Effects of Learning the Long-Run Asset Pricing Model.

Job Market Paper.

This paper documents a significant increase of risk-prices in the presence of learning. We solve a model with long-run risk, where both, the level and persistence of expected consumption growth are unobserved. We introduce a new methodology to quantify the effects of learning about parameter uncertainty and latent variables. The representative consumer chooses state variables that are sufficient statistics of the learning problems and, conditional on her information set, forms posterior distributions of the states and future consumption growth. We present a novel numerical approach that approximates the agent's continuation-value by nesting the solutions of problems with different information sets. This reduces the complexity of the optimization and ensures consistency across information structures. Keeping preference parameters constant, maximum Sharpe ratios increase from .07 in the benchmark case without learning to .45 in the learning economy.

References

Lars P. Hansen (co-chair)	Ruey S. Tsay (co-chair)
Homer J. Livingston Distinguished Service Professor	H.G.B. Alexander Professor of Econometrics & Statistics
Department of Economics, University of Chicago	Booth School of Business, University of Chicago
lhansen@uchicago.edu	Ruey.Tsay@chicagobooth.edu
+1 (773) 702-8170	+1 (773) 702-6750
Pietro Veronesi	Kenneth L. Judd
Roman Family Professor of Finance	Paul H. Bauer Senior Fellow
Booth School of Business, University of Chicago	Hoover Institution, Stanford University
pietro.veronesi@chicagobooth.edu	judd@hoover.stanford.edu
+1 (773) 702-6348	+1 (650) 723-5866

November 23rd, 2009