分布不确定条件下的再保险稳健自留额

Robust Reinsurance Retention under Distribution Uncertainty

提出了在索赔分布信息不完全的条件下,确定再保险自留额的一类稳健优化方法。通过使用KL散度(Kullback-Leibler Divergence)作为分布信息模糊性的度量,对成数再保险和停止-损失再保险这两类常见的分保模式构建了基于稳健性思想的极小-极大随机规划模型,并对两类模型给出了统一的求解算法。通过随机模拟和数值计算,验证了模型和算法的有效性,并比较了两类再保险模式的优劣。

A class of robust retention model is proposed in this paper when the estimation of claim distribution has uncer- tainty. With the use of the KL divergence as the measurement of distribution uncertainty, the author constructs rain-max stochastic optimization models based on the rule of robustness for quota share and stop-loss reinsuranee contracts respectively. An algorithm based on statistical simulation is also presented to solve these models. Numerical results illustrate the efficiency of the proposed method, and comparison between the two kinds of retention is made.