Risk and Dependence Analysis of Australian Stock Market - The Case of Extreme Value Theory

22 Pages Posted: 26 Aug 2012

See all articles by Abhay Kumar Singh

Abhay Kumar Singh

Edith Cowan University

David E. Allen

School of Mathematics and Statistics, The University of Sydney; Financial Research Network (FIRN); Department of Finance; School of Business and Law, Edith Cowan University

Robert J. Powell

Edith Cowan University - School of Accounting, Finance and Economics; Financial Research Network (FIRN)

Date Written: August 25, 2012

Abstract

The quantification of risk and dependence are major components of financial risk modelling. Financial risk modelling frequenty uses the assumption of a normal distribution when considereing the return series which makes modelling easy but is inefficient if the data is not normally distributed or if it exhibits extreme tails. When dealing with extreme financial events to quantify extreme market risk, Extreme Value Theory (EVT) proves to be a natural statistical modelling technique of interest. 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. In this paper we focus on the extreme risk and dependence analysis of the ASX-All Ordinaries (Australian) stock market using using univariate and multivariate EVT based methods. The empirical evidence shows that EVT can be successfully applied to financial market return series for predicting daily VaR using a GARCH(1,1) and EVT based dynamic approach. We also use 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.

Keywords: Extreme risk, Asymptotic dependence, VaR, Extreme Value Theory

JEL Classification: C14, C22, G10, G19

Suggested Citation

Singh, Abhay Kumar and Allen, David Edmund and Powell, Robert J., Risk and Dependence Analysis of Australian Stock Market - The Case of Extreme Value Theory (August 25, 2012). 25th Australasian Finance and Banking Conference 2012. Available at SSRN: https://ssrn.com/abstract=2136294 or http://dx.doi.org/10.2139/ssrn.2136294

Abhay Kumar Singh (Contact Author)

Edith Cowan University ( email )

Joondalup Drive
Perth
Joondalup, WA 6027
Australia

David Edmund Allen

School of Mathematics and Statistics, The University of Sydney ( email )

School of Mathematics and Statistics F07
University of Sydney
Sydney, New South Wales 2006
Australia

HOME PAGE: http://www.maths.usyd.edu.au

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Department of Finance ( email )

Taiwan
Taiwan

School of Business and Law, Edith Cowan University

100 Joondalup Drive
Joondalup, WA 6027
Australia

HOME PAGE: http://www.dallenwapty.com

Robert J. Powell

Edith Cowan University - School of Accounting, Finance and Economics ( email )

Joondalup Campus
Perth
Joondalup 6027, WA
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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