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A diagnostic m-test for distributional specification of parametric conditional heteroscedasticity models for financial data

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  • LEJEUNE, Bernard

Abstract

This paper proposes a convenient and generally applicable diognostic m-test for checking the distributional specification of parametric conditional heteroscedasticity models for financial data such as customary student t GARCH model. The proposed test is based on the moments of probability integral transform of the innovations of the assumed model. Monte-carlo evidence indicates that our suggested test performs well both in terms of size and power.

Suggested Citation

  • LEJEUNE, Bernard, 2002. "A diagnostic m-test for distributional specification of parametric conditional heteroscedasticity models for financial data," LIDAM Discussion Papers CORE 2002024, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2002024
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp2002.html
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    Citations

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    Cited by:

    1. Christian Bontemps & Nour Meddahi, 2012. "Testing distributional assumptions: A GMM aproach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 978-1012, September.

    More about this item

    Keywords

    parametric conditional heteroscedasticity models; distributional specification test; m-testing;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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