Abstract
We investigate a new method for pricing high-dimensional American
options. The method is of ???nite di???erence type but is also related
to Monte Carlo techniques in that it involves a representative sampling of the underlying variables. An approximating Markov chain is built using this sampling and linear programming is used to satisfy local consistency conditions at each point related to the in???nitesimal generator or transition density. The algorithm for constructing the matrix can be parallelised easily, moreover once it has been obtained it can be reused to generate quick solutions for a wide range of related problems.
We provide pricing results for geometric average options in up to ten
dimensions, and compare these to accurate benchmarks.
| Original language | English |
|---|---|
| Title of host publication | [n.n.] |
| Publisher | Unknown Publisher |
| Pages | 31 |
| Number of pages | 31 |
| Publication status | Published - 2003 |