Quasirandom number generators for parallel Monte Carlo algorithms
Journal of Parallel and Distributed Computing
Path-dependent options: extending the Monte Carlo simulation approach
Management Science
Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
Techniques for parallel quasi-Monte Carlo integration with digital sequences and associated problems
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Adaptive parallel computing on heterogeneous networks with mpC
Parallel Computing
Parallel and Distributed Computing Issues in Pricing Financial Derivatives through Quasi Monte Carlo
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Modular Parallel Programming in mpC for Distributed Memory Machines
PAS '97 Proceedings of the 2nd AIZU International Symposium on Parallel Algorithms / Architecture Synthesis
Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
Parameterization based on randomized quasi-Monte Carlo methods
Parallel Computing
Pricing algorithms for financial derivatives
Algorithms and theory of computation handbook
Exploring financial applications on many-core-on-a-chip architecture: a first experiment
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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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, for 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 will distribute 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 heteroogenous parallel platforms.