Thursday 12 December 2019, 18:00-19:00.
Please click here to see the slides.
We first provide a mini-tutorial on Adjoint Algorithmic Differentiation (AAD) (also known as back-propagation in machine learning). We then illustrate how neural networks may be used to compute dynamic values and risks of trading books with applications to risk management of derivatives, valuation adjustments (XVA), counterpart credit risk, FRTB and SIMM margin valuation adjustments (MVA). We also describe new techniques to substantially improve deep learning on simulated data, and discuss how this is analogous to deriving approximate analytics in real time.
Antoine Savine is a mathematician and quant at Superfly Analytics in Danske Bank. He has held multiple leading roles in the derivatives industry in the past 20 years, including Head of Research at BNP-Paribas, and also teaches Volatility and Computational Finance at Copenhagen University. Antoine holds a PhD in Mathematics from Copenhagen University and is the author of 'Modern Computational Finance' (Wiley 2018).
Citi Stirling Square
5-7 Carlton Gardens
London SW1Y 5AD
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