Systemic centrality and systemic communities in financial networks

Jorge A. Chan-Lau; Jorge A. Chan-Lau

doi:10.3934/QFE.2018.2.468

Quantitative Finance and Economics

2018, Volume 2, Issue 2: 468-496. doi: 10.3934/QFE.2018.2.468

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Research article Special Issues

Systemic centrality and systemic communities in financial networks

Jorge A. Chan-Lau ^,

International Monetary Fund, 700 19th St NW, Washington, DC 20431, USA

Received: 19 March 2018 Accepted: 14 May 2018 Published: 13 June 2018

A systemically important firm could be too-connected-to-fail and/or too-important-to-fail, two properties which centrality measures and community detection methods can capture respectively. This paper examines the performance of these measures in a variance decomposition global financial network. Too-connected-to-fail risk and vulnerability rankings are quite robust to the choice of centrality measure. The PageRank centrality measure, however, does not seem as suitable for assessing vulnerabilities. Two community identification methods, edge betweenness and the map equation (Infomap) were used to identify systemic communities, which in turn capture the too-important-tofail dimension of systemic risk. The first method appears more robust to di erent weighting schemes but tends to isolate too many firms. The second method exhibits the opposite characteristics. Overall, the analysis suggests that centrality measures and community identification methods complement each other for assessing systemic risk in financial networks.
- centrality,
- community detection,
- edge-betweenness,
- financial network,
- map equation,
- power law,
- systemic risk
Citation: Jorge A. Chan-Lau. Systemic centrality and systemic communities in financial networks[J]. Quantitative Finance and Economics, 2018, 2(2): 468-496. doi: 10.3934/QFE.2018.2.468

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Abstract

A systemically important firm could be too-connected-to-fail and/or too-important-to-fail, two properties which centrality measures and community detection methods can capture respectively. This paper examines the performance of these measures in a variance decomposition global financial network. Too-connected-to-fail risk and vulnerability rankings are quite robust to the choice of centrality measure. The PageRank centrality measure, however, does not seem as suitable for assessing vulnerabilities. Two community identification methods, edge betweenness and the map equation (Infomap) were used to identify systemic communities, which in turn capture the too-important-tofail dimension of systemic risk. The first method appears more robust to di erent weighting schemes but tends to isolate too many firms. The second method exhibits the opposite characteristics. Overall, the analysis suggests that centrality measures and community identification methods complement each other for assessing systemic risk in financial networks.

