IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i17p2770-d1473333.html
   My bibliography  Save this article

Three-Layer Artificial Neural Network for Pricing Multi-Asset European Option

Author

Listed:
  • Zhiqiang Zhou

    (School of Economics and Management, Xiangnan University, Chenzhou 423000, China)

  • Hongying Wu

    (School of Mathematics and Information Science, Xiangnan University, Chenzhou 423000, China)

  • Yuezhang Li

    (School of Economics and Management, Xiangnan University, Chenzhou 423000, China)

  • Caijuan Kang

    (School of Economics and Management, Xiangnan University, Chenzhou 423000, China)

  • You Wu

    (School of Economics and Management, Xiangnan University, Chenzhou 423000, China)

Abstract

This paper studies an artificial neural network (ANN) for multi-asset European options. Firstly, a simple three-layer ANN-3 is established with undetermined weights and bias. Secondly, the time–space discrete PDE of the multi-asset option is given and the corresponding discrete data are fed into the ANN-3. Then, using least squares error as the objective function, the weights and bias of ANN-3 are trained well. Numerical examples are carried out to confirm the stability, accuracy and efficiency. Experiments show the ANN’s relative error is about 0.8 % . This method can be extended into multi-layer ANN- q ( q > 3 ) and extended into American options.

Suggested Citation

  • Zhiqiang Zhou & Hongying Wu & Yuezhang Li & Caijuan Kang & You Wu, 2024. "Three-Layer Artificial Neural Network for Pricing Multi-Asset European Option," Mathematics, MDPI, vol. 12(17), pages 1-22, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2770-:d:1473333
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/17/2770/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/17/2770/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuanyang Teng & Yicun Li & Xiaobo Wu & Ya Jia, 2022. "Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-39, April.
    2. Hongying Wu & Zhiqiang Zhou & Caijuan Kang & Barbara Martinucci, 2023. "Option Pricing by Willow Tree Method for Generalized Hyperbolic Lévy Processes," Journal of Mathematics, Hindawi, vol. 2023, pages 1-18, October.
    3. Carl Chiarella & Boda Kang & Gunter H Meyer, 2014. "The Numerical Solution of the American Option Pricing Problem:Finite Difference and Transform Approaches," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8736, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. Alòs & F. Antonelli & A. Ramponi & S. Scarlatti, 2021. "Cva And Vulnerable Options In Stochastic Volatility Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-34, March.

    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:jmathe:v:12:y:2024:i:17:p:2770-:d:1473333. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.