Reactive PublishingAdvanced Stochastic Methods in Quantitative Finance goes beyond the classical assumptions that dominate traditional financial modeling and confronts the realities of modern markets. Moving past the Gaussian world of Brownian motion and Black-Scholes, this book provides a rigorous yet applied treatment of the stochastic structures that better capture jumps, heavy tails, volatility clustering, and path-dependent behavior observed in real asset prices.
Designed for quantitatively trained practitioners, researchers, and advanced students, the book introduces Lévy processes, jump-diffusion models, and infinitely divisible distributions as core building blocks for realistic market dynamics. It then progresses into contemporary frameworks such as rough volatility and rough path theory, explaining how these models reshape pricing, hedging, and risk management in environments where classical smoothness assumptions fail.
Rather than focusing on abstract theory alone, the text emphasizes intuition, mathematical structure, and financial interpretation. Readers will learn how and why these models outperform traditional approaches, where they break down, and how they are used in practice across derivatives pricing, volatility modeling, and risk systems. Each topic is positioned within the broader evolution of quantitative finance, highlighting the shift from closed-form elegance to structurally robust, data-consistent models.
This book is ideal for quants who already understand standard stochastic calculus and want to operate at the frontier of modern financial modeling, where uncertainty is discontinuous, volatility is rough, and markets are no longer well described by simple diffusion assumptions.
