Linardi, F., Diks, C., van der Leij, M. and Lazier, I. (2020). Dynamic interbank network analysis using latent space models Journal of Economic Dynamics and Control, 112:.

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{\textquoteright} positions are esti- mated 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.