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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. (2013) Out-of-sample equity premium prediction: A complete subset quantile regression approach. Working paper. Kent Business School 10.2139/ssrn.2335084. (doi:10.2139/ssrn.2335084) (KAR id:45149)

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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. 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. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark and the complete subset mean regression approach.

Item Type: Monograph (Working paper)
DOI/Identification number: 10.2139/ssrn.2335084
Subjects: H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Ekaterini Panopoulou
Date Deposited: 21 Nov 2014 11:42 UTC
Last Modified: 16 Nov 2021 10:18 UTC
Resource URI: (The current URI for this page, for reference purposes)
Panopoulou, Ekaterini:
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