Evaluating extremal dependence in stock markets using Extreme Value Theory

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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.

LanguageEnglish
Title of host publicationMODSIM 2011
Subtitle of host publication19th International Congress on Modelling and Simulation: proceedings
EditorsF. Chan, D. Marinova, R. S. Anderssen
Place of PublicationCanberra
Pages1485-1491
Number of pages7
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty, MODSIM2011 - Perth, WA, Australia
Duration: 12 Dec 201116 Dec 2011

Conference

Conference19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty, MODSIM2011
CountryAustralia
CityPerth, WA
Period12/12/1116/12/11

Fingerprint

Tail Dependence
Extreme Value Theory
Stock Market
Heteroskedasticity
Financial Risk
Tail
Extreme Quantiles
Portfolio Theory
Financial Modeling
Time series
Financial Time Series
Generalized Autoregressive Conditional Heteroscedasticity
Hedging
Univariate
Forecasting
Logarithmic
Extremes
Filter
Series
Modeling

Keywords

  • Extreme Value Theory
  • GARCH
  • Heteroskedasticity
  • Tail dependence

Cite this

Singh, A. K., Allen, D. E., & Powell, R. J. (2011). Evaluating extremal dependence in stock markets using Extreme Value Theory. In F. Chan, D. Marinova, & R. S. Anderssen (Eds.), MODSIM 2011: 19th International Congress on Modelling and Simulation: proceedings (pp. 1485-1491). Canberra.
Singh, Abhay K. ; Allen, David E. ; Powell, Robert J. / Evaluating extremal dependence in stock markets using Extreme Value Theory. MODSIM 2011: 19th International Congress on Modelling and Simulation: proceedings. editor / F. Chan ; D. Marinova ; R. S. Anderssen. Canberra, 2011. pp. 1485-1491
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Singh, AK, Allen, DE & Powell, RJ 2011, Evaluating extremal dependence in stock markets using Extreme Value Theory. in F Chan, D Marinova & RS Anderssen (eds), MODSIM 2011: 19th International Congress on Modelling and Simulation: proceedings. Canberra, pp. 1485-1491, 19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty, MODSIM2011, Perth, WA, Australia, 12/12/11.

Evaluating extremal dependence in stock markets using Extreme Value Theory. / Singh, Abhay K.; Allen, David E.; Powell, Robert J.

MODSIM 2011: 19th International Congress on Modelling and Simulation: proceedings. ed. / F. Chan; D. Marinova; R. S. Anderssen. Canberra, 2011. p. 1485-1491.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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Singh AK, Allen DE, Powell RJ. Evaluating extremal dependence in stock markets using Extreme Value Theory. In Chan F, Marinova D, Anderssen RS, editors, MODSIM 2011: 19th International Congress on Modelling and Simulation: proceedings. Canberra. 2011. p. 1485-1491