Asset Return Predictability and Bayesian Model Averaging

Abstract

This paper studies model uncertainty associated with predictive regressions in asset return predictability research. We comprehensively investigate the performance of Bayesian model averaging (BMA), first introduced to the literature by Avramov (2002) and Cremers (2002), when applied to linear predictive regressions using simulation approaches. We find that, in simple settings, BMA performs fairly satisfactorily even when the true model is not in the model set. It can always identify the powerful predictors and constantly outperform other variable selection methods. The results are robust with respect to non-linearity and prior selections. We confirm that BMA attains best performance when model uncerainty is large, which indicates that it is easier to capture short-run predictability using BMA. However, when we add more structure to the data generating process (DGP), BMA performs less well both insample and out-of-sample. BMA mistakens noise variables for true predictors. This is especially the case when there is a lot of noise in the model set. For out-of-sample prediction, BMA overall model shows little advantage over a no-predictability model, and it tends to under predict. A possible cause could be the complex structure we imposed on the DGP.