Time-varying extreme value dependence with application to leading European stock markets

Daniela Castro, Miguel de Carvalho, Jennifer Wadsworth

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Extremal dependence between international stock markets is of particular interest in today’s global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.
Original languageEnglish (US)
Pages (from-to)283-309
Number of pages27
JournalThe Annals of Applied Statistics
Volume12
Issue number1
DOIs
StatePublished - Mar 9 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank the Editor, Associate Editor, and two anonymous referees. We extend our thanks to António Rua, Vanda Inácio de Carvalho, and Claudia Wehrhahn for helpful discussions. Part of this work was written while D. Castro-Camilo was visiting the University of Cambridge—Statistical Laboratory, and while M. de Carvalho was visiting Banco de Portugal. Supported in part by Fundação para a Ciência e a Tecnologia, through UID/MAT/00006/2013 and by the Chilean National Science Foundation through Fondecyt 11121186, “Constrained Inference Problems in Extreme Value Modeling”.

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