Efficient Semiparametric Estimation of the Fama-French Model and Extensions

This paper develops a new estimation procedure for characteristic-based factor models stock returns. We treat the factor model as a weighted additive nonparametric model, with the factor returns serving as time-varying weights and a set univariate nonparametric functions relating security characteristic to the associated betas. We use a time-series and cross-sectional pooled weighted additive nonparametric methodology to simultaneously estimate the factor returns and -beta functions. By avoiding the curse of dimensionality, our methodology for a larger number of factors than existing semiparametric methods. We the technique to the three-factor Fama–French model, Carhart’s four-factor extension it that adds a momentum factor, and a five-factor extension that adds an -volatility factor. We find that momentum and own-volatility factors are at least as , if not more important, than size and value in explaining equity return comovements. test the multifactor beta pricing theory against a general alternative a new nonparametric test