Dynamic Interbank Network Analysis Using Latent Space Models

Fernando Linardi, Cees Diks, Marco van der Leij and Iuri Lazier

No 487, Working Papers Series from Central Bank of Brazil, Research Department

Abstract: 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.

Date: 2018-11
New Economics Papers: this item is included in nep-ict
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Related works:
Journal Article: Dynamic interbank network analysis using latent space models (2020)
Working Paper: Dynamic Interbank Network Analysis Using Latent Space Models (2017)
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