Exploring Financial Applications on Many-Core-on-a-Chip Architecture: A First Experiment
Computational requirements for solving models of financial derivatives, for example, the option pricing problems, are huge and demand efficient algorithms and high performance computing capabilities. This demand has been rekindled by the recent developments in the mobile technology making wireless trading a possibility. In this paper, we focus on the development of a Monte-Carlo algorithm on a modern multi-core chip architecture, Cyclops-64 (C64) under development at IBM as the experimental platform for our study in pricing options. The timing results on C64 show that various sets of simulations could be done in a real-time fashion while yielding high performance/price improvement over traditional microprocessors for finance applications.
KeywordsOption Price Stochastic Volatility Strike Price Option Price Formula Option Price Problem
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- 2.Rahmayil, S., Shiller, I., Thulasiram, R.K.: Different Estimators of the Underlying Asset’s Volatility and Option Pricing Errors: Parallel Monte Carlo Simulation. In: Proc. Intl. Conf. on Computational Finance and its Applications, Bologna, Italy, April 2004, pp. 121–131 (2004)Google Scholar
- 3.Chen, G., Thulasiram, R.K., Thulasiraman, P.: Distributed Adaptive Quasi-Monte Carlo Algorithm for Option Pricing on HNOWs Using mpC. In: Proc. 9th Annual Simulation Sympoisum, Huntsville, AL, April 2006, pp. 90–97 (2006)Google Scholar
- 4.Kola, K.: WAMAN:Web-mining-Assisted Mobile-computing-enAbled on-line optioN pricing- a software architecture towards autonomic computing. Master’s thesis, Department of Computer Science, The University of Manitoba, Winnipeg, MB, Canada (May 2006)Google Scholar
- 6.Varshney, U., Vetter, R.J.: Mobile commerce:framework, applications and networking support. MONET 7, 3–4 (2002)Google Scholar
- 7.Hull, J.C.: Options, Futures and Other Derivatives, 5th edn. Prentice-Hall, Upper Saddle River (2002)Google Scholar
- 9.Clark, I.J.: Option Pricing Algorithms for the Cray T3D Supercomputer. In: Proceedings of the first National Conference on Computational and Quantitative Finance (September 1998)Google Scholar
- 10.Thulasiram, R.K., Litov, L., Nojumi, H., Downing, C.T., Gao, G.R.: Multithreaded Algorithms for Pricing a Class of Complex Options. In: Proc. Intl. Parallel and Distributed Processing Symp (IPDPS 2001), San Francisco, CA (April 2001)Google Scholar
- 12.Carr, P., Madan, D.B.: Option Valuation using the Fast Fourier Transform. The Journal of Computational Finance 2(4), 61–73 (1999)Google Scholar
- 14.Mayo, A.: Fourth Order Accurate Implicit Finite Difference Method for Evaluating American Options. In: Proc. Intl. Conf. on Computational Finance 2000, London, England (June 2000)Google Scholar
- 15.Thulasiram, R.K., Zhen, C., Chhabra, A., Thulasiraman, P., Gumel, A.: A second order l0 stable algorithm for evaluating european options. In: Intl. Journal of High Performance Computing and Networking (in press, 2006)Google Scholar
- 16.Srinivasan, A.: Parallel an Distributed Computing Issues in Pricing Financial Derivatives through Quasi Monte Carlo. In: Proc. Intl. Parallel and Distributed Processing Symp (IPDPS 2002), Fort Lauderdale, FL (April 2002)Google Scholar
- 18.del Cuvillo, J., Zhu, W., Hu, Z., Gao, G.R.: FAST: A functionally accurate simulation toolset for the Cyclops64 cellular architecture. In: Workshop on Modeling, Benchmarking, and Simulation (MoBS 2005), Madison, WI (June 2005)Google Scholar