Abstract
Since the financial crisis of 2008, the network analysis of financial systems has attracted a lot of attention. In this paper, we analyze the global banking network via the method of Random Matrix Theory. By applying that method on a cross border lending network, it is shown that while the connectivity between different parts of the network has risen and the profile of transactions has diversified, the role of hubs remains important in the weighted perspective. The largest eigenvalue of the transaction matrix as the leading mode of the system shows sharp growth since 2002. As well, it is observed that its growth has diminished since 2008. This indicates that the crisis of 2008 has left a long-lasting footprint on the financial system. Analyzing the mean value of the participation ratio reveals the fact that the role of countries in forming small modes, has increased since 2002. In our final analysis, we provide snapshots of the hubs in the network over time. We observe that the share of countries in total transactions is not equal to their share in shaping the eigenvector of the largest eigenvalue. In 2018 for example, while the United Kingdom leads the share of transactions, it is the United States that has the largest value in the leading eigenvector. The proposed technique in the paper can be useful for analyzing different types of interaction networks between countries.
Highlights
Since the recent global financial crisis, cross-border lending and financial contagions have gained in importance
Random Matrix Theory has been presented by some scholars in nuclear physics such as Mehta [55, 56], for analyzing the energy levels of complex quantum systems
We know that the eigenvalues–in the real matrix–which deviate from the range of the eigenvalues–in the random matrix–possess relatively more complete information from the system [51, 58, 59]
Summary
Since the recent global financial crisis, cross-border lending and financial contagions have gained in importance. The propagated effects [1, 2] of financial crises on political and economic systems [3, 4] are not to be underestimated. Those developments have prompted a lot of research on the systemic dependence of the international banking sector [5,6,7,8,9,10,11,12,13]. Complex networks are useful instruments for describing a large number of financial systems [23,24,25,26,27,28,29,30,31]. Most of the networks have different topological properties such as small-world and scale-free characteristics [24,25,26, 32,33,34,35,36,37,38,39]
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