library(mFilter)
library(forecast)## Warning: package 'forecast' was built under R version 4.3.3## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(lmtest)## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.3.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3library(tseries)## Warning: package 'tseries' was built under R version 4.3.3library(stats)
library(fBasics)## Warning: package 'fBasics' was built under R version 4.3.3library(grid)
library(openxlsx)## Warning: package 'openxlsx' was built under R version 4.3.3library(dynlm)
library(urca)## Warning: package 'urca' was built under R version 4.3.3library(FinTS)## Warning: package 'FinTS' was built under R version 4.3.2##
## Attaching package: 'FinTS'## The following object is masked from 'package:forecast':
##
## Acflibrary(MTS)##
## Attaching package: 'MTS'## The following object is masked from 'package:FinTS':
##
## apcalibrary(ggrepel)## Warning: package 'ggrepel' was built under R version 4.3.3library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)library(readxl)
library(readxl)
Fondos_de_Renta_Fija_1 <- read_excel("Fondos de Renta Fija_1.xlsx")
summary(Fondos_de_Renta_Fija_1)## Dates CAMDCTA CB Equity RLVALBC CB Equity
## Min. :2016-02-29 00:00:00.00 Min. :10009 Min. :11978
## 1st Qu.:2018-08-15 12:00:00.00 1st Qu.:11539 1st Qu.:13812
## Median :2021-01-31 00:00:00.00 Median :15472 Median :15010
## Mean :2021-01-29 09:40:50.42 Mean :19640 Mean :15683
## 3rd Qu.:2023-07-15 12:00:00.00 3rd Qu.:28061 3rd Qu.:17331
## Max. :2025-12-31 00:00:00.00 Max. :32023 Max. :21690
##
## FCABBPU CB Equity FGLDEEM CB Equity ALICXCA CB Equity CRCCFEC CB Equity
## Min. : 8949 Min. :10881 Min. :12493 Min. :10819
## 1st Qu.: 9562 1st Qu.:13203 1st Qu.:15067 1st Qu.:12424
## Median :10343 Median :14011 Median :17024 Median :14257
## Mean :10750 Mean :14246 Mean :17614 Mean :14180
## 3rd Qu.:12042 3rd Qu.:15109 3rd Qu.:19894 3rd Qu.:15974
## Max. :13104 Max. :18118 Max. :24900 Max. :17447
## NA's :57 NA's :62
## ALIGODB CB Equity ITCOPTC CB Equity PENSIO4 CB Equity ALT120A CB Equity
## Min. :10007 Min. : 9906 Min. :12113 Min. :10002
## 1st Qu.:10635 1st Qu.: 10337 1st Qu.:14507 1st Qu.:10656
## Median :11176 Median : 12421 Median :16392 Median :11448
## Mean :12124 Mean :1174547 Mean :17410 Mean :12070
## 3rd Qu.:13848 3rd Qu.:3624557 3rd Qu.:20180 3rd Qu.:13765
## Max. :15811 Max. :4137827 Max. :24727 Max. :15429
## NA's :37 NA's :13 NA's :57
## FAAPPM1 CB Equity BBVEFEA CB Equity CONSOLI CB Equity FIDUCTA CB Equity
## Min. :119964 Min. :2043 Min. :18862 Min. :24699
## 1st Qu.:134834 1st Qu.:2376 1st Qu.:20640 1st Qu.:28452
## Median :142736 Median :2631 Median :21759 Median :30827
## Mean :148256 Mean :2766 Mean :23265 Mean :32398
## 3rd Qu.:159611 3rd Qu.:3120 3rd Qu.:25836 3rd Qu.:35812
## Max. :193764 Max. :3938 Max. :31023 Max. :45008
## NA's :13
## DEREC26 CB Equity LIQBYRB CB Equity OMEFECF CB Equity FINTERES CB Equity
## Min. :10025 Min. :15493 Min. :2448 Min. :15199
## 1st Qu.:11405 1st Qu.:17901 1st Qu.:2558 1st Qu.:17611
## Median :12766 Median :19420 Median :2558 Median :19297
## Mean :13664 Mean :20324 