Mixed‐frequency predictive regressions with parameter learning

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.