### Abstract

Original language | English |
---|---|

Pages (from-to) | 121-145 |

Journal | Australian Journal of Management |

Volume | 29 |

Issue number | 1 |

Publication status | Published - 2004 |

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### Cite this

*Australian Journal of Management*,

*29*(1), 121-145.

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*Australian Journal of Management*, vol. 29, no. 1, pp. 121-145.

**Computer-Intensive Time-Varying Model Approach to the Systematic Risk of Australian Industrial Stock Returns.** / Yao, J.; Gao, Jiti.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Computer-Intensive Time-Varying Model Approach to the Systematic Risk of Australian Industrial Stock Returns

AU - Yao, J.

AU - Gao, Jiti

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

M3 - Article

VL - 29

SP - 121

EP - 145

JO - Australian Journal of Management

JF - Australian Journal of Management

SN - 0312-8962

IS - 1

ER -