{"id":905,"date":"2020-10-17T19:51:06","date_gmt":"2020-10-17T19:51:06","guid":{"rendered":"https:\/\/ricovictor.com\/?page_id=905"},"modified":"2026-01-25T16:26:30","modified_gmt":"2026-01-25T16:26:30","slug":"carteras-con-copulas-matematicas","status":"publish","type":"page","link":"https:\/\/ricovictor.com\/index.php\/carteras-con-copulas-matematicas\/","title":{"rendered":"Modelo de carteras con c\u00f3pulas matem\u00e1ticas"},"content":{"rendered":"\t\t
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WEBINAR GRATIS - CARTERAS DE INVERSI\u00d3N

DCC-GARCH COPULAS<\/h2>\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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\ud83d\udc68\u200d\ud83d\udcbb Los Modelos DCC-GARCH-C\u00f3pulas<\/strong> pueden ser usados para crear carteras de inversi\u00f3n en diferentes activos financieros o tambi\u00e9n para hacer simulaciones a partir de ellos.<\/p>\n

La idea con estos modelos es conjuntar los modelos de volatilidad condicional<\/strong> por un lado con las c\u00f3pulas por otro,todo ello permitiendo crear una estructura que nos permita medir y simular el riesgo de manera multidimensional<\/strong>.<\/p>\n

Aqu\u00ed tienes el Webinar Completo donde explico u m\u00e9todo para crear carteras usando estos modelos \ud83d\udc47\ud83d\udc47<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u00a0<\/h4>

CODIGO R DEL WEBINAR<\/strong><\/h4>

### CLASE CARTERAS VIDEO DCC-GARCH- COPULAS<\/p>

## INDICES- 10<\/p>

#### CODIGO CARTERA INDICES #######
#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)
#install.packages(\u00abHAC\u00bb)
library(HAC)#
#install.packages(\u00abexpm\u00bb)
library(expm)
#install.packages(\u00abfPortfolio\u00bb)
library(fPortfolio)
#install.packages(\u00abgtools)
library(gtools)<\/p>

#?get.hist.quote<\/p>

CAC40=get.hist.quote(\u00ab^FCHI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
DAX=get.hist.quote(\u00ab^GDAXI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
#FTSEMIB=get.hist.quote(\u00abFTSEMIB.MI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-03-13″,end=\u00bb2017-03-13\u00bb)
FTSE=get.hist.quote(\u00ab^FTSE\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
SWISS=get.hist.quote(\u00ab^SSMI\u00bb,quote=\u00bbClose\u00bb,start =\u00bb2016-06-04″,end=\u00bb2017-06-04″)
IBEX=get.hist.quote(\u00ab^IBEX\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
BELG=get.hist.quote(\u00ab^BFX\u00bb,quote=\u00bbClose\u00bb,start =\u00bb2016-06-04″,end=\u00bb2017-06-04″)
DOW=get.hist.quote(\u00ab^DJI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
NASDAQ=get.hist.quote(\u00ab^NDX\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
NIKKEI=get.hist.quote(\u00ab^N225″,quote=\u00bbClose\u00bb,start =\u00bb2016-06-04″,end=\u00bb2017-06-04″)
SINGAPUR=get.hist.quote(\u00ab^STI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
HONG=get.hist.quote(\u00ab^HSI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
HONG1=get.hist.quote(\u00ab^HSI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
SP500=get.hist.quote(\u00ab^GSPC\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
STOXX50=get.hist.quote(\u00ab^STOXX50E\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
#HOLANDA=get.hist.quote(\u00ab^STOXX50E\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-03-28″,end=\u00bb2017-03-28\u00bb)
HOLANDA<- get.hist.quote(\u00ab^AEX\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
#getSymbols(\u00abEURUSD=X\u00bb,src=\u00bbyahoo\u00bb,from = \u00ab2016-06-04\u00bb)
#getSymbols(\u00abCADCHF=X\u00bb,src=\u00bbyahoo\u00bb,from = \u00ab2016-06-04\u00bb)<\/p>

