Cross-border interbank networks, banking risk and contagion

Tonzer, Lena

doi:10.1016/j.jfs.2015.02.002

Cited by 111 publications

(93 citation statements)

References 55 publications

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“…In this enterprise, we take inspiration from a recent strand of spatial econometrics literature that includes finance-based weight matrices in their analysis (Keiler and Eder (2013), Blasques et al (2016), Tonzer (2015), and Billio et al (2017)), and starts to introduce the network terminology in the discussion. However, building on Caporin and Paruolo (2015) differentiates us from the latter papers.…”

Section: A Model For Spatial Econometrics Of Networked Riskmentioning

confidence: 99%

“…Previous applications of spatial methods to financial markets average these time-varying relationships, reducing to a static framework (see, as an example, Tonzer (2015)). This contribution is not completely new; however, we are the first to embed the time-varying nature of financial proximity within a conditional covariance model, going beyond the static weight matrix used in Caporin and Paruolo (2015).…”

Section: A Model For Spatial Econometrics Of Networked Riskmentioning

confidence: 99%

“…The second ingredient is our consideration of the weight matrix as a directed network. Directedness is not present in classical approaches, but is already considered in Billio et al (2017);Tonzer (2015) and Blasques et al (2016).…”

Section: A Model For Spatial Econometrics Of Networked Riskmentioning

confidence: 99%

“…The intersection and interaction of different research fields, such as network science, spatial econometrics, economics, and finance, give rise to new developments and tools. In particular, one of the most advanced strands of the spatial econometrics literature (see Keiler and Eder (2013); Blasques et al (2016); Caporin and Paruolo (2015); Tonzer (2015); Billio et al (2017)) focuses on statistical, economic, and financial relationships. Our work belongs to this strand of the literature and aims at building a bridge between contagion, volatility spillover, and networks.…”

Section: Introductionmentioning

confidence: 99%

“…This is particularly important in applications since, as already remarked, we are not only able to obtain the statistical significance of a given channel, as in Tonzer (2015), but also to distinguish the importance of different nodes by using diagonal matrix parameters. Moreover, the use of directed networks (i.e., asymmetric proximity) allows us to weight the transmission channel according to their direction, and we recover information on the statistically relevant channels of contagion, resulting in a better understanding of risk circulation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Networks in Risk Spillovers: A Multivariate GARCH Perspective

et al. 2018

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We propose a spatiotemporal approach for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and introduce a new bilateral specification. We study covariance stationarity and identification of the model, and analyze consistency and asymptotic normality of the quasi-maximum-likelihood estimator. We show how to isolate risk channels and we discuss how to compute target exposure able to reduce system variance. An empirical analysis on Euro-area cross-country holdings shows that Italy and Ireland are key players in spreading risk, France and Portugal are the major risk receivers, and we uncover Spain's non-trivial role as risk middleman.

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Section: A Model For Spatial Econometrics Of Networked Riskmentioning

confidence: 99%

Section: A Model For Spatial Econometrics Of Networked Riskmentioning

confidence: 99%

Section: A Model For Spatial Econometrics Of Networked Riskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Networks in Risk Spillovers: A Multivariate GARCH Perspective

et al. 2018

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Risk contagion in the cross‐border banking network: Some new evidence

Chen

Peng

et al. 2019

Int. J Fin Econ

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Project (12YJC790004), the Shanghai Education Commission Research and Innovation Key Project (12ZS184) and the Shanghai Young Eastern Scholar (QD2015049) are acknowledged. The data used here are with the permission of the Bank for International Settlement (BIS). Neither the original collectors of the data nor distributors bear any responsibility for the analyses or interpretations presented here. We are also grateful to the anonymous reviewer for providing helpful comments which materially improved quality and presentation this article. However, all remaining errors are our own.

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Basel accords and banking inefficiency: Evidence from the Italian local market

2022

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Using data for Italy from 1994 to 2015, we appraise the nexus between Basel II and III accords and both the cost and the profit inefficiency of profit‐oriented and mutual cooperative banks. Our analysis further deals with the effects of the regulation in different market structures. To assess the efficiency of the two types of banks under scrutiny, a stochastic frontier approach has been proposed. The evidence reported in this paper indicates that the regulatory framework of the Basel accords had heterogeneous effects on the efficiency of Italian banking institutions. Specifically, Basel II is associated with a contraction in the profit inefficiency and an increase in the cost inefficiency for mutual cooperative banks. On the other hand, Basel III worsened the profit inefficiency, though it is found to reduce the profit inefficiency of mutual cooperative banks, with no significant impact on cost inefficiency. Furthermore, once we control for the degree of market competition, the effects of the regulation are shown to be statistically insignificant. The advent of the 2008 recession and the application of the revenue inefficiency do not alter the main findings. There is also some evidence of spatial effects of the Basel II regulation at the LMA level.

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Cross-border interbank networks, banking risk and contagion

Cited by 111 publications

References 55 publications

Networks in Risk Spillovers: A Multivariate GARCH Perspective

Networks in Risk Spillovers: A Multivariate GARCH Perspective

Risk contagion in the cross‐border banking network: Some new evidence

Basel accords and banking inefficiency: Evidence from the Italian local market

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