Financial Networks: I. Financial Cartography


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First lecture of a PhD level course on "Financial Networks" at Center for Financial Research at Goethe University, Frankfurt.

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Financial Networks: I. Financial Cartography

  1. 1. Center for Financial Studies at the Goethe UniversityPhD Mini-courseFrankfurt, 25 January 2013Financial Networks Dr. Kimmo Soramäki Founder and CEO FNA,
  2. 2. About the Course• Objective of the mini-course To give an overview of how network theory can be applied in financial regulation and risk management. To show how to use FNA software to analyze financial networks• Interdisciplinary approach Combining methods from Graph Theory, Economics, Finance, Statistics, Operations Research, Computer Science, Bioinformatics, …• Focus on empirical analysis and real-life applications 2
  3. 3. OrganizationFriday, 25 January, 16:00-19:001. Financial Cartography2. Introduction to Network Theory and FNAFriday, 1 February, 16:00-19:003. Observing Network Structures4. Centrality and Systemic riskFriday, 8 February, 16:00-19:005. Inferring Financial Networks6. Stress Testing Networks 3
  4. 4. Literature• Blog at• Research Library at• ~150 papers on financial networks 4
  5. 5. Software• Financial Network Analytics –software available at• Free to register and use online• All analysis and visualization presented here are developed with the software• For getting started, see Feel free to contact me at: 5
  6. 6. Center for Financial Studies at the Goethe UniversityPhD Mini-courseFrankfurt, 25 January 2013Financial Networks1. Financial Cartography Dr. Kimmo Soramäki Founder and CEO FNA,
  7. 7. “When the crisis came, the serious limitations of existingeconomic and financial models immediately became apparent.[...]As a policy-maker during the crisis, I found the availablemodels of limited help. In fact, I would go further: in the face ofthe crisis, we felt abandoned by conventional tools.” in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 7
  8. 8. We did not have maps … 8
  9. 9. Eratosthenes map of the known world9c. 194 BC
  10. 10. … but what are maps“A set of points, lines, and areasall defined both by position withreference to a coordinate systemand by their non-spatialattributes”Data is encoded assize, shape, value, texture orpattern, color and orientation ofthe points, lines and areas –everything has a meaning Political map of Europe 10
  11. 11. … but what are maps (contd.)Cartographer selects onlythe information that isessential to fulfill thepurpose of the mapMaps reducemultidimensional data intoa two dimensional spacethat is better understood byhumansMaps are intelligenceamplification, they aid indecision making and build Map by John Snow showing the clusters of cholera cases in the London epidemic of 1854intuition 11
  12. 12. I. Mapping II. MappingSystemic Risk Financial Markets 12
  13. 13. Systemic risk ≠ systematic risk News articles mentioning “systemic risk”, Source: 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 13
  14. 14. First Maps 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: Fe Institute Working Paper 03- 14 10-054, 2003.
  15. 15. More Maps Federal funds 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 259-278 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 15 Cross-border bank lending banking:1978-2009. IMF Working Paper WP/11/74.
  16. 16. Exposure networksSam Langfield, Zijun Liu and Tomohiro Ota (2012). Presentation given at ETHConference Economics on the Move on 14/09/12 16
  17. 17. Network Theory can be to Financial Mapswhat Cartography is to Geographic MapsMain premise of network theory:Structure of links between nodesmattersTo understand the behavior of onenode, one must analyze thebehavior of nodes that may beseveral links apart in the networkTopics:Centrality, Communities, Layouts,Spreading and generationprocesses, Path finding, etc. 17
  18. 18. Network aspect is an unexploreddimension of data Variables Observations 18
  19. 19. Centrality Measures forFinancial Systems• Traditional – Degree, Closeness, Betweenness centrality, PageRank, etc.• DebtRank – Battiston et al, Science Reports, 2012 – Feedback-centrality – Solvency cascade• SinkRank – Soramäki and Cook, Kiel Economics DP, 2012 – Transfer along walks – Liquidity absorption 19
