斯克拉定理,copula积,因子模型中的排序结果

IF 0.6 Q4 STATISTICS & PROBABILITY Dependence Modeling Pub Date : 2021-01-01 DOI:10.1515/demo-2021-0113
Jonathan Ansari, L. Rüschendorf
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引用次数: 9

摘要

考虑一个风险向量X = (X1,…,Xd)的完全指定因子模型,其中X的分量与风险因子Z的联合分布和给定Z的X的条件分布是指定的。我们将Darsow et al.[6]和Durante et al.[8]中对于d = 2和连续因子分布所引入的d-copulas的*-积的概念推广到多元不连续情况。我们给出了因子模型的sklar型表示定理,表明这些*-积决定了一个完全指定的因子模型的联结。我们详细研究了*-积的近似、变换和排序性质,并在此基础上推导出完全指定因子模型依赖于它们的规范的一般正交排序结果。本文将已知的最坏情况部分指定风险因子模型的排序结果推广到一般的正相关或负相关风险因子模型。特别地,它开发了一些工具来推导完全指定因子模型的子类中的最坏情况依赖性边界。
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Sklar’s theorem, copula products, and ordering results in factor models
Abstract We consider a completely specified factor model for a risk vector X = (X1, . . ., Xd), where the joint distributions of the components of X with a risk factor Z and the conditional distributions of X given Z are specified. We extend the notion of *-product of d-copulas as introduced for d = 2 and continuous factor distribution in Darsow et al. [6] and Durante et al. [8] to the multivariate and discontinuous case. We give a Sklar-type representation theorem for factor models showing that these *-products determine the copula of a completely specified factor model. We investigate in detail approximation, transformation, and ordering properties of *-products and, based on them, derive general orthant ordering results for completely specified factor models in dependence on their specifications. The paper generalizes previously known ordering results for the worst case partially specified risk factor models to some general classes of positive or negative dependent risk factor models. In particular, it develops some tools to derive sharp worst case dependence bounds in subclasses of completely specified factor models.
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来源期刊
Dependence Modeling
Dependence Modeling STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
18
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
期刊最新文献
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