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Characterizing optimal allocations in quantile-based risk sharing

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  • Wang, Ruodu
  • Wei, Yunran

Abstract

Unlike classic risk sharing problems based on expected utilities or convex risk measures, quantile-based risk sharing problems exhibit two special features. First, quantile-based risk measures (such as the Value-at-Risk) are often not convex, and second, they ignore some part of the distribution of the risk. These features create technical challenges in establishing a full characterization of optimal allocations, a question left unanswered in the literature. In this paper, we address the issues on the existence and the characterization of (Pareto-)optimal allocations in risk sharing problems for the Range-Value-at-Risk family. It turns out that negative dependence, mutual exclusivity in particular, plays an important role in the optimal allocations, in contrast to positive dependence appearing in classic risk sharing problems. As a by-product of our main finding, we obtain some results on the optimization of the Value-at-Risk (VaR) and the Expected Shortfall, as well as a new result on the inf-convolution of VaR and a general distortion risk measure.

Suggested Citation

  • Wang, Ruodu & Wei, Yunran, 2020. "Characterizing optimal allocations in quantile-based risk sharing," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 288-300.
    • Handle: RePEc:eee:insuma:v:93:y:2020:i:c:p:288-300
    DOI: 10.1016/j.insmatheco.2020.06.001
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    References listed on IDEAS

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    2. Xia, Zichao & Zou, Zhenfeng & Hu, Taizhong, 2023. "Inf-convolution and optimal allocations for mixed-VaRs," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 156-164.
    3. Peng Liu & Andreas Tsanakas & Yunran Wei, 2024. "Risk sharing with Lambda value at risk under heterogeneous beliefs," Papers 2408.03147, arXiv.org, revised Sep 2024.
    4. Khreshna Syuhada & Oki Neswan & Bony Parulian Josaphat, 2022. "Estimating Copula-Based Extension of Tail Value-at-Risk and Its Application in Insurance Claim," Risks, MDPI, vol. 10(6), pages 1-26, May.
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    6. Zou, Zhenfeng & Wu, Qinyu & Xia, Zichao & Hu, Taizhong, 2023. "Adjusted Rényi entropic Value-at-Risk," European Journal of Operational Research, Elsevier, vol. 306(1), pages 255-268.
    7. Lauzier, Jean-Gabriel & Lin, Liyuan & Wang, Ruodu, 2023. "Pairwise counter-monotonicity," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 279-287.

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