Collaborative processing of Least-Square Monte Carlo for American options

Abstract

American options are popularly traded in the financial market, so pricing those options becomes crucial in practice. In reality, many popular pricing models do not have analytical solutions. Hence techniques such as Monte Carlo are often used in practice. This paper presents a CPU-FPGA collaborative accelerator using state-of-the-art Least-Square Monte Carlo method, for pricing American options. We provide a new sequence of generating the Monte Carlo paths, and a precalculation strategy for the regression process. Our design is customisable for different pricing models, discretisation schemes, and regression functions. The Heston model is used as a case study for evaluating our strategy. Experimental results show that an FPGA-based solution could provide 22 to 64.5 times faster than a single-core CPU implementation.