Cover Image

Dynamic Interbank Network Analysis Using Latent Space Models

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
Year of publication:
2017
Authors: Linardi, Fernando de Menezes
Other Persons: Diks, Cees G. H. (contributor) ; Leij, Marco van der (contributor) ; Lazier, Iuri (contributor)
Publisher: [2017]: [S.l.] : SSRN
Subject: Theorie | Theory | Interbankenmarkt | Interbank market | Netzplantechnik | Project network techniques | Brasilien | Brazil
Extent:
1 Online-Ressource (38 p)
Series:
Type of publication: Book / Working Paper
Language: English
Notes:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 14, 2017 erstellt
Other identifiers:
10.2139/ssrn.3059618 [DOI]
Classification: C11 - Bayesian Analysis ; D85 - Network Formation ; G21 - Banks; Other Depository Institutions; Mortgages
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://ebvufind01.dmz1.zbw.eu/10012944864
Similar items by subject
Similar items by person