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Antoine Savine
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Tech Talk Event @Bloomberg

Thrilled to introduce Machine Learning and Adjoint Differentiation in Finance (in 15 minutes!!) at the Bloomberg Tech Talk Event, held Wednesday, 27 November 201908:30 – 14:00 GMT at Bloomberg L.P. 3 Queen Victoria Street London, EC4N 4TQ https://bbgevent.com/risk-tech-talks/2019ldn/agenda/

Deep learning derivatives pricing

I made two simplistic TensorFlow (1.x) notebooks for the benefit of my students at Copenhagen University, to demonstrate how vanilla neural nets (deeply) learn pricing of European calls and high dimensional basket options, together with a comparison with conventional polynomial regression models (a la LSM) and a quick, simple introduction to the implementation of deep … Continue reading Deep learning derivatives pricing

Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

Back in March, I gave a series of lectures at Kings College London on automatic adjoint differentiation, backpropagation and machine learning, and how it all connects and applies to risk management of financial derivatives. The lectures were recorded and made freely available online, either from Kings own page: https://nms.kcl.ac.uk/probability/workshopPages.php?id=8 or, on YouTube: It is my … Continue reading Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

QuantMinds 2019, Vienna

We had another fantastic conference from QuantMinds last week in Vienna, where I was honored to chair the Numerical and Computational Finance stream. The agenda was dominated by the application of machine learning to derivatives finance. Brian Huge and I gave a talk on Deep Analytics to a vast audience of delegates, professionals and academics, … Continue reading QuantMinds 2019, Vienna

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Nov 3
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Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

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:…See More
Oct 31
Antoine Savine updated their profile
Oct 30

Profile Information

Short Bio
Antoine Savine is a practitioner and a lecturer in methematical and computational finance. He is the author of the Modern Computational Finance book with Wiley.
Antoine Savine holds a MsC (mathematics) from the University of Paris-Diderot and a PhD (also, 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 current interest is in the application of deep learning and reinforcement learning to Derivatives finance.
My Web Site Or LinkedIn Profile
http://antoinesavine.com
Field of Expertise
Machine Learning, Deep Learning, Other
Professional Status
Technical
Years of Experience:
25
Your Company:
Danske Bank
Industry:
Financial Services
Your Job Title:
Quantitative Research
Interests:
Contributing, Networking

Antoine Savine's Blog

Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

Posted on October 30, 2019 at 7:00am 0 Comments

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:…

Continue

Deep Learning picking momentum in option pricing and financial risk management

Posted on January 11, 2019 at 5:30am 0 Comments

Deep Learning is picking momentum in Quantitative Finance, outside the obvious application to the prediction of asset prices (where to my knowledge it is not particularly effective) and spreading into the more serious application area of option pricing and risk management.

These two recent papers clearly demonstrate the benefits of DL as a pricing technology alternative to the classical FDM and Monte-Carlo in certain contexts:…

Continue

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