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Out‐of‐Sample Return Predictability: A Quantile Combination Approach
Author(s) -
Lima Luiz Renato,
Meng Fanning
Publication year - 2016
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2549
Subject(s) - quantile , predictability , econometrics , lasso (programming language) , quantile regression , computer science , equity premium puzzle , equity (law) , statistics , economics , mathematics , risk premium , world wide web , political science , law
Summary This paper develops a novel forecasting method that minimizes the effects of weak predictors and estimation errors on the accuracy of equity premium forecasts. The proposed method is based on an averaging scheme applied to quantiles conditional on predictors selected by LASSO. The resulting forecasts outperform the historical average, and other existing models, by statistically and economically meaningful margins. Copyright © 2016 John Wiley & Sons, Ltd.