Experience rating in the Cramér-Lundberg model
研究了在连续时间Cramér-Lundberg模型中，基于索赔频率和严重程度的经验费率对保险公司偿付能力的影响，发现同时更新两种信息的模型比仅基于频率的模型破产概率更低且衰减更快。
This paper provides a study of how experience rating on both claim frequency and severity impacts the solvency of an insurance business in the continuous-time Cramér Lundberg model. This is done by treating the claim parameters as random outcomes and continuously updating the premiums using Bayesian estimators. In the analysis, the claim sizes conditional on the severity parameter are assumed to be light-tailed. The main contributions are large deviation results where the asymptotic ruin probability is found for a model updating the premium based upon both frequency and severity. This asymptotic ruin probability is lower and decays faster compared to the one of a model which updates the premium solely based on claim frequency. Our findings are illustrated with examples, where the conditional claim size and the severity parameter are parametrised.