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Bootstrapping Neural tests for conditional heteroskedasticity

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Author Info
Carole Siani () (University of Claude Bernard Lyon 1 (France).)
Christian de Peretti (University of Evry-Val-d'Essonne (France).)

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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

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 301.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:301

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Related research
Keywords: Bootstrap; Artificial Neural Networks; ARCH models; inference tests;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February. [Downloadable!] (restricted)
  2. G. S. Hongyi Li, 1996. "Bootstrapping time series models," Econometric Reviews, Taylor and Francis Journals, vol. 15(2), pages 115-158. [Downloadable!] (restricted)
  3. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, EconWPA, revised 05 Mar 1996. [Downloadable!]
  4. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April. [Downloadable!] (restricted)
  5. Weber, N. C., 1984. "On resampling techniques for regression models," Statistics & Probability Letters, Elsevier, vol. 2(5), pages 275-278, October. [Downloadable!] (restricted)
  6. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April. [Downloadable!] (restricted)
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