A network model of the interbank market

Li, Shouwei; He, Jie; Zhuang, Yaming

doi:10.1016/j.physa.2010.08.057

Cited by 17 publications

(9 citation statements)

References 40 publications

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“…Gai and Kapadia [9] develop a model of contagion using the network approach and assess the fragility of the financial system, depending on the degree of connectivity, and the liquidity of the market. Li et al [10] introduce a network model and show that simulation of their model replicates features of real interbank networks such as low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of outdegree and in-degree. Soramaki et al [11] investigate the network topological properties of Fedwire funds service and argue that network is scale-free and characterized by low average path length and low connectivity.…”

Section: Introductionmentioning

confidence: 99%

Network centrality measures and systemic risk: An application to the Turkish financial crisis

Kuzubaş

Omercikoglu

Saltoğlu

2014

Physica A: Statistical Mechanics and its Applications

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Section: Introductionmentioning

confidence: 99%

Network centrality measures and systemic risk: An application to the Turkish financial crisis

Kuzubaş

Omercikoglu

Saltoğlu

2014

Physica A: Statistical Mechanics and its Applications

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“…A topological analysis of money market flows logged in the Danish large-value payment system (Kronos) in 2006 was reported in [18], where customer-driven transactions are compared with the bank-driven ones. Empirical network studies have been used to guide the development of a network model of the interbank market based on the interbank credit lending relationships [19].Establishing basic topological features of interbank networks is essential for understanding these complex…”

mentioning

confidence: 99%

Loan and nonloan flows in the Australian interbank network

Sokolov¹,

Webster²,

Melatos³

et al. 2012

Physica A: Statistical Mechanics and its Applications

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High-value transactions between banks in Australia are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments and transfers sourced from the SWIFT payment delivery system, the Austraclear securities settlement system, and the interbank transactions entered directly into RITS. In this paper, we analyse a dataset received from the Reserve Bank of Australia that includes all interbank transactions settled in RITS on an RTGS basis during five consecutive weekdays from 19 February 2007 inclusive, a week of relatively quiescent market conditions. The source, destination, and value of each transaction are known, which allows us to separate overnight loans from other transactions (nonloans) and reconstruct monetary flows between banks for every day in our sample. We conduct a novel analysis of the flow stability and examine the connection between loan and nonloan flows. Our aim is to understand the underlying causal mechanism connecting loan and nonloan flows. We find that the imbalances in the banks' exchange settlement funds resulting from the daily flows of nonloan transactions are almost exactly counterbalanced by the flows of overnight loans. The correlation coefficient between loan and nonloan imbalances is about −0.9 on most days. Some flows that persist over two consecutive days can be highly variable, but overall the flows are moderately stable in value. The nonloan network is characterised by a large fraction of persistent flows, whereas only half of the flows persist over any two consecutive days in the loan network. Moreover, we observe an unusual degree of coherence between persistent loan flow values on Tuesday and Wednesday. We probe static topological properties of the Australian interbank network and find them consistent with those observed in other countries.

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“…For instance, Li et al [13] put forward 2 Discrete Dynamics in Nature and Society Table 1: Initial data statistics. 2008 2009 2010 2011 2012 2013 2014 2015 2016 Number of banks 74 100 108 113 145 171 209 236 283 Number of firms 344 386 374 380 684 936 1071 1175 1199 Number of loans 788 951 957 988 1828 3285 4503 5330 6024 an interbank network based on interbank credit lending relationships and it generates some network features including a low clustering coefficient, a relatively short average path length, a community structure, and a two-power-law distribution of degree.…”

Section: Introductionmentioning

confidence: 99%

Co-Loan Network of Chinese Banking System Based on Listed Companies’ Loan Data

et al. 2018

Discrete Dynamics in Nature and Society

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Based on the loan data of Chinese listed companies from 2008 to 2016, this paper constructs a co-loan network of the Chinese banking system and analyzes the topological structures and corresponding evolvement characteristics from the perspective of complex network. Through the empirical studies, we find that the co-loan network always displays a core-periphery structure; for example, ten banks including four state banks and six large commercial banks are always in the core region of the Chinese banking system for nine consecutive years. Furthermore, the co-loan network is a small-world network lasting for nine years.

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A network model of the interbank market

Cited by 17 publications

References 40 publications

Network centrality measures and systemic risk: An application to the Turkish financial crisis

Network centrality measures and systemic risk: An application to the Turkish financial crisis

Loan and nonloan flows in the Australian interbank network

Co-Loan Network of Chinese Banking System Based on Listed Companies’ Loan Data

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