A Sotchastic Mesh Method for Pricing High-Dimensional American Options.

High-dimensional pricing problems frequently arise with financial options (examples include basket options, outperformance options, interest-rate and foreign currency options) and real options. American versions of these options, i.e., where the owner has the right to exercise early, are particularly challenging to price. We present a new 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 and it converges to the correct values as the computational effort increases. Computational evidence is given which indicates the viability of the method.