Tools for Quantitative Oversight of Market Infrastructures


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Tools for Quantitative Oversight of Market Infrastructures

  1. 1. WORKSHOP ON PAYMENT SYSTEMS OVERSIGHTCEMLA - Bank of JamaicaKingston, Jamaica, December 5-7, 2012Tools for Quantitative Oversight ofMarket Infrastructures Dr. Kimmo Soramäki Founder and CEO FNA,
  2. 2. AgendaPart I: Visualization• Financial Cartography• Getting Started with FNA Platform• Tutorial 1 – Loading Networks into FNA• Tutorial 2 – Managing Data in FNA• Tutorial 6 – Network VisualizationPart II: Analytics• Oversight Metrics• Oversight Monitor -application• Tutorial 3 – Network Summary Measures• Tutorial 5 – Connectedness and Components• Tutorial 4 – Centrality MeasuresPart III: Simulations• Payment System Simulations and Stress Tests• Payment System Simulator -application• Tutorial 8 – Payment System Simulations 2
  3. 3. Part I: VisualizationFinancial Cartography
  4. 4. ―When the crisis came, the serious limitations of existingeconomic and financial models immediately became apparent.[...]As a policy-maker during the crisis, I found the availablemodels of limited help. In fact, I would go further: in the face ofthe crisis, we felt abandoned by conventional tools.‖ in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 4
  5. 5. We did not have maps … 5
  6. 6. Eratosthenes map of the known world6c. 194 BC
  7. 7. … but what are maps―A set of points, lines, and areas all defined both by position withreference to a coordinate system and by their non-spatial attributes‖Data is encoded as size, shape, value, texture or pattern, color andorientation of the points, lines and areas – everything has a meaningCartographer selects only the information that is essential to fulfill thepurpose of the mapMaps reduce multidimensional data into a two dimensional space thatis better understood by humansMaps are intelligence amplification, they aid in decision making andbuild intuition 7
  8. 8. I. Mapping II. MappingSystemic Risk Financial Markets 8
  9. 9. Systemic risk ≠ systematic risk News articles mentioning ―systemic risk‖, Source: risk that a system composed of many interactingparts fails (due to a shock to some of its parts).In Finance, the risk that a disturbance in the financialsystem propagates and makes the system unable toperform its function – i.e. allocate capital efficiently. Not:Domino effects, cascading failures, financialinterlinkages, … -> i.e. a process in thefinancial network 9
  10. 10. First Maps Fedwire Interbank Payment 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 (2007), M. Boss, H. Elsinger, M. Summer, S. Thurner, ThePhysica A, Vol. 379, pp 317-333. network topology of the interbank market, SantaSee: Fe Institute Working Paper 03- 10 10-054, 2003.
  11. 11. 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 11
  12. 12. 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 12
  13. 13. Overnight lending networks 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 Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global 13 Cross-border bank lending banking:1978-2009. IMF Working Paper WP/11/74.
  14. 14. Network Theory can be to Financial Mapswhat Cartography is to Geographic MapsMain premise of network theory:Structure of links between nodesmattersTo understand the behavior of onenode, one must analyze thebehavior of nodes that may beseveral links apart in the networkTopics: Centrality, Communities,Layouts, Spreading and generationprocesses, Path finding, etc. 14
  15. 15. Centrality Measures forFinancial Systems• Traditional – Degree, Closeness, Betweenness centrality, PageRank, etc.• DebtRank – Battiston et al, Nature Science Reports, 2012 – Feedback-centrality – Solvency cascade• SinkRank – Soramäki and Cook, Kiel Economics DP, 2012 – Transfer along walks – Liquidity absorption 15
  16. 16. I. Mapping II. MappingSystemic Risk Financial Markets 16
  17. 17. OutlinePurpose of the maps – Identify market dynamics – Reduce complexity – Spot anomalies – Build intuitionThe maps: Heat Maps, Trees, Networksand Sammon‘s ProjectionsBased on asset correlations or taildependenceThese methods are showcased forvisualizing markets around the collapseof Lehman brothers 17
