Implications of Return Predictability across Horizons for Asset Pricing Models
59 Pages Posted: 4 Dec 2012 Last revised: 11 Apr 2017
Date Written: March 28, 2017
We use the evidence on predictability of returns at different horizons to discriminate among competing asset pricing models. Specifically, we employ predictors-based variance bounds, i.e. bounds on the variance of the Stochastic Discount Factors (SDFs) that price a given set of returns conditional on the information contained in a vector of return predictors. We show that return predictability delivers variance bounds that are much tighter than the classical, unconditional Hansen and Jagannathan (1991) bounds. We use the predictors-based bounds to discriminate among three leading classes of asset pricing models: rare disasters, long-run risks and external habit. We find that the rare disasters model of Nakamura, Steinsson, Barro, and Ursua (2013) is the best performer since it satisfies rather comfortably the predictors-based bounds at all horizons. As for long-run risks, while the classical version of Bansal and Yaron (2004) is the model most challenged by the introduction of conditioning information since it struggles to meet the bounds at all horizons, the more general version of Schorfheide, Song, and Yaron (2016), which accounts for multiple volatility components, satisfies the 1- and 5-year bounds as long as the set of test assets includes only equities and T-Bills. The Campbell and Cochrane (1999) habit model lies somehow in the middle: it performs quite well at our longest 5-year horizon while it struggles at the 1-year horizon. Finally, when the set of test assets is augmented with Treasury Bonds, the only model that is able to satisfy the predictors-based bounds is the rare disasters model.
Keywords: return predictability, predictors-based bound, asset pricing models
JEL Classification: G12, E21, E32, E44
Suggested Citation: Suggested Citation