A Regress-Later Algorithm for Backward Stochastic Differential Equations

TL;DR

This paper introduces a new regression-later algorithm combined with least squares Monte Carlo for numerically solving backward stochastic differential equations, providing convergence conditions and practical performance demonstrations.

Contribution

The paper presents a novel regression-later approach for BSDEs that enhances numerical approximation accuracy and convergence analysis.

Findings

Algorithm converges under specified conditions

Demonstrated effectiveness through practical experiments

Improves upon existing Monte Carlo methods for BSDEs

Abstract

This work deals with the numerical approximation of backward stochastic differential equations (BSDEs). We propose a new algorithm which is based on the regression-later approach and the least squares Monte Carlo method. We give some conditions under which our numerical algorithm convergences and solve two practical experiments to illustrate its performance.