Type
ArticleAuthors
Krupskii, Pavel
Genton, Marc G.

KAUST Department
Statistics ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2021-03-19Preprint Posting Date
2020-06-21Online Publication Date
2021-03-19Print Publication Date
2021-09Embargo End Date
2022-03-19Submitted Date
2020-06-08Permanent link to this record
http://hdl.handle.net/10754/663908
Metadata
Show full item recordAbstract
We 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.Citation
Krupskii, P., & Genton, M. G. (2021). Conditional normal extreme-value copulas. Extremes. doi:10.1007/s10687-021-00412-8Sponsors
We would like to thank the associate editor and two anonymous referees for their constructive comments that helped to improve this paper.Publisher
Springer NatureJournal
ExtremesarXiv
2006.11759Additional Links
http://link.springer.com/10.1007/s10687-021-00412-8ae974a485f413a2113503eed53cd6c53
10.1007/s10687-021-00412-8