KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2020-06-21
Online Publication Date2021-03-19
Print Publication Date2021-09
Embargo End Date2022-03-19
Permanent link to this recordhttp://hdl.handle.net/10754/663908
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AbstractWe propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independence models, where the dependence among observed variables is modeled using one unobserved factor. Conditional on this factor, the distribution of these variables is given by the Gaussian copula. This structure allows one to build flexible and parsimonious models for data with complex dependence structures, such as data with spatial dependence or factor structure. We study the extreme-value limits of these models and show some interesting special cases of the proposed class of copulas. We develop estimation methods for the proposed models and conduct a simulation study to assess the performance of these algorithms. Finally, we apply these copula models to analyze data on monthly wind maxima and stock return minima.
CitationKrupskii, P., & Genton, M. G. (2021). Conditional normal extreme-value copulas. Extremes. doi:10.1007/s10687-021-00412-8
SponsorsWe would like to thank the associate editor and two anonymous referees for their constructive comments that helped to improve this paper.