  In the paper, we use and investigate copulas models to represent multivariate
dependence in financial time series. We propose the algorithm of risk measure
computation using copula models. Using the optimal mean-$CVaR$ portfolio we
compute portfolio's Profit and Loss series and corresponded risk measures
curves. Value-at-risk and Conditional-Value-at-risk curves were simulated by
three copula models: full Gaussian, Student's $t$ and regular vine copula.
These risk curves are lower than historical values of the risk measures curve.
All three models have superior prediction ability than a usual empirical
method. Further directions of research are described.
