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Evaluating the forecasts of risk models

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  • Jeremy Berkowitz

Abstract

The forecast evaluation literature has traditionally focused on methods for assessing point-forecasts. However, in the context of risk models, interest centers on more than just a single point of the forecast distribution. For example, value-at-risk (VaR) models, which are currently in extremely wide, use form interval forecasts. Many other important financial calculations also involve estimates not summarized by a point-forecast. Although some techniques are currently available for assessing interval and density forecasts, none are suitable for sample sizes typically available. This paper suggests a new approach to evaluating such forecasts. It requires evaluation of the entire forecast distribution, rather than a value-at-risk quantity. The information content of forecast distributions combined with ex post loss realizations is enough to construct a powerful test even with sample sizes as small as 100.

Suggested Citation

  • Jeremy Berkowitz, 1999. "Evaluating the forecasts of risk models," Finance and Economics Discussion Series 1999-11, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:1999-11
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jose A. Lopez, 1999. "Methods for evaluating value-at-risk estimates," Economic Review, Federal Reserve Bank of San Francisco, pages 3-17.
    2. Asmara Jamaleh, 2002. "Explaining and forecasting the euro/dollar exchange rate through a non-linear threshold model," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 422-448.
    3. Garrat, A. & Lee, K. & Pesaran, M.H. & Shin, Y., 2000. "Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy," Cambridge Working Papers in Economics 0004, Faculty of Economics, University of Cambridge.
    4. Perez-Quiros, Gabriel & Timmermann, Allan, 2001. "Business cycle asymmetries in stock returns: Evidence from higher order moments and conditional densities," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 259-306, July.
    5. Lopez, Jose A. & Saidenberg, Marc R., 2000. "Evaluating credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 151-165, January.
    6. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
    7. Jeremy Berkowitz, 1999. "A coherent framework for stress-testing," Finance and Economics Discussion Series 1999-29, Board of Governors of the Federal Reserve System (U.S.).
    8. Flavio Bazzana, 2001. "I modelli interni per la valutazione del rischio di mercato secondo l'approccio del Value at Risk," Alea Tech Reports 011, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    9. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.

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    Keywords

    Forecasting ; Risk;

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