Pricing American options under rough volatility using deep-signatures and signature-kernels
Mathematical Finance
2025-06-12 v2
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
Cite
@article{arxiv.2501.06758,
title = {Pricing American options under rough volatility using deep-signatures and signature-kernels},
author = {Christian Bayer and Luca Pelizzari and Jia-Jie Zhu},
journal= {arXiv preprint arXiv:2501.06758},
year = {2025}
}