Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)

Anne Péguin-Feissolle and Bilel Sanhaji
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Anne Péguin-Feissolle: GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique

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Abstract: We introduce two multivariate constant conditional correlation tests that require little knowledge of the functional relationship determining the conditional correlations. The first test is based on artificial neural networks and the second one is based on a Taylor expansion of each unknown conditional correlation. These new tests can be seen as general misspecification tests of a large set of multivariate GARCH-type models. We investigate the size and the power of these tests through Monte Carlo experiments. Moreover, we study their robustness to non-normality by simulating some models such as the GARCH−t and Beta−t−EGARCH models. We give some illustrative empirical examples based on financial data.

Keywords: Taylor expansion; multivariate GARCH; neural network (search for similar items in EconPapers)
Date: 2015-03
New Economics Papers: this item is included in nep-cmp and nep-ets
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Working Paper: Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix) (2015)
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