{"id":800,"date":"2020-08-24T16:38:07","date_gmt":"2020-08-24T16:38:07","guid":{"rendered":"https:\/\/ricovictor.com\/?p=800"},"modified":"2026-01-29T22:07:25","modified_gmt":"2026-01-29T22:07:25","slug":"modelos-dcc-garchcorrelacion-dinamica-condicional","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/","title":{"rendered":"Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional)"},"content":{"rendered":"\t\t
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Los modelos DCC_GARCH<\/strong>, son un tipo de modelos econom\u00e9tricos<\/a> que se pueden usar para medir,modelar y predecir la volatilidad<\/strong> en las series de manera din\u00e1mica<\/strong>.<\/p>

Sabemos que la volatilidad expresada estad\u00edsticamente por la varianza es un valor fijo<\/strong>,o un momento poblacional constante<\/strong>. Mediante estos modelos podemos generar una serie de tiempo<\/strong> que nos ayude a su estudio<\/p>

Son una evoluci\u00f3n de los modelos de CCC<\/strong>, y nos pueden permitir hacer predicciones tanto de la varianza como de la convarianza y correlaci\u00f3n cuando tenemos varias series.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Al incluir la especificaci\u00f3n GARCH<\/strong> lo que se busca es que existan puntos de equilibrio central de manera que se hace que la volatilidad tienda a un equilibrio.<\/strong><\/p>

Como su nombre indica es correlaci\u00f3n porque calcularemos el coeficiente de correlaci\u00f3n de Pearson<\/strong> de cada par de rentabilidades, condicional<\/strong> porque primero estimaremos un modelo Garch de cada rentabilidad y Din\u00e1mica<\/strong> porque el coeficiente de correlaci\u00f3n ser\u00e1 dinamico en el tiempo. Es decir, variar\u00e1.<\/p>

Aqu\u00ed tienes un tutorial completo haciendo un ejemplo en R studio.M\u00e1s abajo en el post dejo el c\u00f3digo en R usado para que lo copies si lo deseas.<\/p>

.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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CODIGO VIDEO\u00a0<\/strong><\/p>


###Video DCC – GARCH<\/p>


## CCC<\/p>

# Sirven par modelar,medir y predecir la volatillidad de forma
# de manera din\u00e1mica y multivariada<\/p>

#SOn una evoluci\u00f3n de los CCC y al usar Garch encontramos
# un punto de equilibrio constante<\/p>

#install.packages(\u00abquantmod\u00bb)
library(quantmod)
#install.packages(\u00abtseries\u00bb)
library(tseries)
#install.packages(\u00abfImport\u00bb)
library(fImport)
#install.packages(\u00abrugarch\u00bb) #para ugarchspec
library(rugarch) #rugarch: a package for univariate GARCH fitting, simulation and forecast
#install.packages(\u00abrmgarch\u00bb)
library(rmgarch) #dccspec
#install.packages(\u00abccgarch\u00bb)
library(ccgarch)<\/p>

#install.packages(\u00abfPortfolio\u00bb)
library(fPortfolio)
#install.packages(\u00abgtools)
library(gtools)<\/p>


getSymbols(\u00abEURGBP=X\u00bb,src=\u00bbyahoo\u00bb,from = \u00ab2017-02-18\u00bb)
getSymbols(\u00abEURUSD=X\u00bb,src=\u00bbyahoo\u00bb,from = \u00ab2017-02-18\u00bb)
getSymbols(\u00abGBPCHF=X\u00bb,src=\u00bbyahoo\u00bb,from = \u00ab2017-02-18\u00bb)
getSymbols(\u00abNZDCAD=X\u00bb,src=\u00bbyahoo\u00bb,from = \u00ab2017-02-18\u00bb)<\/p>

EURGBP= `EURGBP=X`[,4]<\/p>

EURUSD= `EURUSD=X`[,4]<\/p>

GBPCHF= `GBPCHF=X`[,4]
NZDCAD= `NZDCAD=X`[,4]<\/p>

FX1=cbind(EURUSD,EURGBP)<\/p>

#Caluclamos los retornos y x 100 para calcularlos en %<\/p>

FX1=returns(FX1)
FX1=100*FX1[,1:ncol(FX1)]
FX<-as.timeSeries(FX1)
FX<-na.omit(FX)
head(FX)<\/p>

apply(FX,2,mean)
mu<-matrix(apply(FX,2,mean))
mu1<-t(mu) #media simple Esta media podr\u00eda calcularse por el metodo de rendimiento simple
plot(FX)
head(FX)<\/p>

##
#Asumimos una distribuci\u00f3n t-student multivariada\/ Ver Normal multivariada<\/p>

dis_garch11<-ugarchspec(mean.model = list(armaOrder = c(0,0), include.mean=F),
variance.model = list(garchOrder = c(1,1),model = \u00absGARCH\u00bb))<\/p>

#DCC-GARCH — VARIAS DISTRIBUIONES<\/p>

DCC_garch11<-dccspec(uspec = multispec(replicate(2, dis_garch11)),
dccOrder = c(1,1),distribution = \u00abmvt\u00bb)<\/p>

