Nature-inspired soft computing for financial option pricing using high-performance analytics

High-performance computing has witnessed the push towards computer hardware design in the past decade. Many real world problems are both data and compute intensive. Designing efficient algorithms is important to make effective use of the hardware resources for fast data analysis. Finance is one application that will benefit from these supercomputers. Options are instruments that give opportunity to profit from market movements without making large investments. However, understanding the asset price behavior and making a decision to enter into an option contract is quite challenging, called option pricing problem, because underlying asset price might vary violently. In this paper, we propose a nature-inspired soft computing, meta-heuristic, particle swarm optimization (PSO) algorithm to price options. We modify the PSO algorithm and incorporate varying volatility parameters to price options. The proposed algorithm, PSO with Varying Volatility (PSOwVV), is experimented with various PSO and financial parametric conditions. We also develop a parallel PSOwVV algorithm and implement on a distributed shared memory multi-core machine. We show that the parallel algorithm performs well when the number of particles is linearly proportional to the number of processors. The parallel algorithm achieves a speedup of approximately 20x with 64 particles on a four node hybrid cluster. Copyright (c) 2014 John Wiley & Sons, Ltd.