A Performance Comparison of Various Bootstrap Methods for Diffusion Processes

Jung S You and Minsoo Jeong

Journal of Economics and Behavioral Studies, 2021, vol. 13, issue 4, 1-7

Abstract: In this paper, we compare the finite sample performances of various bootstrap methods for diffusion processes. Though diffusion processes are widely used to analyze stocks, bonds, and many other financial derivatives, they are known to heavily suffer from size distortions of hypothesis tests. While there are many bootstrap methods applicable to diffusion models to reduce such size distortions, their finite sample performances are yet to be investigated. We perform a Monte Carlo simulation comparing the finite sample properties, and our results show that the strong Taylor approximation method produces the best performance, followed by the Hermite expansion method.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ojs.amhinternational.com/index.php/jebs/article/view/3185/2030 (application/pdf)
https://ojs.amhinternational.com/index.php/jebs/article/view/3185 (text/html)

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:rnd:arjebs:v:13:y:2021:i:4:p:1-7

DOI: 10.22610/jebs.v13i4(J).3185

Access Statistics for this article

More articles in Journal of Economics and Behavioral Studies from AMH International
Bibliographic data for series maintained by Muhammad Tayyab ().