Deep Learning and Computational Graph Techniques for Derivatives Pricing and Analytics
We review some new approaches from research and literature and Wells Fargo’s work to apply deep learning techniques and computational graph techniques (including algorithmic differentiation) to the solution of high-dimensional forward-backward SDE and PDE in derivative pricing, present some fundamental ideas, applications to derivatives pricing and analytics with some results, and some current and planned extensions
Bernhard Hientzsch is the Head of Model, Library, and Tool Development in the Corporate Model Risk Management Group at Wells Fargo. His group is responsible for the implementation of models, libraries, components, and tools for the validation, benchmarking, and oversight of models at Wells Fargo. Prior to joining Wells Fargo, he was a postdoctoral scientist at New York University in several DoE supported projects and consulting on mathematical, financial, and computer modelling in the USA and Germany. Bernhard received his PhD in applied mathematics from the Courant Institute at New York University.