Quantitative Oversight of Financial Market Infrastructures
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Quantitative Oversight of Financial Market Infrastructures

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Slides from a one day seminar and interactive workshop hosted by Bank of Guatemala and CEMLA in Guatemala City.

Slides from a one day seminar and interactive workshop hosted by Bank of Guatemala and CEMLA in Guatemala City.

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  • 1. III Payment System Oversight Workshop CEMLA – Central Bank of Guatemala Guatemala City, 16-18 October 2013 Quantitative Oversight of Financial Market Infrastructures One Day Interactive Workshop Dr. Kimmo Soramäki Founder and CEO Financial Network Analytics, www.fna.fi
  • 2. Agenda • Introduction and Background • Elements of Quantitative Oversight – – – – – – Network Maps Relevant Metrics Real-time Monitoring Predictive Modeling Stress Testing Automation of Analysis • Applying Quantitative Oversight • Interactive Workshop using FNA 2
  • 3. Quantitative Oversight • The recent financial crisis prompted the need and created the expectation for regulators to collect more data about the financial system and to analyze it more efficiently • At he same time, know-how and tools for analyzing large data sets so-called Big Data - have become more prevalent • The continuing reduction in storage costs and increase in computing power has meant that data stored economy wide is growing exponentially. Regulatory data – especially in a data-intensive field such as finance – is no exception • The expectations set by the public and the opportunities created by advances in data analytics will both necessitate and enable a more quantitative approach to the Oversight of Financial Infrastructures Demand – Supply - Expectations 3
  • 4. Systemic Risk News articles mentioning “systemic risk”, Source: trends.google.com Not “The risk that a system composed of many interacting parts fails (due to a shock to some of its parts)” In Finance, the risk that a disturbance in the financial system propagates and makes the system unable to perform its function – i.e. allocate capital efficiently. Or Domino effects, cascading failures, financial interlinkages, … -> i.e. a process in the financial network 4
  • 5. Dragon King Black Swan (Sornette 2009) (Taleb 2001, 2007) vs.
  • 6. Network Models • The financial crisis brought to light the interconnected nature of modern financial systems. Academia and policy-makers have recently developed a stronger awareness of the need for new analytical methods • These new approaches often involve network models, which naturally capture the interconnectedness of the financial system. • In payment system oversight the links may be related to bilateral payment flows, overnight lending relationships, or common participation in different financial infrastructures. • Payment system have led the research on financial networks as the first area where data of the needed granularity has been available from interbank payment systems operated by central banks. 6
  • 7. Network Theory Financial Network Analysis Social Network Analysis Network Science NETWORK THEORY Graph & Matrix Theory Computer Science Biological Network Analysis
  • 8. Main Premise of Network Theory Structure of links between nodes matters • The properties and behavior of a node cannot be analyzed on the basis its own properties and behavior alone. • To understand the behavior of one node, one must analyze the behavior of nodes that may be several links apart in the network. • Financial contexts – Trading networks, payment networks, exposure networks – Networks of interconnected balance sheets – Networks of asset dependencies • Topics: Centrality, Communities, Layouts, Spreading and generation processes, Path finding, etc. 8
  • 9. Networks Brings us Beyond the Data Cube For example: Entities: 100 banks Variables: Liquidity, Opening Balance, … Time: Daily data Links: Bilateral payment flows Links are the 4th dimension to data Information on the links allows us to develop better models for banks' liquidity situation in times of stress 9
  • 10. Network Research • A growing body of empirical research on financial networks • Interbank payment flows – Soramäki et al (2006), Becher et al. (2008), Boss et al. (2008), Pröpper et al. (2009), Embree and Roberts (2009), Akram and Christophersen (2010) … • Overnight loans networks – Atalay and Bech (2008), Bech and Bonde (2009), Wetherilt et al. (2009), Iori et al. (2008) and Heijmans et al. (2010), Craig & von Peter (2010) … • Flow of funds, Credit registry, Stock trading, Markets, … – Castren and Kavonius (2009), Bastos e Santos and Cont (2010), Garrett et al. 2011, Minoiu and Reyes (2011), (Adamic et al. 2009, Jiang and Zhou 2011), Langfield, Liu and Ota (2012) • More at www.fna.fi/blog
