Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - An Environment for Rapid Derivatives Design and Experimentation
AU - Crookes, Danny
AU - Trainor, Sean
AU - Jiang, Richard
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
KW - Financial trading
KW - high performance DSP
KW - pricing engines
U2 - 10.1109/JSTSP.2016.2592619
DO - 10.1109/JSTSP.2016.2592619
M3 - Journal article
VL - 10
SP - 1073
EP - 1082
JO - IEEE Journal of Selected Topics in Signal Processing
JF - IEEE Journal of Selected Topics in Signal Processing
SN - 1932-4553
IS - 6
ER -