卡尔曼滤波在非寿险未决赔款准备金估算中的应用

在历史数据缺乏和数据质量较低的情况下,非寿险公司应用传统的准备金估计方法常存在估计精度不高的问题.本文通过状态空间来描述非寿险赔付过程,应用卡尔曼滤波来估计状态空间的转换参数,并分别预测损失频率和损失程度从而动态地估计未决赔款准备金.实证分析表明,在历史数据较少争存在错误数据的情况下,本方法对改善未决赔款准备金的估计是有效的.

Application of Kalman Filter in Estimating No-life Outstanding Claims Reserving

Because of insufficient historical data and less reliable data,the traditional methods of estimating reserving in non-life insurance companies lack accuracy.By using state-space to describe the process of no-life claims and Kalman filter to estimate the conversion parameters of state-space,this article forecasts the severities and frequencies of claims respectively and then estimates the reserves of the outstanding claims in a dynamic way.The empirical results show that this method is effective in improving...