Python for Rough Volatility: Rough Bergomi and Stochastic Volatility Models

Python for Rough Volatility introduces readers to the practical implementation of rough volatility models in Python, with a focused treatment of the Rough Bergomi model and related stochastic volatility frameworks used in quantitative finance.

This book bridges the gap between advanced mathematical theory and working code. It demonstrates how to build, simulate, calibrate, and apply rough volatility models using Python, with emphasis on numerical methods and computational efficiency suitable for real-world quantitative workflows.

What the book covers:

• Core concepts of rough volatility and the Rough Bergomi model
• Implementation of stochastic volatility models in Python
• High-performance calibration techniques
• Simulation methods for rough paths
• Practical considerations for model application in quantitative finance

Written for quantitative analysts, developers, and researchers working in options pricing, volatility modeling, and financial engineering, the book assumes familiarity with Python programming and basic stochastic calculus. All examples are provided with complete, runnable code that can be adapted for personal or professional use.

The material is presented in a clear, technical style focused on implementation details rather than theoretical proofs, making it a practical resource for those looking to incorporate rough volatility models into their quantitative toolkit.