Abstract

Mixed-Frequency Predictive Regressions ∗ Markus Leippold † and Hanlin Yang ‡ January 21, 2019 Abstract This paper explores the performance of mixed-frequency predictive regressions for stock re- turns from the perspective of a Bayesian investor. We develop a parameter learning approach for sequential estimation, allowing for belief revisions. Empirically, we find that mixed-frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of the time-variation in the volatility of predictors. Mixed-frequency models produce higher volatility timing benefits, compared to temporally aggregate models.Therefore, our results highlight the importance of consistently incorporating predictors of mixed frequencies and correctly specifying the volatility dynamics in predictive regressions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.