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

Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., Vrontos, Spyridon D. (2014) Out-of-sample equity premium prediction: A complete subset quantile regression approach. In: Conference on Econometric Methods for Banking and Finance, September 12-13, 2014, Bank of Portugal, Lisbon, Portugal. (Unpublished) (KAR id:44194)

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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: Conference or workshop item (Paper)
Subjects: H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Ekaterini Panopoulou
Date Deposited: 09 Nov 2014 17:35 UTC
Last Modified: 16 Feb 2021 12:56 UTC
Resource URI: (The current URI for this page, for reference purposes)
Panopoulou, Ekaterini:
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