Mean :2935 Mean :19933
## 3rd Qu.:16016 3rd Qu.:22420 3rd Qu.:3349 3rd Qu.:21926
## Max. :18941 Max. :28181 Max. :4041 Max. :27045
## NA's :37 NA's :15
## ABIERCA CB Equity ITMMKTC CB Equity CAMFVG2 CB Equity CCAPAIS CB Equity
## Min. :14955 Min. :1984 Min. :14055 Min. :12892
## 1st Qu.:16471 1st Qu.:2277 1st Qu.:14454 1st Qu.:14925
## Median :17551 Median :2508 Median :16764 Median :16278
## Mean :18697 Mean :2634 Mean :16851 Mean :16994
## 3rd Qu.:20817 3rd Qu.:2937 3rd Qu.:18857 3rd Qu.:18838
## Max. :25220 Max. :3725 Max. :20491 Max. :23470
## NA's :12 NA's :64
## PARF901 CB Equity FIDUEXC CB Equity RENTFIP CB Equity COVPLT1 CB Equity
## Min. :13396 Min. :13088 Min. :10282 Min. :32844
## 1st Qu.:14778 1st Qu.:15058 1st Qu.:12248 1st Qu.:35905
## Median :15663 Median :16369 Median :14073 Median :37854
## Mean :16503 Mean :17180 Mean :13937 Mean :40191
## 3rd Qu.:18134 3rd Qu.:19019 3rd Qu.:15685 3rd Qu.:44275
## Max. :21537 Max. :23918 Max. :17954 Max. :53303
## NA's :11 NA's :9
## ACCIVAL CB Equity BBVFAMA CB Equity GT10 Govt COLTES
## Min. :5337 Min. :2006 Min. :0.529 Min. : 83.75
## 1st Qu.:6096 1st Qu.:2329 1st Qu.:1.714 1st Qu.: 96.48
## Median :6628 Median :2553 Median :2.503 Median :101.39
## Mean :6904 Mean :2648 Mean :2.688 Mean :101.47
## 3rd Qu.:7561 3rd Qu.:2927 3rd Qu.:3.892 3rd Qu.:107.07
## Max. :9464 Max. :3632 Max. :4.932 Max. :116.28
##
## Dividend Yield of Market
## Min. : 1.925
## 1st Qu.: 2.767
## Median : 3.852
## Mean : 5.255
## 3rd Qu.: 7.533
## Max. :11.752
## PUNTO 1
cols_excluir <- c("Dates", "Dividend Yield of Market", "COLTES", "GT10 Govt")
cols_fondos <- setdiff(names(Fondos_de_Renta_Fija_1), cols_excluir)fondos_Rp <- Fondos_de_Renta_Fija_1 %>%
arrange(Dates) %>%
mutate(across(all_of(cols_fondos), ~ . / lag(.) - 1, .names = "Rp_{.col}")) %>%
select(Dates, starts_with("Rp_")) %>%
slice(-1)fondos_Rlog <- Fondos_de_Renta_Fija_1 %>%
arrange(Dates) %>%
mutate(across(all_of(cols_fondos), ~ log(. / lag(.)), .names = "Rlog_{.col}")) %>%
select(Dates, starts_with("Rlog_")) %>%
slice(-1)retornosprecios_long <- fondos_Rp %>%
select(Dates, starts_with("Rp_")) %>%
pivot_longer(cols = -Dates,
names_to = "fondo",
values_to = "retorno")
retornosprecios_long$Dates <- as.Date(retornosprecios_long$Dates, format = "%Y-%m-%d")
ggplot(retornosprecios_long, aes(x = Dates, y = retorno, color = fondo)) +
geom_line(linewidth = 0.5) +
facet_wrap(~ fondo, scales = "free_y", ncol = 4) +
# Limitar el eje Y para eliminar el efecto de outliers
coord_cartesian(ylim = c(-0.05, 0.05)) + # ajusta según tus datos
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_date(date_breaks = "3 years", date_labels = "%Y") +
theme_minimal(base_size = 9) +
theme(
legend.position = "none",
strip.text = element_text(size = 6.5, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1, size = 6),
axis.text.y = element_text(size = 6),
panel.spacing = unit(0.5, "lines"),
plot.title = element_text(hjust = 0.5, size = 14)
) +
labs(title = "Retornos de precio mensuales por fondo",
x = "Fecha", y = "Retorno")## Warning: Removed 387 rows containing missing values or values outside the scale range
