By Topic

Distributed quasi-Monte Carlo algorithm for option pricing on HNOWs using mpC

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gong Chen ; Dept. of Comput. Sci., Manitoba Winnipeg Univ., Man., Canada ; Thulasiraman, P. ; Thulasiram, R.K.

Monte Carlo (MC) simulation is one of the popular approaches for approximating the value of options and other derivative securities due to the absence of straightforward closed form solutions for many financial models. However, the slow convergence rate, O(N- 12/) for N number of samples of the MC method has motivated research in quasi Monte-Carlo (QMC) techniques. QMC methods use low discrepancy (LD) sequences that provide faster, more accurate results than MC methods. In this paper, we focus on the parallelization of the QMC method on a heterogeneous network of workstations (HNOWs) for option pricing. HNOWs are machines with different processing capabilities and have distinct execution time for the same task. It is therefore important to allocate and schedule the tasks depending on the performance and resources of these machines. We present an adaptive, distributed QMC algorithm for option pricing, taking into account the performances of both processors and communications. The algorithm distributes data and computations based on the architectural features of the available processors at run time. We implement the algorithm using mpC, an extension of ANSI C language for parallel computation on heterogeneous networks. We compare and analyze the performance results with different parallel implementations. The results of our algorithm demonstrate a good performance on heterogenous parallel platforms.

Published in:

Simulation Symposium, 2006. 39th Annual

Date of Conference:

2-6 April 2006