Practical implementation of the BCBS Monitoring indicators for intraday liquidity management


Published on

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

1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Practical implementation of the BCBS Monitoring indicators for intraday liquidity management

  1. 1. Intraday Liquidity Risk Conference 2012London, 28 November 2012Practical implementationof the BCBS Monitoringindicators for intradayliquidity managementCombining the monitoringindicators with data from interbankpayment systems Dr. Kimmo Soramäki Founder and CEO FNA,
  2. 2. ―It may be more efficient for the payment system providers (often central banks) to develop their IT toproduce the reports …‖International Banking Federation―An infrastructure should be equipped to the central bank’s settlement system to enable unified andefficient data collection‖Japanese Bankers Association―As the payment-system owners have […] all relevant intraday data available in their system, werecommend 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 maintainflow data than individual banks …‖Insitute of International Finance―Developing the required reporting capabilities on the actual clearing system would result insignificant efficiencies and cost savings for the overall market‖The ClearingHouse AssociationQuotes from Comments on the consultative document "Monitoring indicatorsfor intraday liquidity management" 2
  3. 3. Starting pointMost 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. 4. Tie to Oversight and Financial StabilityRegulatory environments are in fluxFocus on macroprundential view―Given the close relationship between Operators Overseersthe management of banks’ intradayliquidity risk and the smoothfunctioning of payment and settlementsystems, the indicators are also likely tobe of benefit to overseers of payment Supervisorsand settlement systems. Closecooperation between bankingsupervisors and the overseers isenvisaged.― 4
  5. 5. AgendaBy combining the monitoringindicators with interbank payment datamore meaningful analysis is possible:Network AnalysisThe financial crisis tought us that we cannot thinkof banks in isolation. ―Too interconnected to fail‖Stress SimulationsMake use of the 15 year experience of interbankpayment system simulations by overseers andsystem operatorsNew MetricsDevelop meaningful indicators for identifyingsystemically important and vulnerable banks? 5
  6. 6. Networks 6
  7. 7. Network TheoryMain premise of network theory:Structure of links between nodesmattersTo understand the behavior of onenode, one must analyze the behaviorof nodes that may be several linksapart in the networkIn the context of banking: paymentand liquidity flows, counterpartyexposures, asset correlations 1It is necessary to take a systems view– a network view to liquidity risk 1 1 7
  8. 8. LiquidityA bank’s ability to settle payments (its liquidity risk) depends on itsavailable liquidity and other banks ability to settle payments, whichdepend …The liquidity of other banksmatters only when a bankshas access to little liquidityStrategic interaction isfinely 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. 9. Example: Fedwire InterbankPayment Network Fall 2001 Around 8000 banks, 66 banks comprise 75% of value,25 banks completely connected Similar to other socio- technological networksSoramäki, 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, SantaSee: Fe Institute Working Paper 03- 9 10-054, 2003.
  10. 10. This is still shocking …―In 2006, the Federal Reserve invited a group of researchers tostudy the connections between banks by analyzing data from theFedwire system, which the banks use to back one another up.What they discovered was shocking: Just sixty-six banks — out ofthousands — accounted for 75 percent of all the transfers. Andtwenty five of these were completely interconnected to oneanother, including a firm you may have heard of called LehmanBrothers.‖ Want to Build Resilience? Kill the Complexity Harvard Business Review Blogs, 9/2012 10
  11. 11. Other interbank payment networksBecher, Millard and Soramäki (2008). Agnes Lubloy (2006). Topology of the HungarianThe network topology of CHAPS large-value transfer system. Magyar Nemzeti BankSterling. BoE Working Paper No. 355. Occasional Papers Embree and Roberts (2009). Network Analysis and Canadas Large Value Transfer SystemBoC Discussion Paper 2009-13 11
  12. 12. Demo: FNA Oversight MonitorClick here for interactive demo 12
  13. 13. Stress Simulations 13
  14. 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. 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. 16. Stress simulationsScenarios in BCBS document (i) Own financial stress (ii) Counterparty stress (iii) Customer stress (iv) Market wide credit or liquidity stressProper simulations need information on payment flows between allbanks – feedback effects!It is a Complex adaptive systemA well set-up simulation environment allows exploration of the above(and many more) stress scenariosLarge body of research and policy work on ii and iv carried out withdata from interbank payment systems 16
  17. 17. Demo: FNA Payment SimulatorClick here for interactive demo
  18. 18. New Metrics 18
  19. 19. Common network centrality metricsCentrality metrics aim to summarize some notion ofimportance that takes into account the position of the node inthe networkDegree: number of linksCloseness: distance from/to othernodes via shortest pathsBetweenness: number of shortestpaths going through the nodeEigenvector: nodes that are linked byother important nodes are morecentral, probability of a randomprocess, PageRank
  20. 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. 21. SinkRank vs Disruption Relationship between SinkRank and Disruption Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
  22. 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. 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) 23
  24. 24. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki