We investigate the structural dependencies in the bank-firm credit market of Spain under a multilayer network perspective. In particular, the original bipartite network is decomposed into different layers representing different industrial sectors. We then study the correlations between layers based on normalized measures of overlaps of links and weights of banks between layers. To assess the statistical significance of such correlations, we compare the observed values with the expected ones obtained from random graph models specifying only global constraints, i.e. the total degree or the total strength in single layers, and from configuration models capturing the intrinsic heterogeneity in the local constraints like the observed degree sequence and/or strength sequence in single layers. We find that, first, the raw dependencies between layers of the observed network are highly heterogeneous. Second, when evaluated against the null models, on the one hand, the rescaled correlations after filtering out the effects of the global constraints typically display no significant difference to the observed correlations. In addition, in the binary version, almost all correlations are still present after subtracting the effects of the observed degree sequences in all layers. On the other hand, the observed correlations are partially explained by the local constraints maintained in the weighted configuration models. All in all, comparing the observed network with all referenced null models, we find that the multilayer credit network under scrutiny has a significant, non-random structure of correlations that cannot be explained by more primitive network properties alone. In the binary case, such a non-random structure is, for instance, typically observed in the pairs of layers that have high levels of overlaps and correlations. In contrast, in the weighted case, patterns are found in different pairs of layers that have various levels of overlaps and correlations.

We study how network structure affects the dynamics of collateral in presence of rehypothecation. We build a simple model wherein banks interact via chains of repo contracts and use their proprietary collateral or re-use the collateral obtained by other banks via reverse repos. In this framework, we show that total collateral volume and its velocity are affected by characteristics of the network like the length of rehypothecation chains, the presence or not of chains having a cyclic structure, the direction of collateral flows, the density of the network. In addition, we show that structures where collateral flows are concentrated among few nodes (like in core-periphery networks) allow large increases in collateral volumes already with small network density. Furthermore, we introduce in the model collateral hoarding rates determined according to a Value-at-Risk (VaR) criterion, and we then study the emergence of collateral hoarding cascades in different networks. Our results highlight that network structures with highly concentrated collateral flows are also more exposed to large collateral hoarding cascades following local shocks. These networks are therefore characterized by a trade-off between liquidity and systemic risk. This paper investigates the collateral dynamics when banks are connected in a network of financial contracts and they have the ability to rehypothecate the collateral along chains of contracts.

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We propose a novel approach to investigate the synchronization of business cycles and we apply it to a Eurostat database of manufacturing industrial production time-series in the European Union (EU) over the 2000-2017 period. Our approach exploits Random Matrix Theory and extracts the latent information contained in a balanced panel data by cleaning it from possible spurious correlation. We employ this method to study the synchronization among different countries over time. Our empirical exercise tracks the evolution of the European synchronization patterns and identifies the emergence of synchronization clusters among different EU economies. We find that synchronization in the Euro Area increased during the first decade of the century and that it reached a peak during the Great Recession period. It then decreased in the aftermath of the crisis, reverting to the levels observable at the beginning of the 21st century. Second, we show that the asynchronous business cycle dynamics at the beginning of the century was structured along a East-West axis, with eastern European countries having a diverging business cycle dynamics with respect to their western partners. The recession brought about a structural transformation of business cycles co-movements in Europe. Nowadays the divide can be identified along the North vs. South axis. This recent surge in asynchronization might be harmful for the European Unio because it implies countries' heterogeneous responses to common policies.

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