Abstract
In this paper, by using trapezoidal rule and the integration-by-parts formula of Malliavin calculus, we propose three new numerical schemes for solving decoupled forward-backward stochastic differential equations. We theoretically prove that the schemes have second-order convergence rate. To demonstrate the effectiveness and the second-order convergence rate, numerical tests are given.
Similar content being viewed by others
References
Bender C, Denk R. A forward scheme for backward SDEs. Stochastic Process Appl, 2007, 117: 1793–1812
Bouchard B, Touzi N. Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations. Stochastic Process Appl, 2004, 111: 175–206
Chevance D. Numerical methods for backward stochastic differential equations. In: Numerical methods in finance. Cambridge: Cambridge University Press, 1997, 232–244
Evans L C. Partial Differential Equations. Providence, RI: Amer Math Soc, 1998
Gobet E, Labart C. Error expansion for the discretization of backward stochastic differential equations. Stochastic Process Appl, 2007, 117: 803–829
Karoui N E, Peng S G, Quenez M C. Backward stochastic differential equations in finance. Math Finance, 1997, 7: 1–71
Kloeden P E, Platen E. Numerical Solution of Stochastic Differential Equations. Berlin: Springer-Verlag, 1992
Ladyzenskaja O, Solonnikov V, Uralceva N. Linear and Quasilinear Equations of Parabolic Type. Providence, RI: Amer Math Soc, 1968
Li Y, Zhao W D. L p-error estimates for numerical schemes for solving certain kinds of backward stochastic differential equations. Statist Probab Lett, 2010, 21–22: 1612–1617
Ma J, Protter P, San Martin J, et al. Numerical methods for backward stochastic differential equations. Ann Appl Probab, 2002, 12: 302–316
Ma J, Protter P, Yong J M. Solving forward-backward stochastic differential equations explicitly-a four step scheme. Probab Theory Related Fields, 1994, 98: 339–359
Nualart D. The Malliavin Calculus and Related Topics. Berlin: Springer Verlag, 1995
Pardoux E, Peng S G. Adapted solution of a backward stochastic differntial equation. Systems Control Lett, 1990, 14: 55–61
Peng S G. Probabilistic interpretation for systems of quasilinear parabolic partial differential equations. Stoch Rep, 1991, 37: 61–74
Tocino A, Vigo-aguiar J. Weak second order conditions for stochastic Runge-Kutta methods. SIAM J Sci Comput, 2003, 2: 507–523
Zhang J F. A numerical scheme for BSDEs. Ann Appl Probab, 2004, 14: 459–488
Zhao W D, Chen L F, Peng S G. A new kind of accurate numerical method for backward stochastic differential equations. SIAM J Sci Comput, 2006, 28: 1563–1581
Zhao W D, Li Y, Zhang G N. A Generalized θ-Scheme for solving backward stochastic differential equations. Discrete Contin Dyn Syst Ser B, 2012, 5: 1585–1603
Zhao W D, Wang J L, Peng S G. Error estimates of the θ-scheme for backward stochastic differential equations. Discrete Contin Dyn Syst Ser B, 2009, 4: 905–924
Zhao W D, Li Y, Ju L L. Error estimates of the Crank-Nicolson scheme for solving backward stochastic differential equations. Int J Numer Anal Model, 2013, 4: 876–898
Zhao W D, Zhang G N, Ju L L. A stable multistep scheme for solving backward stochastic differential equations. SIAM J Numer Anal, 2010, 4: 1369–1394
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhao, W., Li, Y. & Fu, Y. Second-order schemes for solving decoupled forward backward stochastic differential equations. Sci. China Math. 57, 665–686 (2014). https://doi.org/10.1007/s11425-013-4764-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11425-013-4764-0
Keywords
- forward backward stochastic differential equations
- second-order scheme
- error estimate
- trapezoidal rule
- Malliavin calculus