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

Raymond Kan, Cesare Robotti and Jay Shanken (jay.shanken@emory.edu)

No 15047, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C1 C12 C13 C4 C52 G12 (search for similar items in EconPapers)
Date: 2009-06
New Economics Papers: this item is included in nep-ecm
Note: AP
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Citations: View citations in EconPapers (16)

Published as Raymond Kan & Cesare Robotti & Jay Shanken, 2013. "Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology," Journal of Finance, American Finance Association, vol. 68(6), pages 2617-2649, December.

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Related works:
Journal Article: Pricing Model Performance and the Two‐Pass Cross‐Sectional Regression Methodology (2013)
Working Paper: Pricing model performance and the two-pass cross-sectional regression methodology (2009)
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