "sged"),
                   models = c("sGARCH", "gjrGARCH", "eGARCH", "iGARCH", "AVGARCH", "TGARCH"))
  }, error = function(e) {
    warning(paste("Error al seleccionar modelo GARCH para la variable", i, ":", e$message))
    NULL
  })
  
  # Si 'u' no es NULL y tiene un mejor modelo, agrega a 'spec'
  if (!is.null(u) && !is.null(u$best_ugarch)) {
    spec[[i]] <- u$best_ugarch
  } else {
    # Si no se seleccionó un modelo, asigna un modelo GARCH(1,1) por defecto
    warning(paste("No se seleccionó ningún modelo GARCH para", NAMES[i], ". Usando modelo GARCH(1,1) con distribución normal"))
    spec[[i]] <- ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),
                            mean.model = list(armaOrder = c(0, 0)),
                            distribution.model = "norm")
  }
}

# Verifica que 'spec' tenga modelos para cada serie en Y
if (length(spec) != k) {
  stop("spec no contiene modelos válidos para todas las series de Y")
}

# Ejecuta el modelo DCC-GARCH si spec es válida
fit <- BivariateDCCGARCH(Y, spec)
```

## Table 4: Evaluation of univariate GARCH performance
```{r message=FALSE, warning=FALSE}
H = fit$H_t
R = fit$R_t

uGARCH_table = NULL
for (i in 1:k) {
  fit = ugarchfit(spec[[i]], Y[,i])
  gt = GARCHtests(fit, lag=20)
  uGARCH_table = rbind(uGARCH_table, gt$TABLE)
}

