





















Abstract:We study the reconstruction of implied volatility surfaces from sparse and noisy option quotes using deep learning models under no-arbitrage constraints. We compare multiple neural architectures, including multilayer perceptrons, convolutional networks, U-Nets, variational autoencoders, and Transformer-based models against classical SVI parameterizations on option market data. Results show that Transformer and U-Net architectures achieve strong reconstruction accuracy, particularly under sparse observation regimes, while soft arbitrage penalties significantly reduce arbitrage violations with moderate impact on reconstruction error. We further analyze the trade-off between accuracy and arbitrage consistency across architectures and regularization strengths.
| Comments: | MSc thesis, Universidad de Buenos Aires, 2026. 94 pages, 27 figures |
| Subjects: | Computational Finance (q-fin.CP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.24031 [q-fin.CP] |
| (or arXiv:2605.24031v1 [q-fin.CP] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24031 arXiv-issued DOI via DataCite (pending registration) |
From: Pablo Rodriguez Manzi [view email]
[v1]
Wed, 20 May 2026 18:39:20 UTC (422 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。