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CONVERGENCE OF THE EULER-MARUYAMA METHOD FOR STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY G-BROWNIAN MOTION

  • Cunxia Liu (School of Mathematics and Information Sciences Yantai University) ;
  • Wen Lu (School of Mathematics and Information Sciences Yantai University)
  • 투고 : 2023.06.15
  • 심사 : 2023.10.19
  • 발행 : 2024.07.31

초록

In this paper, we deal with the Euler-Maruyama (EM) scheme for stochastic differential equations driven by G-Brownian motion (G-SDEs). Under the linear growth and the local Lipschitz conditions, the strong convergence as well as the rate of convergence of the EM numerical solution to the exact solution for G-SDEs are established.

키워드

과제정보

The authors are deeply grateful to the anonymous referees and the editor for their careful reading, valuable comments and correction of some errors, which helped to improve the paper a lot.

참고문헌

  1. X. Bai and Y. Lin, On the existence and uniqueness of solutions to stochastic differential equations driven by G-Brownian motion with integral-Lipschitz coefficients, Acta Math. Appl. Sin. Engl. Ser. 30 (2014), no. 3, 589-610. https://doi.org/10.1007/s10255-014-0405-9
  2. J. Bao and X. Huang, Approximations of McKean-Vlasov stochastic differential equations with irregular coefficients, J. Theoret. Probab. 35 (2022), no. 2, 1187-1215. https://doi.org/10.1007/s10959-021-01082-9
  3. J. Bao, X. Huang, and C. Yuan, Convergence rate of Euler-Maruyama scheme for SDEs with Holder-Dini continuous drifts, J. Theoret. Probab. 32 (2019), no. 2, 848-871. https://doi.org/10.1007/s10959-018-0854-9
  4. J. Bao and C. Yuan, Convergence rate of EM scheme for SDDEs, Proc. Amer. Math. Soc. 141 (2013), no. 9, 3231-3243. https://doi.org/10.1090/S0002-9939-2013-11886-1
  5. S. Deng, C. Fei, W. Fei, and X. Mao, Stability equivalence between the stochastic differential delay equations driven by G-Brownian motion and the Euler-Maruyama method, Appl. Math. Lett. 96 (2019), 138-146. https://doi.org/10.1016/j.aml.2019.04.022
  6. L. Denis, M. Hu, and S. G. Peng, Function spaces and capacity related to a sublinear expectation: Application to G-Brownian motion paths, Potential Anal. 34 (2011), no. 2, 139-161. https://doi.org/10.1007/s11118-010-9185-x
  7. F. Gao, Pathwise properties and homeomorphic flows for stochastic differential equations driven by G-Brownian motion, Stochastic Process. Appl. 119 (2009), no. 10, 3356-3382. https://doi.org/10.1016/j.spa.2009.05.010
  8. D. J. Higham, X. Mao, and A. M. Stuart, Strong convergence of Euler-type methods for nonlinear stochastic differential equations, SIAM J. Numer. Anal. 40 (2002), no. 3, 1041-1063. https://doi.org/10.1137/S0036142901389530
  9. M. Hu and S. Peng, On representation theorem of G-expectations and paths of G-Brownian motion, Acta Math. Appl. Sin. Engl. Ser. 25 (2009), no. 3, 539-546. https://doi.org/10.1007/s10255-008-8831-1
  10. M. Hutzenthaler, A. Jentzen, and P. E. Kloeden, Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients, Ann. Appl. Probab. 22 (2012), no. 4, 1611-1641. https://doi.org/10.1214/11-AAP803
  11. P. E. Kloeden and E. Platen, Numerical solution of stochastic differential equations, Applications of Mathematics (New York), 23, Springer, Berlin, 1992. https://doi.org/10.1007/978-3-662-12616-5
  12. X. Li, X. Lin, and Y. Lin, Lyapunov-type conditions and stochastic differential equations driven by G-Brownian motion, J. Math. Anal. Appl. 439 (2016), no. 1, 235-255. https://doi.org/10.1016/j.jmaa.2016.02.042
  13. X. Li, X. Mao, and G. Yin, Explicit numerical approximations for stochastic differential equations in finite and infinite horizons: truncation methods, convergence in pth moment and stability, IMA J. Numer. Anal. 39 (2019), no. 2, 847-892. https://doi.org/10.1093/imanum/dry015
  14. X. Li and S. Peng, Stopping times and related Ito's calculus with G-Brownian motion, Stochastic Process. Appl. 121 (2011), no. 7, 1492-1508. https://doi.org/10.1016/j.spa.2011.03.009
  15. G. Li and Q. Yang, Convergence and asymptotical stability of numerical solutions for neutral stochastic delay differential equations driven by G-Brownian motion, Comput. Appl. Math. 37 (2018), no. 4, 4301-4320. https://doi.org/10.1007/s40314-018-0581-y
  16. X. Mao, Stochastic Differential Equations and Their Applications, Horwood Publishing Series in Mathematics & Applications, Horwood, Chichester, 1997.
  17. X. Mao, The truncated Euler-Maruyama method for stochastic differential equations, J. Comput. Appl. Math. 290 (2015), 370-384. https://doi.org/10.1016/j.cam.2015.06.002
  18. S. Peng, G-expectation, G-Brownian motion and related stochastic calculus of Ito type, in Stochastic analysis and applications, 541-567, Abel Symp., 2, Springer, Berlin, 2007. https://doi.org/10.1007/978-3-540-70847-6_25
  19. S. Peng, Multi-dimensional G-Brownian motion and related stochastic calculus under G-expectation, Stochastic Process. Appl. 118 (2008), no. 12, 2223-2253. https://doi.org/10.1016/j.spa.2007.10.015
  20. S. Peng, Nonlinear expectations and stochastic calculus under uncertainty, Probability Theory and Stochastic Modelling, 95, Springer, Berlin, 2019. https://doi.org/10.1007/978-3-662-59903-7
  21. H. M. Soner, N. Touzi, and J. Zhang, Martingale representation theorem for the G-expectation, Stochastic Process. Appl. 121 (2011), no. 2, 265-287. https://doi.org/10.1016/j.spa.2010.10.006
  22. Y. Song, Some properties on G-evaluation and its applications to G-martingale decomposition, Sci. China Math. 54 (2011), no. 2, 287-300. https://doi.org/10.1007/s11425-010-4162-9
  23. Y. Song, Uniqueness of the representation for G-martingales with finite variation, Electron. J. Probab. 17 (2012), no. 24, 15 pp. https://doi.org/10.1214/EJP.v17-1890
  24. R. Ullah and F. Faizullah, On existence and approximate solutions for stochastic differential equations in the framework of G-Brownian motion, Eur. Phys. J. Plus (2017), 132: 435 https://doi.org/10.1140/epjp/i2017-11700-9
  25. H. Wu, J. Hu, and C. Yuan, Stability of numerical solution to pantograph stochastic functional differential equations, Appl. Math. Comput. 431 (2022), Paper No. 127326, 13 pp. https://doi.org/10.1016/j.amc.2022.127326
  26. J. Yang and W. Zhao, Numerical simulations for G-Brownian motion, Front. Math. China 11 (2016), no. 6, 1625-1643. https://doi.org/10.1007/s11464-016-0504-9
  27. C. Yuan and X. Mao, A note on the rate of convergence of the Euler-Maruyama method for stochastic differential equations, Stoch. Anal. Appl. 26 (2008), no. 2, 325-333. https://doi.org/10.1080/07362990701857251