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


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… (More)



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