Numerical Methods for Options Pricing: A Guide to Financial Derivatives and Calibration by James Preston
English | September 10, 2026 | ASIN: B0HJBGRSW4 | 533 pages | EPUB | 6.75 Mb
Reactive Publishing Master the mathematical foundations and practical implementations of modern quantitative finance.
Numerical Methods for Options Pricing delivers a comprehensive, rigorous guide to pricing financial derivatives and calibrating market models. Designed for quantitative analysts, financial engineers, and risk managers, this book bridges the gap between theoretical financial mathematics and real-world computational execution.
What You Will Learn Monte Carlo Simulation: Build robust, scalable simulation engines for complex path-dependent options and multi-asset derivatives.
Finite Difference Methods: Implement explicit, implicit, and Crank-Nicolson schemes to solve partial differential equations (PDEs) efficiently.
Volatility Surface Modeling: Calibrate local and stochastic volatility models to market-observed option prices with numerical stability.
Model Calibration Techniques: Apply numerical optimization routines to fit complex derivative models to real-time market data.
Designed for Practitioners and Scholars Whether you are a quantitative developer optimizing trading infrastructure, a student in financial engineering, or a risk practitioner seeking deeper computational clarity, this text provides structured explanations, algorithmic breakdowns, and practical insights necessary to build production-ready pricing models.
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