Bayesian credibility premium with GB2 copulas

IF 0.6 Q4 STATISTICS & PROBABILITY

Dependence Modeling Pub Date : 2019-04-16 DOI:10.2139/ssrn.3373377

Himchan Jeong, Emiliano A. Valdez

引用次数: 2

Abstract

Abstract For observations over a period of time, Bayesian credibility premium may be used to predict the value of a response variable for a subject, given previously observed values. In this article, we formulate Bayesian credibility premium under a change of probability measure within the copula framework. Such reformulation is demonstrated using the multivariate generalized beta of the second kind (GB2) distribution. Within this family of GB2 copulas, we are able to derive explicit form of Bayesian credibility premium. Numerical illustrations show the application of these estimators in determining experience-rated insurance premium. We consider generalized Pareto as a special case.

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GB2 copula的贝叶斯可信度溢价

对于一段时间内的观察，贝叶斯可信度溢价可以用来预测受试者的响应变量的值，给定先前的观察值。本文在copula框架下，推导了概率测度变化下的贝叶斯可信度溢价。用第二类(GB2)分布的多元广义beta证明了这种重新表述。在这类GB2联结中，我们可以推导出贝叶斯可信度溢价的显式形式。算例说明了这些估计量在确定经验费率保险费中的应用。我们把广义帕累托看作一个特例。

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