Stochastic loss reserving using individual information model with over-dispersed Poisson  

作　　者：Zhigao Wang Xianyi Wu Chunjuan Qiu 

机构地区：[1]School of Statistics,East China Normal University,Shanghai,People’s Republic of China

出　　处：《Statistical Theory and Related Fields》2022年第2期114-128,共15页统计理论及其应用（英文）

基　　金：This work was supported by the Natural Science Foundation of China(71771089);the Shanghai Philosophy and Social Sci-ence Foundation(2015BGL001);the National Social Science Foundation Key Program of China(17ZDA091);China Scholarship Council(201906140045)。

摘　　要：For stochastic loss reserving,we propose an individual information model(IIM)which accom-modates not only individual/micro data consisting of incurring times,reporting developments,settlement developments as well as payments of individual claims but also heterogeneity among policies.We give over-dispersed Poisson assumption about the moments of reporting developments and payments of every individual claims.Model estimation is conducted under quasi-likelihood theory.Analytic expressions are derived for the expectation and variance of outstanding liabilities,given historical observations.We utilise conditional mean square error of prediction(MSEP)to measure the accuracy of loss reserving and also theoretically prove that when risk portfolio size is large enough,IIM shows a higher prediction accuracy than individ-ual/micro data model(IDM)in predicting the outstanding liabilities,if the heterogeneity indeed influences claims developments and otherwise IIM is asymptotically equivalent to IDM.Some simulations are conducted to investigate the conditional MSEPs for IIM and IDM.A real data analysis is performed basing on real observations in health insurance.

关 键 词：Risk management loss reserving individual information model over-dispersed Poisson QUASI-LIKELIHOOD MSEP 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]