基于Markov模型的长期护理保险定价

Long Term Care Insurance Pricing Based on the Markov Model

如何发展长期护理保险以应对我国失能老年人的长期护理难题是我国亟待解决的重要问题。本文建立了包含5个状态的健康状态界定体系,并利用CHARLS调查数据,在健康状态符合时间齐性Markov过程的假设下,通过建立有序logit健康状态转移模型,对我国老年人的健康状态转移概率矩阵进行了测算。结果显示,年龄、性别、是否与配偶同居、初始健康状态等因素显著影响老年人的健康状态转移情况。在此基础上,本文利用Markov模型对长期护理保险的趸交精算费率进行了分类计算。测算得到的转移概率矩阵和长期护理保险精算费率能够为我国发展长期护理保险提供现实参考。

How to develop long-term care insurance to cope with the long-term care problem of the disabled elderly in China is an important issue. This paper established the health status definition systems for five states of health and. It used the CHARLS microscopic investigation data, and based on the assumption of time homogeneous Markov process, the paper built an ordered Logit health status transition model to project the health status transition probability matrix. The result showed that factors significantly affecting the transition probability were age, gender, cohabitation with spouse or not, and initial health status. On the basis of this, the Markov model was used to calculate the single premium actuarial rate of long-term care insurance by category. The calculated transfer probability matrix and the long-term care insurance actuarial rate can provide practical references for China to develop long-term care insurance.