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The recent financial crisis brought to the fore the relevance of interconnectedness in general, and in particular in interbank markets. Of critical importance in this context is the identification of the key players in the financial network. While early contributions on the topic have focused on aggregated exposures, it is now increasingly recognised that the web of reciprocal exposures linking bank balance sheets is more intricate and complex. Interbank networks are better characterised as multiplex networks, featuring connections at multiple levels.
In the present paper, authors analyse the multiplex structure of the network of large European banks, making use of a detailed dataset presenting exposures partitioned according to maturity and instrument type.
They find a high level of similarity between the different layers (both by instrument and maturity), a core periphery structure which comprises a large core (relative to studies using country-specific datasets), and positively correlated multiplexity. The results suggest that an institution’s role in the channel of transmission is critical in determining the global importance of such institution, and that the notion of importance may not be related to its traditionally studied core-periphery role. Nevertheless, key forms of non-decomposable (across layers) centrality measures are resilient across layers, indicating that centrality at the layer-level can be robust to the absence of granular data availability.
Authors develop two measures of systemic importance suited to the case in which banks are connected through an arbitrary number of different layers. This allows them to compute systemic importance indicators and decompose them into the contributions of the different layers, providing a holistic analysis that truly incorporates the multiplex structure of the network (instead of doing separate analyses for the different layers and the aggregate network) and is built directly from a consistent accounting representation of the system. Previous literature has justified the need to use granular data by noting that banks can rank differently in different layers in terms of systemic importance and thereby the focus on a single layer can be misleading.
They confirm that, even when centrality is persistent across layers, there is still important information to be obtained from granular data, in particular if one is able to decompose global systemic importance into layer-specific contributions. Nevertheless, they also show that when such granular data is not available, simple measures can be a good second best. Authors illustrate their measures with the dataset on exposures between large European banks, delving deeply into issues of interconnectedness across the various layers of an integrated accounting framework. The results suggest that their proposed measures can be useful tools for practical policy analysis.