An analytical approximation for single barrier options under stochastic volatility models
Hideharu Funahashi () and
Tomohide Higuchi ()
Additional contact information
Hideharu Funahashi: Mizuho Securities Co. Ltd.
Tomohide Higuchi: Mizuho Securities Co. Ltd.
Annals of Operations Research, 2018, vol. 266, issue 1, 129-157
Abstract The aim of this paper is to derive an approximation formula for a single barrier option under local volatility models, stochastic volatility models, and their hybrids, which are widely used in practice. The basic idea of our approximation is to mimic a target underlying asset process by a polynomial of the Wiener process. We then translate the problem of solving first hit probability of the asset process into that of a Wiener process whose distribution of passage time is known. Finally, utilizing the Girsanov’s theorem and the reflection principle, we show that single barrier option prices can be approximated in a closed-form. Furthermore, ample numerical examples will show the accuracy of our approximation is high enough for practical applications.
Keywords: Single barrier option; Analytical approximation; Local and stochastic volatility models; Wiener–Ito chaos expansion (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2559-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:266:y:2018:i:1:d:10.1007_s10479-017-2559-3
Ordering information: This journal article can be ordered from
Access Statistics for this article
Annals of Operations Research is currently edited by Endre Boros
More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla ().