Neural Differential Equations for Option Pricing and Risk in Quantitative Finance

Reactive PublishingNeural Differential Equations for Option Pricing and Risk in Quantitative Finance presents a focused treatment of continuous-time neural models and their application to core problems in quantitative finance.The book examines how neural ordinary and stochastic differential equations can be used to model asset-price dynamics, calibrate to market data, and address option pricing and risk-management tasks. It covers the mathematical foundations of neural differential equations, their relationship to classical stochastic processes, and practical considerations for implementing these models in a financial setting.Topics include the construction and training of neural SDEs, no-arbitrage constraints, calibration to option surfaces, and the use of continuous-time models for risk measures and hedging. Emphasis is placed on clarity of formulation, numerical stability, and the connection between the continuous-time framework and discrete market observations.This work is intended for quantitative analysts, researchers, and advanced practitioners who already possess a working knowledge of stochastic calculus, option pricing theory, and machine-learning methods, and who wish to understand the specific role of neural differential equations in modern quantitative finance.