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Out-of-sample equity premium prediction: a complete subset quantile regression approach

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  • Loukia Meligkotsidou
  • Ekaterini Panopoulou
  • Ioannis D. Vrontos
  • Spyridon D. Vrontos

Abstract

This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark, the complete subset mean regression approach and the single-variable quantile forecast combination approach. Our recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile succeeds in identifying the best subset in a time- and quantile-varying manner.

Suggested Citation

  • Loukia Meligkotsidou & Ekaterini Panopoulou & Ioannis D. Vrontos & Spyridon D. Vrontos, 2021. "Out-of-sample equity premium prediction: a complete subset quantile regression approach," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 110-135, January.
    • Handle: RePEc:taf:eurjfi:v:27:y:2021:i:1-2:p:110-135
    DOI: 10.1080/1351847X.2019.1647866
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    Cited by:

    1. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023. "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, vol. 54(C).
    2. Alexandridis, Antonios K. & Apergis, Iraklis & Panopoulou, Ekaterini & Voukelatos, Nikolaos, 2023. "Equity premium prediction: The role of information from the options market," Journal of Financial Markets, Elsevier, vol. 64(C).

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