Financial Engineering Workshop - Brian Huge & Antoine Savine - Danske Bank - via Microsoft Teams

Abstract: Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels with respect to inputs. It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA.

Brian Huge works for Danske Bank since 2001 as Chief Quantitative Analyst in Copenhagen (in-house system of the year 2015 Risk award, excellence in risk management and modeling RiskMinds award 2019). He holds a Ph.D. in Mathematical Finance from Copenhagen University and won the Risk Quant of the Year award in 2012.

Antoine Savine is a French mathematician and Chief Quantitative Analyst at Danske Bank in Copenhagen. He has held multiple leadership roles in the derivatives industry since 1995, including head of research at BNP-Paribas, and also teaches Volatility, Computational Finance and Machine Learning in Finance at Copenhagen University. Antoine holds a PhD in Mathematics from Copenhagen University and he is the author of the books 'Modern Computational Finance' with John Wiley and sons. He is also a lecturing at Copenhagen University.
 

The Financial Engineering Workshop will now be online only via Microsoft Teams. 
 

For the link to the online seminar please register before 2pm on the day of the seminar otherwise you will not be given access.