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A Note on Diagnosing Multivariate Conditional Heteroscedasticity Models

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  • Y. K. Tse
  • A. K. C. Tsui

Abstract

In this paper we consider several tests for model misspecification after a multivariate conditional heteroscedasticity model has been fitted. We examine the performance of the recent test due to Ling and Li (J. Time Ser. Anal. 18 (1997), 447–64), the Box–Pierce test and the residual‐based F test using Monte Carlo methods. We find that there are situations in which the Ling–Li test has very weak power. The residual‐based diagnostics demonstrate significant under‐rejection under the null. In contrast, the Box–Pierce test based on the cross‐products of the standardized residuals often provides a useful diagnostic that has reliable empirical size as well as good power against the alternatives considered.

Suggested Citation

  • Y. K. Tse & A. K. C. Tsui, 1999. "A Note on Diagnosing Multivariate Conditional Heteroscedasticity Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(6), pages 679-691, November.
  • Handle: RePEc:bla:jtsera:v:20:y:1999:i:6:p:679-691
    DOI: 10.1111/1467-9892.00166
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    File URL: https://doi.org/10.1111/1467-9892.00166
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    Cited by:

    1. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2019. "Tests for conditional heteroscedasticity with functional data and goodness-of-fit tests for FGARCH models," MPRA Paper 93048, University Library of Munich, Germany.
    2. Gregory Rice & Tony Wirjanto & Yuqian Zhao, 2020. "Tests for conditional heteroscedasticity of functional data," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 733-758, November.
    3. Gloria Gonzalez-Rivera & Emre Yoldas, 2010. "Multivariate Autocontours for Specification Testing in Multivariate GARCH Models," Working Papers 201436, University of California at Riverside, Department of Economics.

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