Since the 2008 economic crisis, network research has become increasingly prominent in the world of finance. The complex interrelations and financial interdependencies formed among financial market participants have proved to be critical in times of crisis. In this paper, we explore the network properties of the Hungarian RTGS (VIBER) and also seek an answer to the question of whether the network properties of the system have changed over the long term across the time windows considered, and if so, to what extent. Furthermore, we identify systemically important participants using a variety of network theory tools. We also explore methodologies which – by providing new perspectives for monitoring the evolution of systemically important participants – may contribute to improving the effectiveness of oversight in Hungary. To identify systemically important participants, we apply four methodologies, namely: the LSI index, the model capturing the relation of eigenvector and betweenness, diffusion centrality, and the model exploring the effect of combining multiple nodes. In the Hungarian RTGS, two distinct groups emerge: the first is comprised of participants that play a key role in the transmission of liquidity (“core”), and the other is the cluster of periphery participants. As the composition of the core has remained virtually unchanged, it can be considered stable. While the risk of contagion arising from an operational disruption increased at both the individual and aggregated level during the period under review, it is also apparent that no such link exists in the graph the removal of which would ultimately cut the communication between the banks originally connected by it. The results of each indicator showed that there were no significant changes regarding the network properties across the three time windows, confirming the robustness of the network properties of the Hungarian RTGS and its stability over time.  

JEL codes: D85, E42, E5, G2, G21, L14.

Keywords: Hungarian RTGS (VIBER), network research, financial networks, graph theory, topology, centrality indices, systemically important financial institutions (SIFI).