Presentation held at Infoline's Intraday Liquidity Risk Conference in London on 28 November 2012

1. Intraday Liquidity Risk Conference 2012
London, 28 November 2012
Practical implementation
of the BCBS Monitoring
indicators for intraday
liquidity management
Combining the monitoring
indicators with data from interbank
payment systems
Dr. Kimmo Soramäki
Founder and CEO
FNA, www.fna.fi

2. ―It may be more efficient for the payment system providers (often central banks) to develop their IT to
produce the reports …‖
International Banking Federation
―An infrastructure should be equipped to the central bank’s settlement system to enable unified and
efficient data collection‖
Japanese Bankers Association
―As the payment-system owners have […] all relevant intraday data available in their system, we
recommend that they would be responsible for the data collection …‖
European Association of Co-operative Banks (EACB)
―Central banks and payment and settlement systems are often better placed to collect and maintain
flow data than individual banks …‖
Insitute of International Finance
―Developing the required reporting capabilities on the actual clearing system would result in
significant efficiencies and cost savings for the overall market‖
The ClearingHouse Association
Quotes from Comments on the consultative document "Monitoring indicators
for intraday liquidity management"
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3. Starting point
Most indicators should be calculated by Interbank Payment Systems
(i) Daily maximum liquidity requirement usage √
(ii) Available intraday liquidity √ (partly possible)
(iii) Total payments (sent and received) √
(iv) Time-specific and other critical obligations √ (partly possible)
(v) Value of customer payments made on behalf √ (partly possible)
of financial institution customers
(vi) Intraday credit lines extended to financial X
institution customers
(vii) Timing of intraday payments √
(viii) Intraday throughput √
 Data quality will be better and implementation less constly
 More meaningful analysis can be carried out by augmenting indicators
with other data available in payment systems
 But: systems (securities & payments) vs correspondents, responsibility 3
should be at banks, data confidentiality

4. Tie to Oversight and Financial Stability
Regulatory environments are in flux
Focus on macroprundential view
―Given the close relationship between Operators Overseers
the management of banks’ intraday
liquidity risk and the smooth
functioning of payment and settlement
systems, the indicators are also likely to
be of benefit to overseers of payment
Supervisors
and settlement systems. Close
cooperation between banking
supervisors and the overseers is
envisaged.―
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5. Agenda
By combining the monitoring
indicators with interbank payment data
more meaningful analysis is possible:
Network Analysis
The financial crisis tought us that we cannot think
of banks in isolation. ―Too interconnected to fail‖
Stress Simulations
Make use of the 15 year experience of interbank
payment system simulations by overseers and
system operators
New Metrics
Develop meaningful indicators for identifying
systemically important and vulnerable banks?
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6. Networks
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7. Network Theory
Main premise of network theory:
Structure of links between nodes
matters
To understand the behavior of one
node, one must analyze the behavior
of nodes that may be several links
apart in the network
In the context of banking: payment
and liquidity flows, counterparty
exposures, asset correlations
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It is necessary to take a systems view
– a network view to liquidity risk 1 1
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8. Liquidity
A bank’s ability to settle payments (its liquidity risk) depends on its
available liquidity and other banks ability to settle payments, which
depend …
The liquidity of other banks
matters only when a banks
has access to little liquidity
Strategic interaction is
finely balanced
Galbiati and Soramäki (2011), An Agent based Model of
Payment Systems. Journal of Economic Dynamics and
Control, Vol. 35, Iss. 6, pp 859-875

9. Example: Fedwire Interbank
Payment Network
Fall 2001
Around 8000 banks, 66 banks
comprise 75% of value,25 banks
completely connected
Similar to other socio-
technological networks
Soramäki, Bech, Beyeler, Glass and Arnold M. Boss, H. Elsinger, M. Summer, S. Thurner, The
(2007), Physica A, Vol. 379, pp 317-333. network topology of the interbank market, Santa
See: www.fna.fi/papers/physa2007sbagb.pdf Fe Institute Working Paper 03-
9
10-054, 2003.

