Report
Regression-based estimation of dynamic asset pricing models
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 test. 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 risks that are found to be quantitatively more important than time-varying betas in reducing pricing errors.
Keywords: GMM; dynamic asset pricing; Fama-MacBeth regressions; time-varying betas; reduced rank regression; minimum distance estimation;
JEL Classification: G12; C58; G10;
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Bibliographic Information
Provider: Federal Reserve Bank of New York
Part of Series: Staff Reports
Publication Date: 2014-12-01
Number: 493
Pages: 55 pages
Note: Previous title: “Efficient Regression-Based Estimation of Dynamic Asset Pricing Models”