Time-varying extreme value dependence with application to leading European stock markets
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ArticleDate
2018-03-09Online Publication Date
2018-03-09Print Publication Date
2018-03Permanent link to this record
http://hdl.handle.net/10754/627548
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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.Citation
Castro-Camilo D, de Carvalho M, Wadsworth J (2018) Time-varying extreme value dependence with application to leading European stock markets. The Annals of Applied Statistics 12: 283–309. Available: http://dx.doi.org/10.1214/17-AOAS1089.Sponsors
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”.Publisher
Institute of Mathematical StatisticsJournal
The Annals of Applied StatisticsarXiv
1709.01198Additional Links
https://projecteuclid.org/euclid.aoas/1520564473ae974a485f413a2113503eed53cd6c53
10.1214/17-AOAS1089