Identifying patterns in the bank–sector credit network of Spain

Luu, Duc Thi; Lux, Thomas

doi:10.21314/jntf.2018.037

Cited by 2 publications

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“…Since the one-mode projection network is always less informative than the original bipartite network (e.g. Lehmann et al, 2008;Luu and Lux, 2016) we believe that further research considering the bipartite structure in each layer may provide a more comprehensive analysis of the role of the different fundamental constraints in the multilayer structure of real bank-firm credit networks. In that research direction, the null models defined for bipartite networks should be employed (e.g.…”

Section: Discussionmentioning

confidence: 99%

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Multilayer overlaps and correlations in the bank-firm credit network of Spain

Luu

Lux

2019

Quantitative Finance

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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.

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Section: Discussionmentioning

confidence: 99%

“…In that research direction, the null models defined for bipartite networks should be employed (e.g. see Saracco et al, 2015;Di Gangi et al, 2015;Luu and Lux, 2016). in ensembles of weighted multiplex networks.…”

Section: Discussionmentioning

confidence: 99%

Multilayer overlaps and correlations in the bank-firm credit network of Spain

Luu

Lux

2019

Quantitative Finance

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“…A major disadvantage of the network projection approach is that the information about the monetary value of loans from banks to firms is partially or completely discarded (e.g. see Lehmann et al 2008;Luu and Lux 2018). In addition, often sparser information about the original bipartite structure of credit links is projected into denser one-mode networks.…”

Section: Representation Of a Bank-firm Network Portfolio Overlaps And Projection Matricesmentioning

confidence: 99%

Portfolio Correlations in the Bank-Firm Credit Market of Japan

Luu¹

2021

Comput Econ

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The recent global financial crisis has shown portfolio correlations between agents as one of the major channels of risk contagion and amplification. In this work, we analyse the structure and dynamics of the cross-correlation matrix of banks’ loan portfolios in the yearly bank-firm credit network of Japan during the period from 1980 to 2012. Using the methods of Random Matrix Theory (RMT), Principal Component Analysis and complex networks, we aim to detect non-random patterns in the empirical cross-correlations as well as to identify different states of such correlations over time. Our findings suggest that although a majority of portfolio correlations between banks in lending relations to firms are contributed by noise, the top largest eigenvalues always deviate from the random bulk explained by RMT, indicating the presence of non-random patterns governing the correlation dynamics. In particular, we show that this dynamics is mainly driven by a global common factor and a couple of “groups” factors. Furthermore, different states in the credit market can be identified based on the evolution of eigenvalues and associated eigenvectors. For example, during the asset price bubble period in Japan from 1986 to 1991, we find that banks’ loan portfolios tend to be more correlated, showing a significant increase in the level of systemic risk in the credit market. In addition, building Planar Maximally Filtered Graphs from the correlations of different eigenmodes, notably, we observe that the local interaction structure between banks changes in different periods. Typically, when the dominance of a group of banks in one period gradually vanishes, the credit market starts to build-up a different structure in the next period in which another group of banks will become the main actors in the backbone of the cross-correlations.

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Identifying patterns in the bank–sector credit network of Spain

Cited by 2 publications

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Multilayer overlaps and correlations in the bank-firm credit network of Spain

Multilayer overlaps and correlations in the bank-firm credit network of Spain

Portfolio Correlations in the Bank-Firm Credit Market of Japan

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