COMPOSITE BERNSTEIN COPULAS
COMPOSITE BERNSTEIN COPULAS
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DOI：
10.1017/asb.2015.1
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Jingping Yang;Zhijin Chen;Fang Wang;Ruodu Wang
中科院分区：
文献类型：
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作者：
Jingping Yang;Zhijin Chen;Fang Wang;Ruodu Wang
摘要：Copula函数被广泛应用于保险和金融领域，用于对风险之间的相互依赖关系进行建模。受Sancetta和Satchell （2004, econometrectheory, 20, 535-562）提出的Bernstein copula的启发，我们引入了一类新的多元copula，即复合Bernstein copula，它是由两个copula的组合生成的。这类新的联结函数能够捕获尾相关，并且对三个重要的相关结构：共单调性、反单调性和独立性具有再现性。提出了一种基于经验复合Bernstein copula的估计方法，该方法将先验信息和数据结合到估计中。对金融数据的仿真研究和实证研究表明了经验复合Bernstein copula估计方法的优点，特别是在捕获尾部依赖性方面。
Abstract Copula function has been widely used in insurance and finance for modeling inter-dependency between risks. Inspired by the Bernstein copula put forward by Sancetta and Satchell (2004, Econometric Theory, 20, 535–562), we introduce a new class of multivariate copulas, the composite Bernstein copula, generated from a composition of two copulas. This new class of copula functions is able to capture tail dependence, and it has a reproduction property for the three important dependency structures: comonotonicity, countermonotonicity and independence. We introduce an estimation procedure based on the empirical composite Bernstein copula which incorporates both prior information and data into the estimation. Simulation studies and an empirical study on financial data illustrate the advantages of the empirical composite Bernstein copula estimation method, especially in capturing tail dependence.