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

TL;DR

This paper introduces two parallel Monte Carlo algorithms for pricing multi-dimensional Bermudan/American options, demonstrating their effectiveness and scalability in a heterogeneous computing environment.

Contribution

The paper presents novel parallel Monte Carlo algorithms for multi-dimensional option pricing, including boundary computation and classification methods, evaluated in a desktop grid setting.

Findings

Algorithms effectively price complex options

Performance is scalable in heterogeneous environments

Scalability constraints are identified

Abstract

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.