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.

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 Numerical Finance at Copenhagen University. He is the author of the book Modern Computational Finance with John Wiley and Sons. Antoine holds a PhD in Mathematics, and is best known for his work on volatility with Bruno Dupire, scripting with Jesper Andreasen and Leif Andersen, multi-factor interest rate models with Marek Musiela, or automatic differentiation and parallel Monte-Carlo simulations. He was influential in the adoption of scripting, the application of generalized derivatives in the context of local and stochastic volatility models, and the wide adoption of AAD in financial systems.

Published

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