Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Latin supercube sampling for very high-dimensional simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Efficiency improvements for pricing American options with a stochastic mesh
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Constructing Randomly Shifted Lattice Rules in Weighted Sobolev Spaces
SIAM Journal on Numerical Analysis
The effective dimension and quasi-Monte Carlo integration
Journal of Complexity
An improved simulation method for pricing high-dimensional American derivatives
Mathematics and Computers in Simulation - Special issue: 3rd IMACS seminar on Monte Carlo methods - MCM 2001
Derivatives and credit risk: enhanced quasi-monte carlo methods with dimension reduction
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
A study of variance reduction techniques for American option pricing
WSC '05 Proceedings of the 37th conference on Winter simulation
Fast simulation of equity-linked life insurance contracts with a surrender option
Proceedings of the 40th Conference on Winter Simulation
American option pricing with randomized quasi-Monte Carlo simulations
Proceedings of the Winter Simulation Conference
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Quasi-Monte Carlo (QMC) methods have been used in a variety of problems in finance over the last few years, where they often provide more accurate estimators than the Monte Carlo (MC) method. These results have led many researchers to try to find reasons for the success of QMC methods in finance. A general explanation is that financial problems often have a structure that interacts in a constructive way with the point set used by the QMC method, thus resulting in estimators with reduced error. This positive interaction can be amplified by various fine-tuning techniques, which we review in the first part of this paper. Leaving aside these techniques, we then choose a few randomized QMC methods and test their "robustness" by comparing their performance against MC on different financial problems. Our results suggest that the chosen methods are efficient in a broad sense for financial simulations.