Lower bounds for American option prices with control variates

Abstract

This article discusses the application of optimally sampled control variates in the context of the Least-Squares Monte Carlo algorithm for pricing American options. We demonstrate theoretically that optimal sampling introduces bias when estimated exercise times are not stopping times. Numerical results show that this bias is an accurate proxy for the positive foresight bias of American option price estimates, effectively yielding in-sample lower-bound estimates.