Improving Value-at-Risk Prediction Under Model Uncertainty*

Shige Peng, Shuzhen Yang and Jianfeng Yao

Journal of Financial Econometrics, 2023, vol. 21, issue 1, 228-259

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.

Keywords: empirical finance; G-normal distribution; model uncertainty; sublinear expectation; value-at-risk (search for similar items in EconPapers)
JEL-codes: C58 G32 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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