Energy Efficient Acceleration and Evaluation of Financial Computations towards Real-Time Pricing

  • Christian de Schryver
  • Matthias Jung
  • Norbert Wehn
  • Henning Marxen
  • Anton Kostiuk
  • Ralf Korn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6884)


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.


Asset Price Option Price Stochastic Volatility Model Hardware Accelerator Heston Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andersen, L.: Efficient Simulation of the Heston Stochastic Volatility Model. SSRN eLibrary (2007)Google Scholar
  2. 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. 3.
    Bernemann, A., Schreyer, R., Spanderen, K.: Accelerating Exotic Option Pricing and Model Calibration Using GPUs (February 2011),
  4. 4.
    Black, F., Scholes, M.: The Pricing of Options and Corporate Liabilities. The Journal of Political Economy 81(3), 637–654 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 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
  6. 6.
    Giles, M.B.: Multilevel Monte Carlo path simulation. Operations Research-Baltimore 56(3), 607–617 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Van Haastrecht, A., Pelsser, A.: Efficient, almost exact simulation of the Heston stochastic volatility model. International Journal of Theoretical and Applied Finance 13(1), 1–43 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Heston, S.L.: A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies 6(2), 327 (1993)CrossRefGoogle Scholar
  9. 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
  10. 10.
    Korn, R., Korn, E., Kroisandt, G.: Monte Carlo methods and models in finance and insurance. CRC Press, Boca Raton (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    Lord, R., Koekkoek, R., van Dijk, D.: A comparison of biased simulation schemes for stochastic volatility models. Quantitative Finance 10(2), 177–194 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Schmerken, I.: Deutsche Bank Shaves Trade Latency Down to 1.25 Microseconds (March 2011),
  13. 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. 14.
    Warren, P.: City business races the Games for power. The Guardian (May 2008)Google Scholar
  15. 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. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian de Schryver
    • 1
  • Matthias Jung
    • 1
  • Norbert Wehn
    • 1
  • Henning Marxen
    • 2
  • Anton Kostiuk
    • 2
  • Ralf Korn
    • 2
  1. 1.Departments of Electrical EngineeringUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Departments of MathematicsUniversity of KaiserslauternKaiserslauternGermany

Personalised recommendations