Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology

62 Pages Posted: 8 Jun 2009 Last revised: 14 Aug 2010

See all articles by Raymond Kan

Raymond Kan

University of Toronto - Rotman School of Management

Cesare Robotti

Warwick Business School

Jay A. Shanken

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

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Date Written: June 2009

Abstract

Since Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973), the two-pass cross-sectional regression (CSR) methodology has become the most popular approach for estimating and testing asset pricing models. Statistical inference with this method is typically conducted under the assumption that the models are correctly specified, i.e., expected returns are exactly linear in asset betas. This can be a problem in practice since all models are, at best, approximations of reality and are likely to be subject to a certain degree of misspecification. We propose a general methodology for computing misspecification-robust asymptotic standard errors of the risk premia estimates. We also derive the asymptotic distribution of the sample CSR R2 and develop a test of whether two competing beta pricing models have the same population R2. This provides a formal alternative to the common heuristic of simply comparing the R2 estimates in evaluating relative model performance. Finally, we provide an empirical application which demonstrates the importance of our new results when applied to a variety of asset pricing models.

Suggested Citation

Kan, Raymond and Robotti, Cesare and Shanken, Jay A., Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology (June 2009). NBER Working Paper No. w15047, Available at SSRN: https://ssrn.com/abstract=1415210

Raymond Kan

University of Toronto - Rotman School of Management ( email )

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Cesare Robotti

Warwick Business School ( email )

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United Kingdom

Jay A. Shanken (Contact Author)

Emory University - Goizueta Business School ( email )

1300 Clifton Road
Atlanta, GA 30322-2722
United States
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National Bureau of Economic Research (NBER)

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United States

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