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RoughVolatilityWorkshop

Python translation of the R materials from QuantMinds Rough Volatility Workshop Lectures, with minor updates.

Repository Structure

Jupyter Notebooks:

  • QM2024_1_Econometrics.ipynb:

    • Shape of the volatlity surface

    • Scaling of implied volatility smiles

    • Monofractal scaling of realized variance

    • Estimation of $H$

    • Realized variance forecasting

  • QM2024_2_Rough_volatility_models.ipynb:

    • The forward variance curve

    • Change of measure

    • The rough Bergomi model

    • The rough Heston model

    • The quadratic rough Heston model

    • Financial meaning of parameters

  • QM2024_3_Affine_models.ipynb:

    • The microstructural foundation of affine forward variance models

    • Characteristic function methods

      • Option pricing
      • The ATM skew
      • The skew-stickiness ratio
    • Diamonds and the forest expansion

    • Moment computations

  • QM2024_4_Computations.ipynb:

    • Rational approximation of rough Heston

    • Smile plotting and parameter sensitivities

    • The HQE scheme

Python files:

Python Installation Guide

Option 1: Standard Virtual Environment

  1. Clone the repository:

    git clone https://github.com/fbourgey/RoughVolatilityWorkshop.git
  2. Navigate to the project directory:

    cd RoughVolatilityWorkshop
  3. Create a virtual environment:

    python3 -m venv .venv
  4. Activate the environment:

    source .venv/bin/activate
  5. Install dependencies:

    pip install .
  6. Launch Jupyter Lab (optional):

    jupyter lab

Option 2: Using uv (Recommended)

If you have uv installed, setup is simpler and faster.
After cloning the repository (steps 1–2 above), run:

uv sync

This will automatically create a virtual environment and install all dependencies.

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  • Jupyter Notebook 98.3%
  • Python 1.1%
  • R 0.6%