10. This is still shocking …
―In 2006, the Federal Reserve invited a group of researchers to
study the connections between banks by analyzing data from the
Fedwire system, which the banks use to back one another up.
What they discovered was shocking: Just sixty-six banks — out of
thousands — accounted for 75 percent of all the transfers. And
twenty five of these were completely interconnected to one
another, including a firm you may have heard of called Lehman
Brothers.‖
Want to Build Resilience? Kill the Complexity
Harvard Business Review Blogs, 9/2012
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11. Other interbank payment networks
Becher, Millard and Soramäki (2008). Agnes Lubloy (2006). Topology of the Hungarian
The network topology of CHAPS large-value transfer system. Magyar Nemzeti Bank
Sterling. BoE Working Paper No. 355. Occasional Papers
Embree and Roberts (2009). Network
Analysis and Canada's Large Value Transfer
SystemBoC Discussion Paper 2009-13
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12. Demo: FNA Oversight Monitor
Click here for interactive demo 12

13. Stress Simulations
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14. What are simulations?
• Methodology to understand complex systems – systems that are
large with many interacting elements and or non-linearities (such as
payment systems)
• In contrast to traditional statistical models, which attempt to
find analytical solutions
• Usually a special purpose computer program is used that takes
granular inputs, applies the simulation rules and generates outputs
• Take into account second rounds effects, third round, …
• Inputs can be stochastic or deterministic. Behavior can be
static, pre-programmed, evolving or co-learning

15. Short history of LVPS simulations
• 1997 : Bank of Finland
– Evaluate liquidity needs of banks when Finland’s RTGS system was joined with TARGET
– See Koponen-Soramaki (1998) ―Liquidity needs in a modern interbank payment systems:
• 2000 : Bank of Japan and FRBNY
– Test features for BoJ-Net/Fedwire
• 2001 - : CLS approval process and ongoing oversight
– Test CLS risk management
– Evaluate settlement’ members capacity for pay-ins
– Understand how the system works
• Since: Bank of Canada, Banque de France, Nederlandsche Bank, Norges
Bank, TARGET2, and many others
• 2010 - : Bank of England new CHAPS
– Evaluate alternative liquidity saving mechanisms
– Use as platform for discussions with banks
– Denby-McLafferty (2012) ―Liquidity Saving in CHAPS: A Simulation Study‖

16. Stress simulations
Scenarios in BCBS document
(i) Own financial stress
(ii) Counterparty stress
(iii) Customer stress
(iv) Market wide credit or liquidity stress
Proper simulations need information on payment flows between all
banks – feedback effects!
It is a Complex adaptive system
A well set-up simulation environment allows exploration of the above
(and many more) stress scenarios
Large body of research and policy work on ii and iv carried out with
data from interbank payment systems
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17. Demo: FNA Payment Simulator
Click here for interactive demo

18. New Metrics
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19. Common network centrality metrics
Centrality metrics aim to summarize some notion of
importance that takes into account the position of the node in
the network
Degree: number of links
Closeness: distance from/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, PageRank

20. SinkRank
SinkRanks on unweighed
networks
• Soramäki and Cook
(2012), ―Algorithm for identifying
systemically important banks in
payment systems‖
• Measures how big of a ―sink‖ a bank is
in a payment system
• Based on theory of absorbing markov
chains: average distance to a node via
(weighted) walks from other nodes
• Provides a baseline scenario of no
behavioral changes by banks
• Allows also the identification of most
vulnerable banks

21. SinkRank vs Disruption
Relationship between
SinkRank and Disruption
Highest disruption by
banks who absorb
liquidity quickly from the
system (low SinkRank)

22. 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)
Distance to Sink

23. Summary
• The indicators can be efficiently calculated at (central
bank) payment and settlement systems
• Responsibility is different from implementation
• Complex adaptive system. Simplification dangerous.
• Possibility for joint stress tests?
(overseers/supervisors/banks)
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24. Blog, Library and Demos at www.fna.fi
Dr. Kimmo Soramäki
kimmo@soramaki.net
Twitter: soramaki