Regression Based Estimation of Dynamic Asset Pricing Models
Tobias Adrian (),
Richard Crump () and
Emanuel Moench ()
No 10449, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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 which are found to be quantitatively more important than time varying betas in reducing pricing errors.
Keywords: Dynamic Asset Pricing; Fama-MacBeth Regressions; GMM; Minimum Distance Estimation; Reduced Rank Regression; Time-varying Betas (search for similar items in EconPapers)
JEL-codes: C58 G10 G12 (search for similar items in EconPapers)
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Journal Article: Regression-based estimation of dynamic asset pricing models (2015)
Working Paper: Regression-based estimation of dynamic asset pricing models (2014)
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