Abstract
A Multivariate Gaussian random number generator (MVGRNG) is a pre-requisite for most Monte Carlo simulations for financial applications, especially those that involve many correlated assets. In recent years, Field Programmable Gate Arrays (FPGAs) have received a lot of attention as a target platform for the implementation of such a generator due to the high throughput performance that can be achieved. In this work it is demonstrated that the choice of the objective function employed for the hardware optimization of the MVRNG core, has a considerable impact on the final performance of the application of interest. Two of the most important financial applications, Value-at-Risk estimation and option pricing are considered in this paper. Experimental results have shown that the suitability of the chosen objective function for the optimization of the hardware MVRNG core depends on the structure of the targeted distribution. An improvement in performance of up to 96% is reported for VaR calculation while up to 81% improvement is observed for option pricing when a suitable objective function for the optimization of the MVRNG core is considered while maintaining the same level of hardware resources.
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Saiprasert, C., Bouganis, CS., Constantinides, G.A. (2010). Design of a Financial Application Driven Multivariate Gaussian Random Number Generator for an FPGA. In: Sirisuk, P., Morgan, F., El-Ghazawi, T., Amano, H. (eds) Reconfigurable Computing: Architectures, Tools and Applications. ARC 2010. Lecture Notes in Computer Science, vol 5992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12133-3_18
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DOI: https://doi.org/10.1007/978-3-642-12133-3_18
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