Robust Value at Risk Prediction

Loriano Mancini () and Fabio Trojani ()

University of St. Gallen Department of Economics working paper series 2007 from Department of Economics, University of St. Gallen

Abstract: We propose a general robust semiparametric bootstrap method to estimate conditional predictive distributions of GARCH-type models. Our approach is based on a robust estimator for the parameters in GARCH-type models and a robustified resampling method for standardized GARCH residuals, which controls the bootstrap instability due to influential observations in the tails of standardized GARCH residuals. Monte Carlo simulation showsthat our method consistently provides lower VaR forecast errors, often to a large extent, and in contrast to classical methods never fails validation tests at usual significance levels. We test extensively our approach in the context of real data applications to VaR prediction for market risk, and find that only our robust procedure passes all validation tests at usualconfidence levels. Moreover, the smaller tail estimation risk of robust VaR forecasts implies VaR prediction intervals that can be nearly 20% narrower and 50% less volatile over time. This is a further desirable property of our method, which allows to adapt risky positions to VaR limits more smoothly and thus more efficiently.

Keywords: Backtesting; M-estimator; Extreme Value Theory; Breakdown Point (search for similar items in EconPapers)
JEL-codes: C14 C15 C23 C59 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2007-09
New Economics Papers: this item is included in nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Robust Value at Risk Prediction (2011)
Working Paper: Robust Value at Risk Prediction (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2007:2007-36

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