CAC40=getSymbols(\u00ab^FCHI\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2019-10-06\u00bb)
CAC40= FCHI[,4]
getSymbols(\u00ab^GDAXI\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2019-10-06\u00bb)
head(GDAXI)
DAX= GDAXI[,4]<\/p>

#FTSEMIB=get.hist.quote(\u00abFTSEMIB.MI\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-03-13″,end=\u00bb2017-03-13\u00bb)
#FTSE=getSymbols(\u00ab^FTSE\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)<\/p>

getSymbols(\u00ab^SSMI\u00bb,quote=\u00bbClose\u00bb,from =\u00bb2019-10-06″)
SWISS = SSMI[,4]<\/p>

getSymbols(\u00ab^IBEX\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2019-10-06\u00bb)
IBEX= IBEX[,4]
#BELG=get.hist.quote(\u00ab^BFX\u00bb,quote=\u00bbClose\u00bb,start =\u00bb2016-06-04″,end=\u00bb2017-06-04″)<\/p>

getSymbols(\u00ab^DJI\u00bb,quote=\u00bbClose\u00bb,from =\u00bb2019-10-06″)
DOW= DJI[,4]
getSymbols(\u00ab^NDX\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2019-10-06\u00bb)
NASDAQ=NDX[,4]
getSymbols(\u00ab^N225″,quote=\u00bbClose\u00bb,from =\u00bb2019-10-06″)
NIKKEI=N225[,4]
getSymbols(\u00ab^STI\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2019-03-04\u00bb)
SINGAPUR=STI[,4]
getSymbols(\u00ab^HSI\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2019-10-06\u00bb)
HONG=HSI[,4]
plot(HONG,type=\u00bbl\u00bb)
HONG1=getSymbols(\u00ab^HSI\u00bb,quote=\u00bbClose\u00bb,from =\u00bb2019-10-06″)<\/p>

getSymbols(\u00ab^AEX\u00bb,quote=\u00bbClose\u00bb,from =\u00bb2019-10-06″)
HOLANDA=AEX[,4]<\/p>

#SP500=get.hist.quote(\u00ab^GSPC\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
#STOXX50=get.hist.quote(\u00ab^STOXX50E\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-06-04″,end=\u00bb2017-06-04\u00bb)
#HOLANDA=get.hist.quote(\u00ab^STOXX50E\u00bb,quote=\u00bbClose\u00bb,start = \u00ab2016-03-28″,end=\u00bb2017-03-28\u00bb)<\/p>

#X<-timeSequence(from=\u00bb2017-04-18″,to =\u00bb2017-04-18″,by=\u00bbday\u00bb)
#X
#X2<-23924.54
#P<-timeSeries(X2,X)
#P
#cHONG<-rbind(HONG,P,HONG1)
#plot(cHONG,type=\u00bbl\u00bb)<\/p>

#1. Elegir 10 indicies por independecia lineal<\/p>

FX1=na.omit(cbind(CAC40,DAX,SWISS,IBEX,DOW,NASDAQ,NIKKEI,SINGAPUR,HONG,HOLANDA))
head(FX1)<\/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
mu1
plot(FX)
head(FX)<\/p>

plot(FX[,9],xlab=\u00bbRetornos HONG KONG\u00bb)<\/p>

cor(FX)<\/p>

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

#install.packages(\u00abrugarch\u00bb) #para ugarchspec
#library(rugarch) #rugarch: a package for univariate GARCH fitting, simulation and forecast<\/p>

#install.packages(\u00abrmgarch\u00bb)
#library(rmgarch) #dccspec<\/p>

#install.packages(\u00abccgarch\u00bb)
#library(ccgarch)<\/p>

garch11.spec<-ugarchspec(mean.model = list(armaOrder = c(0,0), include.mean=T),
variance.model = list(garchOrder = c(1,1),model = \u00absGARCH\u00bb))<\/p>

