2020
DOI: 10.48550/arxiv.2007.14447
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Analysis of the Global Banking Network by Random Matrix Theory

Abstract: Since 2008, the network analysis of financial systems is one of the most important subjects in economics. In this paper, we have used the complexity approach and Random Matrix Theory (RMT) for analyzing the global banking network. By applying this method on a cross border lending network, it is shown that the network has been denser and the connectivity between peripheral nodes and the central section has risen. Also, by considering the collective behavior of the system and comparing it with the shuffled one, … Show more

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Cited by 3 publications
(3 citation statements)
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“…Our recent work underscores the utility of edge-based curvature measures in analysis of networks of stocks [49] or global financial indices. In future, curvature measures may also find application in other financial networks including Banking networks [50]. (iii) The largest yet by no means exhaustive survey of network measures to identify potential network-centric indicators of fragility and systemic risk in the system of global financial market indices.…”
Section: Summary and Concluding Remarksmentioning
confidence: 99%
“…Our recent work underscores the utility of edge-based curvature measures in analysis of networks of stocks [49] or global financial indices. In future, curvature measures may also find application in other financial networks including Banking networks [50]. (iii) The largest yet by no means exhaustive survey of network measures to identify potential network-centric indicators of fragility and systemic risk in the system of global financial market indices.…”
Section: Summary and Concluding Remarksmentioning
confidence: 99%
“…The RMT is not only used for the stock markets but other financial entities. For example, Namaki et al 28 used RMT to analyze the global banking network. Zitelli 29 estimated the bias within the sample correlation.…”
mentioning
confidence: 99%
“…It helps to illuminate the difference between random and non-random information [18,[60][61][62]. RMT has been applied extensively in the investigation of time series of financial markets and is one of the im-mensely used methods for studying the correlations in stocks [4,18,20,25,31,34,36,[60][61][62][63][64][65][66][67][68][69][70][71][72]. Analyzing the properties of the cross-correlation matrix (C) on several stock markets was demonstrated to agree with RMT predictions whose elements are uncorrelated [18,60,62].…”
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confidence: 99%