H. Prasain, P. Thulasiraman, R. Thulasiram, and G. Kumar Jha (Canada)
Swarm Intelligence, Particle Swarm Optimization, Option pricing Algorithm, and Multi-core Architecture , and extreme learning machine
An option is a financial contract that gives its holder the right to buy or sell a stock or commodity at a specified price during a specified contract period. In this paper, we use particle swarm optimization (PSO), a novel global search algorithm to price option. In our algorithm, we incorporate both constant volatility at compile time and dynamic volatility at run time to reflect the market reality. We first test our algorithm against the classical Black-Scholes-Merton model for pricing accuracy. Since the option pricing problem is computationally intensive, we also develop a parallel PSO algorithm, and experiment on a cluster of multicore machines. We show that a one-to-one mapping of particles to processors is essential for performance speedup. We get a speed of 20 on a cluster of four nodes with 8 dual-core processors per node.
Important Links:
Go Back
