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Multivariate ARCH and GARCH Models

In: New Introduction to Multiple Time Series Analysis

Author

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  • Helmut Lütkepohl

    (European University Institute)

Abstract

In the previous chapters, we have discussed modelling the conditional mean of the data generation process of a multiple time series, conditional on the past at each particular time point. In that context, the variance or covariance matrix of the conditional distribution was assumed to be time invariant. In fact, in much of the discussion, the residuals or forecast errors were assumed to be independent white noise. Such a simplification is useful and justified in many applications.

Suggested Citation

  • Helmut Lütkepohl, 2005. "Multivariate ARCH and GARCH Models," Springer Books, in: New Introduction to Multiple Time Series Analysis, chapter 16, pages 557-584, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27752-1_16
    DOI: 10.1007/978-3-540-27752-1_16
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