Pricing for perpetual American strangle options under stochastic volatility with fast mean reversion

Mijin Ha, Donghyun Kim, Ji-Hun Yoon and Sun-Yong Choi

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 227, issue C, 41-57

Abstract: A perpetual American strangle option refers to an investment strategy combining the features of both call and put options on a single underlying asset, with an infinite time horizon. Investors are known to use this trading strategy when they expect the stock price to fluctuate significantly but cannot predict whether it will rise or fall. In this study, we consider the perpetual American strangle options under a stochastic volatility model and investigate the corrected option values and free boundaries using an asymptotic analysis technique. Further, we examine the pricing accuracy of the approximated formulas for perpetual American strangle options under stochastic volatility by comparing our solutions with the prices that are obtained from Monte Carlo simulations. We also investigate the sensitivities of the option values and free boundaries with respect to several model parameters.

Keywords: Perpetual American strangle option; Stochastic volatility; Free boundary problem; Fast mean reversion (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475424002908
Full text for ScienceDirect subscribers only

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:eee:matcom:v:227:y:2025:i:c:p:41-57

DOI: 10.1016/j.matcom.2024.07.030

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().