Dynamic Interbank Network Analysis Using Latent Space Models

Arbeitspapier

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model, each node has an unobserved temporal trajectory in a low-dimensional Euclidean space. Model parameters and latent banks' positions are estimated within a Bayesian framework. We apply this methodology to analyze two different datasets: the unsecured and the secured (repo) interbank lending networks. We show that the model that incorporates a latent space performs much better than the model in which the probability of a tie depends only on observed characteristics; the latent space model is able to capture some features of the dyadic data such as transitivity that the model without a latent space is not able to.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 17-101/II

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Network Formation and Analysis: Theory ; Banks; Depository Institutions; Micro Finance Institutions; Mortgages
• Subject: network dynamics ; latent position model ; interbank network ; Bayesian inference

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Linardi, Fernando ; Diks, Cees ; van der Leij, Marco (M.J.) ; Lazier, Iuri
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/177669
• Last update: 10.03.2025, 11:44 AM CET