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Pricing model performance and the two-pass cross-sectional regression methodology

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Author Info
Raymond Kan
Cesare Robotti
Jay Shanken

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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, that is, expected returns are exactly linear in asset betas. This assumption 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 linear beta pricing models have the same population R2. This test 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.

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Paper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 2009-11.

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Date of creation: 2009
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Handle: RePEc:fip:fedawp:2009-11

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Keywords: Econometric models ; Asset pricing;

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Vadim Marmer & Taisuke Otsu, 2009. "Optimal Comparison of Misspecified Moment Restriction Models," Cowles Foundation Discussion Papers 1724, Cowles Foundation, Yale University. [Downloadable!]
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