Modern Computational Finance Scripting for Derivatives and Xva

Modern Computational Finance: Scripting for Derivatives and Xva

By Antoine Savine and Jesper Andreasen

"Scripting of derivatives transactions has been a central piece of financial software since the 1990s. Every derivatives valuation and risk system, either in-house or from external vendors, features at least some kind of scripting technology. Yet, the expertise in that field remains unwritten to date, without any article or publication dedicated to the subject.

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Book Information

Publisher: Wiley
Publish Date: 11/02/2021
Pages: 288
ISBN-13: 9781119540786
ISBN-10: 111954078X
Language: Eng

Full Description

An incisive and essential guide to building a complete system for derivative scripting

In Volume 2 of Modern Computational Finance Scripting for Derivatives and xVA, quantitative finance experts and practitioners Drs. Antoine Savine and Jesper Andreasen deliver an indispensable and insightful roadmap to the interrogation, aggregation, and manipulation of cash-flows in a variety of ways. The book demonstrates how to facilitate portfolio-wide risk assessment and regulatory calculations (like xVA).

Complete with a professional scripting library written in modern C++, this stand-alone volume walks readers through the construction of a comprehensive risk and valuation tool. This essential book also offers:

  • Effective strategies for improving scripting libraries, from basic examples--like support for dates and vectors--to advanced improvements, including American Monte Carlo techniques
  • Exploration of the concepts of fuzzy logic and risk sensitivities, including support for smoothing and condition domains
  • Discussion of the application of scripting to xVA, complete with a full treatment of branching

Perfect for quantitative analysts, risk professionals, system developers, derivatives traders, and financial analysts, Modern Computational Finance Scripting for Derivatives and xVA Volume 2 is also a must-read resource for students and teachers in master's and PhD finance programs.

About the Authors

Antoine Savine is a mathematician and derivatives practitioner with 25 years of leadership experience with global investment banks. He wrote the book on automatic adjoint differentiation (AAD) and co-developed Differential Machine Learning. He was also influential in volatility modeling and many areas of numerical and computational finance. A

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ANTOINE SAVINE is a mathematician and derivatives practitioner with 25 years of leadership experience with global investment banks. He wrote the book on automatic adjoint differentiation (AAD) and co-developed Differential Machine Learning. He was also influential in volatility modeling and many areas of numerical and computational finance. A

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