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Distributionally robust reinsurance with Value-at-Risk and Conditional Value-at-Risk

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  • Liu, Haiyan
  • Mao, Tiantian

Abstract

A basic assumption of the classic reinsurance model is that the distribution of the loss is precisely known. In practice, only partial information is available for the loss distribution due to the lack of data and estimation error. We study a distributionally robust reinsurance problem by minimizing the maximum Value-at-Risk (or the worst-case VaR) of the total retained loss of the insurer for all loss distributions with known mean and variance. Our model handles typical stop-loss reinsurance contracts. We show that a three-point distribution achieves the worst-case VaR of the total retained loss of the insurer, from which the closed-form solutions of the worst-case distribution and optimal deductible are obtained. Moreover, we show that the worst-case Conditional Value-at-Risk of the total retained loss of the insurer is equal to the worst-case VaR, and thus the optimal deductible is the same in both cases.

Suggested Citation

  • Liu, Haiyan & Mao, Tiantian, 2022. "Distributionally robust reinsurance with Value-at-Risk and Conditional Value-at-Risk," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 393-417.
    • Handle: RePEc:eee:insuma:v:107:y:2022:i:c:p:393-417
    DOI: 10.1016/j.insmatheco.2022.09.002
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    References listed on IDEAS

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    More about this item

    Keywords

    Value-at-Risk; Conditional Value-at-Risk; Distributional robust reinsurance; Uncertainty; Stop-loss;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games

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