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    Generalized autoregressive conditional correlation

    137687_20749_pub46560.pdf (247.3Kb)
    Access Status
    Open access
    Authors
    McAleer, M.
    Chan, Felix
    Hoti, S.
    Lieberman, O.
    Date
    2008
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    McAleer, Michael and Chan, Felix and Hoti, Suhejla and Lieberman, Offer. 2008. Generalized autoregressive conditional correlation. Econometric Theory. 24: pp. 1554-1583.
    Source Title
    Econometric Theory
    DOI
    10.1017/S0266466608080614
    ISSN
    0266-4666
    Faculty
    Curtin Business School
    School of Economics and Finance
    Remarks

    © Cambridge University Press 2008

    URI
    http://hdl.handle.net/20.500.11937/33440
    Collection
    • Curtin Research Publications
    Abstract

    This paper develops a generalized autoregressive conditional correlation (GARCC) model when the standardized residuals follow a random coefficient vector autoregressive process. As a multivariate generalization of the Tsay (1987, Journal of the American Statistical Association 82, 590-604) random coefficient autoregressive (RCA) model, the GARCC model provides a motivation for the conditional correlations to be time varying. GARCC is also more general than the Engle (2002, Journal of Business & Economic Statistics 20, 339-350) dynamic conditional correlation (DCC) and the Tse and Tsui (2002, Journal of Business & Economic Statistics 20, 351-362) varying conditional correlation (VCC) models and does not impose unduly restrictive conditions on the parameters of the DCC model. The structural properties of the GARCC model, specifically, the analytical forms of the regularity conditions, are derived, and the asymptotic theory is established. The Baba, Engle, Kraft, and Kroner (BEKK) model of Engle and Kroner (1995, Econometric Theory 11, 122-150) is demonstrated to be a special case of a multivariate RCA process. A likelihood ratio test is proposed for several special cases of GARCC. The empirical usefulness of GARCC and the practicality of the likelihood ratio test are demonstrated for the daily returns of the Standard and Poor's 500, Nikkei, and Hang Seng indexes.

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