E ¢ cient Semiparametric Estimation of the Fama-French Model and Extensions

This paper develops a new estimation procedure for characteristic-based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time-varying weights, and a set of univariate non-parametric functions relating security characteristic to the associated factor betas. We use a time-series and cross-sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic-beta functions. By avoiding the curse of dimensionality our methodology allows for a larger number of factors than exist-ing semiparametric methods. We apply the technique to the three-factor Fama-French model, Carharts four-factor extension of it adding a momentum factor, and a \u85ve-factor extension adding an own-volatility factor. We \u85nd that momentum and own-volatility factors are at least as important if not more important than size and value in explaining equity return comove-ments. We test the multifactor beta pricing theory against the Capital Asset Pricing model using a standard test, and against a general alternative using a new nonparametric test