摘要
利用连续时间时齐马尔可夫链构建关于保险失效或退保概率的预测模型,用以计算任一时刻处于各个状态的概率,并给出参数估计的方法.由于实际中保单的状态在特定的时刻会发生离散事件,因此使用多阶段的马尔可夫链模型来刻画这一特点,即在发生离散事件的特定时刻,定义一个跳跃矩阵描述该时刻的状态转移情况.将该模型应用到保险公司的寿险失效或退保概率的研究中,通过实际的数据对模型参数进行估计和校准,通过校准的马尔可夫链模型对其寿险失效或退保概率进行预测并得到较好的预测结果.
The continuous-time time-homogeneous Markov chain was used to construct a prediction model about the probability of insurance lapse or surrender to calculate the probability of being in various states at any time,and a parameter estimation method was given.In reality,the state of the insurance policy would have discrete events at a specific time,so the multi-stage Markov chain model was used to characterize this feature.That was,at a specific time when a discrete event occurs,a jump matrix was defined to describe the state transition at the specific time.The model was applied to the study of the life insurance lapse or surrender probability of insurance companies,and the model parameters were estimated and calibrated by actual data.Finally,the calibrated Markov chain model was used to predict the life insurance lapse or surrender probability and a good prediction result was obtained.
作者
张影
ZHANG Ying(School of Mathematics Sciences,University of Science and Technology of China,Hefei 230026,China)
关键词
马尔可夫链
强度矩阵
转移概率
寿险失效或退保概率预测
Markov chains
intensity matrix
transition probability
life insurance lapse or surrender probability prediction
