identifying systemically important banks in payment systems
DESCRIPTION
Presentation held at the Latsis Symposium at ETH on 13 September 2012TRANSCRIPT
Identifying Systemically Important Banks in Payment Systems
Kimmo Soramäki, Founder and CEO, FNASamantha Cook, Chief Scientist, FNA
Latsis SymposiumEconomics on the moveZürich, 13 September 2012
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
• 130 out of 290 billion lent to Greece consist of Target 2 balances
Example system across this presentation: A pays B one unit and C two, B pays A one, and C pays B one
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
Agenda
• Centrality • SinkRank • Experiments• Implementation
Common measures of 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 byother important nodes are more central, probability of a random process
Common centrality metrics
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) calculatedonly for “Giant Strongly Connected Component” (GSCC)
• Solution: PageRank
Pagerank
• Solves the problem with a “Damping factor” a which is used to modify the adjacency matrix (M)– Gi,j= aSi,j + (1- )/a N
• Effectively allowing the random process out of dead-ends (dangling nodes), but at the cost of introducing error
• Effect of a– =0 -> a Centrality of each node is 1/N
– =1 -> a Eigenvector Centrality
– Commonly =0.85 a is used
=0.85a
=1 (0.375, 0.375, 0.258)a
Which Measure for Payment Systems?
Network processes
• Centrality depends on network process– Trajectory : geodesic paths, paths, trails or
walks– Transmission : parallel/serial duplication or
transfer
Source: Borgatti (2004)
Distance to Sink
From B
From C
1
2
1
To A
From A
From CTo B
From A
From BTo C
• Markov chains are well-suited to model transfers along walks
• Example:
SinkRank
• SinkRank averages distances 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
SinkRanks on unweighed networks
SinkRank (weighting)
Uniform(A,B,C: 33.3% )
“Real”(A: 5% B: 90% C:5%)
Note: Node sizes scale with 1/SinkRank
PageRank(A: 37.5% B: 37.5% C:25%)
How good is it?
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
Data generation process
Based on extending Barabasi–Albert model of growth and preferential attachment
Distance from Sink vs Disruption
Highest disruption to banks whose liquidity is absorbed first (low distance to sink)
Relationship between Failure Distance and Disruption when the most central bank fails
SinkRank vs Disruption
Relationship between SinkRank and Disruption
Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
Implementing SinkRank
Payment System Simulator
available at www.fna.fi
More information at
www.fna.fi www.fna.fi/blog
Kimmo Soramäki, [email protected]: soramaki
Discussion Paper, No. 2012-43 | September 3, 2012 | http://www.economics-ejournal.org/economics/discussionpapers/2012-43