How to compute conditional correlation matrix by using standardized residuals obtained from the DCC-GARCH estimation and how to obtain variances of the individual return series?


#1

I have fitted a DCC GARCH model to my multivariate financial returns data. Now, I need to compute the time-varying conditional correlation matrix by using the standardized residuals obtained from the DCC-GARCH estimation. Here, the problem is I do not know how to compute conditional correlation matrix by using standardized residuals.

Below is my reproducible code:

#load libraries 
library(rugarch) 
library(rmgarch) 

data(dji30retw)
Dat = dji30retw[, 1:8, drop = FALSE] 
uspec = ugarchspec(mean.model = list(armaOrder = c(0,0)), 
                   variance.model = list(garchOrder = c(1,1), model = "eGARCH"), 
                   distribution.model = "norm") 
spec1 = dccspec(uspec = multispec(replicate(8, uspec)), dccOrder = c(1,1), 
                distribution = "mvnorm") 
fit1 = dccfit(spec1, data = Dat) 
print(fit1)

My question: Is it possible to obtain the time-varying conditional correlation matrix as well as variance of the returns, by using standardized residuals obtained from the DCC-GARCH estimation? I have tried the following code without residuals, but not sure whether it is correct or not:

r1=rcor(fit1, type="cor")

Kindly help me to get the time-varying correlation matrix by using the standardized residuals. I also need help to obtain the variances of each individual returns.

A kind help will be highly appreciated.

Thanks in advance.


#2

Just a friendly note on cross-posting to other forums, be sure to link to them.

It looks like you got some help with this one already,