IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp1807.html
   My bibliography  Save this paper

When Are Stocks Less Volatile in the Long Run?

Author

Listed:
  • Eric Jondeau

    (University of Lausanne and Swiss Finance Institute)

  • Qunzi Zhang

    (Shandong University)

  • Xiaoneng Zhu

    (Shanghai University of Finance and Economics)

Abstract

Pastor and Stambaugh (2012) demonstrate that from a forward-looking perspective, stocks are more volatile in the long run than they are in the short run. We investigate how the economic constraint of non-negative equity premia aspects predictive variance. When investors expect non-negative returns in the market and thus impose the constraint on predictive regressions, they find that stocks are less volatile in the long run, even after taking account of estimation risk and uncertainties on current and future expected stock returns because the constraint provides additional parameter identification condition and prior information for future returns. Thus, it substantially reduces uncertainty on future stock returns. This fact, combined with the mean reversion property of stock return dynamics, leads to lower predictive variance in the long run.

Suggested Citation

  • Eric Jondeau & Qunzi Zhang & Xiaoneng Zhu, 2018. "When Are Stocks Less Volatile in the Long Run?," Swiss Finance Institute Research Paper Series 18-07, Swiss Finance Institute, revised Feb 2018.
  • Handle: RePEc:chf:rpseri:rp1807
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3112068
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    Bayesian method; predictive variance; non-negative equity premium;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • 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:rp1807. 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.