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Asymptotic Equivalence Of Risk Measures Under Dependence Uncertainty

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  • Jun Cai
  • Haiyan Liu
  • Ruodu Wang

Abstract

In this paper, we study the aggregate risk of inhomogeneous risks with dependence uncertainty, evaluated by a generic risk measure. We say that a pair of risk measures is asymptotically equivalent if the ratio of the worst†case values of the two risk measures is almost one for the sum of a large number of risks with unknown dependence structure. The study of asymptotic equivalence is particularly important for a pair of a noncoherent risk measure and a coherent risk measure, as the worst†case value of a noncoherent risk measure under dependence uncertainty is typically difficult to obtain. The main contribution of this paper is to establish general asymptotic equivalence results for the classes of distortion risk measures and convex risk measures under different mild conditions. The results implicitly suggest that it is only reasonable to implement a coherent risk measure for the aggregation of a large number of risks with uncertainty in the dependence structure, a relevant situation for risk management practice.

Suggested Citation

  • Jun Cai & Haiyan Liu & Ruodu Wang, 2018. "Asymptotic Equivalence Of Risk Measures Under Dependence Uncertainty," Mathematical Finance, Wiley Blackwell, vol. 28(1), pages 29-49, January.
    • Handle: RePEc:bla:mathfi:v:28:y:2018:i:1:p:29-49
    DOI: 10.1111/mafi.12140
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    Citations

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    Cited by:

    1. Cosimo Munari & Lutz Wilhelmy & Stefan Weber, 2021. "Capital Requirements and Claims Recovery: A New Perspective on Solvency Regulation," Papers 2107.10635, arXiv.org.
    2. Dimitrios G. Konstantinides & Georgios C. Zachos, 2019. "Exhibiting Abnormal Returns Under a Risk Averse Strategy," Methodology and Computing in Applied Probability, Springer, vol. 21(2), pages 551-566, June.
    3. Ruodu Wang & Yunran Wei & Gordon E. Willmot, 2020. "Characterization, Robustness, and Aggregation of Signed Choquet Integrals," Mathematics of Operations Research, INFORMS, vol. 45(3), pages 993-1015, August.
    4. Chen, Ouxiang & Hu, Taizhong, 2019. "Extreme-aggregation measures in the RDEU model," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 155-163.
    5. Silvana Pesenti & Qiuqi Wang & Ruodu Wang, 2020. "Optimizing distortion riskmetrics with distributional uncertainty," Papers 2011.04889, arXiv.org, revised Feb 2022.
    6. Mao, Tiantian & Hu, Jiuyun & Liu, Haiyan, 2018. "The average risk sharing problem under risk measure and expected utility theory," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 170-179.
    7. Yuyu Chen & Peng Liu & Yang Liu & Ruodu Wang, 2022. "Ordering and inequalities for mixtures on risk aggregation," Mathematical Finance, Wiley Blackwell, vol. 32(1), pages 421-451, January.
    8. Ruodu Wang & Zuo Quan Xu & Xun Yu Zhou, 2019. "Dual utilities on risk aggregation under dependence uncertainty," Finance and Stochastics, Springer, vol. 23(4), pages 1025-1048, October.
    9. Yichun Chi & Zuo Quan Xu & Sheng Chao Zhuang, 2022. "Distributionally Robust Goal-Reaching Optimization in the Presence of Background Risk," North American Actuarial Journal, Taylor & Francis Journals, vol. 26(3), pages 351-382, August.
    10. Cornilly, Dries & Vanduffel, Steven, 2019. "Equivalent distortion risk measures on moment spaces," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 187-192.
    11. Yuyu Chen & Peng Liu & Yang Liu & Ruodu Wang, 2020. "Ordering and Inequalities for Mixtures on Risk Aggregation," Papers 2007.12338, arXiv.org, revised Jun 2021.
    12. Cosimo Munari & Stefan Weber & Lutz Wilhelmy, 2023. "Capital requirements and claims recovery: A new perspective on solvency regulation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 329-380, June.

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