Computing Optimal Checkpointing Strategies for Rollback and Recovery Systems
IEEE Transactions on Computers - Fault-Tolerant Computing
The valuation of path dependent contracts on the average
Management Science
Numerical valuation of high dimensional multivariate European securities
Management Science
Extension and completion of Wynn's theory on convergence of columns of the epsilon table
Journal of Approximation Theory
Path-dependent options: extending the Monte Carlo simulation approach
Management Science
Efficiency improvement by lattice rules for pricing Asian options
Proceedings of the 30th conference on Winter simulation
On selection criteria for lattice rules and other quasi-Monte Carlo point sets
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Variance Reduction via Lattice Rules
Management Science
50th ANNIVERSARY ARTICLE: Option Pricing: Valuation Models and Applications
Management Science
Pricing American-Style Derivatives with European Call Options
Management Science
Dynamic Programming Approach for Valuing Options in the GARCH Model
Management Science
American option pricing with randomized quasi-Monte Carlo simulations
Proceedings of the Winter Simulation Conference
Pricing of early-exercise Asian options under Lévy processes based on Fourier cosine expansions
Applied Numerical Mathematics
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Pricing European-style Asian options based on the arithmetic average, under the Black and Scholes model, involves estimating an integral (a mathematical expectation) for which no easily computable analytical solution is available. Pricing their American-style counterparts, which provide early exercise opportunities, poses the additional difficulty of solving a dynamic optimization problem to determine the optimal exercise strategy. A procedure for pricing American-style Asian options of the Bermudan flavor, based on dynamic programming combined with finite-element piecewise-polynomial approximation of the value function, is developed here. A convergence proof is provided. Numerical experiments illustrate the consistency and efficiency of the procedure. Theoretical properties of the value function and of the optimal exercise strategy are also established.