Parallel Pricing Algorithms for Multi--Dimensional Bermudan/American Options using Monte Carlo methods

In this paper we present two parallel Monte Carlo based algorithms for pricing multi–dimensional Bermudan/American options. First approach relies on computation of the optimal exercise boundary while the second relies on classification of continuation and exercise values. We also evaluate the performance of both the algorithms in a desktop grid environment. We show the effectiveness of the proposed approaches in a heterogeneous computing environment, and identify scalability constraints due to the algorithmic structure.