kable(uGARCH_table)
```


## Figure 3: Dynamic conditional correlations
```{r message=FALSE, warning=FALSE}
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (j in 1:k) {
  plot(date,R[j,j,],type='l',las=1,xaxs='i',yaxs='i',xlab='',ylab='',main=paste(NAMES[j]),tck=-.02,col=j,ylim=c(-1.1,1.1))
  grid(NA,NULL)
  for (i in 1:k) {
    lines(date,R[j,i,],col=i)
    abline(h=0,lty=3)
    box()
  }
  legend("bottom",NAMES[-j],fill=c(1:k)[-j],bty="n",cex=0.75,ncol=k)
}
```


## Table 5: Averaged connectedness measures
```{r message=FALSE, warning=FALSE}
dca = R2Correlations(R)
kable(dca$TABLE)
```

## Figure 4: Dynamic conditional R2 decomposed measures
```{r message=FALSE, warning=FALSE}
r2c = apply(dca$CT,c(1,3),sum)-1
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (j in 1:k) {
  r2dd = matrix(0,ncol=k,nrow=t)
  for (i in 1:t) {
    ded = rep(0,k)
    ded[j] = 1
    r2dd[i,] = cumsum(dca$CT[j,,i]-ded)
  }
  plot(date,100*r2c[j,]*NA,type="l",las=1,xlab="",ylab="",ylim=c(0,100),xaxs="i",tck=-0.01,yaxs="i",main=NAMES[j])
  grid(NA,NULL)
  for (i in k:1) {
    polygon(c(date, rev(date)), c(c(rep(0, t)), rev(100*r2dd[,i])), col=i, border=i)
  }
  box()
  legend("topleft",NAMES[-j],fill=c(1:k)[-j],bty="n",cex=0.75,ncol=1)
}
```

## Figure 5: Dynamic total connectedness
```{r message=FALSE, warning=FALSE}
par(mfcol = c(1,1), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
plot(date,dca$TCI*NA,type='l',las=1,xaxs='i',yaxs='i',xlab='',ylab='',main='',tck=-.01, ylim=c(0,110))#c(max(c(0,min(TCI)-10)),max(TCI)+10))
grid(NA,NULL)
polygon(c(date, rev(date)), c(c(rep(0, t)), rev(dca$TCI)), col=1, border=1)
box()
```

## Figure 6: Net total directional connectedness
```{r message=FALSE, warning=FALSE}
par(mfcol = c(ceiling(k/2),2), oma = c(0, 0, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (i in 1:k) {
  plot(date,dca$NET[,i]*NA,type='l',las=1,xaxs='i',yaxs='i',ylim=c(-20,20),xlab='',ylab='',main=paste("NET",NAMES[i]),tck=-.025)
  grid(NA,NULL)
  polygon(c(date, rev(date)), c(c(rep(0, t)), rev(dca$NET[,i])), col=1, border=1)
  abline(h=0,lty=3)
  box()
}
```


## Table 7: Hedge ratios
```{r message=FALSE, warning=FALSE}
method = "cumsum"
statistics = "Fisher"
metric = "StdDev"
hr = HedgeRatio(Y/100, H, statistics=statistics, method=method, metric=metric, digit=3)
kable(hr$TABLE)
```

## Table 8: Multivariate hedging portfolios
```{r message=FALSE, warning=FALSE}
mhp = MultivariateHedgingPortfolio(Y/100, H, statistics=statistics, method=method, digit=3)
kable(mhp$TABLE)
```

## Figure 3: Dynamic conditional betas
```{r message=FALSE, warning=FALSE}
par(mfcol = c(ceiling(k/2),2), oma = c(0, 1, 0, 0) + 0.5, mar = c(1, 1, 1, 1) + 0.5, mgp = c(1, 0.4, 0))
for (j in 1:k) {
  plot(date,mhp$Beta[j,j,]*NA,type='l',las=1,xaxs='i',yaxs='i',xlab='',ylab='',
       main=paste(NAMES[j]),tck=-.02,col=j,ylim=c(-0.5,70))
  grid(NA,NULL)
  coefs = lm(Y[,j]~Y[,-j])$coefficients[-1]
  for (i in 1:k) {
    if (i!=j) {
      lines(date,mhp$Beta[j,i,],col=i)
      abline(h=0,lty=3)
      box()
    }
  }
  col = 1:k
  col = col[-j]
  abline(h=coefs,lty=3,col=col)
  legend("bottom",NAMES[-j],fill=c(1:k)[-j],bty="n",cex=0.6,ncol=k)
}
```

## Table 9: Optimal bivariate portfolio weights
```{r message=FALSE, warning=FALSE}
bpw = BivariatePortfolio(Y/100, H, statistics=statistics, method=method, metric=metric, digit=3)
## The optimal bivariate portfolios are computed according to:
##  Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. The Review of Financial Studies, 11(4), 817-844.
## 
##           Hedging effectiveness is calculated according to:
##  Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
## 
##           Statistics of the hedging effectiveness measure are implemented according to:
##  Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
kable(bpw$TABLE)
```

## Table 10: Multivariate portfolio analysis
```{r message=FALSE, warning=FALSE}
PCIc = dca$PCI
PCIg = dca$CT
for (l in 1:dim(dca$CT)[3]) {
  for (i in 1:k) {
    for (j in 1:k) {
      PCIg[i,j,l] = (2*R[i,j,l]^2)/(1+R[i,j,l]^2)
    }
  }
}
mvp = MinimumConnectednessPortfolio(Y/100, H, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
##  Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
## 
##           Hedging effectiveness is calculated according to:
##  Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
## 
##           Statistics of the hedging effectiveness measure are implemented according to:
##  Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mcp = MinimumConnectednessPortfolio(Y/100, R, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
##  Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
## 
##           Hedging effectiveness is calculated according to:
##  Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
## 
##           Statistics of the hedging effectiveness measure are implemented according to:
##  Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mpc = MinimumConnectednessPortfolio(Y/100, PCIc, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
##  Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
## 
##           Hedging effectiveness is calculated according to:
##  Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
## 
##           Statistics of the hedging effectiveness measure are implemented according to:
##  Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mpg = MinimumConnectednessPortfolio(Y/100, PCIg, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
##  Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
## 
##           Hedging effectiveness is calculated according to:
##  Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
## 
##           Statistics of the hedging effectiveness measure are implemented according to:
##  Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
mrp = MinimumConnectednessPortfolio(Y/100, dca$CT, statistics=statistics, method=method, metric=metric, digit=3)
## The minimum connectedness portfolio is implemented according to:
##  Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217-253). Palgrave Macmillan, Cham.
## 
##           Hedging effectiveness is calculated according to:
##  Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
## 
##           Statistics of the hedging effectiveness measure are implemented according to:
##  Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
MVA = rbind(mvp$TABLE, mcp$TABLE, mpc$TABLE, mpg$TABLE, mrp$TABLE)
kable(MVA)
```

## Table 11: Portfolio performance
```{r message=FALSE, warning=FALSE}
library(PerformanceAnalytics)
library(xts)
port.mat = data.frame(
                     MVP=mvp$portfolio_return,
                     MCP=mcp$portfolio_return,
                     MPC=mpc$portfolio_return,
                     MPG=mpg$portfolio_return,
                     MRP=mrp$portfolio_return
                     )
port.xts = xts(port.mat, order.by=date)

Rb <- xts(Y[,1]/100, order.by = index(Y))
colnames(Rb) <- "Benchmark"

ir <- matrix(NA, nrow = 1, ncol = ncol(port.xts))
colnames(ir) <- colnames(port.xts)
rownames(ir) <- "GRE"

for (i in 1:ncol(port.xts)) {
  ir[, i] <- InformationRatio(Ra = port.xts[, i], Rb = Rb)
}

k = ncol(port.xts)
table = matrix(NA, ncol=k, nrow=5)
for (i in 1:k) {
  table[,i] = c(
  # Retorno anualizado
  Return.annualized(port.xts[,i]),
  
