{"id":930,"date":"2020-10-21T15:23:35","date_gmt":"2020-10-21T15:23:35","guid":{"rendered":"https:\/\/ricovictor.com\/?p=930"},"modified":"2020-10-21T15:32:49","modified_gmt":"2020-10-21T15:32:49","slug":"modelos-garch-que-son-los-modelos-garch","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2020\/10\/21\/modelos-garch-que-son-los-modelos-garch\/","title":{"rendered":"Modelos GARCH.\u00bfQu\u00e9 son los Modelos GARCH?"},"content":{"rendered":"\t\t
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\"\ud83d\udc9a\" Los Modelos GARCH<\/strong> son un tipo de modelo econom\u00e9tricos<\/a><\/strong>, usados principalmente para la modelizaci\u00f3n de la volatilidad<\/strong> de las series financieras.\u00a0<\/p>

\"\ud83d\udc4c\" Su principal objetivo es evitar la limitaciones que tiene el uso de la varianza tradicional como medida de riesgo<\/strong>(volatilidad).\u00a0<\/p>

En los Modelos GARCH<\/strong> encontramos una nueva forma de c\u00e1lculo de la volatilidad donde la varianza condicional<\/strong> depende de las observaciones pasadas.\u00a0<\/p>

En el siguiente v\u00eddeo tutorial explico algunos ejemplos y paquetes que se pueden usar en R studio<\/strong> para modelizar con GARCH. volatilidad. Tambi\u00e9n puedes copiar el c\u00f3digo usado justo debajo.<\/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 R<\/h2>

\u00a0<\/p>

### V\u00cdDEO MODELOS GARCH<\/p>


#(MOdelos de Autoregresivos de HEterocedasticidad Condicional)<\/p>

# Buscan realizar una modelacion de la volatilidad(varianza),en la
# cu\u00e1l \u00e9sta depender\u00e1 de sus observaciones pasadas
# La volatilidad no ser\u00e1 la volatilidad cl\u00e1sica sino que tendremos una
# nueva forma de calcularla y ver como se comporta<\/p>

\u00a0<\/p>

#1. Surgen para ilimimar las limitaciones de la varianza como indicador de volatilidad<\/p>

#2. Modelos Garch como su nombre indican,se basan en que la estructura
# de la varianza puede ser autorregresiva y puede estar autocorrelacionada<\/p>

# Por tanto los valores del presente pueden depender del pasado.<\/p>


#file:\/\/\/C:\/Users\/Victor-PC\/Documents<\/p>

tabla1<-read.table(\u00abC:\/Users\/Victor-PC\/Documents\/divisasmulti15M.txt\u00bb,header=T,sep=\u00bb\u00bb)<\/p>


tabla<-tabla1[(nrow(tabla1)-1000):nrow(tabla1),]<\/p>

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

library(\u00abquantmod\u00bb)
library(\u00abtseries\u00bb)
library(\u00abfImport\u00bb)<\/p>

#install.packages(\u00abtseries\u00bb)
library(tseries)
#install.packages(\u00abforecast\u00bb)
library(forecast)
#install.packages(\u00abTSA\u00bb)
library(TSA)<\/p>

library(quantmod)<\/p>

library(lmtest)<\/p>

\u00a0<\/p>

\u00a0<\/p>

##VENTANA DE TIEMPO PARA DESVIACIONES<\/p>


AUDUSD<- tabla$AUDUSD_my #audusd<\/p>

rAud<-na.omit(returns(AUDUSD))*100<\/p>

plot(rAud,type=\u00bbl\u00bb)
sd(rAud)
var(rAud)<\/p>

hist(rAud)<\/p>

\u00a0<\/p>

vent<- 50
vent2<- vent-1<\/p>


varianza=c()
for(i in vent:length(rAud))
{f=var(rAud[(i-vent2):i])
varianza[i]=(f)}<\/p>

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

y<-rAud<\/p>

STDM=c()
for(i in vent:length(y))
{f=sd(y[(i-vent2):i])
STDM[i]=(f)}
STDM
STDM2<-STDM*2<\/p>

