We introduce a novel dynamic Bayesian model combination approach for predicting aggregate stock returns. Our method involves combining predictive densities in a data-adaptive fashion and simultaneously features (i) uncertainty about relevant predictor variables, (ii) parameter instability, (iii)...

This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about...