Introduction to Financial Derivatives with Python, Second Editon continues to provide an accessible introduction to derivatives and quantitative finance. Starting from first principles, the book develops the foundations of derivative pricing before progressing to numerical methods and advanced volatility models. Mathematical concepts are introduced progressively, allowing the reader to develop the necessary tools alongside their financial applications. Financial intuition, mathematical foundations, and Python implementation are integrated throughout the book.
The book covers the essential topics in derivative pricing and introduces numerical methods widely used in quantitative finance. It also develops advanced volatility models, including CEV, local volatility, Heston, and SABR.
Features

• Suitable for undergraduate and graduate students, as well as practitioners and anyone seeking an accessible introduction to quantitative finance
• Covers derivative pricing from fundamental principles to advanced volatility models
• Introduces numerical pricing techniques, including binomial trees and Monte Carlo simulation
• Provides chapter summaries, exercises, and examination material
• Accompanied by a GitHub repository containing the Python code used throughout the book
• No prior programming experience is required; introductions to Python and coding are provided.

New to the Second Edition

• Fresh material on the Bachelier model and normal implied volatility
• A new chapter on local volatility covers the motivation for local volatility modelling, the CEV model, and Dupire's formula
• A new chapter on stochastic volatility develops the Heston and SABR models
• Python implementations to help the reader understand the concepts presented
• A new appendix including sample exams. This allows readers to practice the concepts learned throughout the book.