  # Desviación estándar anualizada
  StdDev.annualized(port.xts[,i]),
  
  # Ratio de Sharpe anualizado
  SharpeRatio.annualized(port.xts[,i]),
  
  # Valor en Riesgo (VaR) con distribución normal y percentil del 95%
  VaR(as.numeric(port.xts[,i]), p=0.95, method="gaussian"),
  
  # Expectativa de Pérdida (ES) con distribución normal y percentil del 95%
  ES(as.numeric(port.xts[,i]), p=0.95, method="gaussian")
)
}
colnames(table) = colnames(port.xts)
rownames(table) = c("Return","StdDev","Sharpe Ratio (StdDev)","Sharpe Ratio (VaR)","Sharpe Ratio (CVaR)")
kable(rbind(table, ir))
```


# **Henriques(2025):** Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification

```{r}
pkgs <- c("readr","zoo","xts","ConnectednessApproach","PerformanceAnalytics",
          "riskParityPortfolio","quadprog","rugarch","rmgarch","tseries",
          "urca","FinTS","robustbase","corrplot","matrixStats","psych","vars")

install_if_missing <- function(p) if (!requireNamespace(p, quietly = TRUE)) install.packages(p)
invisible(lapply(pkgs, install_if_missing))
library(readr); library(zoo); library(xts); library(ConnectednessApproach)
library(PerformanceAnalytics); library(riskParityPortfolio); library(quadprog)
library(corrplot); library(vars)

# --- 1) Cargar datos --------------------------------------------------------
data_path <- "C:/Users/AVRIL/Desktop/Spillovers/all_daily_index_returns.csv"
index_alt <- read_csv(data_path, show_col_types = FALSE)

if(!("Date" %in% colnames(index_alt))) stop("No se encontró la columna 'Date'.")
index_alt$Date <- as.Date(index_alt$Date, format = "%d/%m/%Y")
if(any(is.na(index_alt$Date))) index_alt$Date <- as.Date(index_alt$Date, format = "%Y-%m-%d")

data_selected <- index_alt[, c("Date", "AI_INDEX", "Fintech_Index", "Blockchain_Index")]
colnames(data_selected) <- c("Date","AI_INDEX","Fintech","Blockchain")
data_selected <- data_selected[!is.na(data_selected$Date), ]

```

## Detectar si los valores son precios o retornos y preparar series 
```{r}
# Heurística: si hay valores mayores que 1.5 -> tratamos como precios, sino como retornos
is_price_like <- function(x) median(abs(x), na.rm=TRUE) > 1.5
mat <- as.data.frame(data_selected[, -1])

if(any(sapply(mat, is_price_like))) {
  message("Detectado dato tipo precios -> calculando retornos logarítmicos.")
  prices_xts <- xts(mat, order.by = data_selected$Date)
  y.r <- na.omit(diff(log(prices_xts)))
} else {
  message("Detectado dato tipo retornos -> usaremos directamente los retornos.")
  y.r <- xts(mat, order.by = data_selected$Date)
  if(all(abs(coredata(y.r)) > 1)) {
    message("Retornos parecen en porcentaje. Se convierten a decimales (/100).")
    y.r <- y.r / 100
  }
  y.r <- na.omit(y.r)
}

symbols <- colnames(y.r)
cat("Series preparadas:", paste(symbols, collapse=", "), " (n=", nrow(y.r), ")\n")
```

```{r}
p1 <-plot.xts(y.r, auto.legend = TRUE, main="",legend.loc = "topleft")
p1
```



## Estadísticos y correlaciones
```{r}
cat("\n--- Estadísticos descriptivos ---\n")
sum_stats <- data.frame(t(psych::describe(as.data.frame(100 * y.r))))
print(sum_stats)

cat("\n--- Matriz de correlaciones ---\n")
cor_mat <- cor(y.r, use="complete.obs")
print(round(cor_mat,3))
corrplot(cor_mat, type="upper", order="hclust", tl.col="black", tl.srt=45)
```


```{r}
vars::VARselect(y.r)
# standard dy approach
dca = ConnectednessApproach(y.r,
                            nlag=1,
                            nfore=20,
                            window.size=200)

(dca$TABLE)
PlotTCI(dca, ylim=c(0,100))

# tvp-var
dca = ConnectednessApproach(y.r,
                            nlag=1,
                            nfore=20,
                            #window.size=200,
                            corrected = FALSE,
                            model="TVP-VAR")
(dca$TABLE)
PlotTCI(dca, ylim=c(0,100))

str(dca)
(dca$TABLE)
gfevd = dca$CT
dim(gfevd)

(ConnectednessTable(gfevd))
(ConnectednessTable(gfevd)$PCI)

# png("plots/plot_tci.png",  width = 600, height = 480 , pointsize=14)
PlotTCI(dca, ylim=c(0,100))
View (dca$TCI)
max(dca$TCI)
min(dca$TCI)
mean(dca$TCI)
PlotTO(dca, ylim=c(0,100))
PlotFROM(dca, ylim=c(0,100))
PlotNET(dca, ylim=c(-20,20))
PlotNPDC(dca, ylim=c(-15,15))
PlotNetwork(dca)

```


