Mapping Financial Landscapes @ PRMIA

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Presentation at PRMIA Global Risk Conference, New York, 15 May 2012

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Mapping Financial Landscapes @ PRMIA

  1. 1. Concurrent Session 1B:Mapping Financial Landscapes Speaker Kimmo Soramäki
  2. 2. “When the crisis came, the serious limitations of existing economicand financial models immediately became apparent.[...]As a policy-maker during the crisis, I found the available models oflimited help. In fact, I would go further: in the face of the crisis, wefelt abandoned by conventional tools.” in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 2
  3. 3. We did not have maps … 3
  4. 4. Eratosthenes map of the known world4c. 194 BC
  5. 5. Fedwire payment network • Total of ~8000 banks • 66 banks comprise 75% of value • 25 banks completely connected • Scale-free degree distribution • Similar to other socio- technological networksSoramäki, Bech, Beyeler, Glass and Arnold 5(2007), Physica A, Vol. 379, pp 317-333.
  6. 6. Financial and geographic mapsSimilarities• Are visualizations of multivariate data• Have global and local views• Alter and aid decision makingDifferences - Financial Maps:• Relationships are not spatial• Change faster than tectonic plates 6
  7. 7. AgendaMapping Systemic RiskMapping Market DataNetwork Theory 7
  8. 8. Mapping systemic risk 8
  9. 9. Systemic risk ≠ systematic risk News articles mentioning “systemic risk”, Source: trends.google.com• The risk of disruption to a financial entity with spillovers to the real economy• Risk of a crisis that stresses key intermediation markets and leads to their breakdown, which impacts the broader economy and requires government intervention• Risk that critical nodes of a financial network cease to function as designed, disrupting linkages-> some chain of events that starts or gets magnified in the finance sector andmakes us all worse off 9
  10. 10. Background• Regulatory response to recent financial crisis was to strengthen macro-prudential supervision with mandates for more regulatory data.• The challenge now is to understand and utilize the data• Analytics based policy and regulation, i.e. the application of computer technology, operations research, and statistics to support decision Katsushika Hokusai. The great wave off making Kanagawa ~1830 10
  11. 11. Research• A growing body of empirical research on financial networks from a systemic risk perspective• 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 www.fna.fi/library 11
  12. 12. Visualizing 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 Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global Cross-border bank lending banking:1978-2009. IMF Working Paper WP/11/74. 12
  13. 13. The New York Times, 1 May 2010Europes Web of Debt 13
  14. 14. BBC, 18 November 2011Eurozone debt web: Who owes what to whom? 14 http://www.bbc.co.uk/news/business-15748696
  15. 15. Example 1:BIS consolidated banking statistics• The Bank for International Settlements (BIS) consolidated banking sector statistics (item 9D), http://www.bis.org/statistics/consstats.htm• Data on banks on-balance sheet financial claims by country• Provide a measure of the risk exposures of lenders national banking systems.• Only countries reporting both exposures from and to are included net_id from_id to_id value 2005-1Q Australia Austria 499 2005-1Q Australia Belgium 1135 2005-1Q Australia Canada 1884 2005-1Q Australia Finland 553 2005-1Q Australia France 5028 … 15
  16. 16. • ‘Chord diagram’• Using D3 library based on work on Circos• Implemented in FNA• Link to interactive version 16
  17. 17. Centrality• 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/to other important nodes are more central 17
  18. 18. • Network layout• Node size scales with PageRank• Link width scales with value of exposure 18
  19. 19. Mapping financial markets 19
  20. 20. Correlation matrix• Pairwise correlations of 141 assets, 9927 data points 20
  21. 21. • Heatmap• Deep blue -> strong negative correlation• Deep red -> strong positive correlation• Link to interactive version 21
  22. 22. • We consider only the Maximum Spanning Tree of absolute correlations• 140 unique data points -> links 22
  23. 23. • Network visualization of correlation tree• Clear clustering of asset classes• Size of nodes scales with their degree centrality• Joint work with Jochen Papenbrock, Firamis UG (Germany)• Link to interactive version 23
  24. 24. • Link lengths scale with correlation• Mapping change and time-series• Link to interactive version 24
  25. 25. Intelligence Amplification• A solution is to augment human intelligence• Intelligence Amplification vs Artificial Intelligence William Ross Ashby (1956) in ‘Introduction to Cybernetics’• Technology, products and practices change constantly, market knowledge Game of Go (from China). is essential Computer programs only get to human amateur level due to good• Algorithms don’t fare well in periods of pattern recognition capabilities abrupt change, algorithms do not think needed in the game. outside the box• Data reduction 25
  26. 26. Network Theory is to Financial Maps what Cartography is to Geographic Maps 26
  27. 27. Network theory and related fields Financial Network Analysis Social Network Network Analysis Science NETWORK THEORY Graph & Matrix Computer Theory Science Biological Network Analysis 27
  28. 28. Main premise of network theory:Structure of links between nodes matters• The properties and behavior of a node cannot be analyzed on the basis its own properties and behavior alone.• To understand the behavior of one node, one must analyze the behavior of nodes that may be several links apart in the network.• Financial contexts – Trading networks, payment networks, exposure networks – Networks of interconnected balance sheets – Networks of asset dependencies• Topics: Centrality, Communities, Layouts, Spreading and generation processes, Path finding, etc. 28
  29. 29. FNA.fi - Open platform forFinancial Network Analysiswww.fna.fi – Open platform forFinancial Network AnalysisContact: kimmo@soramaki.netTwitter: soramakiThank you! 29

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