Efficient Semiparametric Estimation of the Fama-French Model and Extensions
提出一种新的半参数估计方法，用于股票收益的特征因子模型，可同时估计因子收益和特征-贝塔函数，避免维数灾难，并应用于Fama-French三因子、Carhart四因子及五因子扩展模型，发现动量和自身波动因子至少与规模和价值同等重要。
This paper develops a new estimation procedure for characteristic-based factor models \nof stock returns. We treat the factor model as a weighted additive nonparametric \nregression model, with the factor returns serving as time-varying weights and a set \nof univariate nonparametric functions relating security characteristic to the associated \nfactor betas. We use a time-series and cross-sectional pooled weighted additive nonparametric \nregression methodology to simultaneously estimate the factor returns and \ncharacteristic-beta functions. By avoiding the curse of dimensionality, our methodology \nallows for a larger number of factors than existing semiparametric methods. We \napply the technique to the three-factor Fama–French model, Carhart’s four-factor extension \nof it that adds a momentum factor, and a five-factor extension that adds an \nown-volatility factor. We find that momentum and own-volatility factors are at least as \nimportant, if not more important, than size and value in explaining equity return comovements. \nWe test the multifactor beta pricing theory against a general alternative \nusing a new nonparametric test