The second part of my paper “Computation graphs for AAD and Machine Learning part II: Adjoint differentiation and AAD” was just published in Wilmott, the second in a series of three articles with code dedicated to AAD and Computational Finance in general. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.
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.
View all posts by Antoine Savine