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Efficient Reconfigurable Architecture for Pricing Exotic Options

Published:22 December 2017Publication History
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Abstract

This article presents a new method for Monte Carlo (MC) option pricing using field-programmable gate arrays (FPGAs), which use a discrete-space random walk over a binomial lattice, rather than the continuous space-walks used by existing approaches. The underlying hypothesis is that the discrete-space walk will significantly reduce the area needed for each MC engine, and the resulting increase in parallelisation and raw performance outweighs any accuracy losses introduced by the discretisation. Experimental results support this hypothesis, showing that for a given MC simulation size, there is no significant loss in accuracy by using a discrete space model for the path-dependent exotic financial options. Analysis of the binomial simulation model shows that only limited-precision fixed-point arithmetic is needed, and also shows that pairs of MC kernels are able to share RAM resources. When using realistic constraints on pricing problems, it was found that the size of a discrete-space MC engine can be kept to 370 Flip-Flops and 233 Lookup Tables, allowing up to 3,000 variance-reduced MC cores in one FPGA. The combination of a highly parallelisable architecture and model-specific optimisations means that the binomial pricing technique allows for a 50× improvement in throughput compared to existing FPGA approaches, without any reduction in accuracy.

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          • Published in

            cover image ACM Transactions on Reconfigurable Technology and Systems
            ACM Transactions on Reconfigurable Technology and Systems  Volume 10, Issue 4
            December 2017
            119 pages
            ISSN:1936-7406
            EISSN:1936-7414
            DOI:10.1145/3166118
            • Editor:
            • Steve Wilton
            Issue’s Table of Contents

            Copyright © 2017 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 22 December 2017
            • Accepted: 1 November 2017
            • Received: 1 May 2017
            Published in trets Volume 10, Issue 4

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