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Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures

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  • Jonathan Dark

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    Abstract

    In this paper we extend the univariate FIGARCH and FIAPARCH models to a bivariate framework. We estimate bivariate error correction FIGARCH and FIAPARCH models between the All Ordinaries Index and its SPI futures using constant correlation and diagonal parameterisations. We therefore employ a flexible estimation approach that captures the long run equilibrium relationship between the two markets, bi-directional return causality, long memory and asymmetries in volatility, and time varying correlations. The results strongly support the use of this approach. Strong bi-directional return causality exists with the index bearing the burden of adjustment to deviations from long run equilibrium. The results also illustrate the importance of allowing for long memory, asymmetries in volatility, and time varying correlations.

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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2004/wp4-04.pdf
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    Bibliographic Info

    Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 4/04.

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    Length: 47 pages
    Date of creation: Mar 2004
    Date of revision:
    Handle: RePEc:msh:ebswps:2004-4

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    Keywords: long memory; univariate and bivariate FIGARCH and FIAPARCH; asymmetries in volatility.;

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    Cited by:
    1. Christian Conrad & Menelaos Karanasos & Ning Zeng, 2008. "Multivariate Fractionally Integrated APARCH Modeling of Stock Market Volatility: A multi-country study," Working Papers 0472, University of Heidelberg, Department of Economics, revised Jul 2008.

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