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Computer-Intensive Time-Varying Model Approach to the Systematic Risk of Australian Industrial Stock Returns

Author

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  • Juan Yao

    (Finance Discipline, School of Business, The University of Sydney, NSW 2006.)

  • Jiti Gao

    (Department of Statistics, School of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009.)

Abstract

This paper aims to investigate the form of systematic risk of Australian industrial stock returns. We suggest using four stochastic state-space models for the analysis. The stochastic properties of systematic risk are studied by examining four classes of state-space models: random walk model, random coefficient model, ARMA(1, 1) model and mean reverting model (or moving mean model). We have found that the industrial portfolio betas are unstable. The variation of industrial portfolio beta is either random or mean-reverting. Among the nineteen industrial groups, ten of them have the mean-reverting process betas but six of them seem to have a moving long-term mean. Five of the industrial groups have the random process betas, more specifically; the betas of three of them are the random walk processes while the betas of the other two are just the random coefficients. We have also identified that the betas of five industrial groups seem to follow an ARMR(1,1) process.

Suggested Citation

  • Juan Yao & Jiti Gao, 2004. "Computer-Intensive Time-Varying Model Approach to the Systematic Risk of Australian Industrial Stock Returns," Australian Journal of Management, Australian School of Business, vol. 29(1), pages 121-145, June.
    • Handle: RePEc:sae:ausman:v:29:y:2004:i:1:p:121-145
    DOI: 10.1177/031289620402900113
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    References listed on IDEAS

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    Cited by:

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