We have just presented our latest Risk paper Differential Machine Learning at QuantMinds International 2020. The presentation includes a live demo of how differential ML is implemented in production, and combined with cash-flow scripting to provide truly general means of learning the pricing and risk function of arbitrary financial instruments. See the 5min demonstration below:
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
New research, new breakthroughs, and new opportunities
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.