Implications of Return Predictability Across Horizons for Asset Pricing Models

64 Pages Posted: 22 Nov 2016

See all articles by Carlo A. Favero

Carlo A. Favero

Bocconi University - Department of Finance; Centre for Economic Policy Research (CEPR)

Fulvio Ortu

Bocconi University - Department of Finance

Andrea Tamoni

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick

Haoxi Yang

Nankai University

Multiple version iconThere are 2 versions of this paper

Date Written: November 2016

Abstract

We use the evidence on predictability of returns at diff erent 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: asset pricing models, predictors-based bound, return predictability

JEL Classification: E21, E32, E44, G12

Suggested Citation

Favero, Carlo A. and Ortu, Fulvio and Tamoni, Andrea and Yang, Haoxi, Implications of Return Predictability Across Horizons for Asset Pricing Models (November 2016). CEPR Discussion Paper No. DP11645. Available at SSRN: https://ssrn.com/abstract=2873542

Carlo A. Favero (Contact Author)

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

HOME PAGE: http://www.igier.unibocconi.it\favero

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Fulvio Ortu

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

Andrea Tamoni

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick ( email )

1 Washington Park
Newark, NJ 07102
United States

Haoxi Yang

Nankai University ( email )

Tongyan Road 38
Tianjin, Tianjin 300350
China

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