The Annals of Applied Probability

A regression-based Monte Carlo method to solve backward stochastic differential equations

Emmanuel Gobet, Jean-Philippe Lemor, and Xavier Warin

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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.

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Ann. Appl. Probab., Volume 15, Number 3 (2005), 2172-2202.

First available in Project Euclid: 15 July 2005

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Primary: 60H10: Stochastic ordinary differential equations [See also 34F05] 60H10: Stochastic ordinary differential equations [See also 34F05] 65C30: Stochastic differential and integral equations

Backward stochastic differential equations regression on function bases Monte Carlo methods


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.

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