Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
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Brian Huge and Antoine Savine combine automatic adjoint differentiation with modern machine learning. In addition, they introduce general machinery for training fast, accurate pricing and risk approximations, applicable to arbitrary transactions or trading books, and arbitrary stochastic models, effectively resolving the computational bottlenecks of derivatives risk reports and regulations
Pricing approximation has proved tremendously useful with advanced
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