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

Anne Péguin-Feissolle and Bilel Sanhaji

Annals of Economics and Statistics, 2016, issue 123-124, 77-101

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.

Keywords: Multivariate GARCH; Neural Network; Taylor Expansion (search for similar items in EconPapers)
JEL-codes: C22 C45 C58 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Working Paper: Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:adr:anecst:y:2016:i:123-124:p:77-101

DOI: 10.15609/annaeconstat2009.123-124.0077

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