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Erscheinungsjahr: 
2025
Quellenangabe: 
[Journal:] Finance and Stochastics [ISSN:] 1432-1122 [Volume:] 29 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025 [Pages:] 981-1014
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Signature
Optimal stopping
Rough paths
Monte Carlo
Rough volatility
JEL: 
C63
G12
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Dokumentart: 
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Dokumentversion: 
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