Maynooth University

Maynooth University ePrints and eTheses Archive

Maynooth University Library

Efficient Semiparametric Estimation of the Fama–French Model and Extensions

Connor, Gregory and Hagmann, Matthias and Linton, Oliver (2012) Efficient Semiparametric Estimation of the Fama–French Model and Extensions. Econometrica , 80 (2). pp. 713-754. ISSN 0012-9682

[img] Download (880kB)

Abstract

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

Item Type: Article
Keywords: Additive models; arbitrage pricing theory; characteristic-based factor model; kernel estimation; nonparametric regression;
Subjects: Social Sciences > Economics, Finance & Accounting
Item ID: 3579
Depositing User: Gregory Connor
Date Deposited: 17 Apr 2012 15:33
Journal or Publication Title: Econometrica
Publisher: Econometric Society
Refereed: Yes
URI:

    Repository Staff Only(login required)

    View Item Item control page

    Document Downloads

    More statistics for this item...