```{r}
# portfolio weights
mcp = MinimumConnectednessPortfolio(as.zoo(y.r), dca$PCI, statistics="Fisher")
mcp$TABLE

w.mcp <- mcp$portfolio_weights 
apply(w.mcp,2, mean)

rpp = RiskParityPortfolio(as.zoo(y.r), dca$PCI, statistics="Fisher")
rpp$TABLE

w.rpp <- rpp$portfolio_weights 
apply(w.rpp,2, mean)
head(w.rpp)
tail(w.rpp)
dim(w.rpp)
dim(y.r)
dim(w.mcp)

head (w.rpp*y.r)
w.rpp[1,1] * y.r[1,1]

w.mcp_temp <-  cbind(y.r[,1], w.mcp)
head(w.mcp_temp)
w.mcp_temp <- w.mcp_temp[,-1]
head(w.mcp_temp)

w.rpp_temp <-  cbind(y.r[,1], w.rpp)
head(w.rpp_temp)
w.rpp_temp <- w.rpp_temp[,-1]
head(w.rpp_temp)


################################################################
# portfolios
################################################################

# choice of portfolios; mcp, rpp, ew, rp
# https://cran.r-project.org/web/packages/riskParityPortfolio/vignettes/RiskParityPortfolio.html#a-pratical-example-using-faang-price-data
# for backtesting
# https://cran.r-project.org/web/packages/portfolioBacktest/vignettes/PortfolioBacktest.html#defining-portfolios

y.r_c <- exp(y.r)-1

w.eq <- rep(1/ncol(y.r_c), ncol(y.r_c))
w.rp <- riskParityPortfolio(cov(y.r_c))$w

# https://bookdown.org/compfinezbook/introcompfinr/Efficient-portfolios-of.html
max_sharpe_ratio <- function(dataset) {
  prices <- dataset
  
  # 1. Eliminar columnas o filas con NAs
  prices <- na.omit(prices)
  if (nrow(prices) < 2) return(rep(NA, ncol(prices)))
  
  # 2. Calcular retornos logarítmicos de forma segura
  log_returns <- suppressWarnings(diff(log(prices)))
  log_returns <- na.omit(exp(log_returns) - 1)
  
  if (nrow(log_returns) < 2) return(rep(NA, ncol(prices)))
  
  # 3. Estadísticos básicos
  Sigma <- cov(log_returns, use = "pairwise.complete.obs")
  mu <- colMeans(log_returns, na.rm = TRUE)
  
  # 4. Validación de inputs
  if (any(is.na(mu)) || any(is.na(Sigma))) return(rep(NA, ncol(prices)))
  if (all(mu <= 1e-8, na.rm = TRUE)) return(rep(0, ncol(prices)))
  
  # 5. Optimización clásica de portafolio máximo Sharpe
  Dmat <- 2 * Sigma
  Amat <- cbind(mu, diag(ncol(prices)))
  bvec <- c(1, rep(0, ncol(prices)))
  dvec <- rep(0, ncol(prices))
  
  res <- try(solve.QP(Dmat = Dmat, dvec = dvec, Amat = Amat, bvec = bvec, meq = 1), silent = TRUE)
  
  if (inherits(res, "try-error")) {
    return(rep(NA, ncol(prices)))
  } else {
    w <- res$solution
    return(w / sum(w))
  }
}



# rolling portfolio weights
wl <- 252
roll_rp =  na.omit (rollapply(y.r_c, wl ,function(x) riskParityPortfolio(cov(x))$w, by.column=FALSE,align="right"))
head(roll_rp)
tail(roll_rp)

roll_ms =  na.omit (rollapply(y.r_c, wl ,function(x) max_sharpe_ratio(x), by.column=FALSE,align="right"))
colnames(roll_ms) <- colnames(roll_rp)
head(roll_ms)
tail(roll_ms)


###########################3

# port_eqw  <- Return.portfolio(y.r, weights=w.eq ,   verbose=TRUE, geometric = FALSE)
port_eqw  <- Return.portfolio( y.r_c,   verbose=TRUE,  rebalance_on = "days")
head (port_eqw$BOP.Weight,22)
head (port_eqw$EOP.Weight,22)

# port_rp   <- Return.portfolio(y.r, weights=w.rp,verbose=TRUE)
port_rp   <- Return.portfolio( y.r_c, weights=roll_rp,   verbose=TRUE)
port_mcp  <- Return.portfolio( y.r_c, weights=w.mcp_temp,verbose=TRUE)
port_rpp  <- Return.portfolio( y.r_c, weights=w.rpp_temp,verbose=TRUE)
port_ms   <- Return.portfolio( y.r_c, weights=roll_ms,   verbose=TRUE)


str(port_eqw)

