A Note on the Estimation of Asset Pricing Models Using Simple Regression Betas
Raymond Kan and Cesare Robotti
Working Paper 2009-12
Since Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973), the two-pass cross-sectional regression (CSR) methodology has become the most popular tool for estimating and testing beta asset pricing models. In this paper, we focus on the case in which simple regression betas are used as regressors in the second-pass CSR. Under general distributional assumptions, we derive asymptotic standard errors of the risk premia estimates that are robust to model misspecification. When testing whether the beta risk of a given factor is priced, our misspecification robust standard error and the Jagannathan and Wang (1998) standard error (which is derived under the correctly specified model) can lead to different conclusions.
JEL classification: G12
Key words: two-pass cross-sectional regressions, risk premia, model misspecification, simple regression betas, multivariate betas
Kan gratefully acknowledges financial support from the National Bank Financial of Canada. The views expressed here are the authors' and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors' responsibility.
Please address questions regarding content to Raymond Kan, Joseph L. Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, Ontario, Canada M5S 3E6, or Cesare Robotti (contact author), Federal Reserve Bank of Atlanta, 1000 Peachtree Street, N.E., Atlanta, GA 30309-4470, 404-498-8543, 404-498-8810 (fax), firstname.lastname@example.org; or Jay Shanken, Goizueta Business School, Emory University, 1300 Clifton Road, N.E., Atlanta, GA 30322.
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