The Annals of Applied Probability
- Ann. Appl. Probab.
- Volume 15, Number 3 (2005), 2172-2202.
A regression-based Monte Carlo method to solve backward stochastic differential equations
Emmanuel Gobet, Jean-Philippe Lemor, and Xavier Warin
Full-text: Open access
Abstract
We are concerned with the numerical resolution of backward stochastic differential equations. We propose a new numerical scheme based on iterative regressions on function bases, which coefficients are evaluated using Monte Carlo simulations. A full convergence analysis is derived. Numerical experiments about finance are included, in particular, concerning option pricing with differential interest rates.
Article information
Source
Ann. Appl. Probab., Volume 15, Number 3 (2005), 2172-2202.
Dates
First available in Project Euclid: 15 July 2005
Permanent link to this document
https://projecteuclid.org/euclid.aoap/1121433781
Digital Object Identifier
doi:10.1214/105051605000000412
Mathematical Reviews number (MathSciNet)
MR2152657
Zentralblatt MATH identifier
1083.60047
Subjects
Primary: 60H10: Stochastic ordinary differential equations [See also 34F05] 60H10: Stochastic ordinary differential equations [See also 34F05] 65C30: Stochastic differential and integral equations
Keywords
Backward stochastic differential equations regression on function bases Monte Carlo methods
Citation
Gobet, Emmanuel; Lemor, Jean-Philippe; Warin, Xavier. A regression-based Monte Carlo method to solve backward stochastic differential equations. Ann. Appl. Probab. 15 (2005), no. 3, 2172--2202. doi:10.1214/105051605000000412. https://projecteuclid.org/euclid.aoap/1121433781
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