  • 11. The Journal of Network Theory in Finance • Financial institutions and markets are highly interconnected, but only recently has literature begun to emerge that maps these interconnections and assesses their impact on financial risk and returns • The Journal of Network Theory in Finance (JNTF) is an interdisciplinary journal publishing academically rigorous and practitioner-focused research on the application of network theory in finance and related fields • If you have a paper you would like to submit to the journal, or are interested in contributing in any other way please contact Editor-in-Chief: Kimmo Soramaki – kimmo@fna.fi • For information on subscribing please contact Journals Manager of Risk Journals: Jade Mitchell jade.mitchell@incisivemedia.com 11
  • 12. Network Visualization • Network layout – The relative position of nodes – Many algorithms are available • Two (or 3) dimensions are available for visualization – One variable in One dimension – Many variables in Many dimensions • All details must convey unique meaning – – – – High data-ink ratio No ‘chartjunk’ Correct dimensions No duplication (from Tufte 2001, Tufte’s Rules)
  • 13. Intelligence Amplification • Intelligence Amplification vs Artificial Intelligence William Ross Ashby (1956) in ‘Introduction to Cybernetics’ • Visual Cortex is very developed • Technology, products and practices change constantly, market knowledge is essential • Algorithms don’t fare well in periods of abrupt change, algorithms do not think outside the box • Game of Go (from China). Computer programs only get to human amateur level due to good pattern recognition capabilities needed in the game. Build intuition and mental maps, provide tools for scenario thinking 13
  • 14. “In the absence of clear guidance from existing analytical frameworks, policy-makers had to place particular reliance on our experience. Judgment and experience inevitably played a key role.” in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 14
  • 15. Elements of QO 1. 2. 3. 4. 5. 6. Network Maps Relevant Metrics Real-time Monitoring Predictive Modeling Stress Testing Automation 15
  • 16. 1. Network Maps Fedwire Interbank Payment Network, Fall 2001 Around 8000 banks, 66 banks comprise 75% of value,25 banks completely connected Similar to other sociotechnological networks Soramäki, Bech, Beyeler, Glass and Arnold (2007), Physica A, Vol. 379, pp 317-333. See: www.fna.fi/papers/physa2007sbagb.pdf 16
  • 17. More Maps Federal funds Bech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986. Italian money market Iori G, G de Masi, O Precup, G Gabbi and G Caldarelli (2008): “A network analysis of the Italian overnight money market”, Journal of Economic Dynamics and Control, vol. 32(1), pages 259-278 Unsecured Sterling money market Wetherilt, A. P. Zimmerman, and K. Soramäki (2008), “The sterling unsecured loan market during 2006–2008: insights from network topology“, in Leinonen (ed), BoF Scientific monographs, E 42 Cross-border bank lending Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global banking:1978-2009. IMF Working Paper WP/11/74. 17
  • 18. Interactive Maps Exploration of data and Presentation of information 18
  • 19. 2. Relevant Metrics Starting point: BSBC Monitoring tools for intraday liquidity management “It is envisaged that the introduction of monitoring tools for intraday liquidity will lead to closer co-operation between banking supervisors and the overseers in the monitoring of banks’ payment behaviour.“ A(i) A(ii) A(iii) A(iv) Daily maximum intraday liquidity usage Available intraday liquidity at the start of the business day Total payments Time-specific obligations B(i) B(ii) Value of customer payments made on behalf of correspondent banking customers Intraday credit lines extended to customers C(i) Intraday throughput 19
  • 20. Network Metrics Recently developed financial system specific metrics: • Core-Periphery – • DebtRank – • Craig and von Peter 2010, Optimal classification that matches theoretical core-periphery model Battiston et al, Science Reports 2012, Cascading failures -model SinkRank – Soramäki and Cook, Kiel Economics DP, 2012, Absorbing Markov chains World's Ocean Currents NASA Scientific Visualization Studio 20