###2 porque son 2 serie<\/p>


# APLICAMOS MODELO A NUESTRA TABLA<\/p>

DCC_garch11_mod<-dccfit(DCC_garch11, data = FX) # FX aqu\u00ed le fijamos el modelo
DCC_garch11_mod<\/p>

AICdcc<-infocriteria(DCC_garch11_mod)[1]
AICdcc<\/p>

plot(DCC_garch11_mod)<\/p>


#################<\/p>

#sacar las matrices de correlacion y covarianzas<\/p>

DCC_garch11_mod@mfit$R
DCC_garch11_mod@mfit$H<\/p>

gf_cov<-(DCC_garch11_mod@mfit$H[1:1,2:2,])
gf_cov
plot(gf_cov,type=\u00bbl\u00bb)<\/p>


### Predicciones<\/p>

#Predicci\u00f3n con dcc. Est\u00e1 por defecto 1 pero se puede poner las que se desee<\/p>

Prediccion<-(dccforecast(DCC_garch11_mod))
Prediccion<\/p>

#n.ahead=100<\/p>

Prediccion_50<-(dccforecast(DCC_garch11_mod,n.ahead=30))
Prediccion_50
plot(Prediccion_50)<\/p>


pred_cov<-matrix(Prediccion@mforecast$H[[1]],nrow=2)#Matriz covarianzas pred
pred_cov<\/p>

pred_cor<-matrix(Prediccion@mforecast$R[[1]],nrow=2)#Matriz correlaciones
pred_cor<\/p>

#Prediccion@mforecast<\/p>

#1 fijar los modelos univariados garch
# 2 establecer el modelo dcc y la distribuci\u00f3n multivariada
# 3 fijar el modelo sobre nuesta tabla de rentabilidades<\/p>

# estudiar la volatilidad
# predicciones<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tAprende Arbitraje Estad\u00edstico<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tCurso GRATIS Multicointegraci\u00f3n en FOREX<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Los modelos DCC_GARCH, son un tipo de modelos econom\u00e9tricos que se pueden usar para medir,modelar y predecir la volatilidad en las series de manera din\u00e1mica. Sabemos que la volatilidad expresada estad\u00edsticamente por la varianza es un valor fijo,o un momento poblacional constante. Mediante estos modelos podemos generar una serie de tiempo que nos ayude a …<\/p>\n

Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional)<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":1,"featured_media":801,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[],"yoast_head":"\nModelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional) - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional) - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"Los modelos DCC_GARCH, son un tipo de modelos econom\u00e9tricos que se pueden usar para medir,modelar y predecir la volatilidad en las series de manera din\u00e1mica. Sabemos que la volatilidad expresada estad\u00edsticamente por la varianza es un valor fijo,o un momento poblacional constante. 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Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/","og_locale":"es_ES","og_type":"article","og_title":"Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional) - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas","og_description":"Los modelos DCC_GARCH, son un tipo de modelos econom\u00e9tricos que se pueden usar para medir,modelar y predecir la volatilidad en las series de manera din\u00e1mica. Sabemos que la volatilidad expresada estad\u00edsticamente por la varianza es un valor fijo,o un momento poblacional constante. Mediante estos modelos podemos generar una serie de tiempo que nos ayude a … Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional) Leer m\u00e1s »","og_url":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/","og_site_name":"V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas","article_author":"https:\/\/www.facebook.com\/VictorRicoA\/","article_published_time":"2020-08-24T16:38:07+00:00","article_modified_time":"2026-01-29T22:07:25+00:00","og_image":[{"width":640,"height":480,"url":"https:\/\/ricovictor.com\/wp-content\/uploads\/2020\/08\/fractal-1076735_640.jpg","type":"image\/jpeg"}],"author":"Victor A.Rico","twitter_card":"summary_large_image","twitter_creator":"@https:\/\/www.twitter.com\/RicoVictor_","twitter_misc":{"Escrito por":"Victor A.Rico","Tiempo de lectura":"3 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/#article","isPartOf":{"@id":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/"},"author":{"name":"Victor A.Rico","@id":"https:\/\/ricovictor.com\/#\/schema\/person\/019a6d632b4c12d438e84d000f68ae38"},"headline":"Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional)","datePublished":"2020-08-24T16:38:07+00:00","dateModified":"2026-01-29T22:07:25+00:00","mainEntityOfPage":{"@id":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/"},"wordCount":625,"commentCount":21,"publisher":{"@id":"https:\/\/ricovictor.com\/#organization"},"articleSection":["Econometr\u00eda y Estad\u00edstica"],"inLanguage":"es","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/","url":"https:\/\/ricovictor.com\/index.php\/2020\/08\/24\/modelos-dcc-garchcorrelacion-dinamica-condicional\/","name":"Modelos DCC GARCH(Correlaci\u00f3n Din\u00e1mica Condicional) - V\u00edctor A. 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