Skip to main content
Kent Academic Repository

Out-Of-Sample Equity Premium Prediction: A Complete Subset Quantile Regression Approach

Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., Vrontos, Spyridon D. (2019) Out-Of-Sample Equity Premium Prediction: A Complete Subset Quantile Regression Approach. European Journal of Finance, . ISSN 1351-847X. (doi:10.1080/1351847X.2019.1647866) (KAR id:75490)

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 signiÖcant 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
DOI/Identification number: 10.1080/1351847X.2019.1647866
Uncontrolled keywords: Equity premium; Forecast combination; Predictive quantile regression; Robust point forecasts; Subset quantile regressions
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Ekaterini Panopoulou
Date Deposited: 22 Jul 2019 10:30 UTC
Last Modified: 07 Sep 2023 22:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/75490 (The current URI for this page, for reference purposes)

University of Kent Author Information

Panopoulou, Ekaterini.

Creator's ORCID: https://orcid.org/0000-0001-5080-9965
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.