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Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns

Financial Markets and Portfolio Management volume 33, pages 417–445 (2019)Cite this article

Abstract

This study examines whether incorporating volatility improves the forecast of directional changes in the returns of Australia’s banking, industrial and resource sectors. This study first estimates a benchmark non-volatility logit regression model and assesses it against four estimated volatility logit models measured by mean absolute deviation, standard deviation, return squared (U2) and range. An out-of-sample prediction performance, assessed by Brier’s QPS statistic and hit ratio, confirms that volatility improves the prediction of directional changes of returns. A simple trading strategy is utilized to provide practical improvement in investors’ market timing decisions.

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Fig. 1

Notes

  1. This work was primarily based on Jagannathan and Wang (1996), although Durack et al. (2004) extend the study by evaluating the influence of US market movements over Australian stock returns.

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Acknowledgements

We would like to thank the editor Markus Schmid and the anonymous referees for their constructive recommendations, which helped to improve the quality of this paper. In addition, we would like to thank the feedback received during prior drafts from colleagues Dr. Guneratne Wickremasinghe and Dr. Ranjtih Ihalanayake.

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Authors and Affiliations

  1. Victoria University Business School, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia

    Riza Erdugan, Nada Kulendran & Riccardo Natoli

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  1. Riza Erdugan
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  2. Nada Kulendran
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  3. Riccardo Natoli
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Correspondence to Riccardo Natoli.

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Erdugan, R., Kulendran, N. & Natoli, R. Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns. Financ Mark Portf Manag 33, 417–445 (2019). https://doi.org/10.1007/s11408-019-00338-z

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Keywords

  • Binary regression model
  • Volatility estimates
  • Marginal probability
  • Forecast comparison

JEL Classification

  • G17