Deep Hedging is a simple machine learning inspired algorithm, which produces risk management strategies, which is built upon a given market environment and a given preference structure. In this talk we introduce a novel algorithm inspired by Moritz Duembgen’s and Chris Rogers’ Bayesian approach, which can additionally deal with
model uncertainty. We discuss a machine learning implementation of it, and provide a universality proof when it can be successful (joint work with Matteo Gambara and Thorsten Schmidt).
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Meeting ID: 987 7508 3754