Thrilled and honored to introduce Deep Analytics for Oxford University in Central London, Thursday 12th December 2019.
Slides here: www.deep-analytics.org
Register here: maths.ox.ac.uk/groups/mathematical-finance/frontiers-quantitative-finance-seminar-series
Flying Monday to Amsterdam with my colleagues from Superfly Analytics of Danske Bank, including Brian Huge and Ove Scavenius, to attend the RiskMinds 2019 risk management conference and the award ceremony, where our group is nominated for ‘Excellence in risk management and modeling’.
EDIT: Superfly Analytics now won the award:
We will be presenting our vision of modern risk management systems and the ‘One Analytics’ platform: full front to back consistency with scripting of cash-flows , model hierarchies and AAD. Further, we will present ‘Deep Analytics’: leveraging risk management systems with AI to learn revaluation and risk analytics on the fly. For those unable to attend, we posted our slides online here:
By Brian Huge and Antoine Savine
Deep learning derivatives pricing, and risk, with differential regularisation
See our slides here:
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 learning models in TensorFlow.
Notebook 1: European call in Black and Scholes
Notebook 2: Basket option in Bachelier
The notebooks run directly on Google colab, in the cloud and on GPU, no installation required.
The lecture slides and material are all found here: github.com/asavine/CompFinLecture
Some (much) more advanced considerations for efficiently learning prices of trading books, including “twin” neural nets who learn values and risks, and the super efficient differential regularization, are found here: deep-analytics.org
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 understanding that the audience at Kings found the talks useful, and I hope that they may be beneficial to a wider audience on the internet.
Antoine Savine 