This book delivers the mathematical foundations of modern quantitative finance with a direct, applied focus. Built around stochastic calculus and Brownian motion, it shows how continuous-time models underpin option pricing, risk management, and trading strategies used on today's desks.
You'll move from first principles to advanced applications, learning not only the theory but also how to implement it in practice. Each chapter connects core concepts to real trading problems, so the math isn't just abstract, it's actionable.
Construction and properties of Wiener processes and Ito integrals
Application of Ito's Lemma in derivatives pricing
Stochastic differential equations (SDEs) and their financial interpretation
How stochastic calculus powers the Black-Scholes model, Greeks, and hedging
Practical approaches to volatility modeling and path-dependent options
Python-based Monte Carlo methods and algorithmic trading applications
Quantitative analysts, traders, and risk managers
Financial engineers and graduate students in finance
Python developers working in quantitative modeling
Professionals seeking a practical, mathematically rigorous guide