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

Jay Shanken () and Guofu Zhou

No 12055, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: G12 (search for similar items in EconPapers)
Date: 2006-02
New Economics Papers: this item is included in nep-ecm and nep-fin
Note: AP
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Citations: View citations in EconPapers (5)

Published as Shanken, Jay and Guofu Zhou. “Estimating and Testing Beta Pricing Models: Alternative Methods and Their Performance in Simulations.” Journal of Financial Economics 84, 1 (April 2007): 40-86.

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Related works:
Journal Article: Estimating and testing beta pricing models: Alternative methods and their performance in simulations (2007)
Working Paper: Estimating and testing beta pricing models: Alternative methods and their performance in simulations (2007)
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