## (`geom_line()`).
retornoslog_long <- fondos_Rlog %>%
select(Dates, starts_with("Rlog_")) %>%
pivot_longer(cols = -Dates,
names_to = "fondo",
values_to = "retorno")
retornoslog_long$Dates <- as.Date(retornoslog_long$Dates, format = "%Y-%m-%d")
ggplot(retornoslog_long, aes(x = Dates, y = retorno, color = fondo)) +
geom_line(linewidth = 0.5) +
facet_wrap(~ fondo, scales = "free_y", ncol = 4) +
coord_cartesian(ylim = c(-0.05, 0.05)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_date(date_breaks = "3 years", date_labels = "%Y") +
theme_minimal(base_size = 9) +
theme(
legend.position = "none",
strip.text = element_text(size = 6.5, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1, size = 6),
axis.text.y = element_text(size = 6),
panel.spacing = unit(0.5, "lines"),
plot.title = element_text(hjust = 0.5, size = 14)
) +
labs(title = "Retornos logaritmicos mensuales por fondo",
x = "Fecha", y = "Retorno")## Warning: Removed 387 rows containing missing values or values outside the scale range
## (`geom_line()`).
#RESPUESTA
#PUNTO 2
cols_fondos_log <- setdiff(names(fondos_Rlog), "Dates")
resultados_adf <- lapply(cols_fondos_log, function(col) {
serie <- na.omit(fondos_Rlog[[col]]) # eliminar NA
if (length(serie) > 10) { # verificar que haya suficientes datos
test <- adf.test(serie)
data.frame(
Fondo = col,
Statistic = round(test$statistic, 4),
P_value = round(test$p.value, 4),
Estacionaria = ifelse(test$p.value < 0.05, "Sí", "No")
)
}
})## Warning in adf.test(serie): p-value smaller than printed p-value## Warning in adf.test(serie): p-value greater than printed p-value## Warning in adf.test(serie): p-value smaller than printed p-value
## Warning in adf.test(serie): p-value smaller than printed p-value
## Warning in adf.test(serie): p-value smaller than printed p-valueresultados_adf <- do.call(rbind, resultados_adf)
print(resultados_adf)## Fondo Statistic P_value Estacionaria
## Dickey-Fuller Rlog_CAMDCTA CB Equity -4.7260 0.0100 Sí
## Dickey-Fuller1 Rlog_RLVALBC CB Equity 0.4907 0.9900 No
## Dickey-Fuller2 Rlog_FCABBPU CB Equity -0.9388 0.9402 No
## Dickey-Fuller3 Rlog_FGLDEEM CB Equity -4.2609 0.0100 Sí
## Dickey-Fuller4 Rlog_ALICXCA CB Equity -1.6396 0.7262 No
## Dickey-Fuller5 Rlog_CRCCFEC CB Equity -3.7582 0.0279 Sí
## Dickey-Fuller6 Rlog_ALIGODB CB Equity -1.6683 0.7116 No
## Dickey-Fuller7 Rlog_ITCOPTC CB Equity -4.8165 0.0100 Sí
## Dickey-Fuller8 Rlog_PENSIO4 CB Equity -1.8628 0.6335 No
## Dickey-Fuller9 Rlog_ALT120A CB Equity -3.5549 0.0445 Sí
## Dickey-Fuller10 Rlog_FAAPPM1 CB Equity -2.0369 0.5613 No
## Dickey-Fuller11 Rlog_BBVEFEA CB Equity -1.9017 0.6174 No
