Abstract: Consistent Recalibration models (CRC) have been introduced to capture in necessary generality the dynamic features of term structures of derivatives’ prices. Several approaches have been suggested to tackle this problem, but all of them, including CRC models, suffered from numerical intractabilities mainly due to the presence of complicated drift terms or consistency conditions. We overcome this problem by machine learning techniques, which allow to store the crucial drift term’s information in neural network type functions. This yields dynamic term structure models which can be efficiently simulated.

As an alternative to these classical (modeling) approaches for constraint dynamics we shall also introduce a regression technique, based on signature or randomized signature, to encode the dynamics of volatility surfaces or more general term structures. This is based on joint work with Matteo Gambara.

Bio: Josef Teichmann is a professor for Mathematical Finance at ETH Zurich since 2009. He holds a PhD from Vienna University in the area of infinite dimensional geometry from 1999 and has worked as an Associate Professor at TU Vienna from 2002 to 2009. His research interests lie in Stochastic Finance, Stochastic Partial Differential Equations, Rough Analysis and Machine Learning.