Gogas, P.; Papadimitriou, T.; Matthaiou, M.-A. Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS). J. Risk Financial Manag.2022, 15, 253.
Gogas, P.; Papadimitriou, T.; Matthaiou, M.-A. Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS). J. Risk Financial Manag. 2022, 15, 253.
Gogas, P.; Papadimitriou, T.; Matthaiou, M.-A. Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS). J. Risk Financial Manag.2022, 15, 253.
Gogas, P.; Papadimitriou, T.; Matthaiou, M.-A. Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS). J. Risk Financial Manag. 2022, 15, 253.
Abstract
The global financial crisis of 2008, triggered by the collapse of Lehman Brothers, highlighted a banking system that was widely exposed to systemic risk. The minimization of the systemic risk via a close and detailed monitoring of the entire banking network became a priority. This is a complex and demanding task considering the size of the banking systems: in the US and the EU they include more than 10000 institutions. In this paper, we introduce a methodology which identifies a subset of banks that can: a) efficiently represent the behavior of the whole banking system and b) provide, in the case of a failure, a plausible range of the crisis dispersion. The proposed methodology can be used by the regulators as an auxiliary monitoring tool, to identify groups of banks that are potentially in distress and try to swiftly remedy their problems and minimize the propagation of the crisis by restricting contagion. This methodology is based on Graph Theory and more specifically Complex Networks. We termed this setting a “multivariate Threshold – Minimum Dominating Set” (mT–MDS) and it is an extension of the Threshold – Minimum Dominating Set methodology (Gogas e.a., 2016). The method was tested on a dataset of 570 U.S. banks: 429 solvent and 141 failed ones. The variables used to create the networks are: the total interest expense, the total interest income, the tier 1 (core) risk-based capital and the total assets. The empirical results reveal that the proposed methodology can be successfully employed as an auxiliary tool for the efficient supervision of a large banking network.
Copyright:
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