Robust Value at Risk Prediction

Loriano Mancini and Fabio Trojani ()

Journal of Financial Econometrics, 2011, vol. 9, issue 2, 281-313

Abstract: This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that controls bootstrap instability due to outlying observations. A Monte Carlo simulation shows that our robust method provides more accurate Value at Risk (VaR) forecasts than classical methods, often by a large extent, especially for several days ahead horizons and/or in presence of outlying observations. An empirical application confirms the simulation results. The robust procedure outperforms in backtesting several other VaR prediction methods, such as RiskMetrics, CAViaR, historical simulation, and classical filtered historical simulation methods. We show empirically that robust estimation reduces tail estimation risk, providing more accurate and more stable VaR prediction intervals over time. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Date: 2011
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Working Paper: Robust Value at Risk Prediction (2007)
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Journal of Financial Econometrics is currently edited by Allan Timmermann and Fabio Trojani

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