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Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models

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  • Anne Péguin-Feissolle
  • Bilel Sanhaji

Abstract

We introduce two tests for the constancy of conditional correlations of unknown functional form in multivariate GARCH models. The first test is based on artificial neural networks and the second on a Taylor expansion of each unknown conditional correlation. They can be seen as general misspecification tests for a large set of multivariate GARCH-type models. We investigate their size and their power through Monte Carlo experiments. Moreover, we study the robustness of these tests to nonnormality by simulating some models, such as the GARCH - t and Beta - t - EGARCH. We give some illustrative empirical examples based on financial data.

Suggested Citation

  • Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101.
  • Handle: RePEc:adr:anecst:y:2016:i:123-124:p:77-101
    DOI: 10.15609/annaeconstat2009.123-124.0077
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    Cited by:

    1. Annastiina Silvennoinen & Timo Teräsvirta, 3108. "Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model," CREATES Research Papers 2017-28, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Multivariate GARCH; Neural Network; Taylor Expansion;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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