Robust Value at Risk Prediction
AbstractThis 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: firstname.lastname@example.org., Oxford University Press.
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Bibliographic InfoArticle provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.
Volume (Year): 9 (2011)
Issue (Month): 2 (Spring)
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Other versions of this item:
- Loriano Mancini & Fabio Trojani, 2005. "Robust Value at Risk Prediction," Swiss Finance Institute Research Paper Series 07-31, Swiss Finance Institute, revised Oct 2007.
- Loriano Mancini & Fabio Trojani, 2007. "Robust Value at Risk Prediction," University of St. Gallen Department of Economics working paper series 2007 2007-36, Department of Economics, University of St. Gallen.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
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