## Dickey-Fuller12 Rlog_CONSOLI CB Equity -1.5730 0.7537 No
## Dickey-Fuller13 Rlog_FIDUCTA CB Equity -1.8292 0.6475 No
## Dickey-Fuller14 Rlog_DEREC26 CB Equity -1.0931 0.9179 No
## Dickey-Fuller15 Rlog_LIQBYRB CB Equity -2.5219 0.3599 No
## Dickey-Fuller16 Rlog_OMEFECF CB Equity -3.8699 0.0182 Sí
## Dickey-Fuller17 Rlog_FINTERES CB Equity -3.1877 0.0933 No
## Dickey-Fuller18 Rlog_ABIERCA CB Equity -1.9996 0.5768 No
## Dickey-Fuller19 Rlog_ITMMKTC CB Equity -2.1807 0.5016 No
## Dickey-Fuller20 Rlog_CAMFVG2 CB Equity -2.3619 0.4288 No
## Dickey-Fuller21 Rlog_CCAPAIS CB Equity -2.0151 0.5703 No
## Dickey-Fuller22 Rlog_PARF901 CB Equity -2.5539 0.3469 No
## Dickey-Fuller23 Rlog_FIDUEXC CB Equity -2.0463 0.5574 No
## Dickey-Fuller24 Rlog_RENTFIP CB Equity -4.3241 0.0100 Sí
## Dickey-Fuller25 Rlog_COVPLT1 CB Equity -2.2882 0.4571 No
## Dickey-Fuller26 Rlog_ACCIVAL CB Equity -2.0894 0.5395 No
## Dickey-Fuller27 Rlog_BBVFAMA CB Equity -2.2351 0.4790 Nocols_fondos_log <- setdiff(names(fondos_Rlog), "Dates")
# Aplicar PP a cada fondo
resultados_pp <- lapply(cols_fondos_log, function(col) {
serie <- na.omit(fondos_Rlog[[col]])
if (length(serie) > 10) {
test <- pp.test(serie)
data.frame(
Fondo = col,
Statistic = round(test$statistic, 4),
P_value = round(test$p.value, 4),
Estacionaria = ifelse(test$p.value < 0.05, "Sí", "No")
)
}
})## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in pp.test(serie): p-value greater than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value
## Warning in pp.test(serie): p-value smaller than printed p-value# Convertir a DataFrame
resultados_pp <- do.call(rbind, resultados_pp)
print(resultados_pp)## Fondo Statistic P_value Estacionaria
## Dickey-Fuller Z(alpha) Rlog_CAMDCTA CB Equity -116.7498 0.0100 Sí
## Dickey-Fuller Z(alpha)1 Rlog_RLVALBC CB Equity 9.5543 0.9900 No
## Dickey-Fuller Z(alpha)2 Rlog_FCABBPU CB Equity -25.1035 0.0141 Sí
## Dickey-Fuller Z(alpha)3 Rlog_FGLDEEM CB Equity -100.9082 0.0100 Sí
## Dickey-Fuller Z(alpha)4 Rlog_ALICXCA CB Equity -51.8541 0.0100 Sí
## Dickey-Fuller Z(alpha)5 Rlog_CRCCFEC CB Equity -54.7283 0.0100 Sí
## Dickey-Fuller Z(alpha)6 Rlog_ALIGODB CB Equity -34.8059 0.0100 Sí
## Dickey-Fuller Z(alpha)7 Rlog_ITCOPTC CB Equity -116.9526 0.0100 Sí
## Dickey-Fuller Z(alpha)8 Rlog_PENSIO4 CB Equity -6.9585 0.7131 No
## Dickey-Fuller Z(alpha)9 Rlog_ALT120A CB Equity -79.3908 0.0100 Sí
## Dickey-Fuller Z(alpha)10 Rlog_FAAPPM1 CB Equity -38.6070 0.0100 Sí
## Dickey-Fuller Z(alpha)11 Rlog_BBVEFEA CB Equity -47.4114 0.0100 Sí
## Dickey-Fuller Z(alpha)12 Rlog_CONSOLI CB Equity -17.5615 0.0992 No
## Dickey-Fuller Z(alpha)13 