BianchiGiacomo/deepLearningVolatility
Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.
This project helps quantitative analysts and financial engineers quickly and accurately calculate implied volatility surfaces for option pricing. It takes market option data and various rough volatility model parameters as input, and outputs a complete volatility surface or individual option volatilities, significantly faster than traditional methods like Monte Carlo simulations. The tool is designed for quants, traders, and risk managers who need rapid and precise volatility estimates.
No commits in the last 6 months.
Use this if you need to generate implied volatility surfaces or price options using complex rough volatility models with high speed and accuracy, often for real-time trading or risk management.
Not ideal if you are a casual user needing basic option pricing or are uncomfortable with advanced quantitative finance concepts and neural network frameworks.
Stars
11
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 19, 2025
Commits (30d)
0
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