A Stochastic Mesh Method for Pricing High-Dimensional American Options

High-dimensional problems frequently arise in the pricing of derivative securities—for example, in pricing options on multiple underlying assets and in pricing term structure derivatives. American versions of these options, i.e., where the owner has the right to exercise early, are particularly challenging to price. We introduce a stochastic mesh method for pricing high-dimensional American options when there is a finite, but possibly large, number of exercise dates. The algorithm provides point estimates and confidence intervals; we provide conditions under which these estimates converge to the correct values as the computational effort increases. Numerical results illustrate the performance of the method.