# CCC-Garch predict

So I'm trying to measure the VaR of 2 stock with a multivariate GARCH model, so im using the CCC model. I need to predict the standard-diviation and the mean but the ccgarch package doesn't have a command for that. Is the a way in R for doing it?

A bivariate CCC-GARCH model consists of two univariate GARCH models and a scalar conditional correlation. You would predict the individual conditional variances $\hat\sigma^2_{1,t+1}$ and $\hat\sigma^2_{2,t+1}$ from the individual univariate GARCH models (which is straightforward for one step ahead, and you iterate beyond that):
$$\hat\sigma^2_{i,t+1} = \omega + \alpha_{i,1} \hat\varepsilon^2_{i,t} + \beta_{i,1} \hat\sigma^2_{i,t}$$
for $i=1,2$. You would predict the conditional covariance $\hat\sigma_{ij,t+1}$ by multiplying the square roots of the predicted conditional variances to the estimated conditional correlation:
$$\hat\sigma_{ij,t+1} = \sqrt{\hat\sigma^2_{i,t+1}} \cdot \sqrt{\hat\sigma^2_{j,t+1}} \cdot \hat\rho.$$