An adaptive strategy for offering m-out-of-n insurance policies

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL Probability in the Engineering and Informational Sciences Pub Date : 2022-03-15 DOI:10.1017/S0269964821000504
G. S. Fishman, S. Stidham
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Abstract

A company with $n$ geographically widely dispersed sites seeks insurance that pays off if $m$ out of the $n$ sites experience rarely occurring catastrophes (e.g., earthquakes) during a year. This study describes an adaptive dynamic strategy that enables an insurance company to offer the policy with smaller loss probability than more conventional static policies induce, but at a comparable or lower premium. The strategy accomplishes this by periodically purchasing reinsurance on individual sites. Exploiting rarity, the policy induces zero loss with probability one if no more than one quake occurs during any review interval. The policy also may induce a profit if $m$ or more quakes occur in an interval if no quakes have occurred in previous intervals. The study also examines the benefit of more than one reinsurance policy per active site. The study relies on expected utility to determine indifference premiums and derives an upper bound on loss probability independent of premium.
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提供m-out- n保险策略的自适应策略
如果一个公司有$n$地理上分散的站点,如果$n$站点中的$m$在一年中经历很少发生的灾难(例如,地震),则寻求保险。本研究描述了一种自适应动态策略,该策略使保险公司能够提供比传统静态保单更小的损失概率的保单,但保费相当或更低。该策略通过定期在各个站点购买再保险来实现这一点。利用稀有性,如果在任何审查间隔内不发生一次以上的地震,该策略将导致零损失,概率为1。如果在一段时间内发生了100万次或更多的地震,而在之前的时间间隔内没有发生过地震,该保单也可能获得利润。该研究还考察了每个活跃站点超过一份再保险政策的好处。该研究依靠期望效用来确定无差异保费,并推导出与保费无关的损失概率上界。
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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