Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.
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Updated
Jul 7, 2026 - Python
Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.
Volatility surface modelling library
Mathematical finance research by Theodore Ouyang: provable rough Heston pricing-error bounds from finite-history propagation and shared Fourier errors. Begun H2 2023; main writing H1 2024; published on GitHub in 2026.
GPU-accelerated quantitative derivatives engine: FiLM-FNO rough volatility calibration (<1ms), multi-fidelity PDE ensemble autocall pricing (0.99 bps RMSE, SR 26-2 risk guardian), deep hedging, and Next.js/FastAPI streaming (1,126 tests).
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