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Improving Value-at-Risk Prediction Under Model Uncertainty

Author

Listed:
  • Shige Peng
  • Shuzhen Yang
  • Jianfeng Yao
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.

Suggested Citation

  • Shige Peng & Shuzhen Yang & Jianfeng Yao, 2023. "Improving Value-at-Risk Prediction Under Model Uncertainty," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 228-259.
    • Handle: RePEc:oup:jfinec:v:21:y:2023:i:1:p:228-259.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbaa022
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    Citations

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    Cited by:

    1. Shuzhen Yang & Wenqing Zhang, 2024. "Asset pricing under model uncertainty with finite time and states," Papers 2408.13048, arXiv.org.
    2. Parvin Malekzadeh & Zissis Poulos & Jacky Chen & Zeyu Wang & Konstantinos N. Plataniotis, 2024. "EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning," Papers 2408.12446, arXiv.org, revised Aug 2024.
    3. Minglian Lin & Indranil SenGupta & William Wilson, 2023. "Estimation of VaR with jump process: application in corn and soybean markets," Papers 2311.00832, arXiv.org, revised Jun 2024.
    4. Ravi Kashyap, 2024. "The Concentration Risk Indicator: Raising the Bar for Financial Stability and Portfolio Performance Measurement," Papers 2408.07271, arXiv.org.

    More about this item

    Keywords

    empirical finance; G-normal distribution; model uncertainty; sublinear expectation; value-at-risk;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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