# 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.