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Bayes risk, elicitability, and the Expected Shortfall

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Listed:
  • Paul Embrechts
  • Tiantian Mao
  • Qiuqi Wang
  • Ruodu Wang

Abstract

Motivated by recent advances on elicitability of risk measures and practical considerations of risk optimization, we introduce the notions of Bayes pairs and Bayes risk measures. Bayes risk measures are the counterpart of elicitable risk measures, extensively studied in the recent literature. The Expected Shortfall (ES) is the most important coherent risk measure in both industry practice and academic research in finance, insurance, risk management, and engineering. One of our central results is that under a continuity condition, ES is the only class of coherent Bayes risk measures. We further show that entropic risk measures are the only risk measures which are both elicitable and Bayes. Several other theoretical properties and open questions on Bayes risk measures are discussed.

Suggested Citation

  • Paul Embrechts & Tiantian Mao & Qiuqi Wang & Ruodu Wang, 2021. "Bayes risk, elicitability, and the Expected Shortfall," Mathematical Finance, Wiley Blackwell, vol. 31(4), pages 1190-1217, October.
    • Handle: RePEc:bla:mathfi:v:31:y:2021:i:4:p:1190-1217
    DOI: 10.1111/mafi.12313
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yuanying Guan & Zhanyi Jiao & Ruodu Wang, 2022. "A reverse ES (CVaR) optimization formula," Papers 2203.02599, arXiv.org, revised May 2023.
    2. Xia Han & Bin Wang & Ruodu Wang & Qinyu Wu, 2021. "Risk Concentration and the Mean-Expected Shortfall Criterion," Papers 2108.05066, arXiv.org, revised Apr 2022.
    3. Adil Rengim Cetingoz & Jean-David Fermanian & Olivier Gu'eant, 2022. "Risk Budgeting Portfolios: Existence and Computation," Papers 2211.07212, arXiv.org, revised Sep 2023.
    4. N. V. Gribkova & J. Su & R. Zitikis, 2022. "Empirical tail conditional allocation and its consistency under minimal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 713-735, August.
    5. Qiuqi Wang & Ruodu Wang & Ricardas Zitikis, 2021. "Risk measures induced by efficient insurance contracts," Papers 2109.00314, arXiv.org, revised Sep 2021.
    6. Wang, Qiuqi & Wang, Ruodu & Zitikis, Ričardas, 2022. "Risk measures induced by efficient insurance contracts," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 56-65.
    7. Qinyu Wu & Fan Yang & Ping Zhang, 2023. "Conditional generalized quantiles based on expected utility model and equivalent characterization of properties," Papers 2301.12420, arXiv.org.
    8. Tobias Fissler & Fangda Liu & Ruodu Wang & Linxiao Wei, 2024. "Elicitability and identifiability of tail risk measures," Papers 2404.14136, arXiv.org, revised Jun 2024.
    9. Marcelo Brutti Righi & Fernanda Maria Muller & Marlon Ruoso Moresco, 2022. "A risk measurement approach from risk-averse stochastic optimization of score functions," Papers 2208.14809, arXiv.org, revised May 2023.
    10. Qiuqi Wang & Ruodu Wang & Johanna Ziegel, 2022. "E-backtesting," Papers 2209.00991, arXiv.org, revised Aug 2024.
    11. Tim J. Boonen & Xia Han, 2023. "Optimal insurance with mean-deviation measures," Papers 2312.01813, arXiv.org.

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