IDEAS home Printed from https://ideas.repec.org/a/eme/jrfpps/eb043443.html
   My bibliography  Save this article

Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management

Author

Listed:
  • Francis X. Diebold
  • Til Schuermann
  • John D. Stroughair

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 hold 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 associate 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 abstract was borrowed from another version of this item.)

Suggested Citation

  • Francis X. Diebold & Til Schuermann & John D. Stroughair, 2000. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 1(2), pages 30-35, January.
    • Handle: RePEc:eme:jrfpps:eb043443
    DOI: 10.1108/eb043443
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/eb043443/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/eb043443/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/eb043443?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. J.H.J. Einmahl, 1990. "The empirical distribution function as a tail estimator," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 44(2), pages 79-82, June.
    2. Jon Danielsson & Casper G. de Vries, 1998. "Beyond the Sample: Extreme Quantile and Probability Estimation," FMG Discussion Papers dp298, Financial Markets Group.
    3. Diebold & Lopez, "undated". "Modeling Volatility Dynamics," Home Pages _062, University of Pennsylvania.
    4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    5. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-359, October.
    6. Phillip Kearns & Adrian Pagan, 1997. "Estimating The Density Tail Index For Financial Time Series," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 171-175, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    2. ROCKINGER, Michael & JONDEAU, Eric, 2000. "Conditional Volatility, Skewness, and Kurtosis : Existence and Persistence," HEC Research Papers Series 710, HEC Paris.
    3. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," Scandinavian Journal of Economics, Wiley Blackwell, vol. 106(2), pages 165-185, June.
    4. Shaun Bond & Stephen Satchell, 2006. "Asymmetry and downside risk in foreign exchange markets," The European Journal of Finance, Taylor & Francis Journals, vol. 12(4), pages 313-332.
    5. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    6. Siem Jan Koopman & Eugenie Hol Uspensky, 2002. "The stochastic volatility in mean model: empirical evidence from international stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 667-689.
    7. Rama Cont & Jean-Philippe Bouchaud, 1997. "Herd behavior and aggregate fluctuations in financial markets," Science & Finance (CFM) working paper archive 500028, Science & Finance, Capital Fund Management.
    8. LINTON, Olivier & PERRON, Benoît, 1999. "The Shape of the Risk Premium: Evidence from a Semiparametric Garch Model," Cahiers de recherche 9911, Universite de Montreal, Departement de sciences economiques.
    9. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    10. Verbeek, Marno, 2007. "A Guide to Modern Econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 8(4), pages 125-132.
    11. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, September.
    12. Cheng-Few Lee & Jung-Bin Su, 2012. "Alternative statistical distributions for estimating value-at-risk: theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 39(3), pages 309-331, October.
    13. Teräsvirta, Timo, 2006. "An introduction to univariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 646, Stockholm School of Economics.
    14. Y. Liu & B. Wang & H. Zhan & Y. Fan & Y. Zha & Y. Hao, 2017. "Simulation of Nonstationary Spring Discharge Using Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4875-4890, December.
    15. Buhlmann, Peter & McNeil, Alexander J., 2002. "An algorithm for nonparametric GARCH modelling," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 665-683, October.
    16. Cotter, John, 2004. "Downside Risk for European Equity Markets," MPRA Paper 3537, University Library of Munich, Germany.
    17. Peter Christoffersen & Redouane Elkamhi & Bruno Feunou & Kris Jacobs, 2010. "Option Valuation with Conditional Heteroskedasticity and Nonnormality," The Review of Financial Studies, Society for Financial Studies, vol. 23(5), pages 2139-2183.
    18. Black, Angela J. & McMillan, David G., 2006. "Asymmetric risk premium in value and growth stocks," International Review of Financial Analysis, Elsevier, vol. 15(3), pages 237-246.
    19. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    20. Andersen, Torben G. & Lund, Jesper, 1997. "Estimating continuous-time stochastic volatility models of the short-term interest rate," Journal of Econometrics, Elsevier, vol. 77(2), pages 343-377, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:jrfpps:eb043443. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.