Dynamic interbank network analysis using latent space models

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

Journal of Economic Dynamics and Control, 2020, vol. 112, issue C

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; in particular, the latent space model is able to capture the core-periphery structure of financial networks quite well, whereas the model without a latent space is unable to do so.

Keywords: Network dynamics; Latent space model; Interbank network; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 D85 G21 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Dynamic Interbank Network Analysis Using Latent Space Models (2018)
Working Paper: Dynamic Interbank Network Analysis Using Latent Space Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:112:y:2020:i:c:s0165188919301897

DOI: 10.1016/j.jedc.2019.103792

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