Original Research

Risk estimation for shares on the Johannesburg Stock Exchange using transfer function modeling

Ian K. Craig, Mike T. Bendixen
South African Journal of Business Management | Vol 29, No 1 | a765 | DOI: https://doi.org/10.4102/sajbm.v29i1.765 | © 2018 Ian K. Craig, Mike T. Bendixen | This work is licensed under CC Attribution 4.0
Submitted: 12 October 2018 | Published: 31 March 1998

About the author(s)

Ian K. Craig, Department of Electrical and Electronic Engineering, University of Pretoria, South Africa
Mike T. Bendixen, Graduate School of Business Administration, University of the Witwatersrand, South Africa

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This study investigates whether the estimation of the systematic risk component or the beta of shares on the Johannesburg Stock Exchange (JSE) can be improved using transfer function or MARIMA modeling. Two propositions are tested. Transfer function modeling will result in estimates of systematic risk which are different from those obtained using conventional OLS regression methods. Transfer function models will provide forecasting results which are better than those provided by betas estimated in the conventional way. Proposition I cannot be tested using conventional inferential tests as the standard errors of estimate of the betas estimated from MARIMA modeling cannot, in general, be measured. It is found however that 16.9% of the MARIMA beta estimates fall outside the 95% confidence intervals of the respective OLS regression beta estimates. Similar results are obtained when the OLS regression betas are compared to the University of Cape Town (UCT) Financial Risk Service and BFA-NET beta estimates. Proposition 2 can in general not be supported as the MARIMA and OLS regression forecasts are found not to be statistically significantly different. Cross correlations between index and share returns are in many cases found not to be statistically significant. In such cases one is probably better off using OLS regression. Resulting beta estimates should be used with caution.


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