Dynamic Factors and Asset Pricing

This study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset pricing model, termed the dynamic factor pricing model (DFPM). We then conduct asset pricing te...

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Bibliographic Details
Published inJournal of financial and quantitative analysis Vol. 45; no. 3; pp. 707 - 737
Main Authors He, Zhongzhi (Lawrence), Huh, Sahn-Wook, Lee, Bong-Soo
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.06.2010
Subjects
Applied economics
Asset pricing
Computational methods
Econometrics
Economic models
Finance
Financial analysis
Financial portfolios
Forecasting models
Forecasting techniques
Investment returns
Modeling
Null hypothesis
Parametric models
Portfolio analysis
Price models
Price momentum
Statistical significance
Time series
Time series models
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Summary:This study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset pricing model, termed the dynamic factor pricing model (DFPM). We then conduct asset pricing tests in the in-sample and out-of-sample contexts. Our analyses show that the ex ante factors are a key component in asset pricing and forecasting. By using the ex ante factors, the DFPM improves upon the explanatory and predictive power of other competing models, including unconditional and conditional versions of the Fama and French (1993) 3-factor model. In particular, the DFPM can explain and better forecast the momentum portfolio returns, which are mostly missed by alternative models.
Bibliography:We gratefully appreciate the constructive and thoughtful comments of an anonymous referee, Michael Brennan, Stephen Brown (the editor), and Amit Goyal, which have substantially contributed to improving the paper. Our special thanks go to Chang-Jin Kim, who provided us with the GAUSS routines described in Kim and Nelson (1999). We also thank Antonio Bernardo, Jay M. Chung, Robert Grauer, Shaoan Huang, Jialiu Lu, Avanidhar Subrahmanyam, Kevin Wang, Bob Welch, and seminar participants at Brock University, Central University of Finance and Economics, Shanghai University of Finance and Economics, Shandong University, Sun Yat-Sen University, and the 2008 KAFA-KFAs (Korea-America Finance Association and Korea finance associations) Joint Conference, for valuable feedback. Huh gratefully acknowledges the generous financial support from the Social Sciences and Humanities Research Council of Canada (SSHRC). All errors are solely the authors’ responsibility.
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ArticleID:00020
PII:S0022109010000207
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ISSN:0022-1090
1756-6916
DOI:10.1017/S0022109010000207