Various GARCH models are applied to daily returns of more than 1200 constituents of major stock indices worldwide. The value-at-risk forecast performance is investigated for different markets and industries, considering the test for correct conditional coverage using the false discovery rate (FDR) methodology. For most of the markets and industries, we find the same two conclusions. First, an asymmetric GARCH specification is essential when forecasting the 95% value-at-risk. Second, for both the 95% and 99% value-at-risk, it is crucial that the innovations' distribution is fat-tailed (e.g. Student-t or-even better-a nonparametric kernel density estimate).
- false discovery rate