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Modern Computational Finance: AAD and Parallel Simulations
By 作者: Antoine Savine
ISBN-10 书号: 1119539455
ISBN-13 书号: 9781119539452
Edition 版本: 1
出版日期: 2018-11-20
pages 页数: (592)
$95
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.
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.
Contents
Modern Computational Finance
Preface
Intro
C++code
–Modern Parallel Programming
Introduction
Effective C++
ModernC
ParallelC
—Parallel Simulation
Introduction
Asset Pricing
Monte-Carlo
Serial Implementation
Parallel Implementation
–Constant Time Differentiation
Introduction
Manual Adjoint Differentiation
Algorithmic Adjoint Differentiation
Effective AAD & Memory Management
Discussion & Limitations
Differentiation of the Simulation Library
Check-Pointing & Calibration
Multiple Differentiation in Constant Time
Acceleration with Expression Templates
Debugging AAD Instrumentation
Conclusion
Refs
Index
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