IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v62y2024ipas1544612324001314.html
   My bibliography  Save this article

Relationship between deep hedging and delta hedging: Leveraging a statistical arbitrage strategy

Author

Listed:
  • Horikawa, Hiroaki
  • Nakagawa, Kei

Abstract

In this study, we explore the links between deep hedging and delta hedging using a statistical arbitrage strategy. Specifically, we show that hedging that minimizes loss risk combines delta hedging with a statistical arbitrage strategy. The numerical experiments in a simple Black–Scholes world also verify these results. Moreover, it is known that the existence of statistical arbitrages can hamper proper learning of deep hedging. For this problem, the obtained relationship and analysis of the profit and loss (PnL) distribution of deep hedging provide us with insight into risk measures that are resistant to statistical arbitrages. We conclude that the use of these robust risk measures allows us to ignore the estimation of drift terms in asset price processes.

Suggested Citation

  • Horikawa, Hiroaki & Nakagawa, Kei, 2024. "Relationship between deep hedging and delta hedging: Leveraging a statistical arbitrage strategy," Finance Research Letters, Elsevier, vol. 62(PA).
  • Handle: RePEc:eee:finlet:v:62:y:2024:i:pa:s1544612324001314
    DOI: 10.1016/j.frl.2024.105101
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324001314
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2024.105101?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    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:eee:finlet:v:62:y:2024:i:pa:s1544612324001314. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.