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Exact inference in diagnosing value-at-risk estimates: A Monte Carlo device

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  • Herwartz, Helmut

Abstract

In this note a Monte Carlo approach is suggested to determine critical values for diagnostic tests of Value-at-Risk models that rely on binary random variables. Monte Carlo testing offers exact significance levels in finite samples. Conditional on exact critical values the dynamic quantile test suggested by Engle and Manganelli (2004) turns out more powerful than a recently proposed Portmanteau type test (Hurlin and Tokpavi 2006).

Suggested Citation

  • Herwartz, Helmut, 2008. "Exact inference in diagnosing value-at-risk estimates: A Monte Carlo device," Economics Working Papers 2008-16, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:7411
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    File URL: https://www.econstor.eu/bitstream/10419/27671/1/EWP-2008-16.pdf
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    References listed on IDEAS

    as
    1. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    2. Christophe Hurlin & Sessi Tokpavi, 2006. "Backtesting VaR Accuracy: A New Simple Test," Working Papers halshs-00068384, HAL.
    3. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
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    More about this item

    Keywords

    Value-at-Risk; Monte Carlo test;

    JEL classification:

    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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