期刊
INSURANCE MATHEMATICS & ECONOMICS
卷 107, 期 -, 页码 393-417出版社
ELSEVIER
DOI: 10.1016/j.insmatheco.2022.09.002
关键词
Value-at-Risk; Conditional Value-at-Risk; Distributional robust reinsurance; Uncertainty; Stop-loss
类别
资金
- National Natural Science Foundation of China
- [71671176]
- [71871208]
- [71921001]
This paper studies a distributionally robust reinsurance problem by minimizing the maximum Value-at-Risk of the total retained loss of the insurer for all loss distributions with known mean and variance. A three-point distribution is proposed to achieve the worst-case VaR, and the closed-form solutions of the worst-case distribution and optimal deductible are obtained.
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.(c) 2022 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据