Journal of Financial Econometrics Advance Access originally published online on October 12, 2005
Journal of Financial Econometrics 2006 4(1):53-89; doi:10.1093/jjfinec/nbj002
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Value-at-Risk Prediction: A Comparison of Alternative Strategies
University of Frankfurt
University of Munich, Center for Financial Studies, and Ifo Institute for Economic Research
University of Zurich
Address correspondence to Marc Paolella, Swiss Banking Institute, University of Zürich, Plattenstrasse 14, CH-8032, Zürich, Switzerland, or e-mail: paolella{at}isb.unizh.ch.
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 (VaR) in a univariate context. 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 generalized autoregressive conditionally heteroskedastic (GARCH) filter with an extreme value theory-based approach, performs best overall, closely followed by a variant on a filtered historical simulation, and a new model based on heteroskedastic mixture distributions. Conditional autoregressive VaR (CAViaR) models perform inadequately, though an extension to a particular CAViaR model is shown to outperform the others.
KEYWORDS: empirical finance, extreme value theory, fat tails, GARCH, quantile regression
Received March 23, 2004; revised July 18, 2005; accepted September 6, 2005
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