Connor, Gregory, Hagmann, Matthias and Linton, Oliver (2007) Efficient estimation of a semiparametric characteristic-based factor model of security returns. Discussion paper, 599. Financial Markets Group, London School of Economics and Political Science, London, UK.
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This paper develops a new estimation procedure for characteristic-based factor models of security 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 existing semiparametric methods. We apply the technique to the three-factor Fama-French model, Carhart’s four-factor extension of it adding a momentum factor, and a five-factor extension adding an own-volatility factor. We .nd 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 the Capital Asset Pricing model using a standard test, and against a general alternative using a new nonparametric test.
|Item Type:||Monograph (Discussion Paper)|
|Additional Information:||© 2007 The Authors|
|Uncontrolled Keywords:||Additive Models, Arbitrage pricing theory, Factor model, Fama-French, Kernel estimation, Nonparametric regression, Panel data.|
|Library of Congress subject classification:||H Social Sciences > HG Finance
H Social Sciences > HB Economic Theory
|Journal of Economic Literature Classification System:||G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing; Trading volume; Bond Interest Rates
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
|Sets:||Research centres and groups > Financial Markets Group (FMG)
Collections > Economists Online
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