Antoine Savine is a French mathematician, academic and professional with financial derivatives. Antoine works with Superly Analytics at Danske Bank, winner of the In-House System of the Year 2015 Risk award and RiskMinds’ Excellence in Risk Management and Modelling 2019 award. In the past, he has held multiple leadership positions in quantitative finance, including Global Head of Derivatives Research at BNP-Paribas.
Antoine also lectures at Copenhagen University’s Masters of Science in Mathematics-Economics, with topics including Volatility Modeling, Numerical Finance and Machine Learning in Finance. He also wrote the curriculum Modern Computational Finance with Wiley.
Antoine holds a Masters in Mathematics from the University of Paris-Jussieu and a PhD in Mathematics from Copenhagen University. He is best known for his influential work on risk management, volatility, multi-factor interest rate models, scripting, automatic differentiation (AAD) and parallel Monte-Carlo.
Antoine’s current research interests are in the application of deep learning to relieve the computational burden of capital calculations, CVA/XVA, CCR (counterparty credit risk), FRTB (fundamental review of the trading books) or MVA (initial margin valuation adjustment). In particular, his joint work with Brian Huge develops new machine learning techniques, which massively improve the performance of neural networks with the addition of path-wise differential information, efficiently obtained with AAD.
All of those formed the major theme of the QuantMinds 2019 conference in Vienna, where Antoine chaired the Numerical and Computational Finance stream, emphasizing Danske Bank’s unique vision of One Quantitative Engine for the risk management of derivatives and regulations; and in the RiskMinds 2019 conference in Amsterdam, where Superfly Analytics was awarded the ‘Excellence in Risk Management and Modelling’ trophee.