Abstract:  Deep learning algorithms have been shown empirically to work well in many classical problems from mathematical finance. Theoretical foundations of deep learning in this context, however, are far less developed. Here we present some recent results in this direction. More specifically, the talk starts with examples of recent applications of deep learning related to option pricing. Next, we discuss theoretical foundations of such methods, recent mathematical progress, and key challenges. Finally, we show that in certain situations random neural networks are capable of overcoming these challenges.

Bio: Lukas is a Senior Lecturer at the Department of Mathematics at Imperial College London. His research interests lie at the intersection of stochastics, machine learning and finance. Previously, Lukas obtained his doctoral degree from ETH Zürich, was a postdoctoral researcher at University of St. Gallen and an assistant professor at University of Munich.

Meeting Recording: https://ucsb.zoom.us/rec/share/o-ie8BW4LPsBncM6lQH9Z23Jsp_-jT-BwHUwxBkmMNWn41TZ4T6HA-89DrqDS6IN.1fj3uSES7cimWz_H

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