  • 21. Process in Payment Systems Central bank Payment system 4 Payment account 5 Payment account is debited Bi is credited Bj 6 Depositor account 3 Payment is settled is credited or queued Qi 2 Depositor account Bi > 0 Di Bank i Liquidity Market Dj Qj > 0 Qj Bank j is debited payment, if any, is released 1 Agent instructs bank to send a payment 7 Queued Productive Agent Productive Agent Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 22. Payment System Instructions Summed over the network, instructi ons arrive at a steady rate Liquidity When liquidity is high payments are submitted promptly and banks process payments independently of each other Time Payments Time Payments Instructions Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 23. Payment System Instructions Liquidity Time Payments Frequency Time Reducing liquidity leads to episodes of congestion when queues build, and cascades of settlement activity when incoming payments allow banks to work off queues. Payment processing becomes coupled across the network Payments Cascade Length Instructions Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 24. How to Calculate a Metric for Payment Flows Depends on process that takes place in the network! Trajectory – – – – Geodesic paths (shortest paths) Any path (visit no node twice) Trails (visit no link twice) Walks (free movement) Transmission – Parallel duplication – Serial duplication – Transfer Source: Borgatti (2004)
  • 25. 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 transfer 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 SinkRank on unweighted networks
  • 26. 26
  • 27. 3. Real-time Monitoring Outlier Detection Visual alerts Visualizations can highlight banks or their links that are out of normal bounds by marking them with different color or other visual cues E-mail/SMS alerts In a real-time monitoring environment the system can sen e-mails if aspects of the system are out of normal bounds 27
  • 28. 4. Predictive Modeling • Predictive modeling is the process by which a model is created to try to best predict the probability of an outcome • Given a distribution of liquidity among the banks in the morning, how is it going to be in the afternoon? – What is the distribution if bank A has an operational disruption at noon? – Who is affected first? – Who is affected most? – How is Bank C affected in an hour? • Valuable information for decision making – Crisis management – Participant behavior 28
  • 29. 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
  • 30. 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, preprogrammed, evolving or co-learning
  • 31. Application areas Enhance understanding of system mechanics Evaluate alternative design features Why Simulate? Stress testing and liquidity needs analysis Platform for communication among stakeholders
  • 32. Data Needs for Simulations • Historical transaction data – From interbank payment systems – At minimum: date, time, sender, receiver, value – More data on type of payment, economic purpose, second tier (if any), type of institution, etc. useful • Artificial transaction data – Based on aggregates (possible with Entropy maximization methods) – Based on a network model (defining bilateral flows) – Assumptions • Timing of payments • Value distribution • Correlations – System stability (net flows over longer times)
  • 33. Simulations are Difficult Major challenge to make them – tie in to recent/real-time data – easier to carry out – easier to understand 33
  • 34. 5. Stress Testing • Scenario types – – – – Historical Probabilistic Extreme but plausible market conditions Worst-case • Scenarios in BCBS document – Own financial stress : a bank suffers, or is perceived to be suffering from, a stress event – Counterparty stress : a major counterparty suffers an intraday stress event which prevents it from making payments – Customer bank’s stress: a customer bank of a correspondent bank suffers a stress event – Market-wide credit or liquidity stress 34
  • 35. Stress Simulations 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 many different stress scenarios Large body of research and policy work on various stress testing has been carried out with data from interbank payment systems 35
  • 36. Bank Behavior Payment Systems are “Complex Adaptive Systems”: Interaction of simple events (one debit and one credit) creates complex overall behavior A bank’s ability to settle payments (its liquidity risk) depends on its available liquidity and other banks ability to settle payments, which depend … Galbiati and Soramäki (2011), An Agent based Model of Payment Systems. Journal of Economic Dynamics and Control, Vol. 35, Iss. 6, pp 859-875
  • 37. The FNA Platform FNA has developed a proprietary software platform that runs a wide range of applications (either cloud-based, via intranet, or on individual desktops) for financial data analysis and visualization. The focus is on providing unique analysis capabilities not available from any other solution vendors. The FNA Platform is operational and offers more than 200 functions for modeling, analysing and visualising complex financial data - ranging from graph theory to VaR models. The platform represents more than six man-years of work from highly experienced data scientists and financial market professionals. • FNA’s "secret sauce" is network analysis—algorithms and visualization • Network approaches are the best way for modeling complex systems • FNA leads the way in this new market segment
