EVT and tail-risk modelling

Evidence from market indices and volatility series

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

Research output: Contribution to journalArticle

22 Citations (Scopus)


Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.
Original languageEnglish
Pages (from-to)355-369
Number of pages15
JournalThe North American Journal of Economics and Finance: a journal of financial economics studies
Publication statusPublished - 2013
Externally publishedYes

Bibliographical note

Cited By :16

Export Date: 1 January 2018

Correspondence Address: Allen, D.E.; School of Accounting Finance and Economics, Edith Cowan UniversityAustralia; email: d.allen@ecu.edu.au

Funding details: ARC, Australian Research Council

Funding text: We thank the Australian Research Council for funding support. We are grateful to the reviewers for helpful comments.

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  • CVaR
  • ES
  • EVT
  • VaR

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