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Learning about beta: time-varying factor loadings, expected returns, and the conditional CAPM

Listed author(s):
  • Tobias Adrian
  • Francesco Franzoni

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.

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Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 193.

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Date of creation: 2008
Handle: RePEc:fip:fednsr:193
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