Evolution of Controllability in Interbank Networks

The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

[1]  R. Mantegna,et al.  Scaling behaviour in the dynamics of an economic index , 1995, Nature.

[2]  M. Cropper,et al.  Sulfur Dioxide Control by Electric Utilities: What Are the Gains from Trade? , 1998, Journal of Political Economy.

[3]  Marco Galbiati,et al.  Liquidity-Saving Mechanisms and Bank Behaviour , 2010 .

[4]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[5]  Stefano Battiston,et al.  Default Cascades: When Does Risk Diversification Increase Stability? , 2011 .

[6]  G. Caldarelli,et al.  DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk , 2012, Scientific Reports.

[7]  Albert-László Barabási,et al.  Scale-free networks , 2008, Scholarpedia.

[8]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[9]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[10]  Florian Heider,et al.  Liquidity Hoarding and Interbank Market Spreads: The Role of Counterparty Risk , 2009, SSRN Electronic Journal.

[11]  Joseph Christmas,et al.  The Decade and beyond , 1991 .

[12]  Ching-tai Lin.  Structural controllability , 1974 .

[13]  D. Garlaschelli,et al.  The scale-free topology of market investments , 2003, cond-mat/0310503.

[14]  Franklin Allen,et al.  Financial Contagion , 2000, Journal of Political Economy.

[15]  Guido Caldarelli,et al.  Scaling in currency exchange , 1997 .

[16]  Ricardo J. Caballero,et al.  Macroeconomics after the Crisis: Time to Deal with the Pretense-of-Knowledge Syndrome , 2010 .

[17]  John Horgan,et al.  The New Challenges , 1992 .

[18]  H. Stanley,et al.  Switching processes in financial markets , 2011, Proceedings of the National Academy of Sciences.

[19]  Xavier Freixas Dargallo.  Monetary Policy in a Systemic Crisis , 2009 .

[20]  D. Garlaschelli,et al.  Self-organized network evolution coupled to extremal dynamics , 2006, cond-mat/0611201.

[21]  Hans J. Herrmann,et al.  Mitigation of malicious attacks on networks , 2011, Proceedings of the National Academy of Sciences.

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

[23]  V. Plerou,et al.  Scaling of the distribution of fluctuations of financial market indices. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[24]  J. Monségu.  New challenges... , 2008, Annales de cardiologie et d'angeiologie.

[25]  S. Battiston,et al.  Liaisons Dangereuses: Increasing Connectivity, Risk Sharing, and Systemic Risk , 2009 .

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

[27]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[28]  A. Barabasi,et al.  The network takeover , 2011, Nature Physics.

[29]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[30]  Jari Saramäki,et al.  Effects of time window size and placement on the structure of an aggregated communication network , 2012, EPJ Data Science.

[31]  K. Goh,et al.  Universal behavior of load distribution in scale-free networks. , 2001, Physical review letters.

[32]  H. Stanley,et al.  Bankruptcy risk model and empirical tests , 2010, Proceedings of the National Academy of Sciences.

[33]  Sergey N. Dorogovtsev,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW (Physics) , 2003 .

[34]  Andrew Ross Sorkin,et al.  Too Big to Fail: Inside the Battle to Save Wall Street , 2009 .

[35]  Sergey V. Buldyrev,et al.  Scaling behavior in economics: I Epirical results for company growth , 1997, cond-mat/9702082.

[36]  Andrea Baronchelli,et al.  Quantifying the effect of temporal resolution on time-varying networks , 2012, Scientific Reports.