Deep Learning Volatility

Framework for volatility surface approximation with neural networks. Experience sub-basis-point accuracy with order-of-magnitude speedup over Monte Carlo methods. It includes dataset generators (random grid), neural pricers (grid, pointwise, multi‑regime), Monte Carlo engines for rough/classical models, and post‑processing tools.

Status: research project in progress (APIs may change).

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Key features

• Neural pricers
  • Grid‑based (dense surface on a T×K grid)
  • Pointwise (single queries ( $\theta$, T, K)) with random grid and time buckets
  • Multi‑regime (short/mid/long) with automatic routing
• Data generation: Monte Carlo with absorption handling for rough models (long‑term regime)
• Supported processes (excerpt): Rough Bergomi, Rough Heston, Lifted Heston, GBM, jump‑diffusion processes (Kou/Merton)
• Post‑processing: surface interpolation and smile repair modules
• Examples: scripts for stability analysis, MC debugging, and long‑term absorption

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Results: Pointwise Network Performance

Process: Rough Bergomi model ($H=0.25$, $\eta=2.00$, $\rho=-0.80$, $\xi_0=0.15$) trained on 7,000 random grid surfaces. Neural network predictions (red dashed) vs Monte Carlo reference (blue solid) with 95% confidence intervals.
Performance: MAE = 0.00078, CI Coverage = 89.5%

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Try It Now - Interactive Demo

Experience the framework in action with our interactive demo:

What you'll see:

• Pre-trained neural networks generating volatility surfaces in milliseconds
• Interactive parameter exploration for rough volatility models
• Performance comparisons with traditional Monte Carlo methods
• Real-time implied volatility smile visualization

No installation required - runs entirely in your browser.

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Requirements

• Python >= 3.8.1
• PyTorch >= 1.9, < 3.0
• Numpy, Matplotlib, TQDM
• (Optional) Poetry for environment management

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Local Installation (Optional)

Want to run locally or modify the code? Install the framework:

Poetry (recommended)

git clone https://github.com/BianchiGiacomo/deepLearningVolatility.git
cd deepLearningVolatility
poetry install
poetry shell

pip (alternative)

git clone https://github.com/BianchiGiacomo/deepLearningVolatility.git
cd deepLearningVolatility
pip install -e .

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Quickstart

After installation, explore these examples to understand the framework:

You can run the ready-to-use scripts in the examples/ folder to explore the framework:

# Analyze temporal discretization optimization across regimes
python examples/MonteCarloDebugger_time_discretization.py

# Long-term absorption analysis (1Y–5Y) with smile comparison
python examples/LongTermRegimeAnalyzer.py

# Test trained pointwise network performance
python examples/pointwise_test_local.py

# Test trained multi-regime network performance  
python examples/multiregime_test_local.py

Parameters and thresholds can be tweaked directly inside the scripts.

Run on Google Colab

Train Your Own Models

• dlvol_MultiRegimeGridPricer.ipynb - Multi-regime training
• dlvol_PointwisePricer.ipynb - Pointwise training

To use these notebooks if the repository is private, you must provide a GitHub Personal Access Token (PAT) with scope=repo (or a fine‑grained token with “Contents: Read”) to install the package inside Colab.

For instructions on creating and managing a PAT, see: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens

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Documentation & Results Analysis

Comprehensive documentation for using the framework and analyzing results lives in the docs/ directory.

For quick, runnable demonstrations that generate plots/tables, use the scripts in examples/ (e.g. MonteCarloDebugger_time_discretization.py).

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Project structure (excerpt)

deepLearningVolatility/
├─ instruments/                # Products and payoffs
├─ nn/
│  ├─ dataset_builder/         # Dataset generators (random grid, time buckets)
│  ├─ modules/bs/              # Black–Scholes modules
│  └─ pricer/                  # Neural pricers (grid/pointwise/multi‑regime)
├─ stochastic/
│  ├─ engine.py                # Monte Carlo engine
│  ├─ rough_bergomi.py, rough_heston.py, heston.py, ...
│  └─ wrappers/                # Ready‑to‑use process wrappers
├─ examples/                   # Ready‑to‑run scripts
├─ images/                     # Demonstrative output figures
├─ docs/                       # Documentation (if present)
└─ tests/                      # Tests

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References

• Grid‑based approach (Horváth–Muguruza–Tomas, 2021)
• Pointwise with random grid (Baschetti–Bormetti–Rossi, 2024)
• Project docs and notes on absorption handling (docs/ and images/)

See also examples/LongTermRegimeAnalyzer.py to reproduce additional cases mentioned in the docs.

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Contributing

Contributions and issues are welcome. Open an issue or a pull request describing motivation, impact, and minimal tests. Please keep code style consistent (black, isort) and pass checks (flake8, mypy).

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How to cite

If you use this repository, please cite:

Bianchi, G. (2025). Deep Learning Volatility (v1.0.0) [Software]. https://doi.org/10.5281/zenodo.17018686

BibTeX

@software{Bianchi_DeepLearningVolatility_2025,
  author  = {Bianchi, Giacomo},
  title   = {Deep Learning Volatility},
  version = {1.0.0},
  year    = {2025},
  doi     = {10.5281/zenodo.17018686},
  url     = {https://github.com/BianchiGiacomo/deepLearningVolatility}
}

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License

MIT — see LICENSE.