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Regression-based estimation of dynamic asset pricing models

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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.

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  • Tobias Adrian & Richard K. Crump & Emanuel Moench, 2011. "Regression-based estimation of dynamic asset pricing models," Staff Reports 493, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:493
    Note: Previous title: “Efficient Regression-Based Estimation of Dynamic Asset Pricing Models”
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    More about this item

    Keywords

    GMM; dynamic asset pricing; Fama-MacBeth regressions; time-varying betas; reduced rank regression; minimum distance estimation;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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