FPGA acceleration of quasi-Monte Carlo in finance

Today, quasi-Monte Carlo (QMC) methods are widely used in finance to price derivative securities. The QMC approach is popular because for many types of derivatives it yields an estimate of the price, to a given accuracy, faster than other competitive approaches, like Monte Carlo (MC) methods. The calculation of the large number of underlying asset pathways consumes a significant portion of the overall run-time and energy of modern QMC derivative pricing simulations. Therefore, we present an FPGA-based accelerator for the calculation of asset pathways suitable for use in the QMC pricing of several types of derivative securities. Although this implementation uses constructs (recursive algorithms and double-precision floating point) not normally associated with successful FPGA computing, we demonstrate performance in excess of 50times that of a 3 GHz multi-core processor.

[1]  Maya Gokhale,et al.  Monte Carlo Radiative Heat Transfer Simulation on a Reconfigurable Computer , 2004, FPL.

[2]  F. de Dinechin,et al.  A parameterizable floating-point logarithm operator for FPGAs , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[3]  A. Owen,et al.  Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension , 1997 .

[4]  Peter Jaeckel,et al.  Monte Carlo methods in finance , 2002 .

[5]  Wayne Luk,et al.  A Gaussian noise generator for hardware-based simulations , 2004, IEEE Transactions on Computers.

[6]  I. Sobol.  Uniformly distributed sequences with an additional uniform property , 1976 .

[7]  Julian V. Noble,et al.  The full Monte , 2002, Comput. Sci. Eng..

[8]  Martin Langhammer.  Floating point datapath synthesis for FPGAs , 2008, 2008 International Conference on Field Programmable Logic and Applications.

[9]  Paul Glasserman,et al.  Monte Carlo Methods in Financial Engineering , 2003 .

[10]  D. Hunter.  Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension , 2000 .

[11]  J. Hammersley,et al.  Monte Carlo Methods , 1965 .

[12]  Mario Rometsch,et al.  Quasi-Monte Carlo Methods in Finance , 2009 .

[13]  Wayne Luk,et al.  FPGA-optimised high-quality uniform random number generators , 2008, FPGA '08.

[14]  Deian Stefan,et al.  A hardware framework for the fast generation of multiple long-period random number streams , 2008, FPGA '08.

[15]  I. A. Antonov,et al.  An economic method of computing LPτ-sequences , 1979 .