Conditional Mean Risk Sharing of Independent Discrete Losses in Large Pools

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Michel Denuit, Christian Y. Robert
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引用次数: 0

Abstract

This paper considers a risk sharing scheme of independent discrete losses that combines risk retention at individual level, risk transfer for too expensive losses and risk pooling for the middle layer. This ensures that pooled losses can be considered as being uniformly bounded. We study the no-sabotage requirement and diversification effects when the conditional mean risk-sharing rule is applied to allocate pooled losses. The no-sabotage requirement is equivalent to Efron’s monotonicity property for conditional expectations, which is known to hold under log-concavity. Elementary proofs of this result for discrete losses are provided for finite population pools. The no-sabotage requirement and diversification effects are then examined within large pools. It is shown that Efron’s monotonicity property holds asymptotically and that risk can be eliminated under fairly general conditions which are fulfilled in applications.

Abstract Image

大型资金池中独立离散损失的条件均值风险分担
本文考虑了一种独立离散损失的风险分担方案,它结合了个人层面的风险自留、太昂贵损失的风险转移和中间层的风险共担。这确保了集合损失可被视为是均匀有界的。我们研究了应用条件均值风险分担规则分配集合损失时的无破坏要求和分散效应。无破坏要求等同于条件期望的埃夫隆单调性属性,众所周知,该属性在对数凹凸条件下成立。对于离散损失的这一结果,我们提供了有限人口集合的基本证明。然后研究了大集合中的无破坏要求和多样化效应。结果表明,埃夫隆的单调性特性近似成立,而且在应用中满足了相当普遍的条件,风险可以消除。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
58
审稿时长
6-12 weeks
期刊介绍: Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case studies. Applied probability is a broad research area that is of interest to many scientists in diverse disciplines including: anthropology, biology, communication theory, economics, epidemiology, finance, linguistics, meteorology, operations research, psychology, quality control, reliability theory, sociology and statistics. The following alphabetical listing of topics of interest to the journal is not intended to be exclusive but to demonstrate the editorial policy of attracting papers which represent a broad range of interests: -Algorithms- Approximations- Asymptotic Approximations & Expansions- Combinatorial & Geometric Probability- Communication Networks- Extreme Value Theory- Finance- Image Analysis- Inequalities- Information Theory- Mathematical Physics- Molecular Biology- Monte Carlo Methods- Order Statistics- Queuing Theory- Reliability Theory- Stochastic Processes
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