Year: 2020
Type: preprint
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 first time dynamic term structure models which can be efficiently simulated.
Authors: Gambara, Matteo, Teichmann, Josef
Institution: ETH Zurich
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3036061692