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arXiv · 1805.03890

Improving Value-at-Risk prediction under model uncertainty

Shige Peng·Shuzhen Yang·Jianfeng Yao
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Abstract

Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed-$t$ residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, G-VaR, which follows a novel methodology. Inspired by the recent mathematical theory of sublinear expectation, G-VaR is built upon the concept of model uncertainty, which in the present case signifies that the inherent volatility of financial returns cannot be characterized by a single distribution but rather by infinitely many statistical distributions. By considering the worst scenario among these potential distributions, the G-VaR predictor is precisely identified. Extensive experiments on both the NASDAQ Composite Index and S\&P500 Index demonstrate the excellent performance of the G-VaR predictor, which is superior to most existing benchmark VaR predictors.

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Shige Peng, Shuzhen Yang, Jianfeng Yao. 2018-05-10. Improving Value-at-Risk prediction under model uncertainty. https://doi.org/10.1093/jjfinec%2Fnbaa022

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