The Multiplex Structure of Interbank Networks

Bargigli, Leonardo; Iasio, Giovanni di; Infante, Luigi; Lillo, Fabrizio; Pierobon, Federico

doi:10.2139/ssrn.2352787

Cited by 30 publications

(24 citation statements)

References 37 publications

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“…All the networks we consider show in fact dis-assortative behavior for all assortativity measures, the exception being the CMA network which has positive in-out degree assortativity. These results are in line with those observed in the data (see for instance Bech and Atalay (2008), Bargigli et al (2013) or Alves et al (2013)). Notice that dis-assortative behavior is associated with core-periphery structures; this is true both in the data and in our model (this is also visible by our graphs).…”

supporting

confidence: 93%

“…As noted by Bargigli et al (2013), interbank networks tend to be dis-assortative, implying that high-degree nodes tend to connect to other high-degree nodes less frequently than would be expected under the assumption of a random rewiring of the network that preserves the nodes' degrees. All the networks we consider show in fact dis-assortative behavior for all assortativity measures, the exception being the CMA network which has positive in-out degree assortativity.…”

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confidence: 98%

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Bank networks: Contagion, systemic risk and prudential policy

Aldasoro

Gatti

Faia

2017

Journal of Economic Behavior & Organization

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Banks' liquidity hoarding can account for a large part of loss propagation which occurs during banking crises. And different institutional environments in interbank markets can also contribute or on the contrary alleviate loss contagion (central clearing houses versus bilateral lending). We explore those issues in an interbank market that features risk averse banks, prone to liquidity hoarding in face of large uncertainty, banks' interconnections and fire sale externalities. We analyze the impact of different institutional arrangements by modeling alternative matching algorithms, namely maximum entropy, closest matching and random matching. Our banking network is well in line with empirical facts as it reproduces dis-assortative behavior, core-periphery structure and low clustering coefficients. We asses contagion through different systemic risk metrics, namely centrality measures, input-output metrics and Shapley values. We find that indeed banks' risk aversion plays an important role and that liquidity hoarding amplifies losses beyond the ones due to interconnections externalities.Given the realm of our model we test whether different regulatory policy can alleviate contagion. We find that liquidity requirements are better fit in addressing the policy trade-off between efficiency (measured as overall investment) and risk (measures by systemic risk metrics). Bank Networks: Contagion, Systemic Risk and Prudential Policy January 2015 AbstractWe present a network model of the interbank market in which optimizing risk averse banks lend to each other and invest in non-liquid assets. Market clearing takes place through a tâton-nement process which yields the equilibrium price, while traded quantities are determined by means of a matching algorithm. We compare three alternative matching algorithms: maximum entropy, closest matching and random matching. Contagion occurs through liquidity hoarding, interbank interlinkages and fire sale externalities. The resulting network configurations exhibits a core-periphery structure, dis-assortative behavior and low clustering coefficient. We measure systemic importance by means of network centrality and input-output metrics and the contribution of systemic risk by means of Shapley values. Within this framework we analyze the effects of prudential policies on the stability/efficiency trade-off. Liquidity requirements unequivocally decrease systemic risk but at the cost of lower efficiency (measured by aggregate investment in non-liquid assets); equity requirements tend to reduce risk (hence increase stability) without reducing significantly overall investment.

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supporting

confidence: 93%

mentioning

confidence: 98%

Bank networks: Contagion, systemic risk and prudential policy

Aldasoro

Gatti

Faia

2017

Journal of Economic Behavior & Organization

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“…In the financial economic literature network analysis has mostly been applied to payment systems, interbank lending markets, and more recently extended to capture the mutual exposure of financial institutions to other asset classes, including derivatives contracts, in a multilayer networks framework (Bargigli et al (2015), Leon et al (2014), Molina-Borboa et al (2015), Aldasoro and Alves (2015), Poledna et al (2015)). …”

Section: Network Centrality and Interbank Marketsmentioning

confidence: 99%

Network centrality and funding rates in the e-MID interbank market

Temizsoy

Iori

Montes‐Rojas

2017

Journal of Financial Stability

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This paper empirically investigates the role of banks' network centrality in the interbank market on their funding rates. Specifically we analyze transaction data from the e-MID market, the only electronic interbank market in the Euro Area and US, over the period [2006][2007][2008][2009] that encompasses the global financial crisis. We show that interbank spreads are significantly affected by both local and global measures of connectedness. The effects of network centrality increased as the financial crisis evolved. Local measures show that having more links increases borrowing costs for borrowers and reduces premia for lenders. For global network centrality, borrowers receive a significant discount if they increase their intermediation activity and become more central, while lenders pay in general a premium (i.e. receive lower rates) for centrality. This provides evidence of the 'too-interconnected-to-fail' hypothesis.

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“…Recent contributions are starting to fill this gap. Bargigli et al (2015) study the multiplex structure of interbank networks using Italian data broken down by maturity and by the nature of the contract involved (secured versus unsecured). They find that different layers present several topological and metric properties which are layer-specific, whereas other properties are of a more universal nature.…”

Section: Related Literaturementioning

confidence: 99%

“…Similarity analysis As noted by Bargigli et al (2015), it is important to distinguish between topological similarity and point-wise similarity, as one does not necessarily imply the other. For instance, two networks may be very similar in terms of density, degree distribution, etc., but the existence of a link between two nodes in the first network may be irrelevant to explain the existence of an analogous link in the second network.…”

Section: Critical Differences Across Layers Of the Banking Networkmentioning

confidence: 99%