Structure and Dynamics of the Global Financial Network

In this paper, we study the evolution of the network topology for the global financial market. We evaluate the level of diversification and participation of developed and emerging economies in cross-border exposures and find that the gross exposure network is dense, the vulnerability matrix is sparse, and the network's fragility changes over time. Prior to the financial crisis in 2008, the network was relatively fragile, whereas it became more resilient afterwards, showing a reduction in financial institutions risk appetite. Our results suggest that financial regulators should track down the network evolution in their systemic risk assessment

[1]  B. M. Tabak,et al.  Liquidity Performance Evaluation of the Brazilian Interbank Market using a Network-Based Approach , 2015 .

[2]  Alessandro Vespignani,et al.  Characterization and modeling of weighted networks , 2005 .

[3]  Jukka M. Vesala,et al.  Cross-Border Bank Contagion in Europe , 2006, SSRN Electronic Journal.

[4]  Francesco Picciolo,et al.  Reciprocity of weighted networks , 2012, Scientific Reports.

[5]  Benjamin M. Tabak,et al.  Working Paper Series , 2011 .

[6]  Liang Zhao,et al.  Stochastic Competitive Learning in Complex Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[7]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Burak Saltoǧlu,et al.  Network centrality measures and systemic risk: An application to the Turkish financial crisis , 2014 .

[9]  A. Tahbaz-Salehi,et al.  Systemic Risk and Stability in Financial Networks , 2013 .

[10]  Sergio Rubens Stancato de Souza,et al.  Insolvency and contagion in the Brazilian interbank market , 2015 .

[11]  Liang Zhao,et al.  High-level pattern-based classification via tourist walks in networks , 2015, Inf. Sci..

[12]  Diego Garlaschelli,et al.  Patterns of link reciprocity in directed networks. , 2004, Physical review letters.

[13]  Biliana Alexandrova-Kabadjova,et al.  An Empirical Study of the Mexican Banking System's Network and Its Implications for Systemic Risk , 2012 .

[14]  Hrvoje Štefančić,et al.  Model of Wikipedia growth based on information exchange via reciprocal arcs , 2009, ArXiv.

[15]  Benjamin Miranda Tabak,et al.  Network structure analysis of the Brazilian interbank market , 2016 .

[16]  Liang Zhao,et al.  Network-Based High Level Data Classification , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Benjamin Miranda Tabak,et al.  Evaluating systemic risk using bank default probabilities in financial networks , 2016 .

[18]  Sergio Rubens Stancato de Souza,et al.  Monitoring vulnerability and impact diffusion in financial networks , 2017 .

[19]  Benjamin Miranda Tabak,et al.  The role of banks in the Brazilian interbank market: Does bank type matter? , 2008 .

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

[21]  Benjamin Miranda Tabak,et al.  Connectivity and Systemic Risk in the Brazilian National Payments System , 2013, SITIS.

[22]  M. Scheffer,et al.  Complexity theory and financial regulation , 2016, Science.

[23]  Liang Zhao,et al.  Machine Learning in Complex Networks , 2016, Springer International Publishing.

[24]  Iman van Lelyveld,et al.  Finding the core: Network structure in interbank markets , 2014 .

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

[26]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.