Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2013
Abstract
In this paper, we study the problem of estimating the price of an American option and its price sensitivities via Monte Carlo simulation. Compared to estimating the option price which satisfies a backward recursion, estimating the price sensitivities is more challenging. With the readily-computable pathwise derivatives in a simulation run, we derive a backward recursion for the price sensitivities. We then propose nonparametric estimators, the k-nearest neighbor estimators, to estimate conditional expectations involved in the backward recursion, leading to estimates of the option price and its sensitivities in the same simulation run. Numerical experiments indicate that the proposed method works well and is promising for practical problems.
Discipline
Operations and Supply Chain Management
Research Areas
Operations Management
Publication
Proceedings of the 2013 Winter Simulation Conference
First Page
619
Last Page
700
Identifier
10.5555/2675983.2676073
Publisher
IEEE
City or Country
Washington DC, USA
Citation
FENG, Guiyun; LIU, Guangwu; and SUN, Lihua.
A nonparametric method for pricing and hedging American options. (2013). Proceedings of the 2013 Winter Simulation Conference. 619-700.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/6509
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5555/2675983.2676073