“Pricing American options by simulation using a stochastic mesh with optimized weights”
Coauthor(s): Paul Glasserman, Zachary Ha.
Editors: Stanislav P. Uryasev
This chapter develops a simulation method for pricing path-dependent American options, and American options on a large number of underlying assets, such as basket options. Standard numerical procedures (lattice methods and nite difference methods) are generally inapplicable to such high-dimensional problems, and this has motivated research into simulation-based methods. The optimal stopping problem embedded in the pricing of American options makes this a nonstandard problem for simulation. This chapter extends the stochastic mesh introduced in Broadie and Glasserman. In its original form, the stochastic mesh method required knowledge of the transition density of the underlying process of asset prices and other state variables. This chapter extends the method to settings in which the transition density is either unknown or fails to exist. We avoid the need for a transition density by choosing mesh weights through a constrained optimization problem. If the weights are constrained to correctly price su ciently many simple instruments, they can be expected to work well in pricing a more complex American option. We investigate two criteria for use in the optimization | maximum entropy and least squares. The methods are illustrated through numerical examples.
Source: Probabilistic constrained optimization: Methodology and applications
Broadie, Mark, Paul Glasserman, and Z. Ha. "Pricing American options by simulation using a stochastic mesh with optimized weights." In Probabilistic constrained optimization: Methodology and applications, 26-44. Ed. Stanislav P. Uryasev. Dorwell, MA: Kluwer, 2000.
Place: Dorwell, MA