High performance computing for a financial application using Fast Fourier Transform

Fast Fourier Transform (FFT) has been used in many scientific and engineering applications. In the current study, we have applied the FFT for a novel application in finance. We have improved a recently proposed mathematical model of Fourier transform technique for pricing financial derivatives to help design and develop an effective parallel algorithm using a swapping technique that exploits data locality. We have implemented our algorithm on 20 node SunFire 6800 high performance computing system and compared the new algorithm with the traditional Cooley-Tukey algorithm We have presented the computed option values for various strike prices with a proper selection of strike-price spacing to ensure fine grid integration for FFT computation as well as to maximize the number of strikes lying in the desired region of the asset price.