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

Meligkotsidou, Loukia and Panopoulou, Ekaterini and Vrontos, Ioannis D and Vrontos, Spyridon D (2019) 'Out-of-sample equity premium prediction: a complete subset quantile regression approach.' The European Journal of Finance. ISSN 1351-847X

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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.

Item Type: Article
Uncontrolled Keywords: Equity premium, forecast combination, predictive quantile regression, robust point forecasts, subset quantile regressions
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 12 Aug 2019 08:48
Last Modified: 18 Jun 2020 21:15
URI: http://repository.essex.ac.uk/id/eprint/25138

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