Learning from Lending in the Interbank Network
Data Science in Science
Empirical analysis of a major overnight-funding network of European banks shows that, when liquidity disruptions occur in a part of the network, lending banks in other parts of the network broaden their cohorts of borrowers in the part of the network that is subject to the disruptions. Measures of this broadening are useful new statistics for the amount of information conveyed from one part of the network to another. In our setting, we call this broadening “counterparty sampling,” and present evidence that it improves the network’s stock of information about future interest rates. By comparing to linkages forecast by an LSTM deep learning model for counterparty linkages, we find that the extent of surprising new linkages predicts lower future rates. Our evidence supports the idea that interbank funding networks provide benefits of learning and information aggregation, and our measures suggest new ways of looking at sparse networks with stable structures but dynamically-changing linkages.
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited. This article was originally published in Data Science in Science. The version of record is available at: https://doi.org/10.1080/26941899.2022.2151949
entropy, information, network statistics, interbank network, LIBOR, overnight funds
Paul Laux, Wei Qian & Haici Zhang (2023) Learning from Lending in the Interbank Network, Data Science in Science, 2:1, DOI: 10.1080/26941899.2022.2151949