################################################################
# some checks on the eqw
head(port_eqw$returns)
w.eq *y.r[1:5,]
apply(w.eq *y.r[1:5,],1, sum)
head(port_rp$returns)
tail(port_rp$returns)

table.AnnualizedReturns(apply(w.eq *y.r_c["2017-09-14/"],1,sum), scale=252, Rf=(.01/252))

# in sample eqw
summary(y.r_c["2017-09-14/"])
    w.eq *apply(y.r_c["2017-09-14/"], 2, mean)
sum(w.eq *apply(y.r_c["2017-09-14/"], 2, mean))*252
# 0.06297518
################################################################


port_ret <- cbind(port_eqw$returns, port_rp$returns, port_rpp$returns, port_ms$returns )
colnames(port_ret) <- c("EQW", "RP", "RPC", "MS")
# all portfolios start at same time period
port_ret <- na.omit( port_ret)
head(port_ret)
```


```{r}
# portfolio summary statistics
# puedes fijar una fecha o usar el inicio automático de tus datos
start_date <- start(y.r_c)  
cat("Fecha inicial de los datos:", start_date, "\n")

# Filtrar desde start_date
roll_rp_sub  <- roll_rp[paste0(start_date, "/")]
roll_ms_sub  <- roll_ms[paste0(start_date, "/")]
w_rpp_sub    <- w.rpp_temp[paste0(start_date, "/")]

# promedios de pesos
aw1 <- apply(roll_rp_sub, 2, mean, na.rm = TRUE)
aw2 <- apply(roll_ms_sub, 2, mean, na.rm = TRUE)
aw3 <- apply(w_rpp_sub,   2, mean, na.rm = TRUE)

# estadísticas resumen
summary(roll_rp_sub)
summary(roll_ms_sub)
summary(w_rpp_sub)

# gráficas de pesos
plot(roll_rp_sub, main = "Pesos Rolling Risk Parity (RP)", auto.legend = TRUE, legend.loc = "topleft")
plot(roll_ms_sub, main = "Pesos Rolling Max Sharpe (MS)", auto.legend = TRUE, legend.loc = "topleft")
plot(w_rpp_sub,   main = "Pesos Rolling Risk Parity Connectedness (RPC)", auto.legend = TRUE, legend.loc = "topleft")

# medidas agregadas: media, sd, cv, min, max
aw1_2 <- rbind(
  Mean = apply(roll_rp_sub, 2, mean, na.rm = TRUE),
  SD   = apply(roll_rp_sub, 2, sd, na.rm = TRUE),
  CV   = apply(roll_rp_sub, 2, sd, na.rm = TRUE) / apply(roll_rp_sub, 2, mean, na.rm = TRUE),
  Min  = apply(roll_rp_sub, 2, min, na.rm = TRUE),
  Max  = apply(roll_rp_sub, 2, max, na.rm = TRUE)
)

aw2_2 <- rbind(
  Mean = apply(roll_ms_sub, 2, mean, na.rm = TRUE),
  SD   = apply(roll_ms_sub, 2, sd, na.rm = TRUE),
  CV   = apply(roll_ms_sub, 2, sd, na.rm = TRUE) / apply(roll_ms_sub, 2, mean, na.rm = TRUE),
  Min  = apply(roll_ms_sub, 2, min, na.rm = TRUE),
  Max  = apply(roll_ms_sub, 2, max, na.rm = TRUE)
)

aw3_2 <- rbind(
  Mean = apply(w_rpp_sub, 2, mean, na.rm = TRUE),
  SD   = apply(w_rpp_sub, 2, sd, na.rm = TRUE),
  CV   = apply(w_rpp_sub, 2, sd, na.rm = TRUE) / apply(w_rpp_sub, 2, mean, na.rm = TRUE),
  Min  = apply(w_rpp_sub, 2, min, na.rm = TRUE),
  Max  = apply(w_rpp_sub, 2, max, na.rm = TRUE)
)

# combinar en un solo marco de datos
portfolio_weights_summary <- rbind(
  cbind(Type = "RP",  Stat = rownames(aw1_2), round(aw1_2, 4)),
  cbind(Type = "MS",  Stat = rownames(aw2_2), round(aw2_2, 4)),
  cbind(Type = "RPC", Stat = rownames(aw3_2), round(aw3_2, 4))
)

print(portfolio_weights_summary)

# opcional: guardar resultados
# write.csv(portfolio_weights_summary, "tables_portfolio_weights_summary.csv", row.names = FALSE)

# Curvas acumuladas
chart.CumReturns(port_ret, main = "Equity Curves (EQW, RP, RPC, MS)",
                 ylab = "", geometric = TRUE, wealth.index = TRUE,
                 legend.loc = "topleft")

