
Modern Computational Finance
aad and parallel simulations
$236.47
- Hardcover
592 pages
- Release Date
20 November 2018
Summary
Modern Computational Finance: Algorithmic Adjoint Differentiation Explained
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 o…
Book Details
ISBN-13: | 9781119539452 |
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ISBN-10: | 1119539455 |
Author: | Antoine Savine, Leif Andersen |
Publisher: | John Wiley & Sons Inc |
Imprint: | John Wiley & Sons Inc |
Format: | Hardcover |
Number of Pages: | 592 |
Release Date: | 20 November 2018 |
Weight: | 907g |
Dimensions: | 234mm x 158mm x 38mm |
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About The Author
Antoine Savine
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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