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

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  • Raymond Kan
  • Cesare Robotti
  • Jay Shanken

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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15047.

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Date of creation: Jun 2009
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Publication status: 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.
Handle: RePEc:nbr:nberwo:15047

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Citations

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Cited by:
  1. Rubio, Gonzalo & Lozano, Martin, 2009. "Evaluating alternative methods for testing asset pricing models with historical data," MPRA Paper 23613, University Library of Munich, Germany.
  2. Marmer, Vadim & Otsu, Taisuke, 2012. "Optimal comparison of misspecified moment restriction models under a chosen measure of fit," Journal of Econometrics, Elsevier, vol. 170(2), pages 538-550.
  3. Raymond Kan & Cesare Robotti, 2009. "A note on the estimation of asset pricing models using simple regression betas," Working Paper 2009-12, Federal Reserve Bank of Atlanta.
  4. Victoria Atanasov, 2014. "Common Risk Factors in Equity Markets," Tinbergen Institute Discussion Papers 14-070/IV, Tinbergen Institute.
  5. Pierluigi Balduzzi & Cesare Robotti, 2005. "Asset-pricing models and economic risk premia: a decomposition," Working Paper 2005-13, Federal Reserve Bank of Atlanta.
  6. Grauer, Robert R. & Janmaat, Johannus A., 2010. "Cross-sectional tests of the CAPM and Fama-French three-factor model," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 457-470, February.
  7. Murtazashvili, Irina & Vozlyublennaia, Nadia, 2013. "When do characteristics-sorted factors mechanically explain returns?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 25(C), pages 119-143.
  8. Barclay, Richard & Fletcher, Jonathan & Marshall, Andrew, 2010. "Pricing emerging market stock returns: An update," Emerging Markets Review, Elsevier, vol. 11(1), pages 49-61, March.
  9. Kim, Soon-Ho & Kim, Dongcheol & Shin, Hyun-Soo, 2012. "Evaluating asset pricing models in the Korean stock market," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 198-227.
  10. Hammami, Yacine & Lindahl, Anna, 2014. "An intertemporal capital asset pricing model with bank credit growth as a state variable," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 14-28.
  11. Craig Burnside, 2010. "Identification and Inference in Linear Stochastic Discount Factor Models," NBER Working Papers 16634, National Bureau of Economic Research, Inc.
  12. Fletcher, Jonathan, 2014. "Benchmark models of expected returns in U.K. portfolio performance: An empirical investigation," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 30-46.

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