Financial Network Analysis @ Central Bank of Bolivia


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Presentation at Payments System Oversight Course at Central Bank of Bolivia, La Paz
25 November 2011

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Financial Network Analysis @ Central Bank of Bolivia

  1. 1. Presentation at Payments System Oversight CourseCentral Bank of Bolivia, La Paz25 November 2011Financial Network AnalysisSimulation analysis and stress testingof payment networks – Session 1Kimmo Soramä,
  2. 2. Growing interest in networks 2003 2003 2004
  3. 3. “Is network theory the “Meltdown modeling - “... need for new andbest hope for regulating Could agent-based fundamentalsystemic risk?” computer models prevent understanding of the another financial crisis?” structure and dynamics of economic networks.”CFA Magazine, July 2009 Nature, August 2009 Science, July 2009
  4. 4. In the face of the crisis, we felt abandoned by conventional tools.In the absence of clear guidance from existing analyticalframeworks, policy-makers had to place particular reliance on ourexperience. Judgement and experience inevitably played a key role. Jean-Claude Trichet, (Now former) President of the ECB, Opening address at the ECB Central Banking Conference, Frankfurt, 18 November 2010
  5. 5. Network maps • Recent financial crisis brought to light the need to look at links between financial institutions • Networks are a natural way to visualize the financial system • „Network thinking‟ widespread by regulators • Mapping of the financial system has only begun Eratosthenes map of the known world, c.194 BC.
  6. 6. Visualising financial networks Bech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986. Italian money market Iori G, G de Masi, O Precup, G Gabbi and G Caldarelli (2008): “A network analysis of the Italian overnight money market”, Journal of Economic Dynamics and Control, vol. 32(1), pages 259-278Federal funds Unsecured sterling money market Wetherilt, A. P. Zimmerman, and K. Soramäki (2008), “The sterling unsecured loan market during 2006–2008: insights from network topology“, in Leinonen (ed), BoF Scientific monographs, E 42 Soramaki, K, M.L. Bech, J. Arnold, R.J. Glass and W.E. Beyeler (2007), “The topology of interbank payment flows”, Physica A, Vol. 379, pp 317-333, 2007.Fedwire
  7. 7. Europes Web of Debt(Bill Marsh / The New York Times, 1 May 2010)
  8. 8. Eurozone debt web: Who owes what to whom?(BBC, 18 November 2011)
  9. 9. Network theory Financial Network Analysis Social Network Network Science Analysis NETWORK THEORY Graph & Matrix Computer Theory Science Biological Network Analysis
  10. 10. Main premise of network analysis:the structure of the links between nodes mattersThe properties and behaviour of a node cannotbe analysed on the basis its own properties andbehaviour alone.To understand the behaviour of one node, onemust analyse the behaviour of nodes that may beseveral links apart in the network.Financial context: network of interconnectedbalance sheets
  11. 11. Network basics• Terminology – node/vertex -> Bank/banking group – link/tie/edge/arc-> Financial interlinkages, bilateral positions, exposures – directed vs undirected – weighed vs unweighted – graph + properties = network 2 1• Algorithms/measures 3 4 – Centrality -> Systemical importantance – Flow -> Liquidity – Community/pattern identification – Distance, shortest paths – Connectivity, clustering – Cascades, epidemic spreading -> Contagion
  12. 12. “Prototypical” topologies lattice complete random Ring Weighted random Scale-free 12
  13. 13. “Homophily” – “Birds of one feather flock together”, “herd Spread of obesity behaviour” – Ideas, attributes, etc tend to cluster together and enforce each other – Examples: Some obvious (age, social status), others less (obesity, happiness, divorces) – How about: risk appetite, portfolio decisions, etc.“Small world phenomenon” – “Six degrees of separation” (6.6 on MSN Nicholas A. Christakis, James H. Fowler New England Journal of Medicine 357 (4): 370–379 messenger) (26 July 2007) – The shortest path between any two nodes is very short Fedwire degree distribution – Implications for contagion? Probability (log)“Robust yet fragile“, “Scale-free networks” – “The removal of "small" nodes does not alter the path structure of the remaining nodes, and thus has no impact on the overall network topology. “ Degree (log) 13
  14. 14. Network analysis for Oversight• Network maps: intuitive, provide a deeper understanding of the system via anomaly explanation and visualization• Centrality metrics: such as Pagerank can be used as a proxy for systemic importance, contagious links• Monitor over time: build reference data, detect and understand gradual change• Tied to availability of data: enables “Analytics based policy”, i.e. the application of computer technology, operational research, and statistics to solve regulatory problems
  15. 15. Research• A growing body of empirical research on financial networks• Interbank payment flows – Soramäki et al (2006), Becher et al. (2008), Boss et al. (2008), Pröpper et al. (2009), Embree and Roberts (2009), Akram and Christophersen (2010) …• Overnight loans networks – Atalay and Bech (2008), Bech and Bonde (2009), Wetherilt et al. (2009), Iori et al. (2008) and Heijmans et al. (2010), Craig & von Peter (2010) …• Flow of funds, Credit registry, Stock trading… – Castren and Kavonius (2009), Bastos e Santos and Cont (2010), Garrett et al. 2011, Minoiu and Reyes (2011), (Adamic et al. 2009, Jiang and Zhou 2011) …• More at
  16. 16. Common centrality measures Degree: number of links Closeness: distance to other nodes via shortest paths Betweenness: number of shortest paths going through the node Eigenvector: nodes that are linked by other important nodes are more central, probability of a random process
  17. 17. Centrality depends onnetwork processTrajectory geodesic paths, paths, trails or walksTransmission parallel/serial duplication or transfer Source: Borgatti (2004) 17
  18. 18. Components The GWCC has a ‘Giant Strongly Connected .. a ‘Giant In Component’ (each And several Component’ (which node can reach each disconnected flows into GSCC) other) components .. and a ‘Giant Out Component’ (to which GSCC flows) Empirical networks often consist of a ‘Giant Weakly Connected Component’ 18
  19. 19. Intelligence • Can we algorithmically detect financial crisis? Payment data as early warning tool? • Financial crisis are different and rare. The patterns to be recognized must be frequent enough for computers to learn • Pattern recognition is hard for computers -> the best Go programs only manage to reach an intermediate amateur level • A solution is to augment human intelligence (in contrast to AI) • Intelligence amplification (William Ross Ashby 1956)
  20. 20. Tools• Pajek, Universty of Ljublana, Slovenia – Focus on social network analysis of large networks –• Gephi, Gephi Foundation, France – Focus on graph visualisation “Like Photoshop for graphs” –• FNA, Soramaki Networks, Finland – Focus on Financial/Payment Networks, time-series, dashboards –• Many others – Cytoscape, Graphviz, Network Workbench, NodeXL, ORA, Tableau, Ucinet, Visone, etc.
  21. 21. Interactive visualization examples… from
  22. 22. Ring layout
  23. 23. Force-directed layout
  24. 24. Thank youKimmo Soramä