Implications of Return Predictability across Horizons for Asset Pricing Models

59 Pages Posted: 4 Dec 2012 Last revised: 11 Apr 2017

Carlo A. Favero

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

Fulvio Ortu

Bocconi University - Department of Finance

Andrea Tamoni

London School of Economics & Political Science (LSE)

Haoxi Yang

Nankai University

Multiple version iconThere are 2 versions of this paper

Date Written: March 28, 2017

Abstract

We use the evidence on predictability of returns at different horizons to discriminate among competing asset pricing models. Specifi cally, 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 fi nd that the rare disasters model of Nakamura, Steinsson, Barro, and Ursua (2013) is the best performer since it satisfi es 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, satisfi es 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

Favero, Carlo A. and Ortu, Fulvio and Tamoni, Andrea and Yang, Haoxi, Implications of Return Predictability across Horizons for Asset Pricing Models (March 28, 2017). Available at SSRN: https://ssrn.com/abstract=2184254 or http://dx.doi.org/10.2139/ssrn.2184254

Carlo A. Favero

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)

77 Bastwick Street
London, EC1V 3PZ
United Kingdom

Fulvio Ortu

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

Andrea Tamoni (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom
02079557303 (Phone)

Haoxi Yang

Nankai University ( email )

Tongyan Road 38
Tianjin, Tianjin 300350
China

Paper statistics

Downloads
298
Rank
81,788
Abstract Views
1,697