Antoine SavineChief Quantitative Analyst at Danske Bank
Antoine Savine is a mathematician, academic and Derivatives professional with Superfly Analytics at Danske Bank, winner of the Excellence in Risk Management and Modelling RiskMinds 2019 award. Antoine held multiple leadership positions in quantitative finance since 1995, including Global Head of Research at BNP-Paribas.
Antoine is an expert C++ and Python programmer and one of the key contributors to Danske Bank's Superfly platform, winner of the In-House System of the Year 2015 Risk award.
Antoine wrote the book on Automatic Adjoint Differentiation (AAD) with Wiley: Modern Computational Finance (2018). He published 'Differential Machine Learning: the Shape of Things to Come' in Risk (2020) with Brian Huge, combining AAD with modern Machine Learning to automate pricing and risk of arbitrary Derivatives and resolve bottlenecks in risk reports and regulations like XVA, CCR, FRTB or SIMM-MVA.
Antoine holds a PhD in Mathematics from Copenhagen University, where he teaches Volatility, Computational Finance and Machine Learning in Finance. He is best known for his work on volatility and multifactor interest rate models. He was influential in the adoption of cashflow scripting, the application of generalized derivatives in stochastic volatility models, and the wide adoption of AAD in the financial industry. His many publications and conferences are widely attended in the Finance industry and help shape progress in risk management practices.