  • 38. Automation • End-to-end Automation of Analytics – Speed, no set-up costs – Organizational continuity • Continuous Calculation – Analytics available when needed – Predictions ready when needed • Security – Only available to authorized people • Integration – Analysis available in different application and different formats for online and print 38
  • 39. Interactive Workshop
  • 40. Agenda 1. Network Theory and Payment Networks – Network concepts – Network metrics 2. Liquidity in Payment Networks – Liquidity metrics – Modeling liquidity 3. Simulating Payment Networks – Liquidity Saving Mechanisms – Stress testing Payment Networks 4. International Remittances network Central Bank of Guatemala payment data FNA Platform 40
  • 41. Basics of Network Theory • Constituents – Nodes (vertices) – Links (ties, edges or arcs) • Links can be – Directed vs undirected – Weighed vs unweighted • Graph + properties = Network 41
  • 42. Graph Terminology Trivial Graph Complete graph, K4 Empty Graph Complete graph, K7 Undirected Graph Directed Graph
  • 43. Directed Weighted Graph
  • 44. Graphs Star Lattice Ring 44
  • 45. Graphs Random (Erdos-Renyi) Scale-free (Barabasi-Albert) Note: Scale free property assumes infinite size 45
  • 46. Bipartite Graph
  • 47. Example Application Areas Banks Countries Banks Infrastructures 47
  • 48. Tree
  • 49. Example Application Areas FNA HeavyTails Node color indicates latest daily return - Green = positive - Red = negative Node size indicates magnitude of return Bright green and red indicate an outlier return 49
  • 50. Hands-on: Types of Layouts Circle Data, Remittances 2011 Force-Directed Orbital 50
  • 51. Common Centrality Metrics Centrality metrics aim to summarize some notion of importance 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, visiting probability of a random process
  • 52. Degree Degree In-Degree Out-Degree
  • 53. Degree Distribution The topology of interbank payment flows. Soramäki et al. Physica A: Statistical Mechanics and its Applications 379 (1), 317-333 53
  • 54. Paths, Trails, Walks A Walk is any free movement along the links A Trail is a walk where a given link is visited only once A Path is a walk where a given node is visited only once A Geodesic Path is the Shortest Path A Cycle is a path starting and ending to the same vertex
  • 55. Components 55
  • 56. Strongly Connected Graph Starting from any red node allows one to reach any other red node with a walk along the links. Depending which black node one begins from, one can either reach other black nodes or red nodes
  • 57. Closeness • The Farness of a node is defined as the sum of its distances to all other nodes • The Closeness of a node is defined as the inverse of the farness • Needs a connected graph (or component) • Directed/undirected • Weighed/un-weighted 57
  • 58. Betweenness Centrality • Measures the number of shortest paths going through a vertex or an arc • Algorithm – Calculate shortest paths between each pair of nodes – Determine the fraction of shortest paths that pass through the node in question – Sum this fraction over all pairs of nodes • Directed/undirected; Weighed/unweighted Freeman, Linton (1977). "A set of measures of centrality based upon betweenness". Sociometry 40: 35–4 58
  • 59. Eigenvector Centrality Problem: Eigenvector Centrality can be (meaningfully) calculated only for “Giant Strongly Connected Component” (GSCC) Solution: PageRank
  • 60. PageRank • Algorithm used by Google to rank web pages. Random surfer model. • Solves the problem of dead-ends with a “Damping factor” to modify the adjacency matrix (S) – Gi,j= Si,j • Effectively allowing the random process out of dead-ends (dangling nodes), but at the cost of introducing error • Effect of – Centrality of each node is 1/N – Eigenvector Centrality – Commonly is used which is used
  • 61. Hands-on: Centrality Exploring centrality of countries in Remittances 2011 network 61
  • 62. Agenda 1. Network Theory and Payment Networks – Network concepts – Network metrics 2. Liquidity in Payment Networks – Liquidity metrics – Modeling liquidity 3. Simulating Payment Networks – Liquidity Saving Mechanisms – Stress testing Payment Networks 4. International Remittances network Central Bank of Guatemala payment data FNA Platform 62
