Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/330659 
Year of Publication: 
2025
Citation: 
[Journal:] Finance and Stochastics [ISSN:] 1432-1122 [Volume:] 29 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025 [Pages:] 981-1014
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We propose two signature-based methods to solve an optimal stopping problem – that is, to price American options – in non-Markovian frameworks. Both methods rely on a global approximation result for Lp-functionals on rough-path spaces, using linear functionals of robust, rough-path signatures. In the primal formulation, we present a non-Markovian generalisation of the famous Longstaff–Schwartz algorithm, using linear functionals of the signature as regression basis. For the dual formulation, we parametrise the space of square-integrable martingales using linear functionals of the signature and apply a sample average approximation. We prove convergence for both methods and present first numerical examples in non-Markovian and non-semimartingale regimes.
Subjects: 
Signature
Optimal stopping
Rough paths
Monte Carlo
Rough volatility
JEL: 
C63
G12
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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