张节松, 肖庆宪. 依随机序正相依风险下的最优再保险[J]. 应用概率统计, 2013, 29(6): 642-654.
引用本文: 张节松, 肖庆宪. 依随机序正相依风险下的最优再保险[J]. 应用概率统计, 2013, 29(6): 642-654.
Zhang Jiesong, Xiao Qingxian. Optimal Reinsurance with Risks Positively Dependent through the Stochastic Ordering[J]. Chinese Journal of Applied Probability and Statistics, 2013, 29(6): 642-654.
Citation: Zhang Jiesong, Xiao Qingxian. Optimal Reinsurance with Risks Positively Dependent through the Stochastic Ordering[J]. Chinese Journal of Applied Probability and Statistics, 2013, 29(6): 642-654.

依随机序正相依风险下的最优再保险

Optimal Reinsurance with Risks Positively Dependent through the Stochastic Ordering

  • 摘要: 经典的二元复合Poisson风险模型假定索赔次数通过一个共同的Poisson分布相关, 而索赔额相互独立. 本文中, 我们假定索赔次数与索赔额均依随机序正相依, 通过比较, 发现依随机序正相依是一个比依共同Poisson分布相关更弱的条件. 实际上, 依随机序正相依的假定较独立、共同单调、条件随机递增等都要弱. 在依随机序正相依的风险下, 我们得到了最优再保险策略, 并针对二维与随机多维混杂的相依风险, 在自留损失的方差最小和二次效用最大的准则下, 给出了自留向量的显式表达式, 部分解决了Cai和Wei (2012a)提出的多维相依风险下, 求解此类表达式的问题.

     

    Abstract: In the classic bivariate compound Poisson models, the numbers of claims are assumed to be correlated through a common Poisson distribution, while the claim sizes are independent. In this paper, we assume that both the numbers of claims and claim sizes are positively dependent through the stochastic ordering. Through comparing, we find that the condition of positive dependence through the stochastic ordering is weaker than correlating through a common Poisson distribution. In fact, the assumption of positive dependence through the stochastic ordering is weaker than independence, comonotonicity, conditionally stochastically increasing et al.. With the positively dependent risks through the stochastic ordering, we get the optimal reinsurance strategy. In addition, with the mixed two-dimensional and stochastic-dimensional dependent risks, we give the explicit expressions of retention vector under the criterion of minimizing the variance of the total retained loss and maximizing the quadratic utility, which partially solves the problem, proposed by Cai and Wei (2012a), of getting such expressions with multi-dimensional dependent risks.

     

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