Python translation of the R materials from QuantMinds Rough Volatility Workshop Lectures, with minor updates.
Jupyter Notebooks:
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Shape of the volatlity surface
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Scaling of implied volatility smiles
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Monofractal scaling of realized variance
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Estimation of
$H$ -
Realized variance forecasting
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QM2024_2_Rough_volatility_models.ipynb:
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The forward variance curve
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Change of measure
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The rough Bergomi model
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The rough Heston model
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The quadratic rough Heston model
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Financial meaning of parameters
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The microstructural foundation of affine forward variance models
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Characteristic function methods
- Option pricing
- The ATM skew
- The skew-stickiness ratio
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Diamonds and the forest expansion
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Moment computations
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Rational approximation of rough Heston
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Smile plotting and parameter sensitivities
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The HQE scheme
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Python files:
- fwd_var_curve.py: Forward variance curve construction and smoothing
- heston.py: Classical Heston model functions
- pade.py: Padé/rational approximation coefficients for rough Heston
- rough_bergomi.py: Implementation of the rough Bergomi model
- rough_heston.py: Implementation of the rough Heston model
- utils.py: Utility functions for the workshop
- utils_plot.py: Functions for plotting implied volatility curves and Monte Carlo results
- var_gamma_swaps.py: Variance and gamma swap calculations
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Clone the repository:
git clone https://github.com/fbourgey/RoughVolatilityWorkshop.git
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Navigate to the project directory:
cd RoughVolatilityWorkshop -
Create a virtual environment:
python3 -m venv .venv
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Activate the environment:
source .venv/bin/activate -
Install dependencies:
pip install . -
Launch Jupyter Lab (optional):
jupyter lab
If you have uv installed, setup is simpler and faster.
After cloning the repository (steps 1–2 above), run:
uv syncThis will automatically create a virtual environment and install all dependencies.