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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Mixed-Frequency Predictive Regressions |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| 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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