Asymptotic Finite-Time Ruin Probabilities for a Multidimensional Risk Model with Subexponential Claims

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Dawei Lu, Ting Li, Meng Yuan, Xinmei Shen
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引用次数: 0

Abstract

This paper considers a multidimensional risk model with cádlág investment return processes, in which there exists some dependence structure among claims and claim-arrival time. Specifically, if claims follow the subexponential distribution or the regular variation distribution, we obtain some precise asymptotic estimates for the finite-time ruin probabilities. In addition, some numerical simulations are presented to test the performance of the theoretical results.

Abstract Image

具有次指数索赔的多维风险模型的渐近有限时间毁灭概率
本文考虑的是一个具有 cádlág 投资收益过程的多维风险模型,在该模型中,索赔和索赔到达时间之间存在某种依赖结构。具体来说,如果索赔遵循亚指数分布或正则变异分布,我们将得到有限时间毁损概率的一些精确渐近估计值。此外,我们还提出了一些数值模拟来检验理论结果的性能。
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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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