Probability of ruin in discrete insurance risk model with dependent Pareto claims

IF 0.6 Q4 STATISTICS & PROBABILITY

Dependence Modeling Pub Date : 2019-01-01 DOI:10.1515/demo-2019-0011

C. Constantinescu, T. Kozubowski, Haoyu H. Qian

引用次数: 3

Abstract

Abstract We present basic properties and discuss potential insurance applications of a new class of probability distributions on positive integers with power law tails. The distributions in this class are zero-inflated discrete counterparts of the Pareto distribution. In particular, we obtain the probability of ruin in the compound binomial risk model where the claims are zero-inflated discrete Pareto distributed and correlated by mixture.

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具有相依Pareto索赔的离散保险风险模型的破产概率

摘要我们给出了幂律尾正整数上一类新的概率分布的基本性质，并讨论了其潜在的保险应用。这类分布是帕累托分布的零膨胀离散对应分布。特别地，我们在复合二项式风险模型中获得了破产概率，其中索赔是零膨胀离散Pareto分布的，并通过混合相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Dependence Modeling STATISTICS & PROBABILITY-

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

12 weeks

期刊介绍： The journal Dependence Modeling aims at providing a medium for exchanging results and ideas in the area of multivariate dependence modeling. It is an open access fully peer-reviewed journal providing the readers with free, instant, and permanent access to all content worldwide. Dependence Modeling is listed by Web of Science (Emerging Sources Citation Index), Scopus, MathSciNet and Zentralblatt Math. The journal presents different types of articles: -"Research Articles" on fundamental theoretical aspects, as well as on significant applications in science, engineering, economics, finance, insurance and other fields. -"Review Articles" which present the existing literature on the specific topic from new perspectives. -"Interview articles" limited to two papers per year, covering interviews with milestone personalities in the field of Dependence Modeling. The journal topics include (but are not limited to):　 -Copula methods -Multivariate distributions -Estimation and goodness-of-fit tests -Measures of association -Quantitative risk management -Risk measures and stochastic orders -Time series -Environmental sciences -Computational methods and software -Extreme-value theory -Limit laws -Mass Transportations