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Volatility Surface Reconstruction using Deep Learning und...
[Submitted on 20 May 2026] · 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.

Submission history

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

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