A passion to instruct
A knack for clarity
An obsession with detail
A luminous writer
An instant classic.
Bruno Dupire,
Head of Quantitative Research, Bloomberg L.P.
It would not be much of an exaggeration to say that Antoine Savine’s book ranks as the 21st century peer to Merton’s ‘Continuous-Time Finance’: It makes modern computational techniques such as multi-threaded parallel AAD as accessible to finance professionals as Merton’s introduction of stochastic calculus into finance. A first in a three book series authored by Danske Bank’s powerhouse quant team makes intricate concepts inherent to production-quality implementation of AAD easy to understand and follow through. No other quant finance focused book has gone so deeply into parallel C++ and AAD with such clarity, level of detail and thoroughness. I can hardly wait for the remaining two volumes to seewhat else the wizards of AAD have up their sleeves.
Vladimir V. Piterbarg,
Partner at Rokos Capital Management,
co-author of the three-volume set “Interest Rate Modelling”
This book […] addresses the challenges of AAD head on. […] The exposition is […] ideal for a Finance audience. The conceptual, mathematical, and computational ideas behind AAD are patiently developed in a step-by-step manner, where the many brain-twisting aspects of AAD are de-mystified. For real-life application projects, the book is loaded with modern C++ code and battle-tested advice on how to get AAD to run for real. […] Start reading!
Leif Andersen, Global Head of the Quantitative Strategies Group at Bank of America Merrill Lynch,co-author of the three-volume set “Interest Rate Modelling”
This three-book series is an indispensable resource for any quant. Written by experts in the field and filled with practical examples and industry insights that are hard to find elsewhere, the books set a new standard for computational finance.
Paul Glasserman, Jack R. Anderson Professor of Business, Columbia University
The Global Financial Crisis resulted in profound changes in quants’ Modus Operandi. This timely three-volume set describes some of the tools necessary to deal with these changes. Individual volumes cover in detail several important topics of interest to anyone who wants to stay au courant with modern developments in financial engineering. While the books are predominantly practically oriented, they strike a fine balance between theoretical and applied considerations. The authors are prominent practitioners and indisputable thought-leaders in the field. I recommend this set enthusiastically to anyone who wishes to understand the current and emerging trends in financial engineering.
Alexander Lipton,
Connection science fellow at MIT,
Visiting professor at EPFL,
Cofounder and CTO of SilaMoney,
Former co-head of the Global Analytics Group at BAML
“A passion to instruct
A knack for clarity
An obsession to detail
A luminous writer
An instant classic”
―Bruno Dupire, Head of Quantitative Research, Bloomberg L.P.
“It would not be much of an exaggeration to say that Antoine Savine’s book ranks as the 21st century peer to Merton’s Continuous-Time Finance; it makes modern computational techniques such as multi-threaded parallel AAD as accessible to finance professionals as Merton’s introduction of stochastic calculus into finance. A first in a three-book series authored by Danske Bank’s powerhouse quant team makes intricate concepts inherent to production-quality implementation of AAD easy to understand and follow through. No other quant finance focused book has gone so deeply into parallel C++ and AAD with such clarity, level of detail and thoroughness. I can hardly wait for the remaining two volumes to see what else the wizards of AAD have up their sleeves.”
―Vladimir V. Piterbarg, Partner at Rokos Capital Management, co-author of the three-volume set Interest Rate Modelling
“This book […] addresses the challenges of AAD head on. […] The exposition is […] ideal for a Finance audience. The conceptual, mathematical, and computational ideas behind AAD are patiently developed in a step-by-step manner, where the many brain-twisting aspects of AAD are de-mystified. For real-life application projects, the book is loaded with modern C++ code and battle-tested advice on how to get AAD to run for real. […] Start reading!”
―Leif Andersen, Global Head of the Quantitative Strategies Group at Bank of America Merrill Lynch
“This three-book series is an indispensable resource for any quant. Written by experts in the field and filled with practical examples and industry insights that are hard to find elsewhere, the books set a new standard for computational finance.”
―Paul Glasserman, Jack R. Anderson Professor of Business, Columbia University
“The Global Financial Crisis resulted in profound changes in quants’ Modus Operandi. This timely three-volume set describes some of the tools necessary to deal with these changes. Individual volumes cover in detail several important topics of interest to anyone who wants to stay au courant with modern developments in financial engineering. While the books are predominantly practically oriented, they strike a fine balance between theoretical and applied considerations. The authors are prominent practitioners and indisputable thought-leaders in the field. I recommend this set enthusiastically to anyone who wishes to understand the current and emerging trends in financial engineering.”
―Professor Alexander Lipton
From the Author
AAD was named one of the greatest algorithms of the 20th century. It powers machine learning, financial risk management and many other scientific applications. It makes a major difference for financial software, allowing to compute complex risks in minutes on a laptop instead of overnight in data centers. AAD is also notoriously mind-twisting, and this key technology is still misunderstood by a majority of finance professionals. I wrote this book to explain this technology step by step, guide readers towards a complete, up to date implementation, and use it to accelerate a generic parallel simulation library (which we build in the first parts). I also included the source code of professional AAD and simulation libraries in C++. This publication is the sum of my many years teaching AAD and implementing it professionally. My colleagues in Danske Bank and I implemented AAD in production in the early 2010s and were rewarded with Risk’s In-House System of the Year 2015 award. This book builds on our adventure and provides all the explanations and guidance for a practical, effective implementation in words, mathematics and code.
From the Inside Flap
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 and 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 design 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.
From the Back Cover
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 and 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 design 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.
About the Author
Antoine Savine is a mathematician and derivatives practitioner since 1995. After globally running quantitative research for a leading French investment bank for ten years, Antoine joined Jesper Andreasen to participate in the development of Danske Bank’s systems, which won the In-House System of the Year 2015 Risk award.
Antoine also lectures in the University of Copenhagen’s Masters of Science in Mathematics-Economics, with 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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