Rlog_FIDUCTA CB Equity -16.6851 0.1505 No
## Dickey-Fuller Z(alpha)14 Rlog_DEREC26 CB Equity -5.5221 0.7936 No
## Dickey-Fuller Z(alpha)15 Rlog_LIQBYRB CB Equity -57.0299 0.0100 Sí
## Dickey-Fuller Z(alpha)16 Rlog_OMEFECF CB Equity -100.0044 0.0100 Sí
## Dickey-Fuller Z(alpha)17 Rlog_FINTERES CB Equity -164.7440 0.0100 Sí
## Dickey-Fuller Z(alpha)18 Rlog_ABIERCA CB Equity -49.7339 0.0100 Sí
## Dickey-Fuller Z(alpha)19 Rlog_ITMMKTC CB Equity -22.9842 0.0310 Sí
## Dickey-Fuller Z(alpha)20 Rlog_CAMFVG2 CB Equity -16.1314 0.1433 No
## Dickey-Fuller Z(alpha)21 Rlog_CCAPAIS CB Equity -23.9163 0.0241 Sí
## Dickey-Fuller Z(alpha)22 Rlog_PARF901 CB Equity -54.9807 0.0100 Sí
## Dickey-Fuller Z(alpha)23 Rlog_FIDUEXC CB Equity -20.8628 0.0492 Sí
## Dickey-Fuller Z(alpha)24 Rlog_RENTFIP CB Equity -108.5666 0.0100 Sí
## Dickey-Fuller Z(alpha)25 Rlog_COVPLT1 CB Equity -34.8522 0.0100 Sí
## Dickey-Fuller Z(alpha)26 Rlog_ACCIVAL CB Equity -26.2242 0.0151 Sí
## Dickey-Fuller Z(alpha)27 Rlog_BBVFAMA CB Equity -34.3275 0.0100 Sícols_fondos_log <- setdiff(names(fondos_Rlog), "Dates")
resultados_integracion <- lapply(cols_fondos_log, function(col) {
serie <- na.omit(fondos_Rlog[[col]])
if (length(serie) > 10) {
# Prueba en niveles (retornos log)
adf_nivel <- adf.test(serie)
pp_nivel <- pp.test(serie)
# Prueba en primera diferencia
adf_diff <- adf.test(diff(serie))
pp_diff <- pp.test(diff(serie))
# Determinar orden de integración
estacionaria_nivel <- adf_nivel$p.value < 0.05 & pp_nivel$p.value < 0.05
estacionaria_diff <- adf_diff$p.value < 0.05 & pp_diff$p.value < 0.05
orden <- ifelse(estacionaria_nivel, "I(0)",
ifelse(estacionaria_diff, "I(1)", "I(2)+"))
data.frame(
Fondo = col,
ADF_nivel = round(adf_nivel$p.value, 4),
PP_nivel = round(pp_nivel$p.value, 4),
ADF_diff = round(adf_diff$p.value, 4),
PP_diff = round(pp_diff$p.value, 4),
Orden_Integracion = orden
)
}
})## Warning in adf.test(serie): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(serie): p-value greater than printed p-value## Warning in pp.test(serie): p-value greater than printed p-value## Warning in adf.test(diff(serie)): p-value greater than printed p-value## Warning in pp.test(diff(serie)): p-value greater than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(serie): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(serie): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value
## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value
## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(serie): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(serie): p-value smaller than printed p-value## Warning in adf.test(diff(serie)): p-value smaller than printed p-value## Warning in pp.test(diff(serie)): p-value smaller than printed p-valueresultados_integracion <- do.call(rbind, resultados_integracion)
print(resultados_integracion)## Fondo ADF_nivel PP_nivel ADF_diff PP_diff
## 1 Rlog_CAMDCTA CB Equity 0.0100 0.0100 0.0100 0.01
## 2 Rlog_RLVALBC CB Equity 0.9900 0.9900 0.9900 0.99
## 3 Rlog_FCABBPU CB Equity 0.9402 0.0141 0.6132 0.01
## 4 Rlog_FGLDEEM CB Equity 0.0100 0.0100 0.0100 0.01
## 5 Rlog_ALICXCA CB Equity 0.7262 0.0100 0.0100 0.01
## 6 Rlog_CRCCFEC CB Equity 0.0279 0.0100 0.0100 0.01
## 7 Rlog_ALIGODB CB Equity 0.7116 0.0100 0.0100 0.01
## 8 Rlog_ITCOPTC CB Equity 0.0100 0.0100 0.0100 0.01
## 9 Rlog_PENSIO4 CB Equity 0.6335 0.7131 0.2054 0.01
## 10 Rlog_ALT120A CB Equity 0.0445 0.0100 0.0100 0.01
## 11 Rlog_FAAPPM1 CB Equity 0.5613 0.0100 0.0100 0.01
## 12 Rlog_BBVEFEA CB Equity 0.6174 0.0100 0.0100 0.01
## 13 Rlog_CONSOLI CB Equity 0.7537 0.0992 0.0100 0.01
## 14 Rlog_FIDUCTA CB Equity 0.6475 0.1505 0.0100 0.01
## 15 Rlog_DEREC26 CB Equity 0.9179 0.7936 0.0777 0.01
## 16 Rlog_LIQBYRB CB Equity 0.3599 0.0100 0.0100 0.01
## 17 Rlog_OMEFECF CB Equity 0.0182 0.0100 0.0100 0.01
## 18 Rlog_FINTERES CB Equity 0.0933 0.0100 0.0100 0.01
## 19 Rlog_ABIERCA CB Equity 0.5768 0.0100 0.0100 0.01
## 20 Rlog_ITMMKTC CB Equity 0.5016 0.0310 0.0100 0.01
## 21 Rlog_CAMFVG2 CB Equity 0.4288 0.1433 0.0100 0.01
## 22 Rlog_CCAPAIS CB Equity 0.5703 0.0241 0.0100 0.01
## 23 Rlog_PARF901 CB Equity 0.3469 0.0100 0.0100 0.01
## 24 Rlog_FIDUEXC CB Equity 0.5574 0.0492 0.0100 0.01
## 25 Rlog_RENTFIP CB Equity 0.0100 0.0100 0.0100 0.01
## 26 Rlog_COVPLT1 CB Equity 0.4571 0.0100 0.0100 0.01
## 27 Rlog_ACCIVAL CB Equity 0.5395 0.0151 0.0100 0.01
## 28 Rlog_BBVFAMA CB Equity 0.4790 0.0100 0.0100 0.01
## Orden_Integracion
## 1 I(0)
## 2 I(2)+
## 3 I(2)+
## 4 I(0)
## 5 I(1)
## 6 I(0)
## 7 I(1)
## 8 I(0)
## 9 I(2)+
## 10 I(0)
## 11 I(1)
## 12 I(1)
## 13 I(1)
## 14 I(1)
## 15 I(2)+
## 16 I(1)
## 17 I(0)
## 18 I(1)
## 19 I(1)
## 20 I(1)
## 21 I(1)
## 22 I(1)
## 23 I(1)
## 24 I(1)
## 25 I(0)
## 26 I(1)
## 27 I(1)
## 28 I(1)#RESPUESTA
#PUNTO 3
cols_fondos_log <- setdiff(names(fondos_Rlog), "Dates")
resultados_arima <- lapply(cols_fondos_log, function(col) {
serie <- na.omit(fondos_Rlog[[col]])
modelo <- auto.arima(serie,
seasonal = FALSE,
stepwise = FALSE,
approximation = FALSE)
data.frame(
Fondo = col,
p = arimaorder(modelo)[1],
d = arimaorder(modelo)[2],
q = arimaorder(modelo)[3],
AIC = round(AIC(modelo), 4),
BIC = round(BIC(modelo), 4),
Modelo = paste0("ARIMA(", paste(arimaorder(modelo), collapse = ","), ")")
)
})
