Stochastic Volatility Mastery: From Heston to SABR in Python: Calibrate, Simulate, and Price Options with Heston, SABR, and Bates Models Using Product

By James Preston and Danny Munrow

Reactive Publishing

Volatility isn't constant, and if your models assume it is, your PnL will suffer. Stochastic Volatility Mastery is the definitive guide to building, calibrating, and deploying realistic volatility models that match how markets actually behave.

This book takes you beyond Black-Scholes, showing you how to implement Heston, SABR, and Bates models step-by-step in Python, calibrate them to market smiles, and use them to price options and manage risk at scale.

Inside, you'll master:

Heston Model Foundations - Closed-form solutions, Monte Carlo simulation, and Greeks.

SABR Calibration & Pricing - Fitting skew/smile and handling extreme strikes.

Bates Model with Jumps - Capturing jump risk for better crisis scenario pricing.

FFT and Monte Carlo Methods - Fast, accurate option pricing approaches.

Practical Calibration Workflows - Optimize parameters with least-squares, MLE, and global search.

Speed Optimization - Use Numba, JAX, and vectorization for real-time execution.

Complete with production-quality Python code and calibration examples, this book bridges theory and practice, giving quants, traders, and developers a toolkit for robust, market-consistent option pricing that holds up under stress.

• Format: Paperback
• Language: English
• ISBN: B0FRT214BV
• ISBN13: 9798266142534
• Release Date: September 2025
• Publisher: Independently Published
• Length: 632 Pages
• Weight: 1.84 lbs.
• Dimensions: 1.3" x 6.0" x 9.0"