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A Neyman-Pearson Perspective On Optimal Reinsurance With Constraints

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  • Lo, Ambrose

Abstract

The formulation of optimal reinsurance policies that take various practical constraints into account is a problem commonly encountered by practitioners. In the context of a distortion-risk-measure-based optimal reinsurance model without moral hazard, this article introduces and employs a variation of the Neyman–Pearson Lemma in statistical hypothesis testing theory to solve a wide class of constrained optimal reinsurance problems analytically and expeditiously. Such a Neyman–Pearson approach identifies the unit-valued derivative of each ceded loss function as the test function of an appropriate hypothesis test and transforms the problem of designing optimal reinsurance contracts to one that resembles the search of optimal test functions achieved by the classical Neyman–Pearson Lemma. As an illustration of the versatility and superiority of the proposed Neyman–Pearson formulation, we provide complete and transparent solutions of several specific constrained optimal reinsurance problems, many of which were only partially solved in the literature by substantially more difficult means and under extraneous technical assumptions. Examples of such problems include the construction of the optimal reinsurance treaties in the presence of premium budget constraints, counterparty risk constraints and the optimal insurer–reinsurer symbiotic reinsurance treaty considered recently in Cai et al. (2016).

Suggested Citation

  • Lo, Ambrose, 2017. "A Neyman-Pearson Perspective On Optimal Reinsurance With Constraints," ASTIN Bulletin, Cambridge University Press, vol. 47(2), pages 467-499, May.
  • Handle: RePEc:cup:astinb:v:47:y:2017:i:02:p:467-499_00
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    Citations

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    Cited by:

    1. J. David Cummins & Georges Dionne & Robert Gagné & Abdelhakim Nouira, 2021. "The costs and benefits of reinsurance," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 46(2), pages 177-199, April.
    2. Chen, Yanhong & Cheung, Ka Chun & Zhang, Yiying, 2024. "Bowley solution under the reinsurer's default risk," Insurance: Mathematics and Economics, Elsevier, vol. 115(C), pages 36-61.
    3. Ghossoub, Mario, 2019. "Budget-constrained optimal insurance with belief heterogeneity," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 79-91.
    4. Ambrose Lo & Zhaofeng Tang, 2019. "Pareto-optimal reinsurance policies in the presence of individual risk constraints," Annals of Operations Research, Springer, vol. 274(1), pages 395-423, March.
    5. Nicole Bäuerle & Alexander Glauner, 2021. "Minimizing spectral risk measures applied to Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 94(1), pages 35-69, August.
    6. Jiang, Wenjun & Hong, Hanping & Ren, Jiandong, 2021. "Pareto-optimal reinsurance policies with maximal synergy," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 185-198.
    7. Birghila, Corina & Pflug, Georg Ch., 2019. "Optimal XL-insurance under Wasserstein-type ambiguity," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 30-43.
    8. Ghossoub, Mario, 2019. "Budget-constrained optimal insurance without the nonnegativity constraint on indemnities," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 22-39.
    9. Khreshna Syuhada & Arief Hakim & Suci Sari, 2021. "The Combined Stop-Loss and Quota-Share Reinsurance: Conditional Tail Expectation-Based Optimization from the Joint Perspective of Insurer and Reinsurer," Risks, MDPI, vol. 9(7), pages 1-21, July.
    10. Alexander Glauner, 2020. "Dynamic Reinsurance in Discrete Time Minimizing the Insurer's Cost of Capital," Papers 2012.09648, arXiv.org.
    11. Albrecher, Hansjörg & Cani, Arian, 2019. "On randomized reinsurance contracts," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 67-78.
    12. Ghossoub, Mario, 2019. "Optimal insurance under rank-dependent expected utility," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 51-66.
    13. Katia Colaneri & Julia Eisenberg & Benedetta Salterini, 2022. "Some Optimisation Problems in Insurance with a Terminal Distribution Constraint," Papers 2206.04680, arXiv.org.
    14. Cai, Jun & Liu, Haiyan & Wang, Ruodu, 2017. "Pareto-optimal reinsurance arrangements under general model settings," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 24-37.
    15. Asimit, Alexandru V. & Cheung, Ka Chun & Chong, Wing Fung & Hu, Junlei, 2020. "Pareto-optimal insurance contracts with premium budget and minimum charge constraints," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 17-27.
    16. Cheung, Ka Chun & He, Wanting & Wang, He, 2023. "Multi-constrained optimal reinsurance model from the duality perspectives," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 199-214.
    17. Nicole Bauerle & Alexander Glauner, 2020. "Minimizing Spectral Risk Measures Applied to Markov Decision Processes," Papers 2012.04521, arXiv.org.
    18. Boonen, Tim J. & Jiang, Wenjun, 2024. "Robust insurance design with distortion risk measures," European Journal of Operational Research, Elsevier, vol. 316(2), pages 694-706.
    19. Boonen, Tim J. & Ghossoub, Mario, 2021. "Optimal reinsurance with multiple reinsurers: Distortion risk measures, distortion premium principles, and heterogeneous beliefs," Insurance: Mathematics and Economics, Elsevier, vol. 101(PA), pages 23-37.

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