#DCC-GARCH — VARIAS DISTRIBUIONES
dcc.garch11.spec<-dccspec(uspec = multispec(replicate(10, garch11.spec)),
dccOrder = c(1,1),distribution = \u00abmvt\u00bb)<\/p>

# APLICAMOS MODELO A NUESTRA TABLA<\/p>

garch11.dcc.fit<-dccfit(dcc.garch11.spec, data = FX)
garch11.dcc.fit<\/p>

#max<-garch11.dcc.fit@mfit$log.likelihoods<\/p>

AICdcc<-infocriteria(garch11.dcc.fit)[1]
AICdcc<\/p>

#???plot(garch11.dcc.fit)<\/p>

#?dccfit
#fcst.h<-(rcov(garch11.dcc.fit))
#last(fcst.h)<\/p>

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

fcst<-(dccforecast(garch11.dcc.fit))
fcst<\/p>

fcst.h<-matrix(fcst@mforecast$H[[1]],nrow=10)#Matriz covarianzas pred
fcst.h
fcst.mu<-matrix(fcst@mforecast$mu,nrow=1) #medias arma prediccion
fcst.mu<\/p>

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

#1. activos
# DCC-GARCH — Predecir y varianzas y media predichas( Modelizaci\u00f3n
#volatilidad)<\/p>

#Sacamos los residuos y modelizar con Las copulas<\/p>

garch11.dcc.res<-(garch11.dcc.fit@mfit$stdresid)<\/p>

#garch11.dcc.fit@mfit$stdresid<\/p>

res1<-garch11.dcc.res[,1]
res2<-garch11.dcc.res[,2]
res3<-garch11.dcc.res[,3]
res4<-garch11.dcc.res[,4]
res5<-garch11.dcc.res[,5]
res6<-garch11.dcc.res[,6]
res7<-garch11.dcc.res[,7]
res8<-garch11.dcc.res[,8]
res9<-garch11.dcc.res[,9]
res10<-garch11.dcc.res[,10]<\/p>

plot(res10,type=\u00bbl\u00bb)<\/p>

######################## Copula Fitting and Simulation ##########################<\/p>

#Aplicaci\u00f3n de copulas
#aqu\u00ed fija una copula arquimediana de la forma Hierarquical<\/p>

#install.packages(\u00abHAC\u00bb)
library(HAC)#HAC: a package for hierarchical Archimedean copula<\/p>

results<-estimate.copula(cbind(res1,res2,res3,res4,res5,res6,res7,res8,res9,res10),type=3,margins=’t’)
results
#?estimate.copula#The function estimates the parameters and determines the structure of Hierarchical Archimedean Copulae.
plot(results)<\/p>

##SIMULAMOS COPULA
#??hac2nacopula
na.cop<-hac2nacopula(results)
na.cop<\/p>

boo<-length(res1)<\/p>

###<\/p>

set.seed(2)<\/p>

cop.sim=rnacopula(boo,na.cop)
head(cop.sim)<\/p>

##simulate residuals using the simulated copula distribution
#Simulaci\u00f3n de nuevos resiudos(serie) con los par\u00e1metros de la c\u00f3pula<\/p>

res.sim= cbind(qt(cop.sim[,1],df=length(res1)-1),qt(cop.sim[,2],df=length(res2)-1),
qt(cop.sim[,3],df=length(res3)-1),qt(cop.sim[,4],df=length(res4)-1),
qt(cop.sim[,5],df=length(res5)-1),qt(cop.sim[,6],df=length(res6)-1),
qt(cop.sim[,7],df=length(res7)-1),qt(cop.sim[,8],df=length(res8)-1),
qt(cop.sim[,9],df=length(res9)-1), qt(cop.sim[,10],df=length(res10)-1))<\/p>

