Article with code, Wilmott Nov19

Quantitative Finance

My paper  “Computation graphs for AAD and Machine Learning part I: Introduction to computation graphs and automatic differentiation” was just published in Wilmott, the first in a series of articles with code dedicated to Computational Finance. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

A. Savine, “Computation graphs for aad and machine learning part i: introduction to computation graphs and automatic differentiation,” Wilmott, vol. 2019, iss. 104, p. 36–61, 2019.

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Published by Antoine Savine

Antoine Savine is a French mathematician, academic and a leading derivatives research professional with Danske Bank in Copenhagen. Antoine also teaches Volatility and Computational Finance at Copenhagen University. He is the author of Modern Computational Finance with John Wiley and Sons. Antoine holds a PhD in Mathematics, and is best known for his work on volatility and interest rate models. He was influential in the development of cashflow scripting, the application of generalized derivatives to volatility, and the wide adoption of AAD in financial systems.

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