NDLI: A GPU accelerated hybrid lattice-grid algorithm for options pricing

Content Provider	IEEE Xplore Digital Library
Author	Omeru, J.O. Thomas, D.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK (Omeru, J.O.; Thomas, D.)
Abstract	The pricing of financial derivatives is an important problem in risk analysis and real-time trading. The need for faster and more accurate pricing has led financial institutions to adopt GPU technology, but this means we need new pricing algorithms designed specifically for GPU architectures. This research tackles the design of adaptable algorithms for option evaluation using lattices, a commonly used numerical technique. Usually lattice nodes are placed on a fixed grid at a high resolution, but by coarsening the grid in areas of low error, we can reduce run-time without a reduction in accuracy. We show that this adaptable grid can be designed to map onto the underlying architecture of warp-based GPUs, providing a tradeoff between faster execution at the same error, or lower error for the same execution speed. We implemented this algorithm in platform-independent OpenCL, and evaluated it on the Nvidia Quadro K4000, across different option classes. We present accuracy and speed-up results from using our hybrid lattice mesh model over an equivalent standard lattice implementation.
Starting Page	758
Ending Page	765
File Size	1653933
Page Count	8
File Format	PDF
ISBN	9781479953127
e-ISBN	9781479953134
DOI	10.1109/HPCSim.2014.6903765
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-21
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Lattice methods Options Pricing Computational modeling PU Lattices Graphics processing units Hybrid Mesh ModelG Pricing Computer architecture Acceleration Hybrid Mesh ModelPU
Content Type	Text
Resource Type	Article

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