Tail asymptotic for discounted aggregate claims with one-sided linear dependence and general investment return

作     者：Fenglong Guo Dingcheng Wang 

作者机构：Jiangsu Key Laboratory of Financial Engineering and School of Finance Nanjing Audit University School of Mathematical Sciences University of Electronic Science and Technology of China 

出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)

年 卷 期：2019年第62卷第4期

页      面：735-750页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported by National Natural Science Foundation of China (Grant No. 71501100) the Open Project of Jiangsu Key Laboratory of Financial Engineering (Grant No. NSK2015-02) supported by National Natural Science Foundation of China (Grant No. 71271042) the stage results of the Major Bidding Project of the Chinese National Social Science Foundation (Grant No. 17ZDA072) 

主　　题：Poisson risk model tail probability one-sided linear process heavy-tailed distribution asymptotic upper bound investment return 

摘      要：In this study, we investigate the tail probability of the discounted aggregate claim sizes in a dependent risk model. In this model, the claim sizes are observed to follow a one-sided linear process with independent and identically distributed innovations. Investment return is described as a general stochastic process with c`adl`ag paths. In the case of heavy-tailed innovation distributions, we are able to derive some asymptotic estimates for tail probability and to provide some asymptotic upper bounds to improve the applicability of our study.