Unbiased Sensitivity Estimation of One-Dimensional Diffusion Processes

Wanmo Kang () and Jong Mun Lee ()
Additional contact information
Wanmo Kang: Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
Jong Mun Lee: MERITZ Fire and Marine Insurance, Seoul 06232, Republic of Korea

Mathematics of Operations Research, 2019, vol. 44, issue 1, 334-353

Abstract: In this paper, we propose unbiased sensitivity estimators of the expected functionals of one-dimensional diffusion processes. Under general diffusion models, it is common to rely on discretization methods such as the Euler scheme for the generation of sample paths because of the lack of knowledge in the probability distributions associated with the diffusions. The Euler discretization method is easy to apply, but it is difficult to avoid discretization biases. As an alternative approach, we propose unbiased Monte Carlo estimators of sensitivities by taking advantage of the Beskos-Roberts method, which is an exact simulation algorithm for one-dimensional stochastic differential equations (SDEs), and applying the Poisson kernel method. The proposed estimators can be computed by discretely observed Brownian paths, and thus it is simple to implement our algorithms. We illustrate the ideas and algorithms with examples.

Keywords: unbiased estimator; sensitivity estimation; derivative estimation; Greeks; Beskos-Roberts method; Poisson kernel method (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1287/moor.2017.0926 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:44:y:2019:i:1:p:334-353

Access Statistics for this article

More articles in Mathematics of Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().