Mixed-Frequency Predictive Regressions with Parameter Learning

Markus Leippold and Hanlin Yang
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Markus Leippold: University of Zurich; Swiss Finance Institute
Hanlin Yang: University of Zurich

No 23-39, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: 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.

Keywords: Mixed-frequency data; predictive regressions; stochastic volatility; consumption-wealth ratio; parameter learning; portfolio optimization (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 G11 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2023-03, Revised 2023-06
New Economics Papers: this item is included in nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2339

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