IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp0836.html

Learning about Beta: Time-Varying Factor Loadings, Expected Returns,and the Conditional CAPM

Author

Listed:
  • Francesco FRANZONI

    (University of Lugano and Swiss Finance Institute)

  • Tobias ADRIAN

    (Federal Reserve Bank of New York)

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 ¯lter. 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.

Suggested Citation

  • Francesco FRANZONI & Tobias ADRIAN, 2008. "Learning about Beta: Time-Varying Factor Loadings, Expected Returns,and the Conditional CAPM," Swiss Finance Institute Research Paper Series 08-36, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp0836
    as

    Download full text from publisher

    File URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1304536
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:chf:rpseri:rp0836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.