The Annals of Applied Statistics

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

Daniela Castro-Camilo, Miguel de Carvalho, and Jennifer Wadsworth

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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.

Article information

Source
Ann. Appl. Stat. Volume 12, Number 1 (2018), 283-309.

Dates
Received: March 2017
Revised: June 2017
First available in Project Euclid: 9 March 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1520564473

Digital Object Identifier
doi:10.1214/17-AOAS1089

Keywords
Angular measure bivariate extreme values European stock market integration risk statistics of extremes

Citation

Castro-Camilo, Daniela; de Carvalho, Miguel; Wadsworth, Jennifer. Time-varying extreme value dependence with application to leading European stock markets. Ann. Appl. Stat. 12 (2018), no. 1, 283--309. doi:10.1214/17-AOAS1089. https://projecteuclid.org/euclid.aoas/1520564473


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Supplemental materials

  • Supplementary Monte Carlo evidence and empirical reports. The supplement includes additional simulation results, descriptive statistics for daily stock index negative returns, and further empirical analysis using the NGARCH-filtered residuals and LSCV bandwidths.