Learning about beta: time-varying factor loadings, expected returns, and the conditional CAPM
Tobias Adrian () and
No 193, Staff Reports from Federal Reserve Bank of New York
We complement the conditional capital asset pricing model (CAPM) by introducing unobservable long-run changes in risk factor loadings. In this environment, investors rationally “learn” the long-run level of factor loading by observing 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 ratio, our learning-augmented conditional CAPM fails to be rejected. ; Original title: Learning about beta: a new look at CAPM tests.
Keywords: Capital assets pricing model; Investments (search for similar items in EconPapers)
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Journal Article: Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM (2009)
Working Paper: Learning about Beta: time-varying factor loadings, expected returns and the conditional CAPM (2005)
Working Paper: Learning about Beta: Time-varying factor loadings, expected returns, and the Conditional CAPM (2005)
Working Paper: Learning about Beta: Time-Varying Factor Loadings, Expected Returns,and the Conditional CAPM
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