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Modern Computational Finance: Aad and Parallel Simulations

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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.

592 pages, Hardcover

Published November 20, 2018

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About the author

Antoine Savine

3 books14 followers
Antoine Savine is a mathematician, and finance academic and practitioner. A PhD in Mathematics from Copenhagen University and a quantitative researcher at Danske Bank, Antoine Savine also teaches volatility and numerical finance at Copenhagen University.
Antoine Savine is an expert C++ programmer and one of the key contributors to Danske Bank’s CVA (counterparty value adjustment) system, which won the prestigious In House System of the Year 2015 Risk award, and the Excellence in Risk Management and Modelling RiskMinds 2019 award.
Previously, Antoine Savine was Global Head of Derivatives Analytics at BNP-Paribas, and also held positions at Nikko Securities and Gen Re. Antoine Savine is the author of Modern Computational Finance: AAD and Parallel Simulations (John Wiley and Sons, ISBN 978-1119539452).
In Mathematical Finance, Antoine Savine was influential in the development of scripting and automatic differentiation, and has also contributed to volatility modelling. His current interest is in combining financial modeling with deep learning and the unification of derivatives risk management and capital calculations.

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