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Posted on May 25, 2020 at 11:30am 0 Comments 1 Like
Brian Huge and I just posted a working paper following six months of…
ContinuePosted on April 17, 2020 at 7:53am 0 Comments 0 Likes
Posted on December 10, 2019 at 1:30am 0 Comments 1 Like
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…
ContinuePosted on October 30, 2019 at 7:00am 0 Comments 1 Like
Automatic Adjoint Differentiation (AAD) and back-propagation are key technologies in modern machine learning and finance. It is back-prop that enables deep neural networks to learn to identify faces on photographs in reasonable time. It is AAD that allows financial institutions to compute the risks of complex derivatives books in real time. The two technologies share common roots.
See the AAD book here:…
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