AJM - Abstracts

Volume 29 Number 1 June 2004

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

Juan Yao and Jiti Gao

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.

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Keywords

KALMAN FILTER; MAXIMUM LIKELIHOOD; RISK ANALYSIS; TIME-VARYING MODEL.

Contact Details
Juan Yao
Finance Discipline
School of Business
The University of Sydney, NSW 2006.
E-mail: j.yao@econ.usyd.edu.au
Jiti Gao
Department of Statistics
School of Mathematics and Statistics
The University of Western Australia, Crawley, WA 6009.
E-mail: jiti@maths.uwa.edu.au

This paper is drawn from the PhD dissertation of Juan Yao at Curtin University of Technology. This paper has benefited from the comments and suggestions received from the two anonymous referees, the editor, Graham Partington, Jerry Parwada, Lakshman Alles, and the participants in 30th Annual Conference of Economists, September 2001, Perth, Australia.