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Discussion Paper Details

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Title: Regression Based Estimation of Dynamic Asset Pricing Models

Author(s): Tobias Adrian, Richard K. Crump and Emanuel Moench

Publication Date: March 2015

Keyword(s): Dynamic Asset Pricing, Fama-MacBeth Regressions, GMM, Minimum Distance Estimation, Reduced Rank Regression and Time-varying Betas

Programme Area(s): Financial Economics

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 which are found to be quantitatively more important than time varying betas in reducing pricing errors.

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Bibliographic Reference

Adrian, T, Crump, R and Moench, E. 2015. 'Regression Based Estimation of Dynamic Asset Pricing Models'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=10449