  18. 18. The CaseLehman was the fourth largest investment bank in the US (behindGoldman Sachs, Morgan Stanley, and Merrill Lynch) with 26.000employeesAt bankruptcy Lehman had $750 billion debt and $639 billion assetsCollapse was due to losses in subprime holdings and inability to findfunding due to extreme market conditionsIs seen as a divisive point in the 2007-2009 financial crisisWe create 3 visualization of a 5 month period around the failure (15September 2008) from asset price data 18
  19. 19. The Data Pairwise correlations of return on 141 global assets in 5 asset classes 9870 data points per time interval 5 intervals, 2 months before and 3 months after Lehman collapse 19
  20. 20. i) Heat Maps 2004-2007CorporateBondsCDS onGovernmentDebtFX RatesGovernmentBond Yields Correlation -1StockExchange 0Indices +1 20
  21. 21. Collapse of Lehman, t=month2004-2007 t-2 t-1 t+1 t+2 t+3
  22. 22. ii) Asset TreesOriginally proposed by Rosario Mantegna in 1999Used currently by some major financial institutionsfor market analysis and portfolio optimization andvisualizationMethodology in a nutshell MST 1. Calculate (daily) asset returns 2. Calculate pairwise Pearson correlations of returns 3. Convert correlations to distances 4. Extract Minimum Spanning Tree (MST) 5. Visualize (as phylogenetic trees) 22
  23. 23. DemoClick here for interactive visualization 23
  24. 24. Correlation filtering PMFGBalance between too much and too littleinformationOne of many methods to create networksfrom correlation/distance matrices – PMFGs, Partial Correlation Networks, Influence Networks, Granger Causality, Influence Network NETS, etc.New graph, information-theory, economics& statistics -based models are beingactively developed 24
  25. 25. iii) NETS• Network Estimation for Time- Series• Forthcoming paper by Barigozzi and Brownlees• Estimates an unknown network structure from multivariate data• Captures both comtemporenous and serial dependence (partial correlations and lead/lag effects) 25
  26. 26. iv) Sammon‘s ProjectionProposed by John W. Sammon in IEEE Transactions on Computers 18: 401–409(1969)A nonlinear projection method to map ahigh dimensional space onto a space oflower dimensionality. Example: Iris Setosa Iris Versicolor Iris Virginica 26
  27. 27. DemoClick here for interactive visualization 27
  28. 28. Extensions• Correlation is a linear dependence. The same visual maps can be extended to non-linear dependences.• Instead of correlation, links and positions measure similarity of distances to tail losses• Instead of returns, links can be based on bank balance sheet item, portfolio, etc. co-movements Tail Tree Tail Sammon (Click here for interactive visualization) (click here for interactive visualization)28
  29. 29. ―In the absence of clear guidance from existing analyticalframeworks, policy-makers had to place particular reliance onour experience. Judgment and experience inevitably played akey role.‖ in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 29
  30. 30. Part II: AnalyticsNetwork Metrics for Oversight
  31. 31. Standard reporting• System turnover (value, • Intraday statistics – Payments value over day volume) – Intraday pattern/throughput – Daily (value/volume) – Monthly peak/low/average – Delays due to lack of liquidity – Yearly total – Unsettled payments • Technical – Distribution – Processing times – Settlement mode (by algorithm)• Individual payments • Static information – Average/min/max value – Number/types of participants – Value distribution – Opening/closing balances – Intraday credit limits – Payment type breakdown – Bilateral limits (interbank, ancillary, cb operations, etc) • Incident reports – Priority (urgent, normal) – Breakdown by bank 31
  32. 32. Monitoring Indicators for Intraday LiquidityManagementConsultative report by BCBS. Final document expected 1Q 2013―.. the indicators are also likely to be of benefit to overseers of paymentand settlement systems. Close cooperation between banking supervisorsand the overseers is envisaged.―Most indicators can be calculated from payments data (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 √ 32
  33. 33. (i) Daily maximum liquidity requirement usage 33
  34. 34. Network MetricsPayment Systems are ―Complex Adaptive Systems‖A bank‘s ability to settle payments (its liquidity risk) depends on itsavailable liquidity and other banks ability to settle payments, whichdepend … Galbiati and Soramäki (2011), An Agent based Model of Payment Systems. Journal of Economic Dynamics and Control, Vol. 35, Iss. 6, pp 859-875
  35. 35. Network Theory Financial Network Analysis Social Network Network Science Analysis NETWORK THEORY Graph & Matrix Computer Theory Science Biological Network Analysis
