Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints

• URL: http://arxiv.org/abs/2605.24031v1
• Date: Wed, 20 May 2026 18:39:20 GMT
• Title: Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints
• Abstract summary: We study the reconstruction of implied volatility surfaces from sparse and noisy option quotes using deep learning models under no-arbitrage constraints.<n>Results show that Transformer and U-Net architectures achieve strong reconstruction accuracy, particularly under sparse observation regimes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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