res.sim<\/p>

plot(res.sim[,3],type=\u00bbl\u00bb)<\/p>

######### SIMULACION DE RETORNOS DCC-GARCH-COPULA ##########<\/p>

#simulamos los nuevos retornos con los que calcularemos var y la cartera<\/p>

#install.packages(\u00abexpm\u00bb)
library(expm)<\/p>

logret<-matrix(,nrow=boo,ncol=10)
logret
sqrt.h<-sqrtm(fcst.h)
sqrt.h<\/p>

#logret<-matrix(rep(mu1,each=boo),ncol=10)+t(sqrt.h%*%t(res.sim))<\/p>

logret<-matrix(rep(fcst.mu,each=boo),ncol=10)+t(sqrt.h%*%t(res.sim))
medias<-rbind(mu1,fcst.mu)
medias
plot(logret[,1],type=\u00bbl\u00bb)
plot(logret[,2],type=\u00bbl\u00bb)<\/p>

#FX1<-logret<\/p>

acum<- apply(logret,2,cumsum)
acum
plot(acum[,10],type=\u00bbl\u00bb)<\/p>

FX1<-rbind(FX,logret)<\/p>

colnames(FX1)<-c(\u00abCAC40″,\u00bbDAX\u00bb,\u00bbSWISS\u00bb,\u00bbIBEX\u00bb,\u00bbDOW\u00bb,\u00bbNASDAQ\u00bb,\u00bbNIKKEI\u00bb,\u00bbSINGAPUR\u00bb,\u00bbHONG\u00bb,\u00bbHOLANDA\u00bb)
plot(FX1)<\/p>

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

apply(FX1,2,mean)<\/p>

A<-matrix(apply(FX1,2,mean))
A1<-t(A) #media simple Esta media podr\u00eda calcularse por el metodo de rendimiento simple
A1<\/p>

reb3<-A1<0
reb3
rebb<-replace(reb3,reb3==TRUE,-1)
rebb
REBfinal<-replace(rebb,rebb==0,1)
REBfinal
REBfinal<-t(matrix(REBfinal))
REBfinal
########################################<\/p>

V1<-as.timeSeries(rep(REBfinal[1,1],nrow(FX1)))
V2<-as.timeSeries(rep(REBfinal[1,2],nrow(FX1)))
V3<-as.timeSeries(rep(REBfinal[1,3],nrow(FX1)))
V4<-as.timeSeries(rep(REBfinal[1,4],nrow(FX1)))
V5<-as.timeSeries(rep(REBfinal[1,5],nrow(FX1)))
V6<-as.timeSeries(rep(REBfinal[1,6],nrow(FX1)))
V7<-as.timeSeries(rep(REBfinal[1,7],nrow(FX1)))
V8<-as.timeSeries(rep(REBfinal[1,8],nrow(FX1)))
V9<-as.timeSeries(rep(REBfinal[1,9],nrow(FX1)))
V10<-as.timeSeries(rep(REBfinal[1,10],nrow(FX1)))<\/p>

buena<- cbind(V1,V2,V3,V4,V5,V6,V7,V8,V9,V10)
buena<\/p>

FXX<- FX1*buena<\/p>

colnames(FXX)<-c(\u00abCAC40″,\u00bbDAX\u00bb,\u00bbSWISS\u00bb,\u00bbIBEX\u00bb,\u00bbDOW\u00bb,\u00bbNASDAQ\u00bb,\u00bbNIKKEI\u00bb,\u00bbSINGAPUR\u00bb,\u00bbHONG\u00bb,\u00bbHOLANDA\u00bb)
apply(FXX,2,mean)<\/p>

