Using for-loop for fitted DCC GARCH model in R

Question

I am new user in R. Please help me!

I have 1114 observations with 8 assets. For example, I have fitted a multivariate DCC-GARCH model to the first 1000 data points and I want to do 1-ahead forecast for 3 periods later such as

1) Data[1:1000,] In-sample data, forecast for Data[1001,]
2) Data[1:1001,] In-sample data, forecast for Data[1002,]
3) Data[1:1002,] In-sample data, forecast for Data[1003,]

Below is my reproducible code:-

# load libraries
library(rugarch)
library(rmgarch)
library(FinTS)
library(tseries)
library (fPortfolio)

data(dji30retw)
for (i in 1:3) {
Dat.Initial = dji30retw[, 1:8, drop = FALSE]
Dat <- Dat.Initial[1:(1000+(i-1)), ] 

#Fitting the data
uspec = ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model = "norm")
spec1 = dccspec(uspec = multispec( replicate(8, uspec)), dccOrder = c(1,1), distribution = "mvnorm")
fit1 <- list()
fit1[[i]] = dccfit(spec1, data = Dat, out.sample = 1, fit.control = list(eval.se=T))

#Out of sample forecasting
dcc.focast <- list()
dcc.focast[[i]]=dccforecast(fit1[[i]], n.ahead = 1, n.roll = 0)
print(dcc.focast[[i]])
}

The codes works perfectly. I can now get my dcc.focast values. But why is it, if I execute

 dcc.focast[[1]]
 NULL

it gives me "NULL". Shouldn't it produce the same answer as the "print(dcc.focast[[i]])" as in the loop?

The problem here, it only gives me dcc.focast[[3]]. The rest are are "NULL". What is the mistake that I have done? Anyone can help to explain?

Answer 1

Modify your code to define your lists outside the for . You are deleting the previous computations when redefining your lists inside the loop.

data(dji30retw)
fit1 <- list()
dcc.focast <- list()
for (i in 1:3) {#i=1
  Dat.Initial = dji30retw[, 1:8, drop = FALSE]
  Dat <- Dat.Initial[1:(1000+(i-1)), ] 

  #Fitting the data
  uspec = ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model = "norm")
  spec1 = dccspec(uspec = multispec( replicate(8, uspec)), dccOrder = c(1,1), distribution = "mvnorm")
  fit1[[i]] = dccfit(spec1, data = Dat, out.sample = 1, fit.control = list(eval.se=T))

  #Out of sample forecasting
  dcc.focast[[i]]=dccforecast(fit1[[i]], n.ahead = 1, n.roll = 0)
  print(dcc.focast[[i]])
}
summary(dcc.focast)
dcc.focast[[1]]

> summary(dcc.focast)
     Length Class       Mode
[1,] 1      DCCforecast S4  
[2,] 1      DCCforecast S4  
[3,] 1      DCCforecast S4  
> dcc.focast[[1]]

*---------------------------------*
*       DCC GARCH Forecast        *
*---------------------------------*

Distribution         :  mvnorm
Model                :  DCC(1,1)
Horizon              :  1
Roll Steps           :  0
-----------------------------------

0-roll forecast: 
, , 1

       [,1]   [,2]   [,3]   [,4]   [,5]   [,6]   [,7]   [,8]
[1,] 1.0000 0.3378 0.3244 0.2383 0.2730 0.5086 0.3470 0.5073
[2,] 0.3378 1.0000 0.3498 0.5423 0.5910 0.3321 0.2802 0.3658
[3,] 0.3244 0.3498 1.0000 0.2372 0.2771 0.3479 0.2433 0.3625
[4,] 0.2383 0.5423 0.2372 1.0000 0.5746 0.2877 0.2303 0.3593
[5,] 0.2730 0.5910 0.2771 0.5746 1.0000 0.3063 0.2169 0.3101
[6,] 0.5086 0.3321 0.3479 0.2877 0.3063 1.0000 0.3637 0.4307
[7,] 0.3470 0.2802 0.2433 0.2303 0.2169 0.3637 1.0000 0.3976
[8,] 0.5073 0.3658 0.3625 0.3593 0.3101 0.4307 0.3976 1.0000