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Long term hedging of the Australian All Ordinaries Index using a bivariate error correction FIGARCH model

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Author Info
Jonathan Dark ()
Abstract

This article compares the performance of bivariate error correction GARCH and FIGARCH models when estimating long term dynamic minimum variance hedge ratios (MVHRs) on the Australian All Ordinaries Index. The paper therefore introduces the bivariate error correction FIGARCH model into the hedging literature, which to date has only employed the GARCH class of processes. This is important for those interested in managing long term equity exposures, given that FIGARCH processes exhibit long memory, whilst the GARCH class of processes exhibit short memory. The naïve hedge ratio, the constant MVHR estimated via ordinary least squares (the OLS MVHR), the single period dynamic MVHR and the multi-period dynamic MVHR of Lee (1999) are considered. The results strongly support the estimation of dynamic MVHRs that allow for time varying correlations. Whilst long memory dependencies appear important, a multi-period dynamic MVHR that responds more rapidly to persistent changes in volatility dynamics requires development.

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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/04.

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Length: 49 pages
Date of creation: Mar 2004
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Handle: RePEc:msh:ebswps:2004-7

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Related research
Keywords: Long memory; bivariate FIGARCH; time varying correlations; multi period minimum variance hedge ratios.;

Find related papers by JEL classification:
G0 - Financial Economics - - General
G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

This paper has been announced in the following NEP Reports:

References listed on IDEAS
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