Value–at–Risk Prediction: A Comparison of Alternative Strategies∗

Given the growing need for managing financial risk, risk prediction plays an increasing role in banking and finance. In this study, we compare the out-of-sample performance of existing methods and some new models for predicting Value-at-Risk. Using more than 30 years of the daily return data on the NASDAQ Composite Index, we find that most approaches perform inadequately, although several models are acceptable under current regulatory assessment rules for model adequacy. A hybrid method, combining a heavy-tailed GARCH filter with an extreme value theory-based approach, performs best overall. Keywords—Empirical finance; Extreme value theory; Fat-tails; GARCH; Quantile regression. ∗The authors are grateful to Simone Manganelli for providing his CAViaR-programs. †Corresponding author: [email protected]