Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology
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 speci ed, 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 misspeci cation. We propose a general methodology for computing misspeci cation-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.
Raymond Kan Cesare Robotti Jay Shanken
University of Toronto Federal Reserve Bank of Atlanta Emory University and the National Bureau of Economic Research Goizueta Business School, Emory Univer
国际会议
广州
英文
1-62
2009-07-07（万方平台首次上网日期，不代表论文的发表时间）