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Presentation held at the Latsis Symposium at ETH on 13 September 2012

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- 1. Latsis SymposiumEconomics on the moveZürich, 13 September 2012Identifying SystemicallyImportant Banks in PaymentSystemsKimmo Soramäki, Founder and CEO, FNASamantha Cook, Chief Scientist, FNA
- 2. Interbank Payment Systems• Provide the backbone of all economic transactions• Banks settle claims arising from customers transfers, own securities/FX trades and liquidity management• Target 2 settled 839 trillion in 2010 Example system across this• 130 out of 290 billion lent to presentation: A pays B one Greece consist of Target 2 unit and C two, B pays A one, balances and C pays B one
- 3. Systemic Risk in Payment Systems• Credit risk has been virtually eliminated by system design (real-time gross settlement)• Liquidity risk remains – “Congestion” – “Liquidity Dislocation”• Trigger may be – Operational/IT event – Liquidity event – Solvency event• Time scale = intraday
- 4. Agenda• Centrality• SinkRank• Experiments• Implementation
- 5. Common measures of centrality
- 6. Common centrality metrics 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 other important nodes are more central, probability of a random process
- 7. Eigenvector Centrality• Eigenvector centrality (EVC) vector, v, is given by – vP=v , where P is the transition matrix of an adjacency matrix M – v is also the eigenvector of the largest eigenvalue of P (or M)• Problem: EVC can be (meaningfully) calculated only for “Giant Strongly Connected Component” (GSCC)• Solution: PageRank
- 8. Pagerank• Solves the problem with a “Damping factor” which is used to modify the adjacency matrix (M) – Gi,j= Si,j• Effectively allowing the random process out of dead-ends (dangling nodes), but at the cost of introducing error• Effect of – Centrality of each node is 1/N – Eigenvector Centrality – Commonly is used
- 9. Which Measure for Payment Systems?
- 10. Network processes• Centrality depends on network process – Trajectory : geodesic paths, paths, trails or walks – Transmission : parallel/serial duplication or transfer Source: Borgatti (2004)
- 11. Distance to Sink• Markov chains are well-suited to model transfers along walks• Example: From B 1 To A From C 2 From A To B From C 1 From A To C From B
- 12. SinkRank SinkRanks on unweighed• SinkRank averages distances networks to sink into a single metric for the sink• We need an assumption on the distribution of liquidity in the network – Asssume uniform -> unweighted average – Estimate distribution -> PageRank weighted average – Use real distribution -> Average using real distribution as weights
- 13. SinkRank (weighting) Uniform PageRank “Real” (A,B,C: 33.3% ) (A: 37.5% B: 37.5% C:25%) (A: 5% B: 90% C:5%) Note: Node sizes scale with 1/SinkRank
- 14. How good is it?
- 15. Experiments• Design issues – Real vs artificial networks? – Real vs simulated failures? – How to measure disruption?• Approach taken 1. Create artificial data with close resemblance to the US Fedwire system (Soramäki et al 2007) 2. Simulate failure of a bank: the bank can only receive but not send any payments for the whole day 3. Measure “liquidity dislocation” and “congestion” by non-failing banks 4. Correlate them (the “disruption”) with SinkRank of the failing bank
- 16. Data generation processBased on extending Barabasi–Albert model of growth andpreferential attachment
- 17. Distance from Sink vs Disruption Relationship between Failure Distance and Disruption when the most central bank fails Highest disruption to banks whose liquidity is absorbed first (low distance to sink)
- 18. SinkRank vs Disruption Relationship between SinkRank and Disruption Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
- 19. Implementing SinkRank
- 20. Payment System Simulator available at www.fna.fi
- 21. More information atwww.fna.fi www.fna.fi/blogKimmo Soramäki, D.Sc.kimmo@soramaki.netTwitter: soramakiDiscussion Paper, No. 2012-43 | September 3, 2012 |http://www.economics-ejournal.org/economics/discussionpapers/2012-43

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http://www.economics-ejournal.org/economics/discussionpapers/2012-43