Implementation of the Longstaff and Schwartz American Option Pricing Model on FPGA

X. Tian, Khaled Benkrid

Research output: Contribution to journalArticlepeer-review

Abstract

American style options are widely used financial
products, whose pricing is a challenging problem due to
their path dependency characteristic. Finite difference
methods and tree-based methods can be used for American
option pricing. However, the major drawback of these
methods is that they can often only handle one or two
sources of uncertainty; for more state variables they become
computationally prohibitive, with computation times typically increasing exponentially with the number of state
variables. Alternative solutions are the extended Monte
Carlo methods, such as the Least-Squares Monte Carlo
(LSMC) method suggested by Longstaff and Schwartz,
which uses of regression to estimate continuation values
from simulated paths. In this paper, we present an FPGA
hardware architecture for the acceleration of the LSMC
method, with Quasi-Monte Carlo path generation. Our
FPGA hardware implementation on a Xilinx Virtex-4
XC4VFX100 chip achieves 25× and 18× speed-ups in the
path generation and regression steps, respectively, compared to an equivalent pure software implementation
captured in C++ and run on an Intel Xeon 2.8 GHz CPU.
This provides overall speed-up of 20× compared to a CPUbased implementation. Power measurements also show that
our FPGA implementation is 54× more energy efficient
than the pure software implementation.
Original languageEnglish
Pages (from-to)79-91
Number of pages13
JournalJournal of Signal Processing Systems
Volume67
Issue number1
DOIs
Publication statusPublished - 27 Jan 2010

Keywords

  • American option . Least Squares Monte Carlo . Quasi Monte Carlo . FPGA . Hardware acceleration . Financial computing

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