Traditional options pricing models often assume simple payoff structures, but real-world financial markets demand more complex and exotic derivatives that rely on the entire price path of an asset, rather than just its final value. Path-dependent options-such as Asian, Barrier, Lookback, and Cliquet options-require specialized mathematical models and computational techniques for accurate pricing and risk management.

This book provides a comprehensive, Python-driven approach to implementing path-dependent options pricing models, using advanced Monte Carlo simulations, finite difference methods, and machine learning techniques to enhance pricing accuracy and efficiency.

Understanding Path-Dependent Options - How their payoffs differ from standard European and American options
Monte Carlo Simulations for Exotic Derivatives - Modeling Asian, Barrier, and Lookback options in Python
Finite Difference & PDE Approaches - Applying numerical methods for precise derivative pricing
Risk Analysis and Hedging Strategies - Managing path-dependent risks with volatility modeling
Machine Learning for Exotic Option Pricing - Using AI-driven approaches for faster and more accurate predictions
Python Implementation & Optimization - Hands-on coding with NumPy, SciPy, and TensorFlow for scalable computation

Designed for quantitative traders, risk analysts, and financial engineers, this book bridges theory and practice by providing a detailed, hands-on approach to pricing exotic derivatives.