A regression-based Monte Carlo method to solve backward stochastic differential equations
Top Cited Papers
Open Access
- 1 August 2005
- journal article
- Published by Institute of Mathematical Statistics in The Annals of Applied Probability
- Vol. 15 (3) , 2172-2202
- https://doi.org/10.1214/105051605000000412
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.Keywords
All Related Versions
This publication has 23 references indexed in Scilit:
- A regression-based Monte Carlo method to solve backward stochastic differential equationsThe Annals of Applied Probability, 2005
- A numerical scheme for BSDEsThe Annals of Applied Probability, 2004
- An analysis of a least squares regression method for American option pricingFinance and Stochastics, 2002
- Path regularity for solutions of backward stochastic differential equationsProbability Theory and Related Fields, 2002
- Numberical Method for Backward Stochastic Differential EquationsThe Annals of Applied Probability, 2002
- Valuing American Options by Simulation: A Simple Least-Squares ApproachThe Review of Financial Studies, 2001
- Hedging options for a large investor and forward-backward SDE'sThe Annals of Applied Probability, 1996
- Option Pricing with Differential Interest RatesThe Review of Financial Studies, 1995
- Solving forward-backward stochastic differential equations explicitly — a four step schemeProbability Theory and Related Fields, 1994
- Stochastic Differential UtilityEconometrica, 1992