Regression-based estimation of dynamic asset pricing models

Tobias Adrian, Richard Crump and Emanuel Moench

Journal of Financial Economics, 2015, vol. 118, issue 2, 211-244

Abstract: We propose regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross-sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The estimators explicitly allow for time-varying prices of risk, time-varying betas, and serially dependent pricing factors. Our approach nests the Fama-MacBeth two-pass estimator as a special case. We provide asymptotic multistage standard errors necessary to conduct inference for asset pricing tests. We illustrate our new estimators in an application to the joint pricing of stocks and bonds. The application features strongly time-varying, highly significant prices of risk that are found to be quantitatively more important than time-varying betas in reducing pricing errors.

Keywords: Dynamic asset pricing; Fama-MacBeth regressions; Time-varying betas; Generalized method of moments; Minimum distance estimation; Reduced rank regression (search for similar items in EconPapers)
JEL-codes: C58 G10 G12 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (34)

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Related works:
Working Paper: Regression Based Estimation of Dynamic Asset Pricing Models (2015)
Working Paper: Regression-based estimation of dynamic asset pricing models (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:118:y:2015:i:2:p:211-244

DOI: 10.1016/j.jfineco.2015.07.004

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