Publication: Mixed-Frequency Predictive Regressions
Mixed-Frequency Predictive Regressions
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Leippold, M., & Yang, H. (2023). Mixed-Frequency Predictive Regressions. Journal of Forecasting, 42(8), 1955–1972. https://doi.org/10.1002/for.2999
Abstract
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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 f
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- Yang, Hanlin
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Leippold, M., & Yang, H. (2023). Mixed-Frequency Predictive Regressions. Journal of Forecasting, 42(8), 1955–1972. https://doi.org/10.1002/for.2999
