IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v31y2018i12p4815-4862..html
   My bibliography  Save this article

Can People Learn about ‘Black Swans’? Experimental Evidence

Author

Listed:
  • Elise Payzan-LeNestour

Abstract

How do people cope with tail risk? In a lab experiment that removed informational and incentive confounds, subjects overwhelmingly behaved like Bayesian learners. The results of simulations further revealed that if one is to survive under tail risk, one needs to follow the Bayesian approach, as all boundedly rational alternatives fail. These findings support the Bayesian assumption commonly made in prior studies on tail risk and model uncertainty, and they also demonstrate the importance of optimal learning under tail risk. Received February 15, 2017; editorial decision December 24, 2017 by Editor Andrew Karolyi. Authors have furnished supplementary data and code, which are available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Elise Payzan-LeNestour, 2018. "Can People Learn about ‘Black Swans’? Experimental Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 31(12), pages 4815-4862.
    • Handle: RePEc:oup:rfinst:v:31:y:2018:i:12:p:4815-4862.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhy040
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brice Corgnet & Camille Cornand & Nobuyuki Hanaki, 2020. "Negative Tail Events, Emotions & Risk Taking," Working Papers 2016, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.

    More about this item

    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:oup:rfinst:v:31:y:2018:i:12:p:4815-4862.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.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.