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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.15622 |
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| _version_ | 1866909463350345728 |
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| author | Chatelain, Simon Perreault, Samuel Nešlehová, Johanna G. Fougères, Anne-Laure |
| author_facet | Chatelain, Simon Perreault, Samuel Nešlehová, Johanna G. Fougères, Anne-Laure |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_15622 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Clustered Archimax Copulas Chatelain, Simon Perreault, Samuel Nešlehová, Johanna G. Fougères, Anne-Laure Statistics Theory 60G70, 62H05 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. |
| title | Clustered Archimax Copulas |
| topic | Statistics Theory 60G70, 62H05 |
| url | https://arxiv.org/abs/2210.15622 |