Transformer and U-Net models outperform classical SVI parameterization for volatility surface reconstruction from sparse data, with soft arbitrage penalties reducing violations at modest accuracy cost.
The little Heston trap.Wilmott Magazine, pages 83–92, 2007
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Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints
Transformer and U-Net models outperform classical SVI parameterization for volatility surface reconstruction from sparse data, with soft arbitrage penalties reducing violations at modest accuracy cost.