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Error expansion for the discretization of backward stochastic differential equations

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  • Gobet, Emmanuel
  • Labart, Céline

Abstract

We study the error induced by the time discretization of decoupled forward-backward stochastic differential equations (X,Y,Z). The forward component X is the solution of a Brownian stochastic differential equation and is approximated by a Euler scheme XN with N time steps. The backward component is approximated by a backward scheme. Firstly, we prove that the errors (YN-Y,ZN-Z) measured in the strong Lp-sense (p>=1) are of order N-1/2 (this generalizes the results by Zhang [J. Zhang, A numerical scheme for BSDEs, The Annals of Applied Probability 14 (1) (2004) 459-488]). Secondly, an error expansion is derived: surprisingly, the first term is proportional to XN-X while residual terms are of order N-1.

Suggested Citation

  • Gobet, Emmanuel & Labart, Céline, 2007. "Error expansion for the discretization of backward stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 117(7), pages 803-829, July.
    • Handle: RePEc:eee:spapps:v:117:y:2007:i:7:p:803-829
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    References listed on IDEAS

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    1. Arturo Kohatsu & Roger Pettersson, 2002. "Variance reduction methods for simulation of densities on Wiener space," Economics Working Papers 597, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Bouchard, Bruno & Touzi, Nizar, 2004. "Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 111(2), pages 175-206, June.
    3. N. El Karoui & S. Peng & M. C. Quenez, 1997. "Backward Stochastic Differential Equations in Finance," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 1-71, January.
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    Cited by:

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    2. Guangbao Guo, 2018. "Finite Difference Methods for the BSDEs in Finance," IJFS, MDPI, vol. 6(1), pages 1-15, March.
    3. Crisan, D. & Manolarakis, K. & Touzi, N., 2010. "On the Monte Carlo simulation of BSDEs: An improvement on the Malliavin weights," Stochastic Processes and their Applications, Elsevier, vol. 120(7), pages 1133-1158, July.
    4. Balter, Anne G. & Pelsser, Antoon, 2020. "Pricing and hedging in incomplete markets with model uncertainty," European Journal of Operational Research, Elsevier, vol. 282(3), pages 911-925.
    5. Dirk Becherer & Plamen Turkedjiev, 2014. "Multilevel approximation of backward stochastic differential equations," Papers 1412.3140, arXiv.org.
    6. Christian Bender & Nikolaus Schweizer, 2019. "`Regression Anytime' with Brute-Force SVD Truncation," Papers 1908.08264, arXiv.org, revised Oct 2020.
    7. Chol-Kyu Pak & Mun-Chol Kim & Chang-Ho Rim, 2018. "Adapted $\theta$-Scheme and Its Error Estimates for Backward Stochastic Differential Equations," Papers 1808.02173, arXiv.org.
    8. Antonis Papapantoleon & Dylan Possamai & Alexandros Saplaouras, 2021. "Stability of backward stochastic differential equations: the general case," Papers 2107.11048, arXiv.org, revised Apr 2023.
    9. Ioannis Exarchos & Evangelos Theodorou & Panagiotis Tsiotras, 2019. "Stochastic Differential Games: A Sampling Approach via FBSDEs," Dynamic Games and Applications, Springer, vol. 9(2), pages 486-505, June.
    10. Pagès, Gilles & Sagna, Abass, 2018. "Improved error bounds for quantization based numerical schemes for BSDE and nonlinear filtering," Stochastic Processes and their Applications, Elsevier, vol. 128(3), pages 847-883.
    11. Chol-Kyu Pak & Mun-Chol Kim & O Hun, 2018. "A generalized scheme for BSDEs based on derivative approximation and its error estimates," Papers 1808.02478, arXiv.org.
    12. Wei Zhang & Hui Min, 2021. "Weak Convergence Analysis and Improved Error Estimates for Decoupled Forward-Backward Stochastic Differential Equations," Mathematics, MDPI, vol. 9(8), pages 1-15, April.
    13. Ryan Donnelly & Sebastian Jaimungal, 2022. "Exploratory Control with Tsallis Entropy for Latent Factor Models," Papers 2211.07622, arXiv.org, revised Jan 2024.
    14. Gnameho Kossi & Stadje Mitja & Pelsser Antoon, 2024. "A gradient method for high-dimensional BSDEs," Monte Carlo Methods and Applications, De Gruyter, vol. 30(2), pages 183-203.
    15. Naito Riu & Yamada Toshihiro, 2019. "A second-order discretization for forward-backward SDEs using local approximations with Malliavin calculus," Monte Carlo Methods and Applications, De Gruyter, vol. 25(4), pages 341-361, December.

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