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.
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.
From Risk Minds web page: “This award is for the team who has tirelessly designed, developed and implemented new models and risk management techniques that have helped their financial institution manage risk more accurately and/or helped their institution comply with regulatory requirements.”
RiskMinds is “the world’s largest risk management event. 700+ CROs and experts from banks, buy-side, regulators, academia and beyond cover every hot topic in risk.”
Superfly Analytics presented its One Analytic Engine and Deep Analytics: Risk Management with AI. See all the slides here.
Flying Monday to Amsterdam with my colleagues from Superfly Analytics of Danske Bank, including Brian Huge and Ove Scavenius, to attend the RiskMinds 2019 risk management conference and the award ceremony, where our group is nominated for ‘Excellence in risk management and modeling’.
EDIT: Superfly Analytics now won the award:
We will be presenting our vision of modern risk management systems and the ‘One Analytics’ platform: full front to back consistency with scripting of cash-flows , model hierarchies and AAD. Further, we will present ‘Deep Analytics’: leveraging risk management systems with AI to learn revaluation and risk analytics on the fly. For those unable to attend, we posted our slides online here:
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.