  36. 36. Network basics• Terminology – node/vertex -> Bank/banking group, Asset – link/tie/edge/arc -> Financial interlinkages, bilateral positions, exposures – directed vs undirected – weighed vs unweighted – graph + properties = network 2 1• Algorithms/measures 3 4 – Centrality -> Systemic importance – Flow -> Liquidity – Community/pattern identification -> Core-Periphery (Craig – Von Peter) – Distance, shortest paths -> Liquidity absorption, SinkRank – Connectivity, clustering – Cascades, epidemic spreading -> Contagion 36
  37. 37. Network analysis for Oversight• Network maps: intuitive, provide a deeper understanding of the system via anomaly explanation and visualization• Centrality metrics: such as Pagerank and SinkRank can be used as a proxy for systemic importance, contagious links• Monitor over time: build reference data, detect and understand gradual change• Tied to availability of data: enables ―Analytics based policy‖, i.e. the application of computer technology, operational research, and statistics to solve regulatory problems
  38. 38. 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
  39. 39. Common centrality metricsCentrality metrics aim to summarize some notion of importanceDegree: 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 more central,visiting probability of a random process
  40. 40. Eigenvector CentralityProblem: EVC can be (meaningfully) calculated only for ―GiantStrongly Connected Component‖ (GSCC)Solution: PageRank
  41. 41. PageRank• Algorithm used by Google to rank web pages. Random surfer model.• Solves the problem of dead-ends with a ―Damping factor‖ which is used 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
  42. 42. Which measure to calculate?Depends on process that takes place in the network!Trajectory Transmission – Geodesic paths (shortest paths) – Parallel duplication – Any path (visit no node twice) – Serial duplication – Trails (visit no link twice) – Transfer – Walks (free movement) Source: Borgatti (2004)
  43. 43. 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
  44. 44. 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 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
  45. 45. Distance to SinkAbsorbing Markov Chains give distances: From B 1 To A From C 2 (66.6%) (100%) From A To B From C 1 (33.3%) From A To C From B (100%)
  46. 46. SinkRankSinkRank is the average distance of a unit of liquidity to the sinkActual liquidity distribution can be used in calculating SinkRank Uniform PageRank “Real” (A,B,C: 33.3% ) (A: 37.5% B: 37.5% C:25%) (A: 5% B: 90% C:5%) Note: Node sizes scale with 1/SinkRank
  47. 47. How good is it? Experiments:• Design issues – Real vs artificial networks? – Real vs simulated failures? – How to measure disruption?• Approach taken 1. Create artificial data with close resemblance to the US Fedwire system (BA-type, Soramäki 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 ―Liquidity Dislocation‖ and ―Congestion‖ by non-failing banks 4. Correlate 3. (the ―Disruption‖) with SinkRank of the failing bank
  48. 48. SinkRank vs Disruption Relationship between SinkRank and Disruption Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
  49. 49. 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
  50. 50. To sum up• Existing centrality measure do not accurately reflect the process of payment systems• SinkRank accurately predicts the magnitude of disruption caused by the failure of a bank in a payment system and identifies banks most affected by the failure.• SinkRank is based on absorbing Markov chains, which are well-suited to model liquidity dynamics in payment systems.• We find that the failing bank‘s SinkRank is highly correlated with the resulting disruption in the system overall• Finally, we present software that implements SinkRank in a payment system simulation environment
  51. 51. Side note: Data generation processBased on extending Barabasi–Albert model of growth andpreferential attachment
  52. 52. Network Analysis Tools• Pajek, Universty of Ljublana, Slovenia – Focus on social network analysis of large networks –• Gephi, Gephi Foundation, France – Focus on graph visualisation ―Like Photoshop for graphs‖ –• FNA, Soramaki Networks, Finland – Focus on Financial/Payment Networks/Simulation and interactive visualization –• Many others – Cytoscape, Graphviz, Network Workbench, NodeXL, ORA, Tableau, Ucinet, Visone, etc.
