Convergence of the stochastic mesh estimator for pricing American options

Abstract

Broadie and Glasserman proposed a simulation-based method they named {\em stochastic mesh} for pricing high-dimensional American options. Based on simulated states of the assets underlying the option at each exercise opportunity, the method produces an estimator of the option value at each sampled state. Under the mild assumption of the finiteness of certain moments, we derive an asymptotic upper bound on the probability of error of the mesh estimator, where both the error size and the probability bound vanish as the sample size increases. We include the empirical performance for the test problems used by Broadie and Glasserman in a recent unpublished manuscript. We find that the mesh estimator has large bias that decays very slowly with the sample size, suggesting that in applications it will most likely be necessary to employ bias and/or variance reduction techniques