Financial Networks and Cartography

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Talk at Understanding Financial Catastrophe Risk: Developing a Research Agenda. Centre for Risk Studies, University of Cambridge, 9 April 2013

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Financial Networks and Cartography

  1. 1. Understanding Financial Catastrophe Risk:Developing a Research AgendaCentre for Risk Studies, University of Cambridge9 April 2013Financial Networks andCartography Dr. Kimmo Soramäki Founder and CEO FNA, www.fna.fi
  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, they aid indecision making and build intuition
  4. 4. Systemic risk ≠ systematic risk News articles mentioning “systemic risk”, Source: trends.google.comThe 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. Not:Domino effects, cascading failures, financialinterlinkages, … -> i.e. a process in thefinancial network 4
  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 data 5
  6. 6. Networks brings us beyond the Data Cube" The Tesseract" For example: Entities: 100 banks Variables: Balance sheet items Time: Quarterly data since 2011 Links: Interbank exposures Information on the links allows us to develop better models for banks balance sheets in times of stress 6
  7. 7. First empirics Fedwire Interbank Payment Network, Fall 2001 Around 8000 banks, 66 banks comprise 75% of value,25 banks completely connected Similar to other socio- technological networksSoramäki, Bech, Beyeler, Glass and Arnold (2007), M. Boss, H. Elsinger, M. Summer, S. Thurner, ThePhysica A, Vol. 379, pp 317-333. network topology of the interbank market, SantaSee: www.fna.fi/papers/physa2007sbagb.pdf Fe Institute Working Paper 03- 7 10-054, 2003.
  8. 8. Most central banks have now mapped their interbank payment systemsBecher, Millard and Soramäki (2008). Agnes Lubloy (2006). Topology of the HungarianThe network topology of CHAPS large-value transfer system. Magyar Nemzeti BankSterling. BoE Working Paper No. 355. Occasional Papers Embree and Roberts (2009). Network Analysis and Canadas Large Value Transfer SystemBoC Discussion Paper 2009-13 8
  9. 9. 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, Optimal classification that matches theoritical core-periphery model• DebtRank – Battiston et al, Science Reports 2012, Cascading failures -model• SinkRank – Soramäki and Cook, Kiel Economics Worlds Ocean Currents DP, 2012, Absorbing Markov chain NASA Scientific Visualization Studio 9
  10. 10. Types of financial networks• Observing Networks – Flow: payment, trade, collateral – Stock: exposure, co-exposure, – Bipartite: trader-asset, bank-risk, ...• Inferring networks – Model: correlation, partial correlation, tail dependence, similarity, Granger causality – Data: Asset returns, Balance sheet change, ...• Dimensions – Time: intraday, overnight, long-term – Risk: operational, liquidity, solvency
  11. 11. Inferring NetworksCalculate pairwise Control for common Keep statisticallycorrelations factors (e.g. market) significant correlationsCorrect for multiple Convert to network Visualize as treecomparisons
  12. 12. Map on cross asset correlationsand volatilitiesNodes (circles) represent assets and Node color indicateslinks (lines) represent correlations identified communitybetween the linked assetsNode sizes scale withthe variance of the Mapping multiplereturn: assets with dimensions of the samelarger nodes have more Low correlation data set on a single map (long link)variable returns allows visual inference of connections.Shorter links indicate higher One can focus oncorrelations. High correlation (short link) details - while maintaining an overview.Interactive version at www.fna.fi/demos/crc/viz/asset-tree.html
  13. 13. Priorities for research agenda1. Measuring and mapping interconnectedness (network structure), modelling contagion (network process) and understanding their interplay
  14. 14. Example: Bank-Asset graphs andprojections Bank projection Asset projection 14
  15. 15. Priorities for research agenda1. Measuring and mapping interconnectedness (network structure), modelling contagion (network process) and understanding their interplay2. Developing early warning indicators and visual analytics systems for continuous monitoring of the financial system
  16. 16. Example: Oversight Monitor (network is fictional)The monitor will allow the identification ofsystemically important banks and evaluationof the impact of bank failures on the systemhttp://www.fna.fi/solutions/oversight-monitor 16
  17. 17. Priorities for research agenda1. Measuring and mapping interconnectedness (network structure), modelling contagion (network process) and understanding their interplay2. Developing early warning indicators and visual analytics systems for continuous monitoring of the financial system3. Taking into account the social psychology aspect of the financial system

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