Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14572
Title: A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction
Authors: Jin, X
Li, X 
Tan, HH
Wu, Z
Keywords: American-style option
Dimension reduction
High dimensional
Stochastic dynamic programming
Issue Date: 2013
Publisher: Elsevier Science Bv
Source: European journal of operational research, 2013, v. 231, no. 2, p. 362-370 How to cite?
Journal: European Journal of Operational Research 
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.
URI: http://hdl.handle.net/10397/14572
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2013.05.035
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