Efficient reconfigurable design for pricing asian options

Abstract

Arithmetic Asian options are financial derivatives which have the feature of path-dependency: they depend on the entire price path of the underlying asset, rather than just the instantaneous price. This path-dependency makes them difficult to price, as only computationally intensive Monte-Carlo methods can provide accurate prices. This paper proposes an FPGA-accelerated Asian option pricing solution, using a highly-optimised parallel Monte-Carlo architecture. The proposed pipelined design is described parametrically, facilitating its re-use for different technologies. An implementation of this architecture in a Virtex-5 xc5vlx330t FPGA at 200MHz is 313 times faster than a multi-threaded software implementation running on a Intel Xeon E5420 quad-core CPU at 2.5GHz; it is also 2.2 times faster than the Tesla C1060 GPU at 1.3 GHz.

Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing

“…Instead, Monte-Carlo methods are used to calculate the payoff of Asian options. Using MonteCarlo methods leads to accurate results at the expense of a large number of simulations, due to the slow convergence of this method [16].…”

FPGA acceleration using high-level languages of a Monte-Carlo method for pricing complex options

“…We have made this decision because accuracy has been considered a much more critical issue rather than throughput for our Asian option simulation solution. We consider that our approach presents a more appropriate accuracy-throughput tradeoff than other existing solutions such as [18], [19] or [20]. Moreover, previous works such as [21] shows that despite its pipeline-unfriendliness, MT can be highly benefited using FPGA technology.…”

“…Some approaches have been developed in previous works such as [19] or [20] in which a approximation to the ICDF function is used. A tradeoff must be met between the accuracy requirements and the complex expressions that need to be evaluated in order to meet these accuracy constraints.…”

An Environment for Rapid Derivatives Design and Experimentation

“…FPGAs have also been used for Credit Derivatives pricing [20], which involves simulating many scenarios (Monte Carlo simulation) and calculating the loss in each for a set of assets (debt obligations); then the average of these losses gives the overall expected loss. Other work employing FPGAs for option pricing include Tse et al [21,22], where the FPGA implementation is compared with a GPU implementation and is found to be faster (more than 2 times) and more energy efficient (more than 10 times). However the difficulty in programming FPGAs, and the skills and time required to do so, means that adoption of this technology by financial companies is difficult.…”