Forecasting value-at-risk using time varying copulas and EVT return distributions

Forecasting portfolio risk requires both, estimation of marginal return distributions for individual assets and the dependence structure of returns as well. In this paper, we concentrate on Value at Risk as a popular risk measure and combine elliptical copulas with time varying Dynamic Conditional Correlation (DCC) matrices and Extreme Value Theory (EVT) based models for the marginal return distributions. The approach leads to reliable Value-at-Risk figures with respect to several backtesting criteria. Feasibility and accuracy of the approach is corroborated by an extensive empirical application to different financial portfolios consisting of stocks, market indices and FX-rates.