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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

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.

Suggested Citation

  • Jonathan Dark, 2004. "Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures," Monash Econometrics and Business Statistics Working Papers 4/04, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2004-4
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2004/wp4-04.pdf
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    References listed on IDEAS

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    Cited by:

    1. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
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    3. Chaker Aloui, 2011. "Latin American stock markets’ volatility spillovers during the financial crises: a multivariate FIAPARCH-DCC framework," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 4(2), pages 289-326, May.
    4. Cuong Nguyen & M. Bhatti & Aziz Hayat, 2014. "Volatility linkages in the spot and futures market in Australia: a copula approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(5), pages 2589-2603, September.

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    More about this item

    Keywords

    long memory; univariate and bivariate FIGARCH and FIAPARCH; asymmetries in volatility.;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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