Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes

Finger, Karl; Fricke, Daniel; Lux, Thomas

doi:10.1007/s10287-013-0171-9

“…Finger et al (2012) using bank transactions from the Italian payments system …nd that when they use daily data the network is random, however, when they move to quarterly frequencies the structure becomes more asymmetric. Put di¤erently, the network exhibits a more hierarchical structure at higher aggregation frequencies, though, still lower than those found for other national systems.…”

Section: Topological Structure Of Actual Financial Networkmentioning

confidence: 99%

Complex Financial Networks and Systemic Risk: A Review

Bougheas

¹

,

Kirman

²

2015

Dynamic Modeling and Econometrics in Economics and Finance

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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. In this paper we review recent advances in financial economics in relation to the measurement of systemic risk. We start by reviewing studies that apply traditional measures of risk to financial institutions. However, the main focus of the review is on studies that use network analysis paying special attention to those that apply complex analysis techniques. Applications of these techniques for the analysis and pricing of systemic risk has already provided significant benefits at least at the conceptual level but it also looks very promising from a practical point of view. Terms of use: Documents in EconStor mayJEL-Code: D850, G200.

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“…If no further constraints are added, the outcome of the RAS algorithm is a complete weighted network of interbank claims. Although real interbank networks are far from complete [54][55][56][57] , here for simplicity we focus on this limiting case which allows us to focus on the mechanics of reverse stress testing only, rather than on the interplay between network topology and contagion. In order to test how much our results are driven by the fact that the network we consider is complete, we have replicated our analysis for an incomplete network with density 60%.…”

Section: Methodsmentioning

confidence: 99%

Reverse Stress Testing Interbank Networks

Grigat

¹

,

Caccioli

²

2017

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We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock scenarios in stress testing. We consider in particular the case of distress propagation in an interbank market, and we study a network of 44 European banks, which we reconstruct using data collected from banks statements. By looking at the distribution across banks of the size of smallest exogenous shocks we rank banks in terms of their systemic importance, and we show the effectiveness of a policy with capital requirements based on this ranking. We also study the properties of smallest exogenous shocks as a function of the parameters that determine the endogenous amplification of shocks. We find that the size of smallest exogenous shocks reduces and that the distribution across banks becomes more localized as the system becomes more unstable.Systemic risk -the risk associated with the occurrence of a catastrophic breakdown of the financial systemarises endogenously from interactions between the participants that operate in financial markets. Because some types of interactions between financial institutions (in the following banks for brevity) can be modeled in terms of dynamical processes on networks, a growing body of literature has focused on the study of contagion and distress propagation in financial networks [1][2][3] . This research began in the year 2000 with the work of Allen and Gale, who showed that the topology of financial networks influences financial contagion 4 . Many different algorithms have since then been developed to model the propagation of distress between banks under different assumptions as well as to study the relation between the structure of a financial network and its stability (see for instance 3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] ). In this respect, significant progress has been made in the identification of the main drivers of financial contagion and in the design of new stress test frameworks that, at odds with standard micro-prudential tools, do account for interactions between banks 13,[21][22][23][24][25][26] . While the focus of research carried out so far has been mainly that of developing models to understand how exogenous shocks are amplified by the endogenous dynamics of the system, here we look at the reverse problem. We compute the time trajectories of smallest shocks that need to affect banks to produce a final loss of equity larger than a given threshold, which we therefore refer to as worst case shocks. The solution of this reverse problem is useful to identify stress scenarios whose occurrence would lead to systemic events, thus identifying the vulnerabilities of a financial system.At the level of individual institutions, reverse stress testing is a regulatory requirement in the United Kingdom (UK) and the European Union. The...

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“…Stumpf and Porter, 2012). Finger et al (2013) also show that the data under investigation deviate in other respects from those of prototypical scale-free networks. While "preferential attachment" might have been an attractive mechanism for generating money center banks in a financial network, these results show that this generating principle cannot be adopted one-to-one for credit links between banks.…”

Section: Fitting Structural Models: Scale-free and Core-periphery Modmentioning

confidence: 97%

Network Effects and Systemic Risk in the Banking Sector

Lux

¹

2017

Monetary Policy, Financial Crises, and the Macroeconomy

Self Cite

6

0

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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 inThis paper provides a review of recent research on the structure of interbank relations and theoretical models developed to assess the contagious potential of shocks (default of single units) via the interbank network. The empirical literature has established a set of stylized facts that includes a fat-tailed distribution of the number of banks, disassortativity of credit links and a pronounced persistence of existing links over time. These topological features correspond to the existence of money center banks, the importance of relationship banking and the self-organization of the interbank market into a core-periphery structure. Models designed to replicate these topological features exhibit on average more contagious potential than baseline models for the generation of random networks (such as the Erdös-Renyi or preferential attachment mechanisms) that do not share the stylized facts. Combining different channels of contagion such as interbank exposures, portfolio overlaps and common exposure to non-financial borrowers, one typically finds that different contagion channels interact in a distinctly nonlinear way.Dedicated to Gerhard Illing on the occasion of his 60th birthday. Thomas LuxDepartment of Economics, Kiel University D24098 Kiel Department of Economics Chair in Computational EconomicsUniversity Jaume I Es-Castellon Email: lux@economics.uni-kiel.de Dedicated to Gerhard Illing on the occasion of his 60th birthday.

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Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes

Cited by 68 publications

References 26 publications

Complex Financial Networks and Systemic Risk: A Review

Complex Financial Networks and Systemic Risk: A Review

Reverse Stress Testing Interbank Networks

Network Effects and Systemic Risk in the Banking Sector

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