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.
Deep calibration of rough stochastic volatil- ity models.Quantitative Finance, 19(1):71–86, 2019
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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.