Co-author Brian Huge and I were interviewed by Risk Magazine last week and got a chance to present differential ML and related algorithms in non-technical terms. The podcast is publicly available https://www.risk.net/cutting-edge/views/7880346/podcast-huge-and-savine-on-turbo-charging-derivatives-pricing
In this lightning talk delivered for Bloomberg’s BBQ seminar 28th May 2020, we expose the main ideas of differential machine learning and application to derivatives pricing and risk management.
Complete presentation slides here: http://www.deep-analytics.org
With the results of my latest work with Brian Huge on differential machine learning, along with the latest from Marcos Lopez de Prado, Alexander Antonov, Svetlana Borovkova, and Fabio Mercurio, who have shared their latest insights into machine learning (ML), neural networks, covid-19 and Libor.
Differential machine learning combines ML with automatic differentiation (AAD) to produce accurate pricing and risk approximations for arbitrary derivatives transactions or trading books, quickly, online, with convergence guarantees.
I will be presenting theory, implementation and results of pricing and risk management of derivatives by machine learning.
The webinar is complimentary and hosted by Financial Risk Hub.
Register here: www.financialriskhub.com/deep-learning-applications-in-valuation-and-risk-of-trading-books/
Slides here: www.deep-analytics.org
Antoine Savine 