IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v195y2023ics0167715223000135.html
   My bibliography  Save this article

On the averaging principle for stochastic differential equations driven by G-Lévy process

Author

Listed:
  • Yuan, Mingxia
  • Wang, Bingjun
  • Yang, Zhiyan

Abstract

In this paper, we investigate the averaging principle for stochastic differential equation driven by G-Lévy process. By the BDG inequality for G-stochastic calculus with respect to G-Lévy process, we show that the solution of averaged stochastic differential equation driven by G-Lévy process converges to that of the standard one, under non-Lipschitz condition, in the mean square sense and also in capacity. An example is presented to illustrate the efficiency of the obtained results.

Suggested Citation

  • Yuan, Mingxia & Wang, Bingjun & Yang, Zhiyan, 2023. "On the averaging principle for stochastic differential equations driven by G-Lévy process," Statistics & Probability Letters, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:stapro:v:195:y:2023:i:c:s0167715223000135
    DOI: 10.1016/j.spl.2023.109789
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715223000135
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2023.109789?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.

    References listed on IDEAS

    as
    1. Ren, Liying, 2013. "On representation theorem of sublinear expectation related to G-Lévy process and paths of G-Lévy process," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1301-1310.
    2. Gao, Fuqing, 2009. "Pathwise properties and homeomorphic flows for stochastic differential equations driven by G-Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 119(10), pages 3356-3382, October.
    3. Hu, Mingshang & Wang, Falei, 2021. "Probabilistic approach to singular perturbations of viscosity solutions to nonlinear parabolic PDEs," Stochastic Processes and their Applications, Elsevier, vol. 141(C), pages 139-171.
    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. Yuan, Haiyan & Zhu, Quanxin, 2023. "Discrete-time feedback stabilization for neutral stochastic functional differential equations driven by G-Lévy process," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    2. Li, Xinpeng & Peng, Shige, 2011. "Stopping times and related Itô's calculus with G-Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 121(7), pages 1492-1508, July.
    3. Ren, Yong & Hu, Lanying, 2011. "A note on the stochastic differential equations driven by G-Brownian motion," Statistics & Probability Letters, Elsevier, vol. 81(5), pages 580-585, May.
    4. Zhang, Wei & Jiang, Long, 2021. "Solutions of BSDEs with a kind of non-Lipschitz coefficients driven by G-Brownian motion," Statistics & Probability Letters, Elsevier, vol. 171(C).
    5. Hu, Mingshang & Wang, Falei, 2021. "Probabilistic approach to singular perturbations of viscosity solutions to nonlinear parabolic PDEs," Stochastic Processes and their Applications, Elsevier, vol. 141(C), pages 139-171.
    6. Gao, Fuqing & Jiang, Hui, 2010. "Large deviations for stochastic differential equations driven by G-Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 120(11), pages 2212-2240, November.
    7. Hu, Ying & Lin, Yiqing & Soumana Hima, Abdoulaye, 2018. "Quadratic backward stochastic differential equations driven by G-Brownian motion: Discrete solutions and approximation," Stochastic Processes and their Applications, Elsevier, vol. 128(11), pages 3724-3750.
    8. Wang, Bingjun & Yuan, Mingxia, 2019. "Forward-backward stochastic differential equations driven by G-Brownian motion," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 39-47.
    9. Ma, Li & Li, Yujing & Zhu, Quanxin, 2023. "Stability analysis for a class of stochastic delay nonlinear systems driven by G-Lévy Process," Statistics & Probability Letters, Elsevier, vol. 195(C).
    10. Zhengqi Ma & Hongyin Jiang & Chun Li & Defei Zhang & Xiaoyou Liu, 2024. "Stochastic Intermittent Control with Uncertainty," Mathematics, MDPI, vol. 12(13), pages 1-15, June.
    11. Zhang, Xuekang & Huang, Chengzhe & Deng, Shounian, 2024. "Nonparametric estimation for periodic stochastic differential equations driven by G-Brownian motion," Statistics & Probability Letters, Elsevier, vol. 214(C).
    12. Ibrahim Dakaou & Abdoulaye Soumana Hima, 2021. "Large Deviations for Backward Stochastic Differential Equations Driven by G-Brownian Motion," Journal of Theoretical Probability, Springer, vol. 34(2), pages 499-521, June.
    13. Yin, Wensheng & Cao, Jinde, 2021. "On stability of large-scale G-SDEs: A decomposition approach," Applied Mathematics and Computation, Elsevier, vol. 388(C).
    14. Julian Holzermann, 2019. "Term Structure Modeling under Volatility Uncertainty," Papers 1904.02930, arXiv.org, revised Sep 2021.
    15. Luo, Peng & Wang, Falei, 2015. "On the comparison theorem for multi-dimensional G-SDEs," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 38-44.
    16. Peng Luo & Falei Wang, 2019. "Viability for Stochastic Differential Equations Driven by G-Brownian Motion," Journal of Theoretical Probability, Springer, vol. 32(1), pages 395-416, March.
    17. Xuekang Zhang & Shounian Deng & Weiyin Fei, 2023. "Nonparametric Estimation of Trend for Stochastic Processes Driven by G-Brownian Motion with Small Noise," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-14, June.
    18. Erhan Bayraktar & Alexander Munk, 2014. "An $\alpha$-stable limit theorem under sublinear expectation," Papers 1409.7960, arXiv.org, revised Jun 2016.
    19. Luo, Peng & Wang, Falei, 2014. "Stochastic differential equations driven by G-Brownian motion and ordinary differential equations," Stochastic Processes and their Applications, Elsevier, vol. 124(11), pages 3869-3885.
    20. Hu, Ying & Tang, Shanjian & Wang, Falei, 2022. "Quadratic G-BSDEs with convex generators and unbounded terminal conditions," Stochastic Processes and their Applications, Elsevier, vol. 153(C), pages 363-390.

    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:eee:stapro:v:195:y:2023:i:c:s0167715223000135. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.