Parallel FFT approach for derivative pricing

Ruppa K. Thulasiram; Parimala Thulasiraman

doi:10.1117/12.434870

27 July 2001 Parallel FFT approach for derivative pricing

Ruppa K. Thulasiram, Parimala Thulasiraman

Proceedings Volume 4528, Commercial Applications for High-Performance Computing; (2001) https://doi.org/10.1117/12.434870
Event: ITCom 2001: International Symposium on the Convergence of IT and Communications, 2001, Denver, CO, United States

Abstract

Pricing of derivatives is one of the central problems in Computational Finance. Since the theory of derivative pricing is highly mathematical, numerical techniques such as lattice approach, finite-difference and finite-element techniques among others have been resorted in the past. Recently Fast Fourier Transform (FFT) have been used for such applications as derivative pricing. In the current work, we develop a parallel algorithm for FFT and implement it to price options. Our main aim is to study the performance of this algorithm. For a data size of N and P processors, a blocked data distribution for the algorithm in general produces log(N) - log(P) iterations of local communications and log(P) iterations of remote communications. Therefore, the algorithm is divided into two parts: local and remote. In the local algorithm, the processors perform the computations on their locally partitioned data elements without any communications. In the case of remote algorithm, the processors perform the computation on the local data elements with remote communications. In this paper we focus on the remote communication and computation aspect of the algorithm. We discuss the performance of our algorithm and the results (in general terms) from FFT algorithm and binomial tree algorithm developed and implemented for the same/similar problem. We make some general observation on these two algorithms.

Citation Download Citation

Ruppa K. Thulasiram and Parimala Thulasiraman "Parallel FFT approach for derivative pricing", Proc. SPIE 4528, Commercial Applications for High-Performance Computing, (27 July 2001); https://doi.org/10.1117/12.434870

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