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Pricing American options under rough volatility using deep-signatures and signature-kernels

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arxiv 2501.06758 v2 pith:V5WSBRL2 submitted 2025-01-12 q-fin.MF

Christian Bayer , Luca Pelizzari , Jia-Jie Zhu This is my paper · ORCID
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keywords roughamericandualoptimaloptionspricingprimalstopping
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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.

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  1. Stochastic control with signatures via Riccati equations on the tensor algebra

    math.OC 2026-07 accept novelty 7.0 of 10

    Non-Markovian path-dependent control problems with signature rewards admit optimal feedback controls and value processes as local linear signature expansions whose coefficients solve infinite-dimensional Riccati equat...

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