MM=c()
for(i in vent:length(y))
{f=mean(y[(i-vent2):i])
MM[i]=(f)}
MM<\/p>

#desviacion t\u00edpica<\/p>

#sd(na.omit(y))
#2*sd(na.omit(y))<\/p>

sd1= MM+ STDM
sd1
sd11= MM-STDM
sd11
sd2<-MM+STDM2
sd22<-MM-STDM2<\/p>


plot(rAud,type=\u00bbl\u00bb)
lines(MM, col=\u00bbblue\u00bb)
lines(sd11, col=\u00bbgreen\u00bb)
lines(sd1,col=\u00bbgreen\u00bb)
lines(sd2,col=\u00bbred\u00bb)
lines(sd22,col=\u00bbred\u00bb)<\/p>

####################################
######### MODELOS GARCH ############<\/p>

#install.packages(\u00abfGarch\u00bb)
library(fGarch)
library(rugarch)<\/p>


AoveSem<-AUDUSD<\/p>

plot(AoveSem,type=\u00bbl\u00bb)
AOVEvol<-na.omit(returns(AoveSem))*100 ### Retornos
plot(AOVEvol,type=\u00bbl\u00bb)<\/p>

#?returns<\/p>

#GARCH ESTANDAR (1,1)<\/p>

AOVEgarch <- garchFit(~ garch(1,1),cond.dist=\u00bbnorm\u00bb, data = (AOVEvol),include.mean = FALSE, trace = FALSE)<\/p>


### Cosas que se pueden Optimizar = 1. (p,q) y 2. Distribuci\u00f3n<\/p>


#AOVEgarch <- garchFit(~ garch(1,1),cond.dist=\u00bbnorm\u00bb, data = (AOVEvol), trace = FALSE)<\/p>

AOVEgarch
summary(AOVEgarch)
plot(AOVEgarch)<\/p>

?garchFit<\/p>

vc <- AOVEgarch@h.t ## variaci\u00f3n Condicional
plot(vc,type=\u00bbl\u00bb)<\/p>

SD<-AOVEgarch@sigma.t # Desviaci\u00f3n est\u00e1ndar condicional
plot(SD,type=\u00bbl\u00bb)
MediaSD<- mean(SD)
abline( MediaSD,0,col=\u00bbred\u00bb)<\/p>

Residuos<- AOVEgarch@residuals #residuos
plot(Residuos,type=\u00bbl\u00bb)<\/p>


#Comp<-cbind(AOVEvol,Residuos)<\/p>

\u00a0<\/p>

### PREDICCI\u00d3N CON GARCH<\/p>


fcst<-(predict(AOVEgarch,n.ahead=10))
fcst
plot(fcst$standardDeviation)
abline(MediaSD,0,col=\u00bbred\u00bb)<\/p>

\u00a0<\/p>


### OTRA FORMA CON tseries<\/p>

?garch<\/p>

\u00a0<\/p>

fit<-garch(AOVEvol,order=c(1,1))
fit
coef(fit) ### Coeficientes<\/p>

\u00a0<\/p>

#### Rugarch para Garch y SImulaciones<\/p>


library(rugarch)<\/p>

#?ugarchspec<\/p>

gspec.ru <- ugarchspec(variance.model = list(model = \u00absGARCH\u00bb, garchOrder = c(1, 1)),mean.model=list(
armaOrder=c(0,0),include.mean = FALSE), distribution=\u00bbnorm\u00bb)<\/p>

gfit.ru <- ugarchfit(gspec.ru, AOVEvol)
gfit.ru<\/p>


plot(gfit.ru)<\/p>


coef(gfit.ru) # coeficientes estimados
vcov(gfit.ru) # matriz de covarianzas
infocriteria(gfit.ru) # criterios bayesianos
residuals(gfit.ru) # residuos del modelo
newsimpact(gfit.ru) # curva de impacto
fitted(gfit.ru) #valores fijados
uncvariance(gfit.ru) #variaza incondicional( de largo plazo)
uncmean(gfit.ru) # media incondicional (de Largo plazo)<\/p>

