IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v24y2024i8p1129-1156.html
   My bibliography  Save this article

Neural network empowered liquidity pricing in a two-price economy under conic finance settings

Author

Listed:
  • Matteo Michielon
  • Diogo Franquinho
  • Alessandro Gentile
  • Asma Khedher
  • Peter Spreij

Abstract

In the article at hand neural networks are used to model liquidity in financial markets, under conic finance settings, in two different contexts. That is, on the one hand this paper illustrates how the use of neural networks within a two-price economy allows to obtain accurate pricing and Greeks of financial derivatives, enhancing computational performances compared to classical approaches such as (conic) Monte Carlo. The methodology proposed for this purpose is agnostic of the underlying valuation model, and it easily adapts to all models suitable for pricing in conic financial markets. On the other hand, this article also investigates the possibility of valuing contingent claims under conic assumptions, using local stochastic volatility models, where the local volatility is approximated by means of a (combination of) neural network(s). Moreover, we also show how it is possible to generate hybrid families of distortion functions to better fit the implied liquidity of the market, as well as we introduce a conic version of the SABR model, based on the Wang transform, that still allows for analytical bid and ask pricing formulae.

Suggested Citation

  • Matteo Michielon & Diogo Franquinho & Alessandro Gentile & Asma Khedher & Peter Spreij, 2024. "Neural network empowered liquidity pricing in a two-price economy under conic finance settings," Quantitative Finance, Taylor & Francis Journals, vol. 24(8), pages 1129-1156, August.
  • Handle: RePEc:taf:quantf:v:24:y:2024:i:8:p:1129-1156
    DOI: 10.1080/14697688.2024.2390947
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2024.2390947
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/14697688.2024.2390947?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.

    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:24:y:2024:i:8:p:1129-1156. 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.