Network Approaches for Interbank Markets

1,355 views
1,227 views

Published on

Invited talk on Network Approaches for Interbank Markets -research conference in Castellon, Spain.

Published in: Economy & Finance, Technology
0 Comments
4 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,355
On SlideShare
0
From Embeds
0
Number of Embeds
396
Actions
Shares
0
Downloads
0
Comments
0
Likes
4
Embeds 0
No embeds

No notes for slide

Network Approaches for Interbank Markets

  1. 1. Network Approaches forInterbank MarketsInvited TalkDr. Kimmo SoramäkiFounder and CEOFNA, www.fna.fiUniversity Jaume I of Castellon, the University of Kiel, and the Kiel Institutefor the World EconomyCastellon, Spain30 May 2013
  2. 2. Systemic riskSearch volume for “systemic risk” in the US, Source: trends.google.com2Entered the common vocabulary with the 2008- financial crisisIs largely understood as an issue for the financial systemImportant for regulators, Chief Risk Officers, Enterprise RiskManagament, ...
  3. 3. Systemic riskNot clearly defined. De Bandt and Hartmann(2000) provides an early survey.Here: "The risk that a system composed of manyinteracting parts fails due to a shock to some of itsparts" - complex systems approachIn Finance, the risk that a disturbance in thefinancial system propagates and makes the systemunable to perform its function – i.e. allocatecapital efficientlyDomino effects, cascading failures, financialinterlinkages, … -> i.e. a process in thefinancial network 3Not:
  4. 4. Dragon King(Sornette 2009)Black Swan(Taleb 2001, 2007)vs.
  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. What does this mean? (Agenda)• Lots of empirical analysis is needed - You cantmanage what you cant measure• New models are needed that take into accountinterconnectedness and network contagion• Tools and methods need to be developed thatcan conveniently hande all four dimensions(especially visualization)
  8. 8. Empirics
  9. 9. 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 9M. Boss, H. Elsinger, M. Summer, S. Thurner, Thenetwork topology of the interbank market, SantaFe Institute Working Paper 03-10-054, 2003.
  10. 10. Extremelybig banks aremore likelyto occurthan thepower lawwouldsuggest ->Dragon KingInterbank data generation model available in FNA (Soramaki-Cook 2013)
  11. 11. Most central banks have now mapped theirinterbank payment systems11Agnes 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.
  12. 12. Example: Oversight Monitor12(network is fictional)http://www.fna.fi/solutions/oversight-monitor
  13. 13. Models
  14. 14. Degree: number of linksCloseness: distance from/to othernodes via shortest pathsBetweenness: number of shortestpaths going through the nodeEigenvector: nodes that are linked byother important nodes are more central,probability of a random processCommon centrality metricsCentrality aims to summarize some notion of importance.Operationalizing the concept is more challenging.
  15. 15. Centrality Measures forFinancial SystemsRecently 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 chains15Worlds Ocean CurrentsNASA Scientific Visualization Studio
  16. 16. Centrality depends on networkprocess• Trajectory– Geodesic paths (shortest paths)– Any path (visit a given node once)– Trails (visit a given link once)– Walks (free movement)• Transmission– Parallel duplication– Serial duplication– TransferBorgatti (2005). Centrality and network flow .Social Networks 27, pp. 55–71.
  17. 17. Systemic Risk in Payment Systems• Credit risk has been virtually eliminated by system design (real-timegross settlement)• Liquidity risk remains– “Congestion”– “Liquidity Dislocation”• Trigger may be– Operational/IT event– Liquidity event– Solvency event• Time scale is intraday, spillovers possible
  18. 18. SinkRank: Distance to SinkFrom BFrom C121To AFrom AFrom CTo BFrom AFrom BTo C• Soramaki and Cook (2012)• Markov chains are well-suited to model transfers along walks• Payments can be modelled as a ramdon walk in the network. We cancalculate the following random walk distances:(100%)(100%)(33.3%)(66.6%)
  19. 19. SinkRank• SinkRank is the average distanceto a node via (weighted) walksfrom other nodes• We need an assumption on thedistribution of liquidity in thenetwork at time of failure– Assume uniform ->unweighted average– Estimate distribution -> PageRank -weighted average– Use real distribution ->Real distribution are used as weightsSinkRanks on unweightednetworks
  20. 20. Payment System Simulationhttp://www.fna.fi/solutions/payment-simulator
  21. 21. Tools - FNA
  22. 22. • Complete documentation withtutorials and sample scripts• >200 commands• Web version & REST API isfree for academic research• Ongoing collaboration withseveral universities
  23. 23. Priorities for a Research Agenda
  24. 24. Priorities for the Research Agenda1. Measuring and mapping interconnectedness (networkstructure), modelling contagion (network process) andunderstanding their interplay
  25. 25. 28Bank projectionAsset projectionExample: Bank-Asset graphs andprojectionsNetwork of banks assetinterdependencies
  26. 26. Priorities for the Research Agenda1. Measuring and mapping interconnectedness (networkstructure), modelling contagion (network process) andunderstanding their interplay2. Developing metrics of systemic importance and earlywarning indicators for continuous monitoring of thefinancial system
  27. 27. Priorities for the Research Agenda1. Measuring and mapping interconnectedness (networkstructure), modelling contagion (network process) andunderstanding their interplay2. Developing metrics of systemic importance and earlywarning indicators for continuous monitoring of thefinancial system3. Development of network visualization techniques
  28. 28. Financial CartographyInteractive version: http://www.fna.fi/demos/erm/correlation-tree.html
  29. 29. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki

×