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

x=portfolioSpec()
#setOptimize(x)=\u00bbmaxReturn\u00bb
setType(x)=\u00bbCVAR\u00bb
setAlpha(x)=0.05
setSolver(x)=\u00bbsolveRglpk.CVAR\u00bb
#setSolver(x)=\u00bb solveRquadprog\u00bb<\/p>

rest=c(\u00abminW[1:10]=-0.25″,\u00bbmaxW[1:10]=0.25\u00bb)<\/p>

y=tangencyPortfolio(data=FXX,spec=x,constraints=rest)
print(y)<\/p>

pesos<- y@portfolio@portfolio$weights
pesos<\/p>

Apalancamiento<-sum(abs(pesos))<\/p>

posicionAy<- (pesos\/Apalancamiento)*2
AYONDOWEIGHT<-posicionAy*REBfinal
colnames(AYONDOWEIGHT)<-c(\u00abCAC40″,\u00bbDAX\u00bb,\u00bbSWISS\u00bb,\u00bbIBEX\u00bb,\u00bbDOW\u00bb,\u00bbNASDAQ\u00bb,\u00bbNIKKEI\u00bb,\u00bbSINGAPUR\u00bb,\u00bbHONG\u00bb,\u00bbHOLANDA\u00bb)
AYONDOWEIGHT<\/p>

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

# Transformamos
a = apply(FX1,2,mean)
a
negativos = c()
positivos = c()
for (i in 1:length(a))
{
if (a[i] < 0)
{
negativos = c(negativos,i)
}
else
{
positivos = c(positivos,i)
}
}
positivar = function(x)
{
x*-1
}<\/p>

# Positivemos y cbindemos
apply(FX1[,negativos],2,mean)
FXc = positivar(FX1[,negativos])
apply(FXc,2,mean)
FXc1 = cbind(FXc,FX1[,positivos])
apply(FXc1,2,mean)
FXX<-as.timeSeries(FXc1)
head(FXX)
FXX<-FX1<\/p>

#FECHA <-timeSequence(from=\u00bb2012-01-02″,to=\u00bb2016-12-31″,by= \u00abday\u00bb)
#fxx<-as.xts(FXc1,FECHA)<\/p>

#FXX<-as.timeSeries(fxx[\u00ab2012-01-02::2012-12-31\u00bb])<\/p>

#colnames(FXX)<-c(\u00abCADCHF\u00bb,\u00bbAUDNZD\u00bb,\u00bbAUDUSD\u00bb,\u00bbEURCHF\u00bb,\u00bbEURGBP\u00bb,\u00bbEURJPY\u00bb,\u00bbEURNZD\u00bb,\u00bbEURUSD\u00bb,\u00bbGBPCHF\u00bb,\u00bbNZDCAD\u00bb)<\/p>

#CREaMOS LA CARTERA<\/p>

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

x=portfolioSpec()
setType(x)=\u00bbCVAR\u00bb
setAlpha(x)=0.05
setSolver(x)=\u00bbsolveRglpk.CVAR\u00bb
rest=c(\u00abminW[1:10]=-0.25″,\u00bbmaxW[1:10]=0.25\u00bb)
y=tangencyPortfolio(data=FXX,spec=x,constraints=rest)
print(y)
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OTROS CURSOS QUE TE AYUDAR\u00c1N<\/h3>\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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EN R<\/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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WEBINAR GRATIS – CARTERAS DE INVERSI\u00d3N DCC-GARCH COPULAS \ud83d\udc68\u200d\ud83d\udcbb Los Modelos DCC-GARCH-C\u00f3pulas pueden ser usados para crear carteras de inversi\u00f3n en diferentes activos financieros o tambi\u00e9n para hacer simulaciones a partir de ellos. La idea con estos modelos es conjuntar los modelos de volatilidad condicional por un lado con las c\u00f3pulas por otro,todo ello permitiendo …<\/p>\n

Modelo de carteras con c\u00f3pulas matem\u00e1ticas<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":19,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","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":""},"yoast_head":"\nModelo de carteras con c\u00f3pulas matem\u00e1ticas - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Modelo de carteras con c\u00f3pulas matem\u00e1ticas - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"WEBINAR GRATIS – CARTERAS DE INVERSI\u00d3N DCC-GARCH COPULAS \ud83d\udc68\u200d\ud83d\udcbb Los Modelos DCC-GARCH-C\u00f3pulas pueden ser usados para crear carteras de inversi\u00f3n en diferentes activos financieros o tambi\u00e9n para hacer simulaciones a partir de ellos. 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