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 language | English |
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Pages (from-to) | 362-370 |
Number of pages | 9 |
Journal | European Journal of Operational Research |
Volume | 231 |
Issue number | 2 |
DOIs | |
Publication status | Published - 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