Estimating and Testing Beta Pricing Models: Alternative Methods and Their Performance in Simulations

Shanken, Jay A.; Zhou, Guofu

doi:10.2139/ssrn.877184

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Estimating and Testing Beta Pricing Models: Alternative Methods and Their Performance in Simulations

61 Pages Posted: 21 Jan 2006

See all articles by Jay A. Shanken

Jay A. Shanken

Emory University - Goizueta Business School; National Bureau of Economic Research (NBER)

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

There are 2 versions of this paper

Date Written: January 2006

Abstract

In this paper, we conduct a simulation analysis of the Fama and MacBeth (1973) two-pass procedure, as well as maximum likelihood (ML) and generalized method of moments estimators of cross-sectional expected return models. We also provide some new analytical results on computational issues, the relations between estimators, and asymptotic distributions under model misspecification. The GLS estimator is often much more precise than the usual OLS estimator, but it displays more bias as well. A truncated form of ML performs quite well overall in terms of bias and precision, but produces less reliable inferences than the OLS estimator.

Keywords: Fama and MacBeth, two-pass procedure, GLS, GMM

JEL Classification: G12, G14, G32, C31, C33

Suggested Citation: Suggested Citation

Shanken, Jay A. and Zhou, Guofu, Estimating and Testing Beta Pricing Models: Alternative Methods and Their Performance in Simulations (January 2006). Available at SSRN: https://ssrn.com/abstract=877184 or http://dx.doi.org/10.2139/ssrn.877184