An Environment for Rapid Derivatives Design and Experimentation

Danny Crookes, Sean Trainor, Richard Jiang

Research output: Contribution to journalArticlepeer-review

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Abstract

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.
Original languageEnglish
Pages (from-to)1073-1082
JournalIEEE Journal of Selected Topics in Signal Processing
Volume10
Issue number6
Early online date18 Jul 2016
DOIs
Publication statusPublished - Sep 2016

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