IDEAS home Printed from https://ideas.repec.org/a/wsi/nmncxx/v01y2005i02ns1793005705000159.html
   My bibliography  Save this article

Applying The Genetic-Based Neural Networks To Volatility Trading

Author

Listed:
  • SHINN-WEN WANG

    (Department of Business Administration, College of Management, National Changhua University of Education, No. 2, Shi-Da Rd., Changhua, Taiwan 500, ROC)

Abstract

The Black-Scholes options pricing model is widely applied in various options contracts, including contract design, trading, assets evaluation, and enterprise value estimation, etc. Unfortunately, this theoretical model limited by the influences of many unexpected real world phenomena due to six unreasonable assumptions. If we were to soundly take these phenomena into account, the opportunity to gain an excess return would be created. This research therefore combines both the remarkable effects caused by the implied volatility smile (or skew) and the tick-jump discrepancy between the underlying and derivative prices to establish a two-phase options arbitrage model using a genetic-based neural network (GNN). Using evidence from the warrant market in Taiwan, it is shown that the GNN model with arbitrage operations is superior in terms of performance to the original Black-Scholes-based arbitrage model. The GNN model is found to be suitable for application to various options markets as the valuation factors are modified. This paper helps to integrate the theoretical model with important practical considerations.

Suggested Citation

  • Shinn-Wen Wang, 2005. "Applying The Genetic-Based Neural Networks To Volatility Trading," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 285-293.
  • Handle: RePEc:wsi:nmncxx:v:01:y:2005:i:02:n:s1793005705000159
    DOI: 10.1142/S1793005705000159
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S1793005705000159
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S1793005705000159?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:wsi:nmncxx:v:01:y:2005:i:02:n:s1793005705000159. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/nmnc/nmnc.shtml .

    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.