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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Mixed-Frequency Predictive Regressions
Organization Unit
  • Markus Leippold
  • Hanlin Yang
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Journal of Forecasting
Publisher Wiley-Blackwell Publishing, Inc.
Geographical Reach international
ISSN 0277-6693
Volume 42
Number 8
Page Range 1955 - 1972
Date 2023
Abstract Text We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained 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 high-frequency features such as time-varying volatility. Temporally aggregated models misspecify the evolution frequency of the volatility dynamics, resulting in poor volatility timing and worse portfolio performance than the mixed-frequency specification. These results highlight the importance of preserving the potential mixed-frequency nature of predictors and volatility in predictive regressions.
Free access at DOI
Digital Object Identifier 10.1002/for.2999
Other Identification Number merlin-id:23710
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