Approximation of backward stochastic differential equations using Malliavin weights and least-squares regression

@inproceedings{Gobet2013ApproximationOB,
title={Approximation of backward stochastic differential equations using Malliavin weights and least-squares regression},
author={Emmanuel Gobet and Plamen Turkedjiev},
year={2013}
}

We design a numerical scheme for solving a Dynamic Programming equation with Malliavin weights arising from the time-discretization of backward stochastic differential equations with the integration by parts-representation of the Z-component by [18]. When the sequence of conditional expectations is computed using empirical least-squares regressions, we establish, under general conditions, tight error bounds as the time-average of local regression errors only (up to logarithmic factors). We… CONTINUE READING