Pricing High-Dimensional American Options on Hybrid CPU/FPGA Systems

  • Javier Alejandro Varela
  • Christian Brugger
  • Songyin Tang
  • Norbert Wehn
  • Ralf Korn

Abstract

In today’s markets, high-speed and energy-efficient computations are mandatory in the financial and insurance industry. As American options are amongst the most frequently traded products in the derivatives market, it becomes essential to place the focus on their pricing process. Calculating the price of an American option in particular is a challenging task due to the freedom the holder is given in terms of exercise date and the involved trading strategy. A well known algorithm that solves this task is the Longstaff-Schwartz (LS) algorithm, which applies least-squares linear regression on simulated Monte Carlo (MC) paths. This work presents a novel way to price high-dimensional American options, coined Reverse LS, using techniques of the embedded community. The proposed architecture targets hybrid Central Processing Unit (CPU)/Field Programmable Gate Array (FPGA) systems, and it exploits the FPGA reconfiguration to deliver high-throughput. With a bit-true algorithmic transformation based on recomputation, it is possible to eliminate the memory bottleneck and access costs present in a straightforward implementation. The result is a pricing system that is 16× faster and 268× more energy-efficient than an optimized Intel CPU implementation.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Javier Alejandro Varela
    • 1
  • Christian Brugger
    • 1
  • Songyin Tang
    • 2
  • Norbert Wehn
    • 1
  • Ralf Korn
    • 2
  1. 1.Microelectronic Systems Design Research GroupUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Stochastic Control and Financial Mathematics GroupUniversity of KaiserslauternKaiserslauternGermany

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