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RFS Advance Access published online on October 15, 2003
Review of Financial Studies, doi:10.1093/rfs/hhg058
Review of Financial Studies © The Society for Financial Studies 2003; all rights reserved
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© 2003 The Society for Financial Studies

Original Articles

Extreme-Value Dependence in Financial Markets: Diagnostics, Models and Financial Implications

Ser-Huang Poon 1*, Michael Rockinger 2, and Jonathan Tawn 3
1 Manchester School of Accounting and Finance, University of Manchester, Oxford Road, Manchester M13 9PL, UK
2 HEC-BFSH 1, Department of Management, University of Lausanne, CH-1009 Dorigny, Switzerland
3 Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK

* To whom correspondence should be addressed. E-mail: ser-huang.poon{at}man.ac.uk.


  Abstract

This paper presents a general framework for identifying and modelling joint-tail distribution based on multivariate extreme value theories. We argue that the multivariate approach is the most efficient and effective way to study extreme events such as systemic risk and crisis. We show, using returns on five major stock indices, that the use of traditional dependence measures could lead to inaccurate portfolio risk assessment. We explain how the framework proposed here could be exploited in a number of finance applications such as portfolio selection, risk management, Sharpe ratio targeting, hedging, option valuation and credit risk analysis.


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