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Mixed-Frequency Predictive Regressions


Leippold, Markus; Yang, Hanlin (2019). Mixed-Frequency Predictive Regressions. SSRN 3157988, University of Zurich.

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

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

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Additional indexing

Contributors:University of Zurich
Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:330 Economics
Language:English
Date:21 January 2019
Deposited On:29 Mar 2019 09:46
Last Modified:25 Sep 2019 00:32
Series Name:SSRN
ISSN:1556-5068
OA Status:Green
Official URL:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3157988
Other Identification Number:merlin-id:17708

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Content: Accepted Version
Language: English
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