# dev.off()

# experimenting with colors
#chart.CumReturns(port_ret,  main = "Equity curves", ylab="", geometric = FALSE, wealth.index = TRUE, 
#                 colorset=tim6equal, legend.loc = "topleft", )

#chart.CumReturns(port_ret,  main = "Equity curves", ylab="", geometric = FALSE, wealth.index = TRUE, 
#                 colorset=rich6equal, legend.loc = "topleft", )

# set risk free rate ; from yahoo finance ^IRX
(rfr <- (0.9556624/100)/252)

ta1 <- table.AnnualizedReturns(port_ret, scale=252, geometric = TRUE,  Rf=rfr)

ta2 <- table.DownsideRisk(port_ret, scale=252,  Rf=rfr, MAR =.00, p=.95)
ta3 <- Omega(port_ret, Rf=rfr)
SortinoRatio(port_ret)

rbind(ta1, ta2, ta3)

#################################################################
# weekly re-balancing
#################################################################

rbp <- c("days")
# rbp <- c("weeks")
# rbp <- c("months")


port_eqw  <- Return.portfolio( y.r_c,   verbose=TRUE,  rebalance_on = rbp)
head (port_eqw$BOP.Weight,22)
head (port_eqw$EOP.Weight,22)
tail (port_eqw$BOP.Weight,22)
tail (port_eqw$EOP.Weight,22)



# port_rp   <- Return.portfolio(y.r, weights=w.rp,verbose=TRUE)
ep <- endpoints(roll_rp, on = rbp)
port_rp   <- Return.portfolio( y.r_c, weights= roll_rp[ep,] ,   verbose=TRUE)
head(roll_rp[ep,],22)
head (port_rp$BOP.Weight,22)
head (port_rp$EOP.Weight,22)
tail(roll_rp[ep,],22)
tail (port_rp$BOP.Weight,22)
tail (port_rp$EOP.Weight,22)


ep <- endpoints(w.mcp_temp, on = rbp)
port_mcp  <- Return.portfolio( y.r_c, weights= w.mcp_temp[ep,]  ,verbose=TRUE)

ep <- endpoints(w.rpp_temp, on = rbp)
port_rpp  <- Return.portfolio( y.r_c, weights= w.rpp_temp[ep,],verbose=TRUE)
head(w.rpp_temp[ep,],22)
head (port_rpp$BOP.Weight,22)
head (port_rpp$EOP.Weight,22)


ep <- endpoints(roll_ms, on = rbp)
port_ms   <- Return.portfolio( y.r_c, weights= roll_ms[ep,],   verbose=TRUE)

port_ret <- cbind(port_eqw$returns, port_rp$returns, port_rpp$returns, port_ms$returns )
colnames(port_ret) <- c("EQW", "RP", "RPC", "MS")
# all portfolios start at same time period
port_ret <- na.omit( port_ret)
head(port_ret)

ta1 <- table.AnnualizedReturns(port_ret, scale=252, geometric = TRUE,  Rf=rfr)
ta2 <- table.DownsideRisk(port_ret, scale=252,  Rf=rfr, MAR =.00, p=.95)
ta3 <- Omega(port_ret, Rf=rfr)
SortinoRatio(port_ret)

rbind(ta1, ta2, ta3)
```

```{r}
#################################################################
# Portfolio summary statistics (rebalance monthly)
#################################################################

rbp <- c("months")  # <<< cambio clave respecto al bloque semanal

# Portafolio Equal Weight (EQW)
port_eqw_m  <- Return.portfolio(y.r_c, verbose = TRUE, rebalance_on = rbp)
head(port_eqw_m$BOP.Weight, 22)
head(port_eqw_m$EOP.Weight, 22)

# Portafolio Risk Parity (RP)
ep_m <- endpoints(roll_rp, on = rbp)
port_rp_m <- Return.portfolio(y.r_c, weights = roll_rp[ep_m, ], verbose = TRUE)
head(roll_rp[ep_m, ], 22)
head(port_rp_m$BOP.Weight, 22)
head(port_rp_m$EOP.Weight, 22)

# Portafolio Minimum Connectedness (MCP)
ep_m <- endpoints(w.mcp_temp, on = rbp)
port_mcp_m <- Return.portfolio(y.r_c, weights = w.mcp_temp[ep_m, ], verbose = TRUE)

# Portafolio Risk Parity Connectedness (RPC)
ep_m <- endpoints(w.rpp_temp, on = rbp)
port_rpp_m <- Return.portfolio(y.r_c, weights = w.rpp_temp[ep_m, ], verbose = TRUE)
head(w.rpp_temp[ep_m, ], 22)
head(port_rpp_m$BOP.Weight, 22)
head(port_rpp_m$EOP.Weight, 22)

# Portafolio Máximo Sharpe (MS)
ep_m <- endpoints(roll_ms, on = rbp)
port_ms_m <- Return.portfolio(y.r_c, weights = roll_ms[ep_m, ], verbose = TRUE)

