Please use this identifier to cite or link to this item:
|Title:||A state-space partitioning method for pricing high-dimensional American-style options|
Monte Carlo simulation
Quasi-Monte Carlo sequence
|Source:||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. https://doi.org/10.1111/j.1467-9965.2007.00309.x|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 22, 2018
WEB OF SCIENCETM
checked on Jan 23, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.