Practical implementation of the BCBS Monitoring indicators for intraday liquidity management

Combining the monitoring indicators with data from interbank payment systems

Dr. Kimmo SoramäkiFounder and CEOFNA, www.fna.fi

Intraday Liquidity Risk Conference 2012London, 28 November 2012

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“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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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 Xinstitution 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 should be at banks, data confidentiality

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Tie to Oversight and Financial StabilityRegulatory environments are in flux

Focus on macroprundential view

“Given the close relationship between 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 and settlement systems. Close cooperation between banking supervisors and the overseers is envisaged.“

Operators Overseers

Supervisors

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Agenda

By combining the monitoring indicators with interbank payment data more meaningful analysis is possible:

Network AnalysisThe financial crisis tought us that we cannot think of banks in isolation. “Too interconnected to fail”

Stress SimulationsMake use of the 15 year experience of interbank payment system simulations by overseers and system operators

New MetricsDevelop meaningful indicators for identifying systemically important and vulnerable banks?

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Networks

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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

It is necessary to take a systems view – a network view to liquidity risk

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Liquidity

Galbiati and Soramäki (2011), An Agent based Model of Payment Systems. Journal of Economic Dynamics and Control, Vol. 35, Iss. 6, pp 859-875

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

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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 (2007), Physica A, Vol. 379, pp 317-333.See: www.fna.fi/papers/physa2007sbagb.pdf

M. Boss, H. Elsinger, M. Summer, S. Thurner, The network topology of the interbank market, Santa Fe Institute Working Paper 03-10-054, 2003.

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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 ComplexityHarvard Business Review Blogs, 9/2012

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Other interbank payment networks

Agnes Lubloy (2006). Topology of the Hungarian large-value transfer system. Magyar Nemzeti Bank Occasional Papers

Embree and Roberts (2009). Network Analysis and Canada's Large Value Transfer SystemBoC Discussion Paper 2009-13

Becher, Millard and Soramäki (2008). The network topology of CHAPS Sterling. BoE Working Paper No. 355.

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Demo: FNA Oversight Monitor

Click here for interactive demo

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Stress Simulations

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

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”

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Stress simulationsScenarios 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

Demo: FNA Payment Simulator

Click here for interactive demo

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New Metrics

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 byother important nodes are more central, probability of a random process, PageRank

Common network centrality metricsCentrality metrics aim to summarize some notion of importance that takes into account the position of the node in the network

SinkRank

• 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

SinkRanks on unweighed networks

SinkRank vs Disruption

Relationship between SinkRank and Disruption

Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)

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

Distance to Sink

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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)

Blog, Library and Demos at www.fna.fi

Dr. Kimmo Soramäki kimmo@soramaki.netTwitter: soramaki