Time-varying extremal dependence in leading European stock markets
Daniela A. Castro, Miguel de Carvalho – Pontificia Universidad Catholica de Chile
& Jennifer L. Wadsworth – University of Cambridge

Modeling nonstationarity in marginal distributions has been the focus of much recent literature in applied extreme value modeling. By comparison, approaches to modeling nonstationarity in the extremal dependence structure have received relatively little attention. Working within a framework of asymptotic dependence, we introduce a regression model for the spectral 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 decades, and find evidence of an increase in extremal dependence over recent years.

Miguel de Carvalho

Before moving to Pontificia Universidad Católica de Chile, M. de Carvalho was Post-doctoral Fellow and Lecturer at the Swiss Federal Institute of Technology—Ecole Polytechnique Fédérale de Lausanne. He holds a BSc in Mathematics (Universidade Nova de Lisboa, Portugal), an MSc in Economics (Nova School of Business and Economics Portugal), and a PhD in Statistics (Universidade Nova de Lisboa, Portugal). His research is mainly centered on applied statistical, econometrical, and applied mathematical modeling of risk, and on statistical modeling of medical diagnostics. To know more…

  
“Statistics of extremes is about the applied mathematical modeling the occurrence of rare, catastrophic events, but the need to extrapolate beyond observed data—into the tails of a distribution—often leads to challenging inference problems; examples of such extreme events include the February 2010 Chilean earthquake, the recent floods in Australia, the megathrust earthquake in Japan, the oil spill in the Gulf of Mexico, the pandemic influenza H1N1, or the continuing turbulence in the financial markets.”
Extreme events in finance Extreme events in finance