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Less disagreement, better forecasts: adjusted risk measures in the energy futures market

Author

Listed:
  • Zhang, Ning
  • Gong, Yujing
  • Xue, Xiaohan

Abstract

This paper develops a generic adjustment framework to improve in the market risk forecasts of diverse risk forecasting models, which indicates the degree to which risk is under- and overestimated. In the context of the energy commodity market, a market in which tail risk management is of crucial importance, the empirical analysis shows that after this adjustment framework is applied, the forecasting performance of various risk models generally improves, as verified by a battery of backtesting methods. Additionally, our method also lessens the risk model disagreement among post-adjusted risk forecasts.

Suggested Citation

  • Zhang, Ning & Gong, Yujing & Xue, Xiaohan, 2023. "Less disagreement, better forecasts: adjusted risk measures in the energy futures market," LSE Research Online Documents on Economics 118451, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:118451
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    File URL: http://eprints.lse.ac.uk/118451/
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    References listed on IDEAS

    as
    1. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," Journal of Financial Stability, Elsevier, vol. 23(C), pages 79-91.
    2. František Čech & Jozef Baruník, 2019. "Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1167-1189, September.
    3. Susanne Emmer & Marie Kratz & Dirk Tasche, 2013. "What is the best risk measure in practice? A comparison of standard measures," Papers 1312.1645, arXiv.org, revised Apr 2015.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Gurdip Bakshi & Xiaohui Gao & Alberto G. Rossi, 2019. "Understanding the Sources of Risk Underlying the Cross Section of Commodity Returns," Management Science, INFORMS, vol. 65(2), pages 619-641, February.
    6. Yang-Ho Park & Nicole Abruzzo, 2016. "An Empirical Analysis of Futures Margin Changes: Determinants and Policy Implications," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(1), pages 65-100, February.
    7. Filippo Curti & Ibrahim Ergen & Minh Le & Marco Migueis & Rob T. Stewart, 2016. "Benchmarking Operational Risk Models," Finance and Economics Discussion Series 2016-070, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    energy futures; expected shortfall; finance; model disagreement; value at risk; ES/K002309/1; ES/R009724/1; Wiley deal;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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