End-to-End Neural Calibration of Stochastic Volatility Models via Option Panels - Song, Tian'ai (Transactions on Computer Science and Intelligent Systems Research, 2025)
We propose an end-to-end neural architecture that calibrates stochastic volatility models directly from high-dimensional option panels. A 3-D CNN–Transformer hybrid learns the inverse mapping from implied-volatility surfaces to latent Heston parameters in milliseconds while enforcing no-arbitrage constraints via a differentiable penalty layer. Closed-form gradients of the Fourier pricing formula enable stable training on synthetic and market data. Extensive experiments on SPX and EURO STOXX 50 options show sub-0.3 % RMSE and a 100× speed-up over L-BFGS-B, with calibration times under 2ms on GPU.