Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/230116 
Year of Publication: 
2019
Citation: 
[Journal:] Mathematical Finance [ISSN:] 1467-9965 [Volume:] 30 [Issue:] 3 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2019 [Pages:] 782-832
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
A new paradigm has emerged recently in financial modeling: rough (stochastic) volatility. First observed by Gatheral et al. in high-frequency data, subsequently derived within market microstructure models, rough volatility captures parsimoniously key-stylized facts of the entire implied volatility surface, including extreme skews (as observed earlier by Alòs et al.) that were thought to be outside the scope of stochastic volatility models. On the mathematical side, Markovianity and, partially, semimartingality are lost. In this paper, we show that Hairer's regularity structures, a major extension of rough path theory, which caused a revolution in the field of stochastic partial differential equations, also provide a new and powerful tool to analyze rough volatility models.
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.