摘要
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,在历史数据较少和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。
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 the traditional estimation method of outstanding claims reserves.
出处
《系统工程》
CSCD
北大核心
2009年第1期77-81,共5页
Systems Engineering
基金
国家社会科学基金资助项目(08BJY159)
