A state-space partitioning method for pricing high-dimensional American-style options
Jin, Xing, Tan, Hwee Huat and Sun, Junhua. (2007) A state-space partitioning method for pricing high-dimensional American-style options. Mathematical Finance, Vol.17 (No.3). pp. 399-426. ISSN 0960-1627Full text not available from this repository.
Official URL: http://dx.doi.org/10.1111/j.1467-9965.2007.00309.x
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.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HG Finance
H Social Sciences > HC Economic History and Conditions
Q Science > QA Mathematics
H Social Sciences
|Divisions:||Faculty of Social Sciences > Warwick Business School > Finance Group|
|Journal or Publication Title:||Mathematical Finance|
|Publisher:||Wiley-Blackwell Publishing, Inc.|
|Number of Pages:||28|
|Page Range:||pp. 399-426|
|Access rights to Published version:||Restricted or Subscription Access|
Actions (login required)