  • 63. Standard Reporting • System turnover (value, volume) – – – – – Daily Monthly peak/low/average Yearly total Unsettled payments Distribution • – Payments value over day – Intraday pattern/throughput (value/volume) – Delays due to lack of liquidity • Technical – Processing times – Settlement mode (by algorithm) • Individual payments – Average/min/max value – Value distribution – Payment type breakdown (interbank, ancillary, cb operations, etc) – Priority (urgent, normal) – Breakdown by bank Intraday statistics • Static information – – – – • Number/types of participants Opening/closing balances Intraday credit limits Bilateral limits Incident reports 63
  • 64. BCBS Monitoring tools Starting point: BCBS Monitoring tools for intraday liquidity management “It is envisaged that the introduction of monitoring tools for intraday liquidity will lead to closer co-operation between banking supervisors and the overseers in the monitoring of banks’ payment behaviour.“ A(i) A(ii) A(iii) A(iv) Daily maximum intraday liquidity usage Available intraday liquidity at the start of the business day Total payments Time-specific obligations B(i) B(ii) Value of customer payments made on behalf of correspondent banking customers Intraday credit lines extended to customers C(i) Intraday throughput 64
  • 65. (i) Daily maximum liquidity requirement usage 65
  • 66. 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 is intraday, spillovers possible
  • 67. How to Calculate a Metric for Payment Flows Depends on process that takes place in the network! Trajectory – – – – Geodesic paths (shortest paths) Any path (visit no node twice) Trails (visit no link twice) Walks (free movement) Transmission – Parallel duplication – Serial duplication – Transfer Source: Borgatti (2004)
  • 68. Distance to Sink Absorbing Markov Chains give distances: (66.6%) (100%) To B 1 From C To A From B 2 From A From C (33.3%) To C From A From B (100%) 1
  • 69. 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 transfer 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 SinkRank on unweighted networks
  • 70. SinkRank SinkRank is the average distance of a unit of liquidity to the sink. The actual liquidity distribution can be used in calculating SinkRank Uniform (A,B,C: 33.3% ) C A “Real” (A: 5% B: 90% C:5%) C PageRank (A: 37.5% B: 37.5% C:25%) C B A Note: Node sizes scale with 1/SinkRank B A B
  • 71. Core-Periphery Structure • Craig and von Peter (2010) • Interbank markets are tiered in a Core-Periphery structure The algorithm determines the optimal set of core banks that achieves the best structural match between observed structure and perfectly tiered structure • • • • Core banks are connected to each other. Periphery banks are not connected to other periphery banks. Core banks are connected to (some) periphery banks. Ben Craig and Goetz von Peter (2010). Interbank tiering and money center banks, BIS Working Papers No 322. 71
  • 72. How good is it? Experiments: • Design issues – Real vs artificial networks? – Real vs simulated failures? – How to measure disruption? • Approach taken 1. 2. 3. 4. Create artificial data with close resemblance to the US Fedwire system (BA-type, Soramäki et al 2007) Simulate failure of a bank: the bank can only receive but not send any payments for the whole day Measure “Liquidity Dislocation” and “Congestion” by non-failing banks Correlate 3. (the “Disruption”) with SinkRank of the failing bank
  • 73. SinkRank vs Disruption Relationship between SinkRank and Disruption Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
  • 74. Hands-on: Systemically Important Banks 74
  • 75. 3. Real-time Monitoring Outlier Detection Visual alerts Visualizations can highlight banks or their links that are out of normal bounds by marking them with different color or other visual cues E-mail/SMS alerts In a real-time monitoring environment the system can sen e-mails if aspects of the system are out of normal bounds 75
  • 76. 4. Predictive Modeling • Predictive modeling is the process by which a model is created to try to best predict the probability of an outcome • Given a distribution of liquidity among the banks in the morning, how is it going to be in the afternoon? – What is the distribution if bank A has an operational disruption at noon? – Who is affected first? – Who is affected most? – How is Bank C affected in an hour? • Valuable information for decision making – Crisis management – Participant behavior 76
  • 77. 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
  • 78. Agenda 1. Introduction 2. Payment Networks – Network concepts – Network metrics 3. International Remittances network Liquidity in Payment Networks – Liquidity metrics – Modeling liquidity 4. Simulating Payment Systems – – 5. Liquidity Saving Mechanisms Stress Testing Payment Networks Central Bank of Guatemala payment data FNA Platform 78
  • 79. 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, preprogrammed, evolving or co-learning
