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’. 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:
My paper “Computation graphs for AAD and Machine Learning part I: Introduction to computation graphs and automatic differentiation” was just published in Wilmott, the first in a series of articles with code dedicated to Computational Finance. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.
By Brian Huge and Antoine Savine
Deep learning derivatives pricing, and risk, with differential regularisation
See our slides here:
The Practitioners’ Lecture Series, March 28-29, 2019
Introduction to back-propagation and automatic differentiation (AAD) in machine learning and finance
Much honored to be invited to give the titular workshop lecture at Kings College London on 28 and 29 March. See the event page here: https://www.eventbrite.co.uk/e/the-practitioners-lecture-series-introduction-to-back-propagation-and-automatic-adjoint-tickets-58436780985
See a brief, non-technical abstract on QuantMinds page here. The 6-hour workshop is a technical one. We will discuss the mathematics of deep learning and back-propagation, and the application of AAD with implementations in Python/TensorFlow and C++. The presentation slides are found on my GitHub repo (Intro2AADinMachineLearningAndFinance.pdf), together with supplementary material: code, spreadsheets and notebooks in the folder ‘Workshop’: https://github.com/asavine/CompFinance
The event was arranged by Blanka Horvath, author of Deep Learning Volatility, where the matter of quick European option pricing in rough volatility models is resolved with deep learning methods. Thank you, Blanka.
Registration is absolutely free, but seating is limited to 40 people. I am looking forward to meet an audience interested in the most recent additions to computational finance.
I am working on a set of exercises and assignments for the chapters of the Modern Computational Finance book. In the meantime, interested readers will find below the final hand-in for the computational finance lecture of autumn 2018 at Copenhagen University, where the book is used as curriculum:
Please post your questions, suggestions, comments or reviews of the book Modern Computational Finance: AAD and Parallel Simulations on the author’s page:
or the book’s GoodReads page:
A public preview, including Leif Andersen’s preface, is available on SSRN:
My book is out on Amazon today. You can read about it in my Medium post:
A free preview, including Leif Andersen’s preface, is available here:
The companion code is freely available on GitHub:
Follow the repo to be notified of updates, extensions and fixes.
I will be answering questions, suggestions and comments on my GoodReads page:
I put all my years of promoting, teaching and professionally implementing automatic adjoint differentiation (AAD) in this book, yet it took years of working nights and week-ends to (hopefully) get it right. I hope readers find that it was worth the effort.