Pricing American options under rough volatility using deep-signatures and signature-kernels
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909645526794240 |
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| author | Bayer, Christian Pelizzari, Luca Zhu, Jia-Jie |
| author_facet | Bayer, Christian Pelizzari, Luca Zhu, Jia-Jie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_06758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pricing American options under rough volatility using deep-signatures and signature-kernels Bayer, Christian Pelizzari, Luca Zhu, Jia-Jie Mathematical Finance 60G40, 60L10, 91G20, 91G60 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. |
| title | Pricing American options under rough volatility using deep-signatures and signature-kernels |
| topic | Mathematical Finance 60G40, 60L10, 91G20, 91G60 |
| url | https://arxiv.org/abs/2501.06758 |