Your SlideShare is downloading. ×
Financial Cartography - Center for Financial Research
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Financial Cartography - Center for Financial Research

211
views

Published on

Slides from a PhD Seminar at the Center for Financial Studies at the Goethe University of Frankfurt on 30 January 2013. …

Slides from a PhD Seminar at the Center for Financial Studies at the Goethe University of Frankfurt on 30 January 2013.

Published in: Economy & Finance

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
211
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. CFS PhD SeminarFrankfurt, 30 January 2013Financial CartographyCFS Seminar Dr. Kimmo Soramäki Founder and CEO FNA, www.fna.fi
  • 2. “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 2
  • 3. We did not have maps … 3
  • 4. Eratosthenes map of the known world4c. 194 BC
  • 5. … 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 as size, shape,value, texture or pattern, colorand orientation of the points,lines and areas – everything hasa meaning Political map of Europe 5
  • 6. … 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 6
  • 7. I. Mapping II. MappingSystemic Risk Financial Markets 7
  • 8. 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 8
  • 9. 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. 9
  • 10. Network aspect is an unexploreddimension of data e Tim Variables Observations 10
  • 11. 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: www.fna.fi/papers/physa2007sbagb.pdf Fe Institute Working Paper 03- 11 10-054, 2003.
  • 12. More Maps: Federal Funds1997 - 2006 Source: Bech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986.• 2600 loans worth $335 billion per day• First Circle: 165 Second Circle: 271 Rest: 42 12
  • 13. More Maps: Italian money market Italian (very small) Italian (small) Italian (large) Foreign Source: 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 13
  • 14. More Maps: DebtRankAugust 2007 to April 2008 October 2008 to April 2010Nodes: Financial institutions Source: Battiston et al, NatureLinks: Impact of an institution to another Scientific Reports 2-54, 2012Nodes closer to center are more important (as are big and red) 14
  • 15. Where are we today?Regulatory response to recent financial crisiswas to strengthen macro-prudentialsupervision with mandates for moreregulatory data“Big data” and “Complex Data”-> Providingtools and challenge to understand, utilize andoperationalize the data (network is fictional)Financial Networks are starting to get theirown literature and metrics different from Case: Oversight Monitor at Norges Bankother fields of Network Theory The monitor will allow the identification of systemically important banks and evaluation of the impact of bank failures on the system Intraday Liqudidy Network -example 15
  • 16. I. Mapping II. MappingSystemic Risk Financial Markets 16
  • 17. 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 17
  • 18. Collapse of LehmanLehman was the fourth largest investmentbank in the US (behind Goldman Sachs,Morgan Stanley, and Merrill Lynch) with26.000 employeesAt bankruptcy Lehman had $750 billion debtand $639 billion assetsCollapse was due to losses in subprimeholdings and inability to find funding due toextreme market conditionsIs seen as a divisive point in the 2007-2009financial crisis 18
  • 19. The Data Pairwise correlations of return on 118 global assets in 4 asset classes 9870 data points per time interval Time windows 2 months before and 2 months after Lehman collapse 19
  • 20. i) Heat Maps JanuaryCorporate 2007BondsFX RatesGovernmentBond Yields Correlation -1StockExchange 0Indices +1 20
  • 21. January 2007 t-2 t-1CorporateBondsFX RatesGovernmentBondsStocks t+1 t+2CorporateBondsFX RatesGovernmentBondsStocks 21
  • 22. ii) Asset TreesOriginally proposed by Rosario Mantegna in 1999Used currently by some major financial institutionsfor market analysis and portfolio optimization andvisualizationMethodology in a nutshell 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) 22
  • 23. Minimum Spanning TreeA spanning tree of a graph is a subgraph that:1.is a tree and2.connects all the nodes togetherLength of a tree is the sum of its links. Minimum spanning tree (MST) is a spanningtree with shortest length.MST reflects the hierarchical structure of the correlation matrix
  • 24. Demo: Asset Trees Color of node denotes asset class: Dow Jones Size of node reflects volatility (variance) of returns Ireland 10 year Links between nodes reflect government bond backbone correlationsEMU CorporateAAA, 1-3 years - short link = high correlation - long link = low correlation EUR/USDClick here for interactive visualization 24
  • 25. Correlation filtering PMFG Balance between too much and too little information, signal vs noiseOne 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 25
  • 26. iii) NETS• Network Estimation for Time- Series• Forthcoming paper by Barigozzi and Brownlees• Estimates an unknown network structure from multivariate data• Based on partial correlations• Captures both comtemporenous and serial dependence (partial correlations and lead/lag effects) 26
  • 27. 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 27
  • 28. Demo: Sammon Projection EMU Corporate AAA, 1-3 years Color of node denotes asset class: Dow Jones Size of node reflects volatility Ireland 10 year (variance) of returns government bond Distance between nodes reflects EUR/USD similarity of correlation profiles - close = similar - far apart = differentClick here for interactive visualization 28
  • 29. 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 www.extreme-value-theory.com• 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 29
  • 30. “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 30
  • 31. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki