An Environment for Rapid Derivatives Design and Experimentation

Crookes, Danny and Trainor, Sean and Jiang, Richard (2016) An Environment for Rapid Derivatives Design and Experimentation. IEEE Journal of Selected Topics in Signal Processing, 10 (6). pp. 1073-1082. ISSN 1932-4553

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In the highly competitive world of modern finance, new derivatives are continually required to take advantage of changes in financial markets, and to hedge businesses against new risks. The research described in this paper aims to accelerate the development and pricing of new derivatives in two different ways. First, new derivatives can be specified mathematically within a general framework, enabling new mathematical formulae to be specified rather than just new parameter settings. This Generic Pricing Engine (GPE) is expressively powerful enough to specify a wide range of standard pricing engines. Second, the associated price simulation using the Monte Carlo method is accelerated using GPU or multicore hardware. The parallel implementation (in OpenCL) is automatically derived from the mathematical description of the derivative. As a test, for a Basket Option Pricing Engine (BOPE) generated using the GPE, on the largest problem size, an NVidia GPU runs the generated pricing engine at 45 times the speed of a sequential, specific hand-coded implementation of the same BOPE. Thus, a user can more rapidly devise, simulate, and experiment with new derivatives without actual programming.

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Journal Article
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IEEE Journal of Selected Topics in Signal Processing
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19 Mar 2019 09:05
Last Modified:
22 Nov 2022 07:12