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Antoine Savine
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  • Copenhagen
  • Denmark
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farooq hassan liked Antoine Savine's blog post Deep Analytics: Risk Management with AI
yesterday
Antoine Savine's blog post was featured

Deep Analytics: Risk Management with AI

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 discuss…See More
Thursday
PHAM-HI liked Antoine Savine's blog post Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance
Nov 3
Antoine Savine's blog post was featured

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

Deep Analytics: Risk Management with AI

Posted on December 10, 2019 at 1:30am 0 Comments

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…

Continue

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:…

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