Pricing American Options Under Rough Volatility Using Signatures

Christian Bayer (), Luca Pelizzari () and Jia-Jie Zhu ()
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Christian Bayer: Weierstrass Institut (WIAS)
Luca Pelizzari: Weierstrass Institut (WIAS)
Jia-Jie Zhu: Weierstrass Institut (WIAS)

A chapter in Stochastic Analysis and Applications 2025, 2026, pp 375-398 from Springer

Abstract: Abstract We extend the signature-based primal and dual solutions to the optimal stopping problem recently introduced in [Bayer et al.: Primal and dual optimal stopping with signatures, to appear in Finance & Stochastics 2025], by integrating deep-signature and signature-kernel learning methodologies. These approaches are designed for non-Markovian frameworks, in particular enabling the pricing of American options under rough volatility. We demonstrate and compare the performance within the popular rough Heston and rough Bergomi models.

Keywords: Signature; Optimal stopping; Rough volatility; Deep learning; Kernel learning; 60G40; 60L10; 91G20; 91G60 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-03914-9_13

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DOI: 10.1007/978-3-032-03914-9_13

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