Working Paper: CEPR ID: DP10449
Authors: Tobias Adrian; Richard K. Crump; Emanuel Moench
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.
Keywords: Dynamic Asset Pricing; Fama-Macbeth Regressions; GMM; Minimum Distance Estimation; Reduced Rank Regression; Time-Varying Betas
JEL Codes: C58; G10; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| state variables (C29) | expected returns (G17) |
| time variation in prices of risk (G19) | expected excess returns (G17) |
| risk exposure to pricing factors (G13) | market risk premiums (G19) |
| time-varying prices of risk (G19) | pricing errors (D49) |
| traditional asset pricing methods (G19) | pricing errors (D49) |