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Found 3 papers, 3 papers with code
Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints
We explore the abilities of two machine learning approaches for no-arbitrage interpolation of European vanilla option prices, which jointly yield the corresponding local volatility surface: a finite dimensional Gaussian process (GP) regression approach under no-arbitrage constraints based on prices, and a neural net (NN) approach with penalization of arbitrages based on implied volatilities.
Nowcasting Networks
The latter is meant in a broad sense encompassing completion of gridded values, interpolation, or outlier detection, in the context of financial time series of curves or surfaces (also applicable in higher dimensions, at least in theory).
Deep Local Volatility
Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks.
