Description

Reactive Publishing

In AI-Driven Derivatives: Neural PDE Solvers, Deep Hedging, and Generative Option Pricing, James Preston takes you to the cutting edge of quantitative finance, where machine learning meets advanced derivative modeling. This groundbreaking guide reveals how neural networks, auto-differentiation, and generative architectures are reshaping the way traders, quants, and researchers value, hedge, and manage risk in complex markets.

You’ll learn how to implement neural PDE solvers to approximate pricing equations in real time, design deep hedging frameworks that adapt dynamically to shifting volatility regimes, and build generative models capable of synthesizing realistic option surfaces and stress scenarios. Each concept is backed by practical examples in Python, bridging academic innovation with real-world trading applications.

From volatility smiles to stochastic local models, Preston integrates theory, code, and intuition to create a cohesive playbook for the next era of derivatives analytics. Whether you’re a quant developer, portfolio manager, or AI researcher entering the financial domain, this book will show you how to harness artificial intelligence to build faster, smarter, and more adaptive pricing engines that redefine market edge.