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A Hardware Generator of Multi-point Distributed Random Numbers for Monte Carlo Simulation

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Abstract

Monte Carlo simulation of weak approximations of stochastic differential equations constitutes an intensive computational task. In applications such as finance, for instance, to achieve "real time" execution, as often required, one needs highly efficient implementations of the multi-point distributed random number generator underlying the simulations. In this paper a fast and flexible dedicated hardware solution on a field programmable gate array is presented. A comparative performance analysis between a software-only and the proposed hardware solution demonstrates that the hardware solution is bottleneck-free, retains the flexibility of the software solution and significantly increases the computational efficiency. Moreover, simulations in applications such as economics, insurance, physics, population dynamics, epidemiology, structural mechanics, chemistry and biotechnology can benefit from the obtained speedup.

Suggested Citation

  • Nicola Bruti-Liberati & Filippo Martini & Massimo Piccardi & Eckhard Platen, 2005. "A Hardware Generator of Multi-point Distributed Random Numbers for Monte Carlo Simulation," Research Paper Series 156, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:156
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    File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp156.pdf
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    1. Nicola Bruti Liberati & Eckhard Platen, 2004. "On the Efficiency of Simplified Weak Taylor Schemes for Monte Carlo Simulation in Finance," Research Paper Series 114, Quantitative Finance Research Centre, University of Technology, Sydney.
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    Cited by:

    1. Nicola Bruti-Liberati, 2007. "Numerical Solution of Stochastic Differential Equations with Jumps in Finance," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1, July-Dece.
    2. Tisan, A. & Cirstea, M., 2013. "SOM neural network design – A new Simulink library based approach targeting FPGA implementation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 134-149.
    3. Sergio Chavez & Eckhard Platen, 2008. "Distributional Deviations in Random Number Generation in Finance," Research Paper Series 228, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Nicola Bruti-Liberati, 2007. "Numerical Solution of Stochastic Differential Equations with Jumps in Finance," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2007.

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    More about this item

    Keywords

    random number generators; random bit generators; hardware implementation; field programmable gate arrays (FPGAs); Monte Carlo simulation; weak Taylor schemes; multi-point distributed random variables;
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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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