Identifying Systemically Important Banks in Payment Systems
1. Latsis Symposium
Economics on the move
Zรผrich, 13 September 2012
Identifying Systemically
Important Banks in Payment
Systems
Kimmo Soramรคki, Founder and CEO, FNA
Samantha Cook, Chief Scientist, FNA
2.
3. Interbank Payment Systems
โข Provide the backbone of all
economic transactions
โข Banks settle claims arising from
customers transfers, own
securities/FX trades and liquidity
management
โข Target 2 settled 839 trillion in
2010
Example system across this
โข 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
4. Systemic Risk in Payment Systems
โข Credit risk has been virtually eliminated by system
design (real-time gross settlement)
โข Liquidity risk remains
โ โCongestionโ
โ โLiquidity Dislocationโ
โข Trigger may be
โ Operational/IT event
โ Liquidity event
โ Solvency event
โข Time scale = intraday
7. 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
8. Eigenvector Centrality
โข Eigenvector centrality (EVC) vector, v, is given by
โ vP=v , where P is the transition matrix of an adjacency matrix M
โ v is also the eigenvector of the largest eigenvalue of P (or M)
โข Problem: EVC can be
(meaningfully) calculated
only for โGiant Strongly
Connected Componentโ
(GSCC)
โข Solution: PageRank
9. Pagerank
โข Solves the problem with a โDamping factorโ which is used to
modify the adjacency matrix (M)
โ Gi,j= Si,j
โข Effectively allowing the random process out of dead-ends (dangling
nodes), but at the cost of introducing error
โข Effect of
โ Centrality of each node is 1/N
โ Eigenvector Centrality
โ Commonly is used
11. Network processes
โข Centrality depends on network process
โ Trajectory : geodesic paths, paths, trails or walks
โ Transmission : parallel/serial duplication or transfer
Source: Borgatti (2004)
12. Distance to Sink
โข Markov chains are well-suited to model transfers along walks
โข Example:
From B 1
To A
From C 2
From A
To B
From C 1
From A
To C
From B
13. SinkRank
SinkRanks on unweighed
โข SinkRank averages distances networks
to sink into a single metric for
the sink
โข We need an assumption on
the distribution of liquidity in
the network
โ Asssume uniform ->
unweighted average
โ Estimate distribution ->
PageRank weighted average
โ Use real distribution ->
Average using real distribution as
weights
16. Experiments
โข Design issues
โ Real vs artificial networks?
โ Real vs simulated failures?
โ How to measure disruption?
โข Approach taken
1. Create artificial data with close resemblance to the US Fedwire
system (Soramรคki 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 โliquidity dislocationโ and โcongestionโ by non-failing
banks
4. Correlate them (the โdisruptionโ) with SinkRank of the failing
bank
18. 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)
19. SinkRank vs Disruption
Relationship between
SinkRank and Disruption
Highest disruption by
banks who absorb
liquidity quickly from the
system (low SinkRank)
22. More information at
www.fna.fi www.fna.fi/blog
Kimmo Soramรคki, D.Sc.
kimmo@soramaki.net
Twitter: soramaki
Discussion Paper, No. 2012-43 | September 3, 2012 |
http://www.economics-ejournal.org/economics/discussionpapers/2012-43