A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction |
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Authors: | Xing Jin Xun Li Hwee Huat Tan Zhenyu Wu |
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Institution: | 1. University of Warwick, United Kingdom;2. The Hong Kong Polytechnic University, Hong Kong;3. National University of Singapore, Singapore;4. University of Manitoba, Canada |
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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. |
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Keywords: | High dimensional American-style option Dimension reduction Stochastic dynamic programming |
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