Equity premium predictability using Bayesian regression mixtures

Xinxin Shang, Egon Kalotay

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution


This study investigates the predictability of equity returns in a mixture modelling framework that address several potential shortcomings of standard regression-based tools. We find that static single-equation predictive models are strongly rejected by the data in light of 2 and 3-component mixture regression specifications. We generalise standard regression mixtures to accommodate the possibility that predictive variables can be used to forecast time-variation in mixing weights. While within-sample variation in mixing weights appear forecastable, we find little benefit to forecasting such variation on an out-of-sample basis.
Original languageEnglish
Title of host publicationWorld finance conference venice, July 2 - 4, 2014 e-proceedings
PublisherWorld Finance Conference
Number of pages1
ISBN (Print)9789899881617
Publication statusPublished - 2014
EventWorld finance conference - Venice
Duration: 2 Jul 20144 Jul 2014


ConferenceWorld finance conference


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