Connor, Gregory and Hagmann, Matthias and Linton, Oliver
Efficient Semiparametric Estimation of the Fama–French Model and Extensions.
Econometrica , 80 (2).
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 nonparametric 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 existing semiparametric methods. We
apply the technique to the three-factor Fama–French model, Carhart’s four-factor extension
of it that adds a momentum factor, and a five-factor extension that adds an
own-volatility factor. We find that momentum and own-volatility factors are at least as
important, if not more important, than size and value in explaining equity return comovements.
We test the multifactor beta pricing theory against a general alternative
using a new nonparametric test
||Additive models; arbitrage pricing theory; characteristic-based factor model; kernel estimation; nonparametric regression;
||Social Sciences > Economics, Finance & Accounting
||17 Apr 2012 15:33
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