Learning about beta : time-varying factor loadings, expected returns, and the conditional CAPM
- Adrian,Tobias Federal Reserve Bank of New York, New York, United States
- Franzoni, Francesco Istituto di finanza (IFin), Facoltà di scienze economiche, Università della Svizzera italiana, Svizzera
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2009
Published in:
- Journal of empirical finance. - Elsevier. - 2009, vol. 16, no. 4, p. 537-556
English
We amend the conditional CAPM to allow for unobservable long-run changes in risk factor loadings. In this environment, investors rationally “learn” the long-run level of factor loadings from the observation of realized returns. As a consequence of this assumption, we model conditional betas 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 passes the specification tests.
- Language
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- English
- Classification
- Economics
- License
- License undefined
- Identifiers
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- RERO DOC 28565
- DOI 10.1016/j.jempfin.2009.02.003
- ARK ark:/12658/srd1318327
- Persistent URL
- https://n2t.net/ark:/12658/srd1318327
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