A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction

Xing Jin, Xun Li, Hwee Huat Tan, Zhenyu Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

This paper studies the problem of pricing high-dimensional American options. We propose a method based on the state-space partitioning algorithm developed by Jin et al. (2007) and a dimension-reduction approach introduced by Li and Wu (2006). By applying the approach in the present paper, the computational efficiency of pricing high-dimensional American options is significantly improved, compared to the extant approaches in the literature, without sacrificing the estimation precision. Various numerical examples are provided to illustrate the accuracy and efficiency of the proposed method. Pseudcode for an implementation of the proposed approach is also included.
Original languageEnglish
Pages (from-to)362-370
Number of pages9
JournalEuropean Journal of Operational Research
Volume231
Issue number2
DOIs
Publication statusPublished - 1 Dec 2013

Keywords

  • American-style option
  • Dimension reduction
  • High dimensional
  • Stochastic dynamic programming

ASJC Scopus subject areas

  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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