重尾破产时间值的修正相位型近似

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2019-11-21 DOI:10.1515/strm-2019-0009
D. Geiger, A. Adekpedjou
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引用次数: 1

摘要

摘要在最近引入的风险模型中,我们近似具有重尾索赔的Gerber–Shiu函数,该模型具有取决于索赔规模的索赔时间和保费。我们应用了一种被称为“校正相位类型近似”的技术。这导致在具有相位类型声明大小的Gerber–Shiu函数中添加了一个校正项。校正项包含每个卷积最多一次的重尾行为,并捕获真实Gerber–Shiu函数的尾行为。我们使尾部行为在一类保险风险的经典情况下是特定的。在说明了这种近似的使用之后,我们对一些特定惩罚函数和索赔分布的近似的相对误差进行了数值研究。
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On corrected phase-type approximations of the time value of ruin with heavy tails
Abstract We approximate Gerber–Shiu functions with heavy-tailed claims in a recently introduced risk model having both interclaim times and premiums depending on the claim sizes. We apply a technique known as “corrected phase-type approximations”. This results in adding a correction term to the Gerber–Shiu function with phase-type claim sizes. The correction term contains the heavy-tailed behavior at most once per convolution and captures the tail behavior of the true Gerber–Shiu function. We make the tail behavior specific in the classical case of one class of risk insured. After illustrating a use of such approximations, we study numerically the approximations’ relative errors for some specific penalty functions and claims distributions.
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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