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

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

European Journal of Operational Research, 2013, vol. 231, issue 2, 362-370

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.

Keywords: High dimensional American-style option; Dimension reduction; Stochastic dynamic programming (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:231:y:2013:i:2:p:362-370

DOI: 10.1016/j.ejor.2013.05.035

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