Network formation in the interbank money market: An application of the actor-oriented model

Abstract This paper investigates the driving forces behind banks’ link formation in the interbank market by applying the stochastic actor oriented model (SAOM). Our data consists of quarterly networks constructed from the transactions on an electronic trading platform (e-MID) for interbank credit over the period from 2001 to 2010. The analysis strongly supports the hypothesis that the existence and extent of past credit relationships is a major determinant of credit provision (i.e., link formation) in subsequent periods. We also find explanatory power of size-related characteristics, but little influence of past interest rates. The actor-based analysis, thus, confirms the prevalent view that interbank credit is mainly determined by lasting business relationships and less so by competition for the best price (interest rate). Our findings also show that topological features exert a certain influence on the network formation process. The major changes found for the period after the onset of the financial crisis are that: (1) large banks and those identified as ‘core’ intermediaries became even more sought of as counterparties and (2) indirect counterparty risk appeared to be more of a concern as we find a higher tendency to avoid indirect exposure as indicated by clustering effects.

[1]  Michael Boss,et al.  Network topology of the interbank market , 2003, cond-mat/0309582.

[2]  Martin G. Everett,et al.  Models of core/periphery structures , 2000, Soc. Networks.

[3]  Hein de Vries,et al.  Article in Press G Model Social Networks Dynamics of Adolescent Friendship Networks and Smoking Behavior , 2022 .

[4]  Filip Agneessens,et al.  Similarity in friendship networks: Selection or influence? The effect of constraining contexts and non-visible individual attributes , 2010, Soc. Networks.

[5]  Roma,et al.  Fitness model for the Italian interbank money market. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Filip Agneessens,et al.  Where do intra-organizational advice relations come from? The role of informal status and social capital in social exchange , 2012, Soc. Networks.

[7]  C. Steglich,et al.  Partners in Power: Job Mobility and Dynamic Deal‐Making , 2007 .

[8]  Inferring trading dynamics for an OTC market: the case of the euro area overnight money market , 2011 .

[9]  P. Holland,et al.  A Method for Detecting Structure in Sociometric Data , 1970, American Journal of Sociology.

[10]  Ben R. Craig,et al.  Interbank Tiering and Money Center Banks , 2010 .

[11]  T. Snijders,et al.  Using social network analysis tools in ecology: Markov process transition models applied to the seasonal trophic network dynamics of the Chesapeake Bay. , 2009 .

[12]  Thomas Lux,et al.  Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes , 2013, Comput. Manag. Sci..

[13]  Tom A. B. Snijders,et al.  Introduction to stochastic actor-based models for network dynamics , 2010, Soc. Networks.

[14]  John M. Light,et al.  Fundamental principles of network formation among preschool children , 2010, Soc. Networks.

[15]  M. Tumminello,et al.  Networked Relationships in the e-MID Interbank Market: A Trading Model with Memory , 2014, 1403.3638.

[16]  T. Snijders,et al.  A multilevel network study of the effects of delinquent behavior on friendship evolution , 2003 .

[17]  T. Snijders Stochastic actor-oriented models for network change , 1996 .

[18]  Jacob E. Cheadle,et al.  The 'friendship dynamics of religion,' or the 'religious dynamics of friendship'? A social network analysis of adolescents who attend small schools. , 2012, Social science research.

[19]  H. Robbins A Stochastic Approximation Method , 1951 .

[20]  Walter E. Beyeler,et al.  The topology of interbank payment flows , 2007 .

[21]  Laurence Moore,et al.  Actor-based analysis of peer influence in A Stop Smoking In Schools Trial (ASSIST) , 2012, Soc. Networks.

[22]  João F. Cocco,et al.  Lending Relationships in the Interbank Market , 2003 .

[23]  Morten L. Bech,et al.  The Topology of the Federal Funds Market , 2008, SSRN Electronic Journal.

[24]  Johan H. Koskinen,et al.  Modelling the evolution of a bipartite network - Peer referral in interlocking directorates , 2012, Soc. Networks.

[25]  Alain Durré,et al.  Nonlinear Liquidity Adjustments in the Euro Area Overnight Money Market , 2012 .

[26]  Emmanuel Lazega,et al.  Norms, status and the dynamics of advice networks: A case study , 2012, Soc. Networks.

[27]  P. Zappa,et al.  Network formation in the Euro interbank market: A longitudinal analysis of the turmoil , 2012 .

[28]  M. Affinito,et al.  Do Interbank Customer Relationships Exist? And How Did They Function Over the Crisis? Learning from Italy , 2011 .

[29]  G. Caldarelli,et al.  A Network Analysis of the Italian Overnight Money Market , 2005 .

[30]  Giulia Iori,et al.  Systemic Risk on the Interbank Market , 2004 .

[31]  R. May,et al.  Systemic risk in banking ecosystems , 2011, Nature.

[32]  T. Lux,et al.  On the distribution of links in the interbank network: evidence from the e-MID overnight money market , 2015 .

[33]  T. Lux,et al.  Core–Periphery Structure in the Overnight Money Market: Evidence from the e-MID Trading Platform , 2015 .

[34]  Hideki Takayasu,et al.  Fractal Network derived from banking transaction -- An analysis of network structures formed by financial institutions -- , 2004 .

[35]  J. Rochet,et al.  Microeconomics of banking , 1997 .

[36]  Jing Yang,et al.  Network Models and Financial Stability , 2007 .