RFS Advance Access originally published online on October 15, 2003
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Rev Fin 2004; 17:581-610
The Review of Financial Studies Vol. 17, No. 2, pp. 581–610 © 2004 The Society for Financial Studies; all rights reserved.
Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications
University of Manchester
University of Lausanne
Lancaster University
Address correspondence to Ser-Huang Poon, Manchester School of Accounting and Finance, University of Manchester, Oxford Road, Manchester M13 9PL, UK, or e-mail: ser-huang.poon{at}man.ac.uk.
This article presents a general framework for identifying and modeling the 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.