References

[1]	Alzahrani T (2016) Complex Information Networks - Detecting Community Structure in Bipartite Networks. PhD thesis, RMIT.
[2]	Alzahrani T, Horadam KJ, Boztas S (2014) Community Detection in Bipartite Networks Using Random Walks. Springer Studies in Computational Intelligence 549: 157–165.
[3]	Banco de Mexico (2016) Reporte Sobre el Sistema Financiero.
[4]	Barabasi A-L, Albert R (1999) Emergence of Scaling in Random Networks. Science 286: 509–512.
[5]	Barrat A, Barthelemy M, Pastor-Satorras R, et al. (2004) The Architecture of Complex Weighted Networks. Proc Natl Acad Sci USA 101: 3747–3752.
[6]	Basel Committee on Banking Supervision (2013) Global Systemically Important Banks: Updated Assessment Methodology and the Higher Loss Absorbency Requirement. Bank for International Settlements.
[7]	Battiston S, Puliga M, Kaushik R, et al. (2012) DebtRank: Too Central to Fail? Financial Networks, the Fed, and Systemic Risk. Sci Rep-UK 2: 541.
[8]	Bech M, Bergstrom C, Rosvall M, et al. (2014) Mapping Change in the Overnight Money Market. Physica A 424: 44–51.
[9]	Billio M, Getmansky M, Lo A, et al. (2012) Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors. J Financ Econ 104: 535–559.
[10]	Bonacich P (1987) Power and Centrality: A Family of Measures. Am J Sociol 92: 1170–1182.
[11]	Bonacich P, Lloyd P (2001) Eigenvector-Like Measures of Centrality for Asymmetric Relations. Soc Networks 23: 191–201.
[12]	Boss M, Elsinger H, Lehar A, et al. (2004) The Network Topology of the Interbank Market Quant Financ 4: 677–684.
[13]	Chan-Lau JA (2010) Balance Sheet Network Analysis of Too-Connected-to-Fail Risk in Global and Domestic Banking Systems. International Monetary Fund.
[14]	Chan-Lau JA (2013) Systemic Risk Assessment and Oversight. Risk Books.
[15]	Chan-Lau JA (2016) Variance Decomposition Networks: Potential Pitfalls and Simple Solutions. Working Paper. International Monetary Fund; Risk Management Institute, National University of Singapore.
[16]	Chan-Lau JA, Chuang C, Duan J-C, et al. (2016) Banking Network and Systemic Risk via Forward-Looking Partial Default Correlation. International Monetary Fund; National University of Singapore.
[17]	Chen H, Cummins JD, Sun T, et al. (2015) Tail Risk Spillover and Its Contribution to Systemic Risk: A Network Analysis of Global Reinsurers. Temple University.
[18]	Clauset A, Shalizi CR, Newman MEJ (2009) Power-Law Distributions in Empirical Data. SIAM Rev 51: 661–703.
[19]	Cont R, Santos E, Moussa A (2013) Network Structure and Systemic Risk in Banking Systems. In: Fouque J, Langsam J, Handbook of Systemic Risk, New York: Cambridge University Press.
[20]	Demekas D, Chan-Lau JA, Rendak N, et al. (2013) Mandatory Financial Stability Assessments Under the Financial Sector Assessment Program: Update. International Monetary Fund.
[21]	Diebold FX, Yilmaz K (2014) On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms. J Econometrics 182: 119–134.
[22]	Eisenberg L, Noe TH (2001) Systemic Risk in Financial Systems. Manage Sci 47: 236–249.
[23]	Elouaer-Mrizak S, Chastand M (2013) Detecting Communiities with French Intercorporate Network. Procedia Soc Behav Sci 79: 82–100.
[24]	Financial Stability Board (2011) Policy Measures to Address Systemically Important Financial Institutions.
[25]	Fortunato S, Hric D (2016) Community Detection in Networks: A User Guide. Phys Rep 659: 1–44.
[26]	Freeman LC (1977) A Set of Measures of Centrality Based on Betweenness. Sociometry 40: 35–41.
[27]	Girvan M, Newman MEJ (2002) Community Structure in Social and Biological Networks. P Natl Acad Sci USA 99: 7821–7826.
[28]	Ho JH (2012) Managing Systemic Risk from the Perspective of the Financial Network Under Macroeconomic Distress. Financial Stability Institute, Bank for International Settlements.
[29]	Jiang XF, Chen TT, Zheng B (2014) Structure of Local Interactions in Complex Financial Dynamics. Sci Rep-UK 4: 5321.
[30]	Kennett D, Tumminello M, Madi A, et al. (2010) Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. PLOS One.
[31]	Kruskal J (1956) On the Shortest Spanning Subtree and the Traveling Salesman Problem. P Am Math Soc 7: 48–50.
[32]	Kuzubas T, Omercikoglu I, Saltoglu B (2013) Network Centrality Measures and Systemic Risk: An Application to the Turkish Financial Crisis. Bogazici University.
[33]	Lanne M, Nyberg H (2016) Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models. Oxford B Econ Stat 78: 595–603.
[34]	Leon C, Perez J (2014) Assessing Financial Market Infrastructures' Systemic Importance with Authority and Hub Centrality. J Financ Market Infrastruct 2: 67–87.
[35]	Malliaros F, Vazirgiannis M (2013) Clustering and Community Detection in Directed Networks: A Survey. Phys Rep 533: 95–142.
[36]	Mantegna R (1999) Hierarchical Structure in Financial Markets. Eur Phys J B 11: 193–197.
[37]	Martinez-Jaramillo S, Alexandrova-Kabadjova B, Bravo-Benitez B, et al. (2014) An Empirical Study of the Mexican Banking System's Network and Its Implications for Systemic Risk. J Econ Dyn Control 40: 242–265.
[38]	McLaughlin J, Minson A, Palmer N, et al. (2018) The OFR Financial System Vulnerabilities Monitor. 18-01. O_ce of Financial Research, U. S. Department of the Treasury.
[39]	Newman MEJ (2001) Scientific Collaboration Networks. Ii. Shortest Paths, Weighted Networks and Centrality. Phys Rev El 64: 016132.
[40]	Opsahl T, Agneessens F, Skvoretz J (2010) Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Soc Networksl 32: 245–251.
[41]	Page L, Brin S, Motwani R, et al. (1999) The Pagerank Citation Ranking: Bringing Order to the Web. Stanford InfoLab.
[42]	Palla G, Derenyi I, Farkas I, et al. (2005) Uncovering the Overlapping Community Structure of Complex Networks in Naturel and Society. Naturel 435: 814–818.
[43]	Palla G, Farkas I, Pollner P, et al. (2007) Directed Network Modules. New J Phys 9: 186.
[44]	Pesaran HH, Shin Y (1998) Generalized Impulse Response Analysis in Linear Multivariate Models. Econ Lett 58: 17–29.
[45]	Prim R (1957) Shortest Connection Networks and Some Generalizations. Bell Syst Tech J 36: 1389– 1401.
[46]	Rosvall M, Axelsson D, Bergstrom CT (2010) The Map Equation. Eur J Phys 178: 13–23.
[47]	Rosvall M, Bergstrom CT (2008) Maps of Random Walks on Complex Networks Reveal Community Structure. P Natl Acad Sci USA 105: 1118–1123.
[48]	Schaub M, Delvenne J-C, Rosvall M, et al. (2017) The Many Facets of Community Detection in Complex Networks. Appl Netw Sci 2: 1–13.
[49]	Shannon C (1948a) A Mathematical Theory of Communication. Bell Syst Tech J 27: 379–423.
[50]	Shannon C (1948b) A Mathematical Theory of Communication. Bell Syst Tech J 27: 623–656.
[51]	Sun AJ, Chan-Lau JA (2017) Financial Networks and Interconnectedness in an Advanced Emerging Market Economy. Quant Financ 17: 1833–1858.
[52]	Tumminello M, Lillo F, Piilo J, et al. (2012) Identification of Clusters of Investors from Their Real Trading Activity in a Financial Market. New J Phyicsls 14.
[53]	Upper C (2011) Simulation Methods to Assess the Danger of Contagion in Interbank Markets. J Financ Stab 7: 111–125.
[54]	Upper C, Worms A (2002) Estimating Bilateral Exposures in the German Interbank Market: Is There a Danger of Contagion? Discussion Paper 48: 827–849.
[55]	Vitali S, Battiston S (2012) The Community Structure of the Global Corporate Network. PLOS One, August.
[56]	Wang G-J, Xie C, Stanley HE (2016) Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation Networks. Comput Econ 51: 1–29.

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