Conditional tail independence in Archimedean copula models

DOI10.1017/JPR.2019.48zbMath1436.60050arXiv1902.03947OpenAlexW3102224349MaRDI QIDQ5235056

Florian Wisheckel, Michael Falk, Simone A. Padoan

Publication date: 7 October 2019

Published in: Journal of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.03947

zbMATH Keywords

domain of attractionextreme value distributionconditional distributionArchimedean copula\(D\)-normasymptotic tail independencearchimax copula

Mathematics Subject Classification ID

Characterization and structure theory for multivariate probability distributions; copulas (62H05) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)

Cites Work

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• An introduction to copulas.
• Multivariate Archimedean copulas, \(d\)-monotone functions and \(\ell _{1}\)-norm symmetric distributions
• Testing asymptotic independence in bivariate extremes
• Tails of multivariate Archimedean copulas
• Inference for asymptotically independent samples of extremes
• Bivariate tail estimation: dependence in asymptotic independence
• Multivariate Archimax copulas
• Laws of Small Numbers: Extremes and Rare Events
• Multivariate Extreme Value Theory and D-Norms

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