Pricing model performance and the two-pass cross-sectional regression methodology
By: Raymond Kan, Cesare Robotti, Jay Shanken.
Contributors: Robotti, Cesare.; Shanken, Jay.; National Bureau of Economic Research.
Series: NBER working paper series -- working paper 15047; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 15047.
Publisher: National Bureau of Economic Research
Published: 2009
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Description: "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"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 6/9/2009.

Includes bibliographical references.

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