Dynamic Interbank Network Analysis Using Latent Space Models

Fernando Linardi (), Cees (C.G.H.) Diks (), Marco van der Leij and Iuri Lazier ()
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Fernando Linardi: University of Amsterdam, The Netherlands; Central Bank of Brazil, Brazil
Cees (C.G.H.) Diks: University of Amsterdam, The Netherlands; Tinbergen Institute, The Netherlands
Iuri Lazier: Central Bank of Brazil, Brazil

No 17-101/II, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: network dynamics; latent position model; interbank network; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 D85 G21 (search for similar items in EconPapers)
Date: 2017-10-26
New Economics Papers: this item is included in nep-ecm and 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 (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20170101

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