WIAS Preprint No. 3172, (2025)

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


Authors

  • Bayer, Christian
    ORCID: 0000-0002-9116-0039
  • Pelizzari, Luca
  • Zhu, Jia-Jie

2020 Mathematics Subject Classification

  • 60G40 60L10 91G20

Keywords

  • Signature, optimal stopping, rough volatility, deep learning, kernel learning

DOI

10.20347/WIAS.PREPRINT.3172

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 ap- pear in Finance & Stochastics 2025], by integrating deep-signature and signature-kernel learning methodologies. These approaches are designed for non-Markovian frameworks, in particular en- abling 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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