Document Type

Conference Proceeding

Publisher

Modelling and Simulation Society of Australia and New Zealand

Faculty

Faculty of Business and Law

School

School of Accounting, Finance and Economics / Finance, Economics, Markets and Accounting Research Centre

RAS ID

12963

Comments

This article was originally published as: Singh, A.K. , Allen, D. E., & Powell, R.J. (2011). Evaluating extremal dependence in stock markets using extreme value theory. Paper presented at the 19th International Congress on Modelling and Simulation (MODSIM 2011) . Australian Mathematical Sciences Institute. Perth, Australia. Original article available here

Abstract

Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst others. Extreme Value Theory (EVT) that provides well established methods for univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. This paper 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 right and left tails of the return distribution in extreme quantiles. It is investigated whether the asymptotic dependence between these markets is related to the heteroskedasticity present in the logarithmic return series using GARCH filters. The empirical evidence from bivariate EVT methods show that the asymptotic dependence between the extreme tails of the stock markets does not necessarily exist and rather can be associated with the heteroskedasticity present in the financial time series of the various stock markets.

Access Rights

free_to_read

 
COinS