  • 80. Simulations vs analytical models • Simulations (e.g. Koponen-Soramaki 1998, Leinonen, ed. 2005, work at FRB, ECB, BoC, BoJ, BoE) have so far not endogenized bank behaviour – behaviour has been assumed to remain unchanged in spite of other changes in the system – or to change in a predetermined manner – due to the use of actual data, difficult to generalize • Game theoretic models (e.g. Angelini 1998, Kobayakawa 1997, Bech-Garratt 2003) need to make many simplifying assumptions – on settlement process / payoffs – topology of interactions – do not give quantitative answers 80
  • 81. 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”
  • 82. Tools • Bof-PSS2 – – – – Bank of Finland, 1997- (BoF-PSS1) RTGS, RRGS, Net, many optimization methods www.bof.fi/sc/bof-pss Free, Support & Training available, Annual workshop • FNA – Financial Network Analytics Ltd. (UK), 2009– RTGS, RRGS, many optimization methods, visual exploration of results, network analysis – www.fna.fi – Free online, License, Support & Training available • Proprietary tools or general purpose programs – Matlab, SAS, Excel, …
  • 83. Hands-on: Simulation on LSM Register at www.fna.fi 83
  • 84. Framework – Liquidity Optimization Settlement speed END OF DAY IMMEDIATE MIN Liquidity Used HIGH Source: Koponen-Soramäki (1997). Intraday liquidity needs in a modern interbank payment system - a Simulation Approach , Bank of Finland Studies in Economics and Finance 14. 84
  • 85. Framework – RTGS Instructions RTGS Settlement Queue RTGS Settlement Instructions Queue Optimization 85
  • 86. Framework – LSM Time critical payments RTGS Stream Settlement Queue Instructions Optimization LSM Stream Non-time critical payments 86
  • 87. Optimization What? For example: How? • I have 20m and a payment of 30m to settle • Splitting (eg. CLS) • I have a 30m payment to B, but only 20m on account. B has a 10m payment to me • Bilateral offsetting (eg. CHAPS) • I have no money, a payment of 10m to B, B has a payment of 10m to C, C has a payment of 10m to me • Multilateral netting (eg. CLS) • Partial netting (e.g. Kronos) 10 10 10 30 10 10 20 10 10 10 10 87
  • 88. Optimization • What is being optimized? – Minimize idle liquidity – Maximize use of queued incoming payments • Five main methods – – – – Splitting Ordering/Bypassing Bilateral netting (offsetting) Multilateral netting (offsetting, circles processing, gridlock resolution) – New payments 88
  • 89. Bilateral offsetting with new payments Bilaterally offset liquidity needs New payments aimed at minimizing multilateral positions Bilaterally offset, reduced liquidity needs Source: www.lmrkts.com89
  • 90. Issues • What LSM mechanisms to use? LSM simulations (have a long tradition and) are standard now • Some options may have legal ambiguity (status of split payments) or issues with netting agent in multilateral netting • Banks need to learn to co-use the LSM features. “If you build it they will come” is not likely to work 90
  • 91. Stress Testing • Scenario models – – – – Historical Probabilistic Extreme but plausible market conditions Worst-case • Scenarios in BCBS document – – – – Own financial stress Counterparty stress Customer stress Market wide credit or liquidity stress • Predictive modeling 91
  • 92. Stress Simulations 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 many different stress scenarios Large body of research and policy work on various stress testing has been carried out with data from interbank payment systems 92
  • 93. FNA Payment Simulator • Allow testing of Liquidity saving mechanisms – – – – – Queuing (FIFO + priorities + bypass) Bilateral limits, overdraft limits, opening balances Two-stream operation Bilateral offsetting (first, fifo, best) Queue optimization (Bech-Soramaki) • Allow simulation of Stress scenarios, such as the scenarios in BCBS document “Monitoring Indicators for Intraday Liquidity Management” – – – – (i) Own financial stress (ii) Counterparty stress (iii) Customer stress (iv) Market wide credit or liquidity stress • Any functionality can be implemented in a custom Payment Simulator application 93
  • 94. Hands-on: Stress Simulations Register at www.fna.fi 94
  • 95. Interactive Workshop Agenda 1. Network Theory and Payment Networks – Network concepts – Network metrics 2. Liquidity in Payment Networks – Liquidity metrics – Modeling liquidity 3. Simulating Payment Networks – Liquidity Saving Mechanisms – Stress testing Payment Networks 4. International Remittances network Central Bank of Guatemala payment data FNA Platform 95
  • 96. Hands-on: FNA Platform http://www.fna.fi 96
  • 97. Thank you Blog, library and demos are available at www.fna.fi Dr. Kimmo Soramäki kimmo@soramaki.net Twitter: soramaki 97