  20. 20. Where are we today?Regulatory response to recent financial crisiswas to strengthen macro-prudentialsupervision with mandates for moreregulatory data“Big data” and “Complex Data”-> Challengeto understand, utilize and operationalize thedata (network is fictional)Promise of “Analytics based policy andregulation”, i.e. the application of computertechnology, operations research, and Example: Oversight Monitor at Norges Bankstatistics to support human decision making The monitor will allow the identification of systemically important banks and evaluation of the impact of bank failures on the system 20
  21. 21. I. Mapping II. MappingSystemic Risk Financial Markets 21
  22. 22. OutlinePurpose of the maps – Identify price driving themes and market dynamics – Reduce complexity – Spot anomalies – Build intuitionThe maps: Heat Maps, Trees, Networksand Sammon‟s ProjectionsBased on asset correlations or taildependenceThese methods are showcased forvisualizing markets around the collapseof Lehman brothers 22
  23. 23. The CaseLehman was the fourth largest investment bank in the US (behindGoldman Sachs, Morgan Stanley, and Merrill Lynch) with 26.000employeesAt bankruptcy Lehman had $750 billion debt and $639 billion assetsCollapse was due to losses in subprime holdings and inability to findfunding due to extreme market conditionsIs seen as a divisive point in the 2007-2009 financial crisisWe create 3 visualization of a 5 month period around the failure (15September 2008) from asset price data 23
  24. 24. The Data Pairwise correlations of return on 141 global assets in 5 asset classes 9870 data points per time interval 5 intervals, 2 months before and 3 months after Lehman collapse 24
  25. 25. i) Heat Maps 2004-2007CorporateBondsCDS onGovernmentDebtFX RatesGovernmentBond Yields Correlation -1StockExchange 0Indices +1 25
  26. 26. Collapse of Lehman, t=month2004-2007 t-2 t-1 t+1 t+2 t+3
  27. 27. ii) Asset TreesOriginally proposed by Rosario Mantegna in 1999Used currently by some major financial institutionsfor market analysis and portfolio optimization andvisualizationMethodology in a nutshell MST 1. Calculate (daily) asset returns 2. Calculate pairwise Pearson correlations of returns 3. Convert correlations to distances 4. Extract Minimum Spanning Tree (MST) 5. Visualize (as phylogenetic trees) 27
  28. 28. DemoClick here for interactive visualization 28
  29. 29. Correlation filtering PMFGBalance between too much and too littleinformationOne of many methods to create networksfrom correlation/distance matrices – PMFGs, Partial Correlation Networks, Influence Networks, Granger Causality, Influence Network NETS, etc.New graph, information-theory, economics& statistics -based models are beingactively developed 29
  30. 30. iii) NETS• Network Estimation for Time- Series• Forthcoming paper by Barigozzi and Brownlees• Estimates an unknown network structure from multivariate data• Captures both comtemporenous and serial dependence (partial correlations and lead/lag effects) 30
  31. 31. iv) Sammon‟s ProjectionProposed by John W. Sammon in IEEE Transactions on Computers 18: 401–409(1969)A nonlinear projection method to map ahigh dimensional space onto a space oflower dimensionality. Example: Iris Setosa Iris Versicolor Iris Virginica 31
  32. 32. DemoClick here for interactive visualization 32
  33. 33. Tail dependence• Correlation is a linear dependence. The same visual maps can be extended to non-linear dependences.• Joint work with Firamis (Jochen Papenbrock) and RC Banken (Frank Schmielewski), see• Instead of correlation, links and positions measure similarity of distances to tail losses Tail Tree Tail Sammon (Click here for interactive visualization) (click here for interactive visualization) 33
  34. 34. Intelligence Amplification• Intelligence Amplification vs Artificial Intelligence William Ross Ashby (1956) in „Introduction to Cybernetics‟• Technology, products and practices change constantly, market knowledge is essential Game of Go (from China).• Algorithms don‟t fare well in periods of Computer programs only get to abrupt change, algorithms do not think human amateur level due to good outside the box pattern recognition capabilities needed in the game.• Build intuition and mental maps, provide tools for trading strategies 34
  35. 35. “In the absence of clear guidance from existing analyticalframeworks, policy-makers had to place particular reliance onour experience. Judgment and experience inevitably played akey role.” in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 35
  36. 36. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki