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

Emmanuel Gobet; Jean Philippe Lemor; Xavier Warin

doi:10.1214/105051605000000412

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

Emmanuel Gobet
, Jean Philippe Lemor
, Xavier Warin

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	2172-2202
Number of pages	31
Journal	Annals of Applied Probability
Volume	15
Issue number	3
DOIs	https://doi.org/10.1214/105051605000000412
Publication status	Published - 1 Aug 2005

Keywords

Backward stochastic differential equations
Monte Carlo methods
Regression on function bases

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10.1214/105051605000000412

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title = "A regression-based Monte Carlo method to solve backward stochastic differential equations",

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.",

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author = "Emmanuel Gobet and Lemor, \{Jean Philippe\} and Xavier Warin",

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AU - Lemor, Jean Philippe

AU - Warin, Xavier

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N2 - 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.

AB - 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.

KW - Backward stochastic differential equations

KW - Monte Carlo methods

KW - Regression on function bases

UR - https://www.scopus.com/pages/publications/29144464083

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DO - 10.1214/105051605000000412

M3 - Article

AN - SCOPUS:29144464083

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VL - 15

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EP - 2202

JO - Annals of Applied Probability

JF - Annals of Applied Probability

IS - 3

ER -

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

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Keywords

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