Highly volatile scenarios, such as those provoked by the recent subprime and sovereign debt crises, have questioned the accuracy of current risk forecasting methods. This paper adds fuel to this debate by comparing the performance of alternative specifications for modeling the returns filtered by an ARMA-GARCH Parametric distributions (Student's t and skewed-t), the extreme value theory (EVT), semi-nonparametric methods based on the Gram–Charlier (GC) expansion and the normal (benchmark). We implement backtesting techniques for the pre-crisis and crisis periods for stock index returns and a hedge fund of emerging markets. Our results show that the Student's t fails to forecast VaR during the crisis, while the EVT and GC accurately capture market risk, the latter representing important savings in terms of efficient regulatory capital provisions.
