Illuminating Interconnectedness and Contagion

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Presentation at Enterprise Risk Management Symposium in Chicago on 23 April 2013. See http://www.ermsymposium.org/2013/

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Illuminating Interconnectedness and Contagion

  1. 1. IlluminatingInterconnectednessand ContagionDr. Kimmo SoramäkiFounder and CEOFNA, www.fna.fiEnterprise Risk Management SymposiumChicago, 23 April 2013
  2. 2. Map of 1854 Broad Street choleraoutbreak by John Snow
  3. 3. AgendaNetworks "connect the dots". They operationalize theconcept of financial interconnectedness that underpinssystemic risk.The epidemiology of finance is the study of contagion.Contagion models are often based on network models. Thegoal is often to identify and contain "super-spreaders" or"systemically important banks"Network visualizations allow us to "map the financialsystem". Maps are intelligence amplification, theyilluminate multidimensional data and aid in decisionmaking and build intuition
  4. 4. Systemic risk ≠ systematic riskThe risk that a system composed of many interactingparts fails (due to a shock to some of its parts).In Finance, the risk that a disturbance in the financialsystem propagates and makes the system unable toperform its function – i.e. allocate capital efficiently.Domino effects, cascading failures, financialinterlinkages, … -> i.e. a process in thefinancial networkNews articles mentioning “systemic risk”, Source: trends.google.com4Not:
  5. 5. Network Theory is applied widelyMain premise of network theory:Structure of links between nodesmattersLarge empirical networks aregenerally very sparseNetwork analysis is not analternative to other analysismethodsNetwork aspect is an unexploreddimension of ANY data5
  6. 6. 6For example:Entities:100 banksVariables:Balance sheet itemsTime:Quarterly data since 2011Links:Interbank exposuresInformation on the linksallows us to develop bettermodels for banks balancesheets in times of stressNetworks brings us beyond the Data Cube" The Tesseract"
  7. 7. Observing vs inferring• Observing links– Exposures, payment flow, trade, co-ownership, joint boardmembership, etc.– Cause of link is known• Inferring links– Observing the effects and inferringa relationship e.g. via correlations– Cause of link is unknown– Time series on asset prices, tradevolumes, balance sheet items7
  8. 8. First empirics Fedwire Interbank PaymentNetwork, Fall 2001Around 8000 banks, 66 bankscomprise 75% of value,25 bankscompletely connectedSimilar to other socio-technological networksSoramäki, Bech, Beyeler, Glass and Arnold (2007),Physica A, Vol. 379, pp 317-333.See: www.fna.fi/papers/physa2007sbagb.pdf 8M. Boss, H. Elsinger, M. Summer, S. Thurner, Thenetwork topology of the interbank market, SantaFe Institute Working Paper 03-10-054, 2003.
  9. 9. Most central banks have now mapped theirinterbank payment systems9Agnes Lubloy (2006). Topology of the Hungarianlarge-value transfer system. Magyar Nemzeti BankOccasional PapersEmbree and Roberts (2009). NetworkAnalysis and Canadas Large Value TransferSystemBoC Discussion Paper 2009-13Becher, Millard and Soramäki (2008).The network topology of CHAPSSterling. BoE Working Paper No. 355.
  10. 10. Centrality Measures forFinancial SystemsMetrics developed in other fields andwith other network processes inmind:• Degree, Closeness, Betweenness,PageRank, etc.Recently developed financial systemspecific metrics:• Core-Periphery– Craig and von Peter 2010, Optimalclassification that matches theoriticalcore-periphery model• DebtRank– Battiston et al, Science Reports 2012,Cascading failures -model• SinkRank– Soramäki and Cook, Kiel EconomicsDP, 2012, Absorbing Markov chain 10
  11. 11. Worlds Ocean CurrentsNASA Scientific Visualization Studio
  12. 12. 12Bank projectionAsset projectionExample: Bank-Asset graphs andprojections
  13. 13. Benefit of visualization13Mean of x 9Variance of x 11Mean of y ~7.50Variance of y ~4.1Correlation ~0.816Linear regression:y = 3.00 + 0.500xAnscombes Quartet: Constructed in 1973 by Francis Anscombe todemonstrate both the importance of graphing data before analyzing itand the effect of outliers on statistical properties
  14. 14. Visualizing correlationsCalculate pairwisecorrelations for 31 ETFs invarious geographies andasset classes(465 correlations)Color code correlations:Problem:We are making manyestimates, some of whichare likely false positives-1 +12007-20082012-2013
  15. 15. 15Example - distribution of correlation in 30trials with random numbers20 pairs 50 pairs100 pair 200 pairs
  16. 16. Significant correlationsKeep statistically significantcorrelations with 95% confidencelevelCarry out Multiple comparison -correction -> Expected error rate<5%Problem:Heatmaps can bemisleading due tohuman color perception2012-2013Last month
  17. 17. About Color PerceptionA and B are the sameshade of gray
  18. 18. About Color PerceptionA and B are the sameshade of gray
  19. 19. Correlation networkAllows for mappingmultiple dimensions ofthe same data set on asingle map allows visualinference of connections.One can focus on details -while maintaining anoverview.
  20. 20. Map on correlationsand volatilitiesNodes (circles) representassets and links (lines)represent correlations betweenthe linked assetsNode sizes scale with thevariance of the return: assetswith larger nodes have morevariable returnsShorter links indicate highercorrelations.Node color indicates identifiedcommunityhttp://www.fna.fi/demos/erm/correlation-tree.html
  21. 21. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki

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