Tail dependence analysis of stock markets using extreme value theory

Abhay K. Singh*, David E. Allen, Robert J. Powell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Financial risk modelling frequently uses the assumption of a normal distribution when considering the return series which is inefficient if the data is not normally distributed or if it exhibits extreme tails. Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst applications. Extreme Value Theory (EVT) provides well established methods for considering univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. The empirical analysis in this article uses nonparametric measures based on bivariate EVT to investigate asymptotic dependence and estimate the degree of tail dependence of the ASX-All Ordinaries daily returns with four other international markets, viz., the S&P-500, Nikkei-225, DAX-30 and Heng-Seng for both extreme right and left tails of the return distribution. It is investigated whether the asymptotic dependence between these markets is related to the heteroscedasticity present in the logarithmic return series using GARCH filters. The empirical evidence shows that the asymptotic extreme tail dependence between stock markets does not necessarily exist and rather can be associated with the heteroscedasticity present in the financial time series of the various stock markets.

Original languageEnglish
Pages (from-to)4588-4599
Number of pages12
JournalApplied Economics
Issue number45
Publication statusPublished - 2017
Externally publishedYes


  • asymptotic dependence
  • Extreme risk
  • extreme value theory
  • heteroscedasticity


Dive into the research topics of 'Tail dependence analysis of stock markets using extreme value theory'. Together they form a unique fingerprint.

Cite this