A simple approach is to assume that dependence structure between variables (which is characterized by copula) is constant throughout the process. In this case, you may apply log-likelihood estimation of copula parameters to ranked AR-GARCH process residuals.
A more complicated approach is to invent a model of how copula parameters depend on the previous values of the process. This *might* provide a better description of some empirical effects. But in reality, these models are not reliable enough, because you are likely to deal with many more parameters than in univariate case, and there is no general way to estimate the parameters of copula evolution. However, attempts are still made (see for example package mgarchBEKK). Andrey ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
