IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v4y2004i5p549-557.html
   My bibliography  Save this article

Hedging European and Barrier options using stochastic optimization

Author

Listed:
  • Michael Villaverde

Abstract

We hedge European and Barrier options in a discrete time and discrete space setting by uwing stochastic optimization to minimize the mean downside hedge error under transaction costs. Scenario trees are generated using a method which ensures the absence of arbitrage and which matches the mean and variance of the underlying asset price in the sampled scenarios to those of a given distribution. The stochastic optimization based strategy is benchmarked to the method of delta hedging for the case where the underlying asset price following a discretized geometric Brownian motion and implemented for the case where the underlying asset prices is driven by a discretized Variance Gamms proces.

Suggested Citation

  • Michael Villaverde, 2004. "Hedging European and Barrier options using stochastic optimization," Quantitative Finance, Taylor & Francis Journals, vol. 4(5), pages 549-557.
    • Handle: RePEc:taf:quantf:v:4:y:2004:i:5:p:549-557
    DOI: 10.1080/14697680400000037
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/14697680400000037
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697680400000037?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.

    Citations

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


    Cited by:

    1. Mathias Barkhagen & Jörgen Blomvall, 2016. "Modeling and evaluation of the option book hedging problem using stochastic programming," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 259-273, February.

    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:taf:quantf:v:4:y:2004:i:5:p:549-557. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.