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Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints
Pablo Rodrig · 2026-05-26 · via cs.LG updates on arXiv.org

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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)

Submission history

From: Pablo Rodriguez Manzi [view email]
[v1] Wed, 20 May 2026 18:39:20 UTC (422 KB)