#************************************************************ 
# GARCH Out of Sample Forecasting Evaluation in GARCH models
# Here is an example code to use R to evaluate the performance of GARCH type of model
# User can modify the garch_forc function to find many interesting statistics
# install.packages("rugarch")
# install.packages("tseries")
# install.packages("fGarch")
# install.packages("AER")
#------------------------------------------------------------

library(tseries)
library(quantmod)
library(rugarch)
library(forecast)

# Data from Yahoo and input data to dy 
getSymbols("GOOG",from="2000-01-02", to="2017-11-01") 
dy <- as.ts(100*diff(log(GOOG$GOOG.Adjusted)))
dy <- na.omit(dy)
plot(dy,main="Google Daily Stock Returns")



in_obs = length(dy)
forc_ahead = 10             # Defind the out of forecasting obs

# Automatic ARIMA decision usint AIC of BIC 
pq <- arimaorder(auto.arima(dy,ic="bic",trace=TRUE))
d <- c(pq[1],pq[3])

# Function for out of sample forecasting using GARCH type of model 
garch_forc <- function(gtype) {
   g1 <- ugarchspec(variance.model = gtype ,
                  mean.model = list(armaOrder = d, include.mean = TRUE,archm=TRUE) )
   fit1  <- ugarchfit(g1, dy,out.sample = 10)
   fout1 <- ugarchforecast(fit1,n.ahead=10,out.sample=10,n.roll=0)
   ffit  <- fitted(fout1)
   fdy   <-  dy[(in_obs-forc_ahead+1):in_obs]
   rse <- ((fdy - ffit)^2)^0.5
   ape <-  abs(fdy - ffit)/abs(fdy)
   outstat <- c(mean(rse), mean(ape))
   outstat <- as.matrix(outstat)
   rownames(outstat) <- c("RMSE","MAPE")
return(outstat)
}

gtype1 <- list(garchOrder = c(1,1),model="sGARCH")
test1 <- garch_forc(gtype1)
colnames(test1) <- "GARCH" 

gtype2 <- list(garchOrder = c(1,1),model="fGARCH",submodel="TGARCH")
test2 <- garch_forc(gtype2)
colnames(test2) <- "TGARCH" 

gtype3 <- list(garchOrder = c(1,1),model="gjrGARCH")
test3 <- garch_forc(gtype3)
colnames(test3) <- "gjrGARCH" 

vol_forc <- data.frame(test1,test2,test3)
vol_forc