EVALUATING THE TAIL RISK OF MULTIVARIATE AGGREGATE LOSSES

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-07-15 DOI:10.1017/asb.2022.14

Wenjun Jiang, Jiandong Ren

引用次数: 0

Abstract

Abstract In this paper, we study the tail risk measures for several commonly used multivariate aggregate loss models where the claim frequencies are dependent but the claim sizes are mutually independent and independent of the claim frequencies. We first develop formulas for the moment (or size biased) transforms of the multivariate aggregate losses, showing their relationship with the moment transforms of the claim frequencies and claim sizes. Then, we apply the formulas to compute some popular risk measures such as the tail conditional expectation and tail variance of the multivariate aggregated losses and to perform capital allocation analysis.

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评估多元累计损失的尾部风险

摘要本文研究了几种常用的多元累计损失模型的尾部风险度量，其中索赔频率是相互依赖的，但索赔规模与索赔频率是相互独立的。我们首先开发了多元累计损失的矩变换(或大小偏差)的公式，显示了它们与索赔频率和索赔规模的矩变换的关系。然后，应用该公式计算了多元累计损失的尾部条件期望和尾部方差等常用的风险度量，并进行了资本配置分析。

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

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.