AI RESEARCH
Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints
arXiv CS.LG
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ArXi:2605.24031v1 Announce Type: cross 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.