Interconnectedness, G-SIBs and network dynamics of global banking

Bongini, Paola; Clemente, Gian Paolo; Grassi, Rosanna

doi:10.1016/j.frl.2018.03.002

Cited by 26 publications

(17 citation statements)

References 19 publications

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“…As in [7], [21] and [27] we designed a global banking network using the Bank for International Settlements (BIS) consolidated statistics, which measure bank exposures to different countries. These statistics capture worldwide-consolidated claims of internationally active banks headquartered in BIS reporting countries.…”

Section: Numerical Analysismentioning

confidence: 99%

“…In line with [7], [27] and [31], this paper deals with the systemic risk assessment through the analysis of the clustering coefficient of a network.We move from a way to formalize a concept of community more general and informative than the standard clustering coefficient. In fact, communities might be appreciated not only at the level of adjacent nodes, but also by considering nodes which are at the periphery of the network.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the network structure of national interbank markets has been studied, at the global level, using the Bank of International Settlements (BIS) data set. Systemic risk is mainly related to the interbank context and the considered empirical data allow to provide meaningful insights also to this type of literature (see, for instance, [7], [18], [21], [20], [26], [27]).…”

Section: Introductionmentioning

confidence: 99%

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Systemic risk assessment through high order clustering coefficient

2020

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In this article we propose a novel measure of systemic risk in the context of financial networks.To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in [10]. Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.

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Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systemic risk assessment through high order clustering coefficient

2020

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“…Following previous contributions in the quantitative finance literature, we have retrieved the empirical quarterly data from the Bank for International Settlements (BIS) database (see e.g. Bongini et al 2018;Cerqueti et al 2020;Cinelli et al 2021;Demir and Onder 2019;Giudici and Spelta 2016;Minoiu and Reyes 2013;Minoiu et al 2015). The BIS is owned by 62 central banks covering about 95% of the world's GDP (BIS database www.bis.org).…”

Section: Introductionmentioning

confidence: 99%

Financial interbanking networks resilience under shocks propagation

et al. 2022

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The concept of resilience-i.e., the ability of a unified structure to absorb shocks-is of high relevance in the context of network modelling and analysis, mainly when referred to finance. This paper starts from this premise, and deals with the resilience of a financial interbanking system. At this aim, we firstly introduce a new measure of the resilience of a network, by taking into full consideration the influence of the topology of the network and the weights of its links in the shocks propagation; then, we build one financial network model related to the quarterly-based interbanking sector, whose weights are calibrated on high quality empirical data; lastly, we compute the resilience measure of the considered networks. A discussion of the results is provided, by considering both finance and network theory perspectives.

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“…Introduction. Modern economic and financial systems are characterized by a vast collection of interacting agents [1,6,12,37,47,55]. In economic systems, for instance, the interdependence among entities characterizes the trade and exchange of goods in nonanonymous markets as well as in risk-sharing agreements in developing countries [1].…”

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confidence: 99%

Risk-Dependent Centrality in Economic and Financial Networks

Bartesaghi¹,

Benzi²,

Clemente³

et al. 2020

SIAM J. Finan. Math.

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Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node ``importance"" produced not only by the variation of the topology of the system but also as a consequence of the external levels of risk to which the network as a whole is subjected. Starting from the ``Susceptible-Infected"" (SI) model of epidemics and its relation to the communicability functions of networks, we develop a series of risk-dependent centralities for nodes in (financial and economic) networks. We analyze here some of the most important mathematical properties of these risk-dependent centrality measures. In particular, we study the newly observed phenomenon of ranking interlacement, by means of which two entities may interlace their ranking positions in terms of risk in the network as a consequence of the change in the external conditions only, i.e., without any change in the topology. We test the risk-dependent centralities by studying two realworld systems: the network generated by collecting assets of the S\&P 100 and the corporate board network of the U.S. top companies, according to Forbes in 1999. We found that a high position in the ranking of the analyzed financial companies according to their risk-dependent centrality corresponds to companies more sensitive to the external market variations during the periods of crisis.

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Interconnectedness, G-SIBs and network dynamics of global banking

Cited by 26 publications

References 19 publications

Systemic risk assessment through high order clustering coefficient

Systemic risk assessment through high order clustering coefficient

Financial interbanking networks resilience under shocks propagation

Risk-Dependent Centrality in Economic and Financial Networks

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