Overview

Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book.

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9781119539452

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ANTOINE SAVINE is a mathematician and derivatives practitioner with leading investment banks. After globally running quantitative research in a major French bank for ten years, Antoine joined Jesper Andreasen to participate in the development of Danske Bank's award winning systems.Antoine also lectures in the University of Copenhagen's Masters of Science in Mathematics-Economics program, on topics including volatility modeling and numerical finance, for which this book is the curriculum. Antoine holds a Masters in Mathematics from the University of Paris-Jussieu and a PhD in Mathematics from the University of Copenhagen. He is best known for his work on volatility, multi-factor interest rate models, scripting, AAD and parallel Monte-Carlo. His computational finance books combine the unique insight of a leading practitioner with the rigor and pedagogy of an accomplished lecturer.

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