IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p340-d731547.html
   My bibliography  Save this article

Adopting Feynman–Kac Formula in Stochastic Differential Equations with (Sub-)Fractional Brownian Motion

Author

Listed:
  • Bodo Herzog

    (Economics Department, ESB Business School, Reutlingen University, 72762 Reutlingen, Germany
    Reutlingen Research Institute (RRI), 72762 Reutlingen, Germany
    Institute of Finance and Economics (IFE), Reutlingen University, 72762 Reutlingen, Germany)

Abstract

The aim of this work is to establish and generalize a relationship between fractional partial differential equations (fPDEs) and stochastic differential equations (SDEs) to a wider class of stochastic processes, including fractional Brownian motions { B t H , t ≥ 0 } and sub-fractional Brownian motions { ξ t H , t ≥ 0 } with Hurst parameter H ∈ ( 1 2 , 1 ) . We start by establishing the connection between a fPDE and SDE via the Feynman–Kac Theorem, which provides a stochastic representation of a general Cauchy problem. In hindsight, we extend this connection by assuming SDEs with fractional- and sub-fractional Brownian motions and prove the generalized Feynman–Kac formulas under a (sub-)fractional Brownian motion. An application of the theorem demonstrates, as a by-product, the solution of a fractional integral, which has relevance in probability theory.

Suggested Citation

  • Bodo Herzog, 2022. "Adopting Feynman–Kac Formula in Stochastic Differential Equations with (Sub-)Fractional Brownian Motion," Mathematics, MDPI, vol. 10(3), pages 1-13, January.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:340-:d:731547
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/340/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/3/340/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Bodo Herzog, 2023. "Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI," Mathematics, MDPI, vol. 11(9), pages 1-11, April.

    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:10:y:2022:i:3:p:340-:d:731547. 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: 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.