resultados_arima <- do.call(rbind, resultados_arima)
print(resultados_arima)## Fondo p d q AIC BIC Modelo
## p Rlog_CAMDCTA CB Equity 0 0 0 -189.5430 -186.7723 ARIMA(0,0,0)
## p1 Rlog_RLVALBC CB Equity 0 0 0 -442.8361 -440.0654 ARIMA(0,0,0)
## p2 Rlog_FCABBPU CB Equity 1 0 0 -241.3448 -237.1230 ARIMA(1,0,0)
## p3 Rlog_FGLDEEM CB Equity 0 0 0 -596.0304 -590.4890 ARIMA(0,0,0)
## p4 Rlog_ALICXCA CB Equity 3 1 2 -1100.1997 -1083.6267 ARIMA(3,1,2)
## p5 Rlog_CRCCFEC CB Equity 0 0 0 -157.1312 -155.1059 ARIMA(0,0,0)
## p6 Rlog_ALIGODB CB Equity 3 1 0 -679.2893 -669.7611 ARIMA(3,1,0)
## p7 Rlog_ITCOPTC CB Equity 0 0 0 198.5221 201.2928 ARIMA(0,0,0)
## p8 Rlog_PENSIO4 CB Equity 5 1 0 -1240.0889 -1224.2226 ARIMA(5,1,0)
## p9 Rlog_ALT120A CB Equity 1 0 0 -339.1935 -332.8608 ARIMA(1,0,0)
## p10 Rlog_FAAPPM1 CB Equity 0 1 2 -1037.5442 -1029.2577 ARIMA(0,1,2)
## p11 Rlog_BBVEFEA CB Equity 0 1 1 -1058.1204 -1052.5961 ARIMA(0,1,1)
## p12 Rlog_CONSOLI CB Equity 0 1 2 -994.6221 -986.6890 ARIMA(0,1,2)
## p13 Rlog_FIDUCTA CB Equity 4 1 0 -1126.3671 -1112.5562 ARIMA(4,1,0)
## p14 Rlog_DEREC26 CB Equity 0 1 1 -879.2411 -874.4770 ARIMA(0,1,1)
## p15 Rlog_LIQBYRB CB Equity 0 1 1 -1005.5018 -999.9775 ARIMA(0,1,1)
## p16 Rlog_OMEFECF CB Equity 0 1 1 -659.4894 -654.2394 ARIMA(0,1,1)
## p17 Rlog_FINTERES CB Equity 0 1 2 -832.2350 -823.9485 ARIMA(0,1,2)
## p18 Rlog_ABIERCA CB Equity 0 1 3 -936.4173 -925.8015 ARIMA(0,1,3)
## p19 Rlog_ITMMKTC CB Equity 0 1 2 -1102.0212 -1093.7347 ARIMA(0,1,2)
## p20 Rlog_CAMFVG2 CB Equity 4 1 0 -450.5275 -440.6761 ARIMA(4,1,0)
## p21 Rlog_CCAPAIS CB Equity 0 1 2 -1091.6472 -1083.3606 ARIMA(0,1,2)
## p22 Rlog_PARF901 CB Equity 1 0 2 -799.6697 -786.3056 ARIMA(1,0,2)
## p23 Rlog_FIDUEXC CB Equity 0 1 2 -1106.6042 -1098.3176 ARIMA(0,1,2)
## p24 Rlog_RENTFIP CB Equity 0 0 4 -821.0437 -804.4196 ARIMA(0,0,4)
## p25 Rlog_COVPLT1 CB Equity 4 1 0 -959.4971 -946.0864 ARIMA(4,1,0)
## p26 Rlog_ACCIVAL CB Equity 2 1 3 -1097.4743 -1080.9012 ARIMA(2,1,3)
## p27 Rlog_BBVFAMA CB Equity 0 1 2 -1032.2324 -1023.9459 ARIMA(0,1,2)#PUNTO 4
# 1. Crear DataFrame con fechas, Rf y Rm
datos_mercado <- Fondos_de_Renta_Fija_1 %>%
arrange(Dates) %>%
mutate(
Rf = `GT10 Govt` / 100 / 12,
Rm = log(COLTES / lag(COLTES)) + (`Dividend Yield of Market` / 100 / 12)
) %>%
select(Dates, Rf, Rm) %>%
slice(-1)
# 2. Unir por fecha con los retornos logarítmicos
fondos_CAPM <- fondos_Rlog %>%
left_join(datos_mercado, by = "Dates")
# 3. Columnas de los fondos
cols_fondos_log <- setdiff(names(fondos_Rlog), "Dates")
# 4. Correr CAPM para cada fondo
resultados_capm <- lapply(cols_fondos_log, function(col) {
datos <- fondos_CAPM %>%
