The "Out of Sample" Performance of Long-run Risk Models
This paper studies the ability of long-run risk models to explain out-of-sample asset returns during 1931-2009. The long-run risk models perform relatively well on the momentum effect. A cointegrated version of the model outperforms the classical, stationary version. Both the long-run and the short run consumption shocks in the models are empirically important for the models' performance. The models' average pricing errors are especially small in the decades from the 1950s to the 1990s. When we restrict the risk premiums to identify structural parameters, this results in larger average pricing errors but often smaller error variances. The mean squared errors are not substantially better than those of the classical CAPM, except for Momentum.
We are grateful to an anonymous referee, Ravi Bansal, Jason Beeler, David P. Brown, Dana Kiku, Ayse Imrohoroglu, Selahattin Imrohoroglu, Chris Jones, Sergei Sarkissian, Zhiguang Wang, Jianfeng Yu, Guofu Zhou, and to participants in workshops at the University of British Columbia, Claremont McKenna College, Florida International University, the University of California San Diego, the University of North Carolina Charlotte, University of Notre Dame, Ohio State University, the University of Oregon, the Oxford-Man Institute, Queens University, the University of Southern California, the University of Utah, and the University of Washington for discussions and comments. We are also grateful to participants at the 2010 Duke Asset Pricing Conference, the 2010 First World Finance Conference and the 2010 Conference on Financial Economics and Accounting. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
"The 'out of sample' Performance of Long-run Risk Models," with Biqin Xie and Suresh Nallareddy, 2013, Journal of Financial Economics 107 (3) 537-556.