Reactive PublishingTraditional 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 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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