select(Dates, retorno = all_of(col), Rf, Rm) %>%
na.omit() %>%
mutate(
exceso_fondo = retorno - Rf,
exceso_mercado = Rm - Rf
)
modelo <- lm(exceso_fondo ~ exceso_mercado, data = datos)
data.frame(
Fondo = col,
Alpha = round(coef(modelo)[1], 3),
Beta = round(coef(modelo)[2], 3),
R2 = round(summary(modelo)$r.squared, 4),
P_Alpha = round(summary(modelo)$coefficients[1,4], 4),
P_Beta = round(summary(modelo)$coefficients[2,4], 4)
)
})
# 5. Consolidar resultados
resultados_capm <- do.call(rbind, resultados_capm)
print(resultados_capm)## Fondo Alpha Beta R2 P_Alpha P_Beta
## (Intercept) Rlog_CAMDCTA CB Equity -0.007 0.494 0.0069 0.4630 0.3721
## (Intercept)1 Rlog_RLVALBC CB Equity -0.001 0.051 0.0006 0.8398 0.7889
## (Intercept)2 Rlog_FCABBPU CB Equity -0.003 0.774 0.1405 0.4806 0.0029
## (Intercept)3 Rlog_FGLDEEM CB Equity 0.000 0.659 0.3900 0.9400 0.0000
## (Intercept)4 Rlog_ALICXCA CB Equity 0.003 0.048 0.1103 0.0000 0.0002
## (Intercept)5 Rlog_CRCCFEC CB Equity -0.001 0.731 0.0390 0.8576 0.1446
## (Intercept)6 Rlog_ALIGODB CB Equity 0.003 0.087 0.1675 0.0000 0.0001
## (Intercept)7 Rlog_ITCOPTC CB Equity -0.050 0.756 0.0006 0.3351 0.7919
## (Intercept)8 Rlog_PENSIO4 CB Equity 0.004 0.014 0.0342 0.0000 0.0588
## (Intercept)9 Rlog_ALT120A CB Equity 0.004 -0.001 0.0000 0.0291 0.9924
## (Intercept)10 Rlog_FAAPPM1 CB Equity 0.002 0.095 0.2426 0.0000 0.0000
## (Intercept)11 Rlog_BBVEFEA CB Equity 0.003 0.074 0.1864 0.0000 0.0000
## (Intercept)12 Rlog_CONSOLI CB Equity 0.002 0.093 0.2853 0.0000 0.0000
## (Intercept)13 Rlog_FIDUCTA CB Equity 0.003 0.079 0.2071 0.0000 0.0000
## (Intercept)14 Rlog_DEREC26 CB Equity 0.005 0.044 0.1961 0.0000 0.0000
## (Intercept)15 Rlog_LIQBYRB CB Equity 0.003 0.094 0.2152 0.0000 0.0000
## (Intercept)16 Rlog_OMEFECF CB Equity 0.003 0.001 0.0000 0.0077 0.9884
## (Intercept)17 Rlog_FINTERES CB Equity 0.002 0.071 0.0329 0.0003 0.0494
## (Intercept)18 Rlog_ABIERCA CB Equity 0.002 0.081 0.1908 0.0000 0.0000
## (Intercept)19 Rlog_ITMMKTC CB Equity 0.003 0.085 0.2379 0.0000 0.0000
## (Intercept)20 Rlog_CAMFVG2 CB Equity 0.004 0.158 0.3450 0.0000 0.0000
## (Intercept)21 Rlog_CCAPAIS CB Equity 0.003 0.100 0.3180 0.0000 0.0000
## (Intercept)22 Rlog_PARF901 CB Equity 0.002 0.193 0.2923 0.0009 0.0000
## (Intercept)23 Rlog_FIDUEXC CB Equity 0.003 0.085 0.2306 0.0000 0.0000
## (Intercept)24 Rlog_RENTFIP CB Equity 0.002 0.199 0.2075 0.0053 0.0000
## (Intercept)25 Rlog_COVPLT1 CB Equity 0.002 0.100 0.2359 0.0000 0.0000
## (Intercept)26 Rlog_ACCIVAL CB Equity 0.002 0.092 0.2875 0.0000 0.0000
## (Intercept)27 Rlog_BBVFAMA CB Equity 0.002 0.125 0.3706 0.0000 0.0000