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Antoine Savine's Page
Profile Information
- Short Bio:
- Antoine Savine is a leading practitioner and a lecturer in methematical and computational finance. He is the author of the Modern Computational Finance book with Wiley.
Antoine holds a MsC (mathematics) from the University of Paris-Diderot and a PhD (mathematics) from Copenhagen University. He is well known in the quantitative finance community for influential work on cash-flow scripting, multi-factor interest rate models, generalized derivatives in the context of local and stochastic volatility models, and the wide adoption of AAD in financial systems. His latest research focuses on 'Differential Machine Learning' and application in finance.
- Company:
- Danske Bank
- Job Title:
- Quantitative Research -- Superfly Analytics
- Seniority:
- Staff
- Job Function:
- AI/ML Models
- Number of employees:
- 10.000 to 24.999
- Industry:
- Financial/Banking
- LinkedIn Profile:
- http://antoinesavine.com
- Interests:
- Contributing, Networking
Antoine Savine's Blog
Differential ML on TensorFlow and Colab
Posted on May 25, 2020 at 11:30am 0 Comments 1 Like
Brian Huge and I just posted a working paper following six months of…
ContinueAutomatic Differentiation in 15 Minutes -- video tutorial with application in machine learning and finance
Posted on April 17, 2020 at 7:53am 0 Comments 0 Likes
Deep Analytics: Risk Management with AI
Posted on December 10, 2019 at 1:30am 0 Comments 1 Like
We first provide a mini-tutorial on Adjoint Algorithmic Differentiation (AAD) (also known as back-propagation in machine learning). We then illustrate how neural networks may be used to compute dynamic values and risks of trading books with applications to risk management of derivatives, valuation adjustments (XVA), counterpart credit risk, FRTB and SIMM margin valuation adjustments (MVA). We also describe new techniques to substantially improve deep learning on simulated data, and…
ContinueRecorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance
Posted on October 30, 2019 at 7:00am 0 Comments 1 Like
Automatic Adjoint Differentiation (AAD) and back-propagation are key technologies in modern machine learning and finance. It is back-prop that enables deep neural networks to learn to identify faces on photographs in reasonable time. It is AAD that allows financial institutions to compute the risks of complex derivatives books in real time. The two technologies share common roots.
See the AAD book here:…
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