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Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints

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  • Pablo Rodriguez Manzi

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.

Suggested Citation

  • Pablo Rodriguez Manzi, 2026. "Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints," Papers 2605.24031, arXiv.org.
    • Handle: RePEc:arx:papers:2605.24031
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