Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets

TL;DR

This paper introduces a regression model for time-varying extremal dependence between stock markets, revealing increasing dependence in European markets over recent decades.

Contribution

It develops a novel regression framework for the angular density in bivariate extreme value distributions to model non-stationary extremal dependence.

Findings

Evidence of increasing extremal dependence among European stock markets

Model captures how extremal dependence changes with a covariate over time

Application to three decades of data demonstrates evolving financial risk

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.