Abstract
Recent literature has trumpeted the claim that extreme value theory (EVT) holds promise for accurate estimation of extreme quantiles and tail probabilities of financial asset returns, and hence holds promise for advances in the management of extreme financial risks. Our view, based on a disinterested assessment of EVT from the vantage point of financial risk management, is that the recent optimism is partly appropriate but also partly exaggerated, and that at any rate much of the potential of EVT remains latent. We substantiate this claim by sketching a number of pitfalls associated with use of EVT techniques. More constructively, we show how certain of the pitfalls can be avoided, and we sketch a number of explicit research directions that will help the potential of EVT to be realized
This includes, tor example, the fitting of stable distributions, as in McCulloch (1996).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bollerslev T., Chou R.Y. and Kroner K.F. (1992), “ARCH Modeling in Finance A Selective Review of the Theory and Empirical Evidence,” Journal o1 Econometrics, 52, 5–59
Campbell J.Y., Lo., A.W. and MacKinlay A.C. (1997), The Economics ol Financial Markets. Princeton: Princeton University Press
Danielsson J. and deVries C.G. (1997a), “Beyond the Sample: Extreme Quantile and Probability Estimation,” Manuscript, Tinbergen Institute Rotterdam
Danielsson J. and deVries C.G. (1997b), “Tail Index and Quantile Estimation with Very High Frequency Data,” Journal of Empirical Finance, 4, 241–257
Danielsson J. and deVries C.G. (1997c), “Extreme Returns, Tail Estimation, and Value-at-Risk,”, Manuscript, Tinbergen Institute Rotterdam
Diebold F.X. and Lopez J. (1995), “Modeling Volatility Dynamics,” in Kevin Hoover (ed.), Macroeconometrics: Developments, Tensions and Prospects. Boston: Kluwer Academic Press, 427–472.
Diebold F.X. and Senhadji A. (1996), “The Uncertain Unit Root in U.S. GNP: Comment,” American Economic Review, 86, 1291–1298.
Einmahl J.H.J. (1990), “The Empirical Distribution Function as a Tail Estimator,” Statistica Neerlandica, 44, 79–82.
Embrechts P., Klüppelberg C. and Mikosch T. (1997), Modelling Extremal Events. New York: Springer-Verlag.
Engle R.F. and González-Rivera G. (1991), “Semiparametric ARCH Models,” Journal of Business and Economic Statistics, 9, 345–359.
Hill B.M. (1975), “A Simple General Approach to Inference About the Tail of a Distribution,” Annals of Statistics, 3, 1163–1174.
Inoue A. (1998), “A Conditional Goodness-of-Fit Test for Time Series,” Manuscript, Department of Economics, University of Pennsylvania.
Kearns P. and Pagan A.R. (1997), “Estimating the Tail Density Index for Financial Time Series,” Review of Economics and Statistics, 79, 171–175.
Koedijk K.G., Schafgans M.A. and deVries C.G. (1990), “The Tail Index of Exchange Rate Returns,” Journal of International Economics, 29, 93–108.
Leadbetter M.R., Lindgren G. and Rootzen H. (1983), Extremes and Related Properties of Random Sequences and Processes. New York: Springer-Verlag.
McCulloch J.H. (1996), “Financial Applications of Stable Distributions,” Handbook of Statistics, Vol. 14, 393–425.
Paul-Choudhury S. (1998), “Beyond Basle,” Risk, 11, 89. (Introduction to a symposium on new methods of assessing capital adequacy, Risk, 11, 90–107).
Silverman B.W. (1986), Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Diebold, F.X., Schuermann, T., Stroughair, J.D. (1998). Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_1
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5625-1_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-7923-8309-3
Online ISBN: 978-1-4615-5625-1
eBook Packages: Springer Book Archive