摘要
信度模型是非寿险经验费率厘定的主要方法。传统的Buhlmann-Straub信度模型可以表示为随机截距模型,并假设随机效应服从正态分布。但在实际的保险损失数据中,部分个体风险的损失可能远远高于总体平均水平,从而使得不同个体风险之间的风险差异呈现右偏特征。在这种情况下,Buhlmann-Straub模型可能低估高风险的信度保费。本文在随机截距模型中假设随机效应服从偏正态分布,求得了偏正态随机效应假设下的信度保费。可以证明,Buhlmann-Straub信度保费是其特例。模拟分析和实证研究的结果都表明,偏正态随机效应假设下的信度模型可以更好地预测高风险的信度保费,从而改进传统信度模型的保费估计结果。
Credibility models are main methods of experience ratemaking for non-life insurance. Classical Buhlmann- Straub credibility model can be expressed as a random intercept model. Random intercept model assumes that the random effect is normally distributed. In insurance reality, some individual risks may cause much higher losses than the population average. In this case, random effect is right-skewed and Buhlmann-Straub model may under-estimate the credibility premium with higher risks. The paper assumes that the random effect is skew-normally distributed in random intercept model, and a new credibility premium may be calculated. It can be shown that Buhlmann-Straub model is included in this new model as a special ease. Simulation study and case study show that the new credibility model improves the credibility premium of higher risks.
出处
《统计研究》
CSSCI
北大核心
2015年第1期73-78,共6页
Statistical Research
基金
教育部重点研究基地重大项目"随机效应模型及其在非寿险风险管理中的应用"(12JJD790025)
国家自然科学基金项目"考虑风险相依的非寿险精算模型研究"(71171193)
中国人保财险灾害研究基金项目"车型分类及定价分析"(2013B07)资助
关键词
随机效应模型
信度保费
偏正态分布
非寿险
Random Effect Model
Credibility Premium
Skew-normal Distribution
Non-life Insurance