\u00a0<\/p>


##### Simmulaciones con GARCH<\/p>

sim<-ugarchsim(gfit.ru, n.sim=100, n.start=1, m.sim=1, startMethod=\u00bbsample\u00bb)
plot(sim)
head(sigma(sim))<\/p>

#?ugarchsim<\/p>

SIMU<-sim@simulation<\/p>

plot(SIMU$sigmaSim,type=\u00bbl\u00bb,xlab= \u00abHorizonte\u00bb,ylab=\u00bbVOLATILIDAD\u00bb,col=\u00bbblue\u00bb)<\/p>

\u00a0<\/p>

\u00a0<\/p>

## Faltar\u00eda hacer un backtest de la efectividad del Modelos<\/p>

### Crear nuevas series Simuladas
### Predicciones tambi\u00e9n de las series<\/p>


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


getSymbols(\u00ab^IBEX\u00bb,quote=\u00bbClose\u00bb,from = \u00ab2014-06-20\u00bb)
IBEX= IBEX[,4]
IBEX1<-na.omit(IBEX)
dIBEX1<-na.omit(returns(IBEX1))*100
plot(dIBEX1,type=\u00bbl\u00bb)
#install.packages(\u00abfGarch\u00bb)
library(fGarch)<\/p>

spec = ugarchspec()
fit = ugarchfit(dIBEX1, spec = spec)<\/p>

plot(fit)
desv<-fit@fit$sigma
desv<\/p>

sim = ugarchsim(fit,n.sim=1, n.start=1, m.sim=1, startMethod=\u00bbsample\u00bb)
sim@simulation$sigmaSim<\/p>

\u00a0<\/p>

SIM<-c()
SERIE<-c()
set.seed(10)<\/p>


for(i in 1:100){
sim <- ugarchsim(fit,n.sim=1, n.start=1, m.sim=1, startMethod=\u00bbsample\u00bb)
SIM[i]<- sim@simulation$sigmaSim
SERIE[i]<-sim@simulation$seriesSim
}<\/p>


SERIE
SIM
plot(SIM,type=\u00bbl\u00bb)
orden<-sort(SIM)<\/p>

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


orden<0.9505<\/p>

S=S[ , colSums(is.na(S)) == 0]
S<\/p>

############################# SERIE DE RUIDO BLANCO ##########<\/p>

#Ejemplo Ruido Blanco
#Generamos los datos<\/p>

ruido_blanco=rnorm(1000,0,1)<\/p>

#Graficamos la serie de tiempo<\/p>

plot.ts(ruido_blanco, main=\u00bbEjemplo de Ruido Blanco\u00bb, xlab=\u00bbTiempo\u00bb, ylab=\u00bbValores\u00bb,col=\u00bb6″)<\/p>

#Graficamos su correlograma<\/p>

acf(ruido_blanco,main=\u00bbCorrelograma\u00bb,col=\u00bb2″,lag=50)<\/p>

#Calculamos la media<\/p>

mean(ruido_blanco)
#[1] 0.03784017<\/p>


#Ejemplo Ruido Blanco
#Prueba Ljung-Box<\/p>

Box.test(ruido_blanco)<\/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 GARCH son un tipo de modelo econom\u00e9tricos, usados principalmente para la modelizaci\u00f3n de la volatilidad de las series financieras.  Su principal objetivo es evitar la limitaciones que tiene el uso de la varianza tradicional como medida de riesgo(volatilidad).  En los Modelos GARCH encontramos una nueva forma de c\u00e1lculo de la volatilidad donde la …<\/p>\n

Modelos GARCH.\u00bfQu\u00e9 son los Modelos GARCH?<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":19,"featured_media":936,"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":[21,2,13],"tags":[4],"yoast_head":"\nModelos GARCH.\u00bfQu\u00e9 son los Modelos GARCH? - 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\/10\/21\/modelos-garch-que-son-los-modelos-garch\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Modelos GARCH.\u00bfQu\u00e9 son los Modelos GARCH? - V\u00edctor A. 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