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Internal Model Validation in Brazil: Analysis of VaR Backtesting Methodologies

Author

Listed:
  • Alan Cosme Rodrigues da Silva

    (Central Bank of Brazil)

  • Claudio Henrique da Silveira Barbedo

    (Central Bank of Brazil)

  • Gustavo Silva Araújo

    (Central Bank of Brazil)

  • Myrian Beatriz Eiras das Neves

    (Central Bank of Brazil)

Abstract

The purpose of this paper is to analyze backtesting methodologies of VaR, focusing on aspects as suitability to volatile markets and limited data set. We verify, from regulatory standpoint, tests to complement the Basel traffic light results, using simulated and real data. The results indicate that tests based on failures proportion are not adequate for small samples even fro 1,000 observations. The Basel criterion is conservative and has low power, which does not invalidate its application, as the criterion is only one of the procedures adopted in internal model validation process. Thus, it is suggested using tests that capture the shape of returns distribution, as the Kuiper test, in addition to the Basel criterion.

Suggested Citation

  • Alan Cosme Rodrigues da Silva & Claudio Henrique da Silveira Barbedo & Gustavo Silva Araújo & Myrian Beatriz Eiras das Neves, 2006. "Internal Model Validation in Brazil: Analysis of VaR Backtesting Methodologies," Brazilian Review of Finance, Brazilian Society of Finance, vol. 4(1), pages 97-118.
    • Handle: RePEc:brf:journl:v:4:y:2006:i:1:p:97-118
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    References listed on IDEAS

    as
    1. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    2. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    3. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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