# Combinar retornos de todos los portafolios
port_ret_m <- cbind(
  port_eqw_m$returns,
  port_rp_m$returns,
  port_rpp_m$returns,
  port_ms_m$returns
)
colnames(port_ret_m) <- c("EQW", "RP", "RPC", "MS")
port_ret_m <- na.omit(port_ret_m)
head(port_ret_m)

# Estadísticas anuales y de riesgo
ta1_m <- table.AnnualizedReturns(port_ret_m, scale = 252, geometric = TRUE, Rf = rfr)
ta2_m <- table.DownsideRisk(port_ret_m, scale = 252, Rf = rfr, MAR = 0.00, p = 0.95)
ta3_m <- Omega(port_ret_m, Rf = rfr)
sr_m  <- SortinoRatio(port_ret_m)

# Consolidar tabla resumen mensual
summary_monthly <- rbind(ta1_m, ta2_m, ta3_m, sr_m)
print(summary_monthly)

# Curva de rendimiento acumulado mensual
chart.CumReturns(
  port_ret_m,
  main = "Equity Curves (Monthly Rebalance)",
  ylab = "",
  geometric = TRUE,
  wealth.index = TRUE,
  legend.loc = "topleft"
)

```




## Connectedness: TVP-VAR
```{r eval=FALSE, include=FALSE}
cat("\nEstimando TVP-VAR Connectedness...\n")
nlag <- 1; nfore <- 20; window.size <- 200
dca <- ConnectednessApproach(y.r,
                             nlag=nlag, nfore=nfore, model="TVP-VAR",
                             window.size=window.size,
                             VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="BayesPrior")))

cat("\n--- Resultados Connectedness ---\n")
gfevd_array <- dca$CT
gfevd_avg <- apply(gfevd_array, c(1,2), mean, na.rm=TRUE)
colnames(gfevd_avg) <- rownames(gfevd_avg) <- symbols
print(round(gfevd_avg,3))

TCI_series <- dca$TCI
TCI_mean <- mean(TCI_series, na.rm=TRUE)
cat("\nPromedio TCI:", round(TCI_mean,2), "\n")
plot(index(TCI_series), coredata(TCI_series), type="l",
     main="Total Connectedness Index (TCI)", xlab="Fecha", ylab="TCI")
abline(h=TCI_mean, col="red", lty=2)

TO_series <- dca$TO; FROM_series <- dca$FROM; NET_series <- dca$NET
TO_avg <- colMeans(TO_series, na.rm=TRUE)
FROM_avg <- colMeans(FROM_series, na.rm=TRUE)
NET_avg <- colMeans(NET_series, na.rm=TRUE)
dir_summary <- data.frame(Variable=symbols, TO_avg, FROM_avg, NET_avg)
cat("\n--- Dirección promedio (TO/FROM/NET) ---\n")
library(dplyr)
dir_summary %>%
  mutate_if(is.numeric, ~round(., 3)) %>%
  print()

pci_obj <- tryCatch({ ConnectednessTable(gfevd_array)$PCI }, error=function(e) gfevd_avg)
cat("\n--- Pairwise Connectedness Index (PCI) promedio ---\n")
print(round(pci_obj,3))

matplot(index(NET_series), NET_series, type='l', lty=1,
        main="NET dynamic (TO - FROM)", xlab="Date", ylab="NET")
legend("topright", legend=symbols, col=1:length(symbols), lty=1, cex=0.8)

```

## Extraer medidas clave (GFEVD, TCI, TO, FROM, NET, PCI) 
```{r eval=FALSE, include=FALSE}
y_r_zoo <- as.zoo(y.r)
w_eq <- rep(1/ncol(y.r), ncol(y.r)); names(w_eq) <- symbols
cov_mat <- cov(coredata(y.r), use="complete.obs")

w_rp <- tryCatch({ riskParityPortfolio(cov_mat)$w },
                 error=function(e){ message("Error RiskParityPortfolio -> EQW usado."); w_eq })
names(w_rp) <- symbols

cat("\nPesos EQW:\n"); print(round(w_eq,3))
cat("\nPesos Risk Parity:\n"); print(round(w_rp,3))

mcp <- tryCatch({
  MinimumConnectednessPortfolio(as.zoo(y.r), pci_obj, statistics="Fisher",
                                method="cumsum", metric="StdDev")
}, error=function(e){ message("Error MCP: ", e$message); NULL })

if(!is.null(mcp)) {
  cat("\nPesos Minimum Connectedness Portfolio (MCP):\n")
  print(round(mcp$portfolio_weights,3))
}

rpp <- tryCatch({
  RiskParityPortfolio(as.zoo(y.r), pci_obj, statistics="Fisher")
}, error=function(e){ message("Error RPC: ", e$message); NULL })

if(!is.null(rpp)) {
  cat("\nPesos Risk-Parity Portfolio (RPC):\n")
  print(round(rpp$portfolio_weights,3))
}
```

## Portafolios: EQW, RiskParity, MinimumConnectednessPortfolio, MS 

