Pricing high-dimensional American options using local consistency conditions

S.J. Berridge, J.M. Schumacher

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

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 languageEnglish
Title of host publication[n.n.]
PublisherUnknown Publisher
Pages31
Number of pages31
Publication statusPublished - 2003

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sampling
linear programing
Markov chain
matrix
method

Cite this

Berridge, S. J., & Schumacher, J. M. (2003). Pricing high-dimensional American options using local consistency conditions. In [n.n.] (pp. 31). Unknown Publisher.
Berridge, S.J. ; Schumacher, J.M. / Pricing high-dimensional American options using local consistency conditions. [n.n.]. Unknown Publisher, 2003. pp. 31
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Berridge, SJ & Schumacher, JM 2003, Pricing high-dimensional American options using local consistency conditions. in [n.n.]. Unknown Publisher, pp. 31.

Pricing high-dimensional American options using local consistency conditions. / Berridge, S.J.; Schumacher, J.M.

[n.n.]. Unknown Publisher, 2003. p. 31.

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

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Berridge SJ, Schumacher JM. Pricing high-dimensional American options using local consistency conditions. In [n.n.]. Unknown Publisher. 2003. p. 31