IDEAS home Printed from https://ideas.repec.org/a/taf/oaefxx/v8y2020i1p1803524.html
   My bibliography  Save this article

Price discovery in the cryptocurrency option market: A univariate GARCH approach

Author

Listed:
  • Pierre J. Venter
  • Eben Mare
  • Edson Pindza
  • David McMillan

Abstract

In this paper, two univariate generalised autoregressive conditional heteroskedasticity (GARCH) option pricing models are applied to Bitcoin and the Cryptocurrency Index (CRIX). The first model is symmetric and the other takes asymmetric effects into account. Furthermore, the accuracy of the GARCH option pricing model applied to Bitcoin is tested. Empirical results indicate that asymmetry is not an important factor to consider when pricing options on Bitcoin or CRIX, this is consistent with findings in the literature. In addition, the GARCH option pricing model provides realistic price discovery within the bid-ask spreads suggested by the market.

Suggested Citation

  • Pierre J. Venter & Eben Mare & Edson Pindza & David McMillan, 2020. "Price discovery in the cryptocurrency option market: A univariate GARCH approach," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1803524-180, January.
  • Handle: RePEc:taf:oaefxx:v:8:y:2020:i:1:p:1803524
    DOI: 10.1080/23322039.2020.1803524
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23322039.2020.1803524
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/23322039.2020.1803524?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. Pierre J. Venter & Eben Maré, 2021. "Univariate and Multivariate GARCH Models Applied to Bitcoin Futures Option Pricing," JRFM, MDPI, vol. 14(6), pages 1-14, June.
    2. Christian Bucio-Pacheco & Miriam Sosa-Castro & Francisco Reyes-Zarate, 2023. "Volatilidad dinamica en el sector bancario en Mexico: evidencia DCC-GARCH vs Copula-GARCH," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 20(2), pages 69-93, Julio-Dic.

    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:oaefxx:v:8:y:2020:i:1:p:1803524. 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/OAEF20 .

    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.