IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v15y2022i5p223-d818811.html
   My bibliography  Save this article

Deep Partial Hedging

Author

Listed:
  • Songyan Hou

    (Department of Mathematics, ETH Zurich, 8092 Zürich, Switzerland)

  • Thomas Krabichler

    (Centre for Banking & Finance, Eastern Switzerland University of Applied Sciences, 9001 St. Gallen, Switzerland)

  • Marcus Wunsch

    (Institute of Wealth & Asset Management, ZHAW Zurich University of Applied Sciences, 8400 Winterthur, Switzerland)

Abstract

Using techniques from deep learning, we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by Föllmer and Leukert. Not only does this approach better accommodate the realistic setting of hedging in discrete time, it also allows for the inclusion of transaction costs as well as general market dynamics. It needs to be noted that, without further modifications, the approach works only if the risk aversion is beyond a certain level.

Suggested Citation

  • Songyan Hou & Thomas Krabichler & Marcus Wunsch, 2022. "Deep Partial Hedging," JRFM, MDPI, vol. 15(5), pages 1-5, May.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:5:p:223-:d:818811
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/15/5/223/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/15/5/223/
    Download Restriction: no
    ---><---

    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:gam:jjrfmx:v:15:y:2022:i:5:p:223-:d:818811. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.