Using linear congruential generators for parallel random number generation
WSC '89 Proceedings of the 21st conference on Winter simulation
Multithreaded Algorithms for Pricing a Class of Complex Options
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Option Valuation With Generalized Ant Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Performance Evaluation of a Multithreaded Fast Fourier Transform Algorithm for Derivative Pricing
The Journal of Supercomputing
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Adaptive genetic programming for option pricing
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A bioinspired algorithm to price options
Proceedings of the 2008 C3S2E conference
Option pricing using Particle Swarm Optimization
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
HPCC '10 Proceedings of the 2010 IEEE 12th International Conference on High Performance Computing and Communications
Collaborative multi-swarm PSO for task matching using graphics processing units
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture
Information Sciences: an International Journal
High performance computing for a financial application using fast fourier transform
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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An option is a financial instrument that derives its value from an underlying asset, for example, a stock. There are a wide range of options traded today from simple and plain (European options) to exotic (chooser options) that are very difficult to evaluate. Both buyers and sellers continue to look for efficient algorithms and faster technology to price options for better profit and to beat the competition. There are mathematical models like the Black---Scholes---Merton model used to price options approximately for simple and plain options in the form of closed form solution. However, the market is flooded with various styles of options, which are difficult to price, and hence there are many numerical techniques proposed for pricing. The computational cost for pricing complex options using these numerical techniques is exorbitant for reasonable accuracy in pricing results. Heuristic approaches such as particle swarm optimization (PSO) have been proposed for option pricing, which provide same or better results for simple options than that of numerical techniques at much less computational cost. In this study, we first map the PSO parameters to option pricing parameters. Analyzing the characteristics of PSO and option pricing, we propose a strategy to normalize some of the parameters, which helps in better understanding of the sensitivity of these and other parameters on option pricing results. We then avail of the inherent concurrency of the PSO algorithm while searching for an optimum solution, and design an algorithm for implementation on a modern state-of-the-art graphics processor unit (GPU). Our implementation makes use of the architectural features of GPU in accelerating the computing performance while maintaining accuracy on the pricing results.