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Bootstrapping Neural tests for conditional heteroskedasticity

Author

Listed:
  • Carole Siani

    (University of Claude Bernard Lyon 1 (France).)

  • Christian de Peretti

    (University of Evry-Val-d'Essonne (France).)

Abstract

We deal with bootstrapping tests for detecting conditional heteroskedasticity in the context of standard and nonstandard ARCH models. We develope parametric and nonparametric bootstrap tests based both on the LM statistic and a neural statistic. The neural tests are designed to approximate an arbitrary nonlinear form of the conditional variance by a neural function. While published tests are valid asymptotically, they are not exact in finite samples and suffer from a substantial size distortion: the finite-sample error remains non-negligible, even for several hundred observations. Here, we treat this problem using bootstrap methods, making possible a better finite-sample estimate of the distribution of the test statistic. A graphical presentation employing a size-correction principle is used to show the true power of the tests rather than the spurious nominal power typically given

Suggested Citation

  • Carole Siani & Christian de Peretti, 2006. "Bootstrapping Neural tests for conditional heteroskedasticity," Computing in Economics and Finance 2006 301, Society for Computational Economics.
    • Handle: RePEc:sce:scecfa:301
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    References listed on IDEAS

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    More about this item

    Keywords

    Bootstrap; Artificial Neural Networks; ARCH models; inference tests;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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