Approximating the Asset Pricing Kernel



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    • Finance Department, Graduate School of Business, The University of Texas at Austin. A portion of this work was completed while I was on leave at the William E. Simon Graduate School of Business Administration at the University of Rochester. I thank Tom Cooley, David Ellis, Ludger Hentschel, Burton Hollifield, John Long, Neil Pearson, Ehud Ronn, Bill Schwert, Jay Shanken, Dan Slesnick, Laura Starks, René Stulz (the editor), Stanley Zin, an anonymous referee, and participants in the Finance workshops at Texas A&M University, the University of British Columbia, the William E. Simon Graduate School of Business at the University of Rochester, and the University of Illinois at Urbana-Champaign.


This article tests a simple consumption-based asset pricing model by approximating the true asset pricing kernel using low-order orthonormal polynomials based on the model's state variables. Approximated kernels based solely on next period's consumption growth are not rejected by overall measures of model fit, but they produce statistically and economically large pricing errors. Approximated kernels based on two quarters of future consumption growth and technology shocks have substantially improved overall fit. In particular, the best of these kernels are capable of eliminating the small firm effect.