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
Neuro-Dynamic Programming
Variance with alternative scramblings of digital nets
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Dynamic Programming Procedure for Pricing American-Style Asian Options
Management Science
Randomized Quasi-Monte Carlo: a tool for improving the efficiency of simulations in finance
WSC '04 Proceedings of the 36th conference on Winter simulation
A study of variance reduction techniques for American option pricing
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Constructing Robust Good Lattice Rules for Computational Finance
SIAM Journal on Scientific Computing
New Brownian bridge construction in quasi-Monte Carlo methods for computational finance
Journal of Complexity
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Simulation of a Lévy process by PCA sampling to reduce the effective dimension
Proceedings of the 40th Conference on Winter Simulation
A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains
Operations Research
Smoothness and dimension reduction in Quasi-Monte Carlo methods
Mathematical and Computer Modelling: An International Journal
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
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We study the pricing of American options using least-squares Monte Carlo combined with randomized quasi-Monte Carlo (RQMC), viewed as a variance reduction method. We find that RQMC reduces both the variance and the bias of the option price obtained in an out-of-sample evaluation of the retained policy, and improves the quality of the returned policy on average. Various sampling methods of the underlying stochastic processes are compared and the variance reduction is analyzed in terms of a functional ANOVA decomposition.