# Explain the unconditional covariance in Dynamic Conditional correlation( DCC ) GARCH model

Confused about the unconditional covariance matrix in a DCC GARCH model. Could anyone help me understand it? My understanding is that we get the unconditional covariance before based on the data sets. For example, two data sets, A and B, then the unconditional covariance matrix is built by the variance of A and B respectively and covariance of them, is that true? Thanks.

• Do you have a link to what you are reading about DCC GARCH at the moment? – Ric Apr 1 '15 at 14:27
• Yes, @Richard. [link](pages.stern.nyu.edu/~rengle/Dcc-Sheppard.pdf ). Words to explain the unconditional covariance is on the page 5 under equation (2). Thanks for you reply. – Fly_back Apr 1 '15 at 16:27

Your question is about the $\overline{Q}$, right?
If so, it is the covariance between the error terms $E_{i,t}$ and $E_{j,t}$.(The sub term $i$ comes from asset $i$, and $j$ for asset $j$)
• Thanks @SRKX. I understand it now. So one more question, $\bar{Q}$ can be estimated by the standard residuals, so is this matrix is the parameters in the model, i n other words, I want to estimate the Bayesian Information Criterion (BIC), and I need to know the number of parameters, I wonder whether the three parameters in the matrix needs to be considered. If not considered, then there will be 8 parameter in a bivariate case, otherwise, there will be 11 parameters. – Fly_back May 14 '15 at 14:47
• Dear @SRKX, sorry for the previous question, I have already understood the model now. The elements in $\bar{Q}$ should be considered in. – Fly_back May 14 '15 at 16:38