Please use this identifier to cite or link to this item:
|Title:||A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction|
Stochastic dynamic programming
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 17, 2019
WEB OF SCIENCETM
checked on Jan 9, 2019
checked on Jan 18, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.