论文标题
聚集的Archimax Copulas
Clustered Archimax Copulas
论文作者
论文摘要
在对多元现象进行建模时，正确捕获关节极端行为通常是许多担忧之一。在渐近依赖性的情况下，Archimax Copulas似乎是成功的候选人。在本文中，Archimax Copulas的类别通过其随机表示扩展到聚类结构。这些聚集的Archimax Copulas的特征是将随机变量的分区分为由径向copula链接的组。每个群集都是Archimax，因此由其自己的Archimedean Generator和稳定的尾部依赖功能定义。所提出的扩展允许群集之间的渐近依赖性和独立性，这是一种在环境科学和金融应用中所寻求的特性。该模型还从Archimax Copulas捕获前超级级别上变量之间依赖性的能力继承。建立了模型的渐近行为，从而导致一系列稳定的尾巴依赖功能。
When modeling multivariate phenomena, properly capturing the joint extremal behavior is often one of the many concerns. Archimax copulas appear as successful candidates in case of asymptotic dependence. In this paper, the class of Archimax copulas is extended via their stochastic representation to a clustered construction. These clustered Archimax copulas are characterized by a partition of the random variables into groups linked by a radial copula; each cluster is Archimax and therefore defined by its own Archimedean generator and stable tail dependence function. The proposed extension allows for both asymptotic dependence and independence between the clusters, a property which is sought, for example, in applications in environmental sciences and finance. The model also inherits from the ability of Archimax copulas to capture dependence between variables at pre-extreme levels. The asymptotic behavior of the model is established, leading to a rich class of stable tail dependence functions.
