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

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  • Herwartz, Helmut
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    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). --

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    File URL: http://econstor.eu/bitstream/10419/27671/1/EWP-2008-16.pdf
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    Bibliographic Info

    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

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    Keywords: Value-at-Risk; Monte Carlo test;

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    1. Jean-Marie Dufour, 2005. "Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and non-standard asymptotics," CIRANO Working Papers 2005s-02, CIRANO.
    2. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
    3. Christophe Hurlin & Sessi Tokpavi, 2006. "Backtesting VaR Accuracy: A New Simple Test," Working Papers halshs-00068384, HAL.
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