IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/6612284.html
   My bibliography  Save this article

Pricing of American Carbon Emission Derivatives and Numerical Method under the Mixed Fractional Brownian Motion

Author

Listed:
  • Yuling Wang
  • Jing Wang
  • Lijun Pei

Abstract

This paper studies the pricing of American carbon emission derivatives and its numerical method under the mixed fractional Brownian motion. To capture the long memory properties such as self-similarity and long-range dependence in the price process, we proposed a model based on a fractional Black–Scholes, which is more in line with the actual characteristics of the option market. We have outlined a power penalty approach using parabolic variation inequality and linear complementarity (LCP) which arises from mixed fractional Brownian motion. In addition, we introduced a nonuniform grid-based modification of the fitted finite volume method for numerical solution. Numerically, we show the impact of Hurst exponent on the pricing and analyze the convergence rates of the proposed penalty method. In conclusion, since mfBm is a well-developed mathematical model of strongly correlated stochastic processes, this model will be an efficient model for pricing carbon financial derivative.

Suggested Citation

  • Yuling Wang & Jing Wang & Lijun Pei, 2021. "Pricing of American Carbon Emission Derivatives and Numerical Method under the Mixed Fractional Brownian Motion," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-8, April.
  • Handle: RePEc:hin:jnddns:6612284
    DOI: 10.1155/2021/6612284
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2021/6612284.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/ddns/2021/6612284.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/6612284?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
    ---><---

    Citations

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


    Cited by:

    1. Yue Qi & Yue Wang, 2023. "Innovating and Pricing Carbon-Offset Options of Asian Styles on the Basis of Jump Diffusions and Fractal Brownian Motions," Mathematics, MDPI, vol. 11(16), pages 1-22, August.
    2. Xinchen Liu & Xuanwei Ning & Chengliang Wu & Yang Zhang, 2024. "Evolutionary Trends in Carbon Market Risk Research," Energies, MDPI, vol. 17(18), pages 1-28, September.

    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:hin:jnddns:6612284. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.