A parallel Particle swarm optimization algorithm for option pricing

Option pricing is one of the challenging problems of computational finance. Nature-inspired algorithms have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. Particle swarm optimization (PSO) is one of the novel global search algorithms based on swarm intelligence. We first show that PSO could be effectively used for the option pricing problem. The results are compared with standard classical Black-Scholes-Merton model for simple European options. In this study, we developed two algorithms for option pricing using Particle Swarm Optimization (PSO). The first algorithm we developed is synchronous option pricing algorithm using PSO (SPSO), and the second is parallel synchronous option pricing algorithm. The pricing results of these algorithms are close when compared with classical Black-Scholes-Merton model for simple European options. We test our Parallel Synchronous PSO algorithm in three architectures: shared memory machine using OpenMP, distributed memory machine using MPI and on a homogeneous multicore architecture running MPI and OpenMP (hybrid model). The results show that the hybrid model handles the load well as we increase the number of particles in simulation while maintaining equivalent accuracy.