IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i2p323-d1571356.html
   My bibliography  Save this article

Density Formula in Malliavin Calculus by Using Stein’s Method and Diffusions

Author

Listed:
  • Hyun-Suk Park

    (Division of Data Science, Data Science Convergence Research Center, Hallym University, Chuncheon 24252, Republic of Korea)

Abstract

Let G be a random variable of functionals of an isonormal Gaussian process X defined on some probability space. Studies have been conducted to determine the exact form of the density function of the random variable G . In this paper, unlike previous studies, we will use the Stein’s method for invariant measures of diffusions to obtain the density formula of G . By comparing the density function obtained in this paper with that of the diffusion invariant measure, we find that the diffusion coefficient of an Itô diffusion with an invariant measure having a density can be expressed as in terms of operators in Malliavin calculus.

Suggested Citation

  • Hyun-Suk Park, 2025. "Density Formula in Malliavin Calculus by Using Stein’s Method and Diffusions," Mathematics, MDPI, vol. 13(2), pages 1-15, January.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:323-:d:1571356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/2/323/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/2/323/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kusuoka, Seiichiro & Tudor, Ciprian A., 2012. "Stein’s method for invariant measures of diffusions via Malliavin calculus," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1627-1651.
    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. Eden, Richard & Víquez, Juan, 2015. "Nourdin–Peccati analysis on Wiener and Wiener–Poisson space for general distributions," Stochastic Processes and their Applications, Elsevier, vol. 125(1), pages 182-216.
    2. Peng Chen & Ivan Nourdin & Lihu Xu & Xiaochuan Yang & Rui Zhang, 2022. "Non-integrable Stable Approximation by Stein’s Method," Journal of Theoretical Probability, Springer, vol. 35(2), pages 1137-1186, June.
    3. Gaunt, Robert E., 2019. "Stein operators for variables form the third and fourth Wiener chaoses," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 118-126.
    4. Tudor, Ciprian A., 2014. "Chaos expansion and asymptotic behavior of the Pareto distribution," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 62-68.
    5. Christophe Ley & Gesine Reinert & Yvik Swan, 2014. "Approximate Computation of Expectations: the Canonical Stein Operator," Working Papers ECARES ECARES 2014-36, ULB -- Universite Libre de Bruxelles.
    6. Ley, Christophe, 2023. "When the score function is the identity function - A tale of characterizations of the normal distribution," Econometrics and Statistics, Elsevier, vol. 26(C), pages 153-160.
    7. Arras, Benjamin & Azmoodeh, Ehsan & Poly, Guillaume & Swan, Yvik, 2019. "A bound on the Wasserstein-2 distance between linear combinations of independent random variables," Stochastic Processes and their Applications, Elsevier, vol. 129(7), pages 2341-2375.
    8. Yoon-Tae Kim & Hyun-Suk Park, 2023. "Bound for an Approximation of Invariant Density of Diffusions via Density Formula in Malliavin Calculus," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    9. Privault, N. & Yam, S.C.P. & Zhang, Z., 2019. "Poisson discretizations of Wiener functionals and Malliavin operators with Wasserstein estimates," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3376-3405.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:13:y:2025:i:2:p:323-:d:1571356. 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.