A Simple Econometric Approach for Utility-Based Asset Pricing Models

Authors

  • DAVID P. BROWN,

  • MICHAEL R. GIBBONS

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    • School of Business, Indiana University and the Graduate School of Business, Stanford University, respectively. This research has benefited from the helpful comments made by our friends at Dartmouth, Harvard, Stanford, UBC, Wharton, and Yale where this paper was presented. We also thank Wayne Ferson, Allan Kleidon, Paul Pfleiderer, Peter Reiss, Mark Rubinstein, and Ken Singleton for many useful discussions. Of course, all remaining errors are our responsibility. Financial support was provided in part by the Stanford Program in Finance. This work was completed while the second author was a Batterymarch Fellow. Both sources of financial support are gratefully acknowledged.


ABSTRACT

Utility-based models of asset pricing may be estimated with or without assuming a distribution for security returns; both approaches are developed and compared here. The chief strength of a parametric estimator lies in its computational simplicity and statistical efficiency when the added distributional assumption is true. In contrast, the nonparametric estimator is robust to departures from any particular distribution, and it is more consistent with the spirit underlying utility-based asset pricing models since the distribution of asset returns remains unspecified even in the empirical work. The nonparametric approach turns out to be easy to implement with precision nearly indistinguishable from its parametric counterpart in this particular application. The application shows that log utility is consistent with the data over the period 1926–1981.

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