Energy Efficient Acceleration and Evaluation of Financial Computations towards Real-Time Pricing
Modern financial markets are as vivid as never before. Asset prices - and therefore the prices of all related financial products - change within several milliseconds nowadays. However, not only due to the financial crisis in 2008, calculating fair and meaningful prices for these products is much more important than in the past. In order to obtain reliable prices, sophisticated simulation models have to be used. Pricing in these models in general has a very high computational complexity and can in many cases only be approximately done by using numerical methods. On the other hand, we all know that energy costs will become more and more significant in the future. The gap between the increasing computational complexity and the consumed energy can only be bridged by using more tailored computation engines, like dedicated hardware accelerators or application specific instruction set processors (ASIPs). In this paper we present a comprehensive methodology for the efficient design of optimal hardware accelerators and the evaluation thereof. We give two case studies: a new hardware random number generator for arbitrary distributions and a dedicated hardware accelerator for calculating European barrier option prices.
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- 1.Andersen, L.: Efficient Simulation of the Heston Stochastic Volatility Model. SSRN eLibrary (2007)Google Scholar
- 2.Bernemann, A., Schreyer, R., Spanderen, K.: Pricing Structured Equity Products on GPUs. In: 2010 IEEE Workshop on High Performance Computational Finance (WHPCF), pp. 1–7 (November 2010)Google Scholar
- 3.Bernemann, A., Schreyer, R., Spanderen, K.: Accelerating Exotic Option Pricing and Model Calibration Using GPUs (February 2011), http://ssrn.com/abstract=1753596
- 5.de Schryver, C., Schmidt, D., Wehn, N., Korn, E., Marxen, H., Korn, R.: A New Hardware Efficient Inversion Based Random Number Generator for Non-Uniform Distributions. In: 2010 International Conference on Reconfigurable Computing and FPGAs (ReConFig), pp. 190–195 (December 2010)Google Scholar
- 9.Kaganov, A., Chow, P., Lakhany, A.: FPGA Acceleration of Monte-Carlo based Credit Derivative Pricing. In: Proc. Int. Conf. Field Programmable Logic and Applications, FPL 2008, pp. 329–334 (September 2008)Google Scholar
- 12.Schmerken, I.: Deutsche Bank Shaves Trade Latency Down to 1.25 Microseconds (March 2011), http://www.advancedtrading.com/infrastructure/229300997
- 13.Thomas, D.B., Luk, W.: Credit Risk Modelling using Hardware Accelerated Monte-Carlo Simulation. In: Proc. 16th Int. Symp. Field-Programmable Custom Computing Machines, FCCM 2008, pp. 229–238 (April 2008)Google Scholar
- 14.Warren, P.: City business races the Games for power. The Guardian (May 2008)Google Scholar
- 15.Woods, N.A., Van Court, T.: FPGA Acceleration of Quasi-Monte Carlo in Finance. In: Proc. Int. Conf. Field Programmable Logic and Applications, FPL 2008, pp. 335–340 (2008)Google Scholar
- 16.Zhang, B., Oosterlee, C.W.: Acceleration of Option Pricing Technique on Graphics Processing Units. Technical Report 10-03, Delft University of Technology (February 2010)Google Scholar
- 17.Zhang, J.E., Shu, J.: Pricing s&p 500 index options with heston’s model. In: Proc. IEEE Int. Computational Intelligence for Financial Engineering Conf., pp. 85–92Google Scholar