Endogenous Banks' Networks, Cascades and Systemic Risk

Bluhm, Marcel; Faia, Ester; Krahnen, Jan Pieter

doi:10.2139/ssrn.2235520

Cited by 49 publications

(61 citation statements)

References 32 publications

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“…Under mark-to-market accounting, the endogenous fall in market prices negatively affects other banks' balance sheets. Cifuentes et al (2005) and Bluhm et al (2013) also formalize this mechanism. Our model encompasses both views and shows that both are important to account for risk propagation.…”

Section: Related Literaturementioning

confidence: 90%

“…First, it contributes to the literature which tries to assess the trade-offs between risk sharing and risk propagation. Using an interbank network modelled as a credit chain, Allen and Gale (2000) show the existence of 8 See also Bluhm et al (2013) and Halaj and Kok (2014).…”

Section: Related Literaturementioning

confidence: 99%

“…This is the price corresponding to zero aggregate sales and banks fulfilling regulatory requirements. The occurrence of shocks to banks' non-liquid asset holdings may force them to put some of their stock of assets on the market 24 Bluhm et al (2013) employ this matching algorithm. In their model however banks are either borrowers or lenders and this simplifies the workings of the algorithm.…”

Section: Price Tâtonnement In the Market For Non-liquid Assetsmentioning

confidence: 99%

“…For brevity we decided to report the analytical description of the ex ante metrics and their performance in the numerical analysis in Appendix D. In the main text we focus on one ex post metric, the Shapley value (see Shapley (1953), and Bluhm et al (2013) and Drehmann and Tarashev (2011) for applications to banking), and we comment on the comparison with other ex ante metrics. The Shapley value, borrowed from the literature on cooperative and non-cooperative game theory, provides the contribution (through permutations) of each bank to an aggregate value.…”

Section: Systemic Risk and Systemic Importancementioning

confidence: 99%

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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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Section: Related Literaturementioning

confidence: 90%

Section: Related Literaturementioning

confidence: 99%

Section: Price Tâtonnement In the Market For Non-liquid Assetsmentioning

confidence: 99%

Section: Systemic Risk and Systemic Importancementioning

confidence: 99%

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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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“…is limited (Bluhm et al, 2013;Bluhm and Krahnen, 2014). Aymanns and Georg (2015) additionally argue that the vulnerability of the financial sector to common shocks is stronger if banks pursue similar investment strategies.…”

Section: Introductionmentioning

confidence: 99%

Interbank loans, collateral and modern monetary policy

Wolski

Leur

2016

Journal of Economic Dynamics and Control

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.

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An endogenous network model of banking systems

Xin

2016

Complexity

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In this article, we propose an endogenous network model for banking systems. Starting from the balance sheets of a banking system and its update algorithms, the interbank credit lending was established based on the partner selection mechanism. Through simulation analysis, we find that the endogenous network model displays a structure with multiple liquidity centers, that the in-degree distribution exhibits a Pareto tail, and that the bank asset distribution is a lognormal distribution with a Pareto tail.

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Endogenous Banks' Networks, Cascades and Systemic Risk

Cited by 49 publications

References 32 publications

Bank networks: Contagion, systemic risk and prudential policy

Bank networks: Contagion, systemic risk and prudential policy

Interbank loans, collateral and modern monetary policy

An endogenous network model of banking systems

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