```{r eval=FALSE, include=FALSE}
max_sharpe_w <- function(prices_window) {
  tryCatch({
    ret <- as.matrix(prices_window)
    mu <- colMeans(ret, na.rm = TRUE)
    Sigma <- cov(ret, use = "complete.obs")
    N <- length(mu)
    Dmat <- 2 * Sigma; dvec <- rep(0, N)
    Amat <- cbind(rep(1, N), mu)
    bvec <- c(1, 0)
    res <- solve.QP(Dmat, dvec, Amat, bvec=bvec, meq=1)
    w <- pmax(res$solution, 0)
    w / sum(w)
  }, error=function(e) rep(1/ncol(prices_window), ncol(prices_window)))
}

cat("\nCalculando portafolio Max-Sharpe rolling...\n")
wl <- min(252, nrow(y.r))
roll_ms <- rollapply(as.matrix(y.r), width=wl, FUN=function(x) max_sharpe_w(x),
                     by.column=FALSE, align="right")
cat("Rolling Mean-Sharpe listo.\n")

port_ret <- Return.portfolio(y.r, weights=w_eq)
chart.CumReturns(port_ret, main="Curva acumulada EQW", legend.loc="topleft", wealth.index=TRUE)

TCI_dates <- as.Date(rownames(TCI_series))
TCI_xts <- xts(as.numeric(TCI_series), order.by=TCI_dates)
common_idx <- intersect(index(TCI_xts), index(port_ret))
par(mfrow=c(1,2))
plot(index(TCI_xts[common_idx]), coredata(TCI_xts[common_idx]), type="l", main="TCI", ylab="TCI", xlab="Date")
plot(index(port_ret[common_idx,]), coredata(port_ret[common_idx,"portfolio.returns"]),
     type="l", main="EQW Returns", ylab="Return", xlab="Date")

```

## Performance summary and relación con TCI
```{r eval=FALSE, include=FALSE}
rfr_daily <- 0  # tasa libre de riesgo diaria
cat("\n--- Indicadores de desempeño ---\n")

# Métricas clásicas
ta1 <- table.AnnualizedReturns(port_ret, scale = 252, Rf = rfr_daily)
ta2 <- table.DownsideRisk(port_ret, scale = 252, Rf = rfr_daily, MAR = 0, p = .95)
ta3 <- Omega(port_ret, Rf = rfr_daily)

# Unir en una tabla
perf_all <- rbind(ta1, ta2, ta3)
print(round(perf_all, 4))

# --- Correlación entre TCI y retornos del portafolio ------------------------
TCI_dates <- as.Date(rownames(TCI_series))
TCI_xts <- xts(as.numeric(TCI_series), order.by = TCI_dates)
common_idx <- intersect(index(TCI_xts), index(port_ret))

if (length(common_idx) > 10) {
  cors <- cor(coredata(TCI_xts[common_idx]),
              coredata(port_ret[common_idx,]),
              use = "complete.obs")
  cat("\n--- Correlación entre TCI y portafolio(s) ---\n")
  print(round(cors, 4))
}

# --- Gráficos de desempeño --------------------------------------------------
cat("\nMostrando curvas acumuladas de portafolio(s)...\n")
chart.CumReturns(port_ret, main="Equity Curves (Portafolios)",
                 legend.loc="topleft", wealth.index=TRUE)

# --- Gráfico TCI vs Retornos de portafolio ---------------------------------
cat("\nMostrando TCI vs EQW returns...\n")
par(mfrow=c(1,2))
plot(index(TCI_xts[common_idx]), coredata(TCI_xts[common_idx]), type="l",
     main="TCI", ylab="TCI", xlab="Date")
plot(index(port_ret[common_idx,]), coredata(port_ret[common_idx,"portfolio.returns"]),
     type="l", main="EQW Returns", ylab="Return", xlab="Date")
```

```{r eval=FALSE, include=FALSE}
gfevd_array <- dca$CT
gfevd_avg <- apply(gfevd_array, c(1,2), mean, na.rm=TRUE)
colnames(gfevd_avg) <- rownames(gfevd_avg) <- symbols
print(round(gfevd_avg,3))


TCI_series <- dca$TCI
TCI_mean <- mean(TCI_series, na.rm=TRUE)
cat("\nPromedio TCI:", round(TCI_mean,2), "\n")
#plot(index(TCI_series), coredata(TCI_series), type="l", ...)

# PCI (Pairwise Connectedness Index)
pci_obj <- tryCatch({
  ConnectednessTable(gfevd_array)$PCI
}, error=function(e) gfevd_avg)
print(round(pci_obj,3))
```

