Robust Parametric Tests of Constant Conditional Correlation in a MGARCH model

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Abstract

This paper provides a rigorous asymptotic treatment of new and existing asymptotically valid Conditional Moment testing procedures of the Constant Conditional Correlation assumption in a multivariate GARCH model. Full and partial Quasi Maximum Likelihood Estimation frameworks are considered, as is the robustness of these tests to non-normality. In particular, the asymptotic validity of the LM procedure proposed by Tse (2000) is analyzed and new asymptotically robust versions of this test are proposed for both estimation frameworks. A Monte Carlo study suggests that a robust Tse test procedure exhibits good size and power properties, unlike the original variant which exhibits size distortion under non-normality. In order to conserve space, the Supplementary paper provides all Monte Carlo results/tables referred to in the Main Paper and detailed proofs of all results.

Keyword(s)

Conditional Moment Tests; Constant Conditional Correlation; Monte Carlo; Multivariate GARCH models; Robustness

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