Improving Value-at-Risk prediction under model uncertainty

Shige Peng, Shuzhen Yang and Jianfeng Yao

Papers from arXiv.org

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.

Date: 2018-05, Revised 2020-06
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (3)

Published in Journal of Financial Econometrics, 2020/07

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1805.03890

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