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Learning About Beta: Time-Varying Factor Loadings, Expected Returns, and the Conditional CAPMTobias AdrianFederal Reserve Bank of New York Francesco A. FranzoniUniversity of Lugano; Swiss Finance Institute October 8, 2008 Federal Reserve Bank of New York Staff Report No. 193 Abstract: 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, C11 working papers seriesDate posted: April 9, 2003 ; Last revised: October 14, 2008Suggested CitationContact Information
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