Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ejor.2013.05.035
Title: A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction
Authors: Jin, X.
Li, X.
Tan, H.H. 
Wu, Z.
Keywords: American-style option
Dimension reduction
High dimensional
Stochastic dynamic programming
Issue Date: 1-Dec-2013
Citation: Jin, X., Li, X., Tan, H.H., Wu, Z. (2013-12-01). A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction. European Journal of Operational Research 231 (2) : 362-370. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejor.2013.05.035
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. © 2013 Elsevier B.V. All rights reserved.
Source Title: European Journal of Operational Research
URI: http://scholarbank.nus.edu.sg/handle/10635/102623
ISSN: 03772217
DOI: 10.1016/j.ejor.2013.05.035
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

6
checked on May 16, 2018

WEB OF SCIENCETM
Citations

5
checked on May 16, 2018

Page view(s)

45
checked on May 18, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.