Network Risk and Key Players: A Structural Analysis of Interbank Liquidity

Denbee, Edward; Julliard, Christian; Li, Ye; Yuan, Ke‐Hai

doi:10.2139/ssrn.2884461

Cited by 36 publications

(28 citation statements)

References 63 publications

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“…In addition, (13) states that the level of exposure to a firm at minimal distance is the same as that to a direct shock, while (14) says that the exposure becomes vanishing small when the distance to the source of the shock is maximal in the component. Conditions (12), (13), and (14), together with the adding-up constraint (10) on the total fraction of the risk externalized by firms, capture the essential features displayed by the exposure function in the discrete setup.…”

Section: The Continuum Approximationmentioning

confidence: 99%

“…They take this measure, which takes into account the feedback effect that failures can have on neighbors at different distances, and estimate its value for US data. Similarly, Denbee et al (2011) propose a measure of centralityá la Katz-Bonacich, following Ballester, Calvó-Armengol and Zenou (2006), and apply it to English data. Of particular interest in this respect is Elsinger, Lehar and Summer (2011) who, using Austrian data, show that correlation in banks' asset portfolios is the main source of systemic risk.…”

Section: Introductionmentioning

confidence: 99%

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Risk Sharing and Contagion in Networks

Cabrales

Gottardi

Vega‐Redondo

2017

The Review of Financial Studies

175

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The aim of this paper is to investigate how the capacity of an economic system to absorb shocks depends on the specific pattern of interconnections established among financial firms. The key trade-off at work is between the risk-sharing gains enjoyed by firms when they become more interconnected and the large-scale costs resulting from an increased risk exposure. We focus on two dimensions of the network structure: the size of the (disjoint) components into which the network is divided, and the "relative density" of connections within each component. We find that when the distribution of the shocks displays "fat" tails extreme segmentation is optimal, while minimal segmentation and high density are optimal when the distribution exhibits "thin" tails. For other, less regular distributions intermediate degrees of segmentation and sparser connections are also optimal. We also find that there is typically a conflict between efficiency and pairwise stability, due to a "size externality" that is not internalized by firms who belong to components that have reached an individually optimal size. Finally, optimality requires perfect assortativity for firms in a component.JEL Classification: D85, C72, G21.

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Section: The Continuum Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Sharing and Contagion in Networks

Cabrales

Gottardi

Vega‐Redondo

2017

The Review of Financial Studies

175

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“…First, we contribute to the applied spatial econometrics literature. Spatial models are widely used in applied geographic and regional science studies, and have recently also been applied in empirical finance; see Fernandez (2011) for a CAPM model augmented by spatial dependencies, Wied (2013), Arnold et al (2013), and Asghar-ian et al (2013) for analyses of spatial dependencies in stock markets, Denbee et al (2013) for a network approach to assess interbank liquidity, and Saldias (2013) for a spatial error model to identify sector risk determinants. The study closest to ours is Keiler and Eder (2013).…”

Section: Introductionmentioning

confidence: 99%

Spillover dynamics for systemic risk measurement using spatial financial time series models

Blasques

Koopman

Schaumburg

2016

Journal of Econometrics

106

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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. Terms of use: Documents in AbstractWe introduce a new model for time-varying spatial dependence. The model extends the wellknown static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the static parameters is consistent and asymptotically normal. We also study the information theoretic optimality of the updating steps for the time-varying spatial dependence parameter. We adopt the model to empirically investigate the spatial dependence between eight European sovereign CDS spreads over the period [2009][2010][2011][2012][2013][2014], which includes the European sovereign debt crisis. We construct our spatial weight matrix using cross-border lending data and include country-specific and Europe-wide risk factors as controls. We find a high, time-varying degree of spatial spillovers in the sovereign CDS spread data. There is a downturn in spatial dependence after the first half of 2012, which is consistent with policy measures taken by the European Central Bank. The findings are robust to a wide range of alternative model specifications.

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“…Many other behaviors are mediated through prices, but in a way that it matters how agents are in contact with each other. Production and financial networks are natural examples (e.g., Atalay, Hortacsu, Roberts, and Syverson (2011) for the first and Denbee, Julliard, Li, and Yuan (2014) or Bonaldi, Hortacsu, and Kastl (2014) for the second). The main conduit in these examples is the intervening role of "connections": who is in direct or indirect contact with whom.…”

Section: Introductionmentioning

confidence: 99%

Econometrics of network models

Paula¹

2016

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In this article I provide a (selective) review of the recent econometric literature on networks. I start with a discussion of developments in the econometrics of group interactions. I subsequently provide a description of statistical and econometric models for network formation and approaches for the joint determination of networks and interactions mediated through those networks. Finally, I give a very brief discussion of measurement issues in both outcomes and networks. My focus is on identification and computational issues, but estimation aspects are also discussed.

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Network Risk and Key Players: A Structural Analysis of Interbank Liquidity

Cited by 36 publications

References 63 publications

Risk Sharing and Contagion in Networks

Risk Sharing and Contagion in Networks

Spillover dynamics for systemic risk measurement using spatial financial time series models

Econometrics of network models

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