A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
This work addresses network formation in financial markets, specifically the interbank market, but is incremental as it builds on existing dynamic network modeling approaches.
The authors tackled the problem of modeling dynamic networks by proposing a model that incorporates persistent links and node-specific latent variables, applied to the e-MID interbank market to forecast links and analyze trading behaviors, showing that ignoring time-varying topologies overestimates preferential linkage.
We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognise preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.