Longitudinal network data are increasingly available allowing researchers to model how networks evolve over time and make inference on their dependence structure. In this paper, the dynamic latent space approach of Sewell and Chen (2015) 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 dataset: the unsecured and the secured (repo) interbank lending networks. We show that the model which 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 and reciprocity that the model without a latent space is not able to.