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

  • Herwartz, Helmut
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    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).

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    File URL: http://econstor.eu/bitstream/10419/27671/1/EWP-2008-16.pdf
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    Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2008,16.

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    Date of creation: 2008
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    Handle: RePEc:zbw:cauewp:7411
    Contact details of provider: Postal: D-24098 Kiel,Wilhelm-Seelig-Platz 1
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    Web page: http://www.wiso.uni-kiel.de/econ/

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    1. DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 03-2005, Centre interuniversitaire de recherche en ├ęconomie quantitative, CIREQ.
    2. 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.
    3. Christophe Hurlin & Sessi Tokpavi, 2006. "Backtesting VaR Accuracy: A New Simple Test," Working Papers halshs-00068384, HAL.
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