Exploring reconfigurable architectures for explicit finite difference option pricing models

Jin, Qiwei; Thomas, David B.; Luk, Wayne

doi:10.1109/fpl.2009.5272549

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…Some recent studies have shifted the focus to pipelined tree based methods [1], finite-difference methods [2] and quadrature methods [3]. All of these studies have provided solutions for the pricing of certain options (such as American options) which cannot be handled easily by Monte Carlo methods.…”

Section: Related Workmentioning

confidence: 99%

Option pricing with multi-dimensional quadrature architectures

Tse

Thomas

Luk

2009

2009 International Conference on Field-Programmable Technology

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Abstract-Quadrature based methods for numerical integration provide a means of quickly and accurately pricing financial products such as options. These methods can be applied to multidimensional products, such as options on multiple underlying assets, but suffer from an exponential increase in computational complexity as the dimension increases. This paper examines the theoretical complexity of quadrature methods for pricing multi-dimensional options, and then relates this to practical performance in contemporary hardware. An automated system for generating hardware architectures for quadrature is used to explore the performance of increasing dimensionality in FPGA implementations, and then compared them to GPU and CPU solutions. We find that a single-precision FPGA can provide 25 times speedup over software in three dimensions, and offers slightly improved performance over a GPU using comparable technology. The latest GPUs are 2.7 times faster than the older technology Virtex-4 FPGA, but the FPGA still provides over 9 times the energy efficiency.

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Section: Related Workmentioning

confidence: 99%

Option pricing with multi-dimensional quadrature architectures

Tse

Thomas

Luk

2009

2009 International Conference on Field-Programmable Technology

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“…This is because our implementation is based on a highly optimised framework with an efficient floating point library, while the reference is based on a high level synthesis language, Hyperstreams [3]. .…”

Section: Implementation and Evaluationmentioning

confidence: 99%

“…Existing work addresses physical problems [2] which do not require high arithmetical precision, or focus on accelerating individual options rather than the entire set of possible options [3]. In this paper we consider the explicit method, which has a regular computational stencil which fits well in hardware.…”

Section: Introductionmentioning

confidence: 99%

Unifying Finite Difference Option-Pricing for Hardware Acceleration

Jin

Luk

Thomas

2011

2011 21st International Conference on Field Programmable Logic and Applications

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Explicit finite difference method is widely used in finance for pricing many kinds of options. Its regular computational pattern makes it an ideal candidate for acceleration using reconfigurable hardware. However, because the corresponding hardware designs must be optimised both for the specific option and the target platform, it is challenging and time consuming to develop all the designs efficiently. This paper presents a framework for describing and automatically implementing financial explicit finite difference procedures in reconfigurable hardware, allowing parallelised fully pipelined implementations to be created from high-level mathematical expressions. The proposed methodology is demonstrated using three option pricing problems. Our results show that the implementation generated by our framework on a Virtex-6 device at 310MHz is more than 24 times faster than a software implementation fully optimised by the Intel compiler on a four-core Xeron CPU at 2.66GHz. In addition, the latency of the FPGA solvers is up to 90 times lower than the corresponding software solvers.

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“…There has been great interest in applying reconfigurable solutions to genetic programming [5][6][7][8][9][10][11][12][13][14][15][16][17][18] and substantial progress has been achieved, however, there are still important limitations which restrict the applicability of these solutions in real environments and that we propose to address in our work such as: high latency due to fitness evaluation, simple trading strategies due to GPs represented with reduced complexity s.a. bit-strings instead of trees, small number of individuals evaluated, small number of iterations to reach the maturity of a GP population.…”

Section: Introductionmentioning

confidence: 99%

“…This could enable identifying more complex data patterns such as those which could exist within Foreign Exchange market data and eventually pave the way for more advanced trading strategies [9], potentially higher returns and better risk monitoring.…”

Section: Introductionmentioning

confidence: 99%

Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies

Funie

Grigoras

Burovskiy

et al. 2017

J Sign Process Syst

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Genetic programming can be used to identify complex patterns in financial markets which may lead to more advanced trading strategies. However, the computationally intensive nature of genetic programming makes it difficult to apply to real world problems, particularly in realtime constrained scenarios. In this work we propose the use of Field Programmable Gate Array technology to accelerate the fitness evaluation step, one of the most computationally demanding operations in genetic programming. We propose to develop a fully-pipelined, mixed precision design using run-time reconfiguration to accelerate fitness evaluation. We show that run-time reconfiguration can reduce resource consumption by a factor of 2 compared to previous solutions on certain configurations. The proposed design is up to 22 times faster than an optimised, multithreaded software implementation while achieving comparable financial returns.

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Exploring reconfigurable architectures for explicit finite difference option pricing models

Cited by 17 publications

References 13 publications

Option pricing with multi-dimensional quadrature architectures

Option pricing with multi-dimensional quadrature architectures

Unifying Finite Difference Option-Pricing for Hardware Acceleration

Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies

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