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Title: A state-space partitioning method for pricing high-dimensional American-style options
Authors: Jin, X.
Tan, H.H. 
Sun, J.
Keywords: American-style option
Monte Carlo simulation
Quasi-Monte Carlo sequence
State-space partitioning
Issue Date: Jul-2007
Citation: Jin, X., Tan, H.H., Sun, J. (2007-07). A state-space partitioning method for pricing high-dimensional American-style options. Mathematical Finance 17 (3) : 399-426. ScholarBank@NUS Repository.
Abstract: The pricing of American-style options by simulation-based methods is an important but difficult task primarily due to the feature of early exercise, particularly for high-dimensional derivatives. In this paper, a bundling method based on quasi-Monte Carlo sequences is proposed to price high-dimensional American-style options. The proposed method substantially extends Tilley's bundling algorithm to higher-dimensional situations. By using low-discrepancy points, this approach partitions the state space and forms bundles. A dynamic programming algorithm is then applied to the bundles to estimate the continuation value of an American-style option. A convergence proof of the algorithm is provided. A variety of examples with up to 15 dimensions are investigated numerically and the algorithm is able to produce computationally efficient results with good accuracy. © 2007 The Author. Journal compilation © 2007 Blackwell Publishing Inc.
Source Title: Mathematical Finance
ISSN: 09601627
DOI: 10.1111/j.1467-9965.2007.00309.x
Appears in Collections:Staff Publications

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