A diagnostic m-test for distributional specification of parametric conditional heteroscedasticity models for financial data
AbstractThis 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.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2002024.
Date of creation: 00 Apr 2002
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parametric conditional heteroscedasticity models; distributional speciﬁcation test; m-testing;
Find related papers by 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 &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- 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, 09.
- N. Meddahi & C. Bontemps, 2004. "Testing Distributional Assumptions: A GMM Approach," Econometric Society 2004 North American Winter Meetings 487, Econometric Society.
- Bontemps, Christian & Meddahi, Nour, 2007. "Testing Distributional Assumptions: A GMM Approach," IDEI Working Papers 486, Institut d'Économie Industrielle (IDEI), Toulouse.
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