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

Jonathan Dark ()

No 4/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: long memory; univariate and bivariate FIGARCH and FIAPARCH; asymmetries in volatility. (search for similar items in EconPapers)
JEL-codes: C3 C51 G0 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2004-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin, nep-fmk and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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