Path-Dependent Options and Exotic Derivatives Pricing with Python

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.
Key Topics Covered:
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.
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