Learning About Beta: Time-Varying Factor Loadings, Expected Returns, and the Conditional CAPM
Federal Reserve Bank of New York
Francesco A. Franzoni
University of Lugano; Swiss Finance Institute
October 8, 2008
Federal Reserve Bank of New York Staff Report No. 193
We complement the conditional CAPM by introducing unobservable long-run changes in risk factor loadings. In this environment, investors rationally 'learn' the long-level of factor loadings from the observation of realized returns. As a direct consequence of this assumption, conditional betas are modeled using the Kalman filter. Because of its focus on low frequency variation in betas, our approach circumvents recent criticisms of the conditional CAPM. When tested on portfolios sorted by size and book-to-market, our learning-augmented conditional CAPM fails to be rejected.
Number of Pages in PDF File: 48
Keywords: Asset Pricing, Bayesian Learning, CAPM Anomalies
JEL Classification: G12, C11working papers series
Date posted: April 9, 2003 ; Last revised: October 14, 2008
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