  53. 53. Where are we today?Regulatory response to recent financial crisiswas to strengthen macro-prudentialsupervision with mandates for moreregulatory data―Big data‖ and ―Complex Data‖-> Challengeto understand, utilize and operationalize thedata (network is fictional)Promise of ―Analytics based policy andregulation‖, i.e. the application of computertechnology, operations research, and Example: Oversight Monitor at Norges Bankstatistics to support human decision making The monitor will allow the identification ofGrowing body of empirical research, see systemically important banks and evaluation of the impact of bank failures on the 53
  54. 54. FNA Oversight Monitor• Allow identification of systemically important banks – Values/volumes – SinkRank• Allows a network view to payment systems – Liquidity flows – Throughput• Allows visualization of network and other statistics – Statistics can be calculated in real-time or at regular intervals directly from raw payment data – The default indicators consist of metrics proposed in the BIS/BCBS report on "Monitoring indicators for intraday liquidity management―• Can help in crisis management 54
  55. 55. Demo: FNA Oversight MonitorTry it at 55
  56. 56. Part III: SimulationsLSM and Stress Analysis
  57. 57. 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
  58. 58. 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‖
  59. 59. Framework 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.
  60. 60. Application Areas Enhance Evaluate alternative understanding of design features system mechanics Why Simulate? Stress testing and Platform for liquidity needs communication analysis among stakeholders
  61. 61. 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)
  62. 62. Tools• Bof-PSS2 – Bank of Finland, 1997- (BoF-PSS1) – RTGS, RRGS, Net, many optimization methods – – Free, Support & training available, Annual workshop• FNA – Soramaki Networks, 2009- – RTGS, RRGS, many optimization methods, visual exploration of results, network analysis – – Free online, License, support & training available• Proprietary tools or general purpose programs – Matlab, SAS, Excel, …
  63. 63. 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 63
  64. 64. Demo: FNA Payment Simulator 64
  65. 65. FNA Platform• Go to• Register account (click login on top right)• Watch ‗Getting started with FNA‘ video• More documentation available at 65
  66. 66. Getting Started: Commands• FNA operates on commands that are submitted to FNA server for execution. Commands explore the database, alter it or create visualizations from it• Command syntax: commandname –parameter1 value1 –parameter2 value2 … e.g. loada -file sample-arcs.csv -preserve false (load arcs from sample-arcs.csv file and don‘t preserve any existing networks in database)• Each command is on a single line. Character # marks a comment line• Commands can be bundled into scripts and executed in one go 66
  67. 67. Data Modelloada -file sample-arcs.csv -preserve falsesample-arcs.csvnetwork,source,target,value2005-1Q,Australia,Austria,4992005-1Q,Australia,Belgium,1135 Stores the data into2005-1Q,Australia,Canada,1884 a graph database... on FNA Servernet_id : 2005-1Q arc_id : Australia-Austria vertex_id : Austria value : 499 … vertex_id : Australia vertex_id : Belgium … vertex_id : Canada 67
  68. 68. CSV files Comment rows # The data shows banking system exposures to particular countries. (1 and d 2) # See for details. Empty row (3)Header field (with net_id,arc_id,from_id,to_id,value value ‘net_id’) 2010-1Q,Australia-Austria,Australia,Austria,211 Header row (4) 2010-1Q,Australia-Belgium,Australia,Belgium,1128 2010-1Q,Australia-Canada,Australia,Canada,12231 2010-1Q,Australia-Chile,Australia,Chile,335 2010-1Q,Australia-France,Australia,France,8865Field (with value 2010-1Q,Australia-Germany,Australia,Germany,11702 ‘2010-1Q’) 2010-1Q,Australia-Greece,Australia,Greece,12 2010-1Q,Australia-India,Australia,India,2180 2010-1Q,Australia-Ireland,Australia,Ireland,3583 Data rows (5-) 2010-1Q,Australia-Italy,Australia,Italy,8657 2010-1Q,Australia-Japan,Australia,Japan,4035 2010-1Q,Australia-Netherlands,Australia,Netherlands,5970 2010-1Q,Australia-Portugal,Australia,Portugal,776 2010-1Q,Australia-Spain,Australia,Spain,2776 2010-1Q,Australia-Sweden,Australia,Sweden,773 2010-1Q,Australia-Switzerland,Australia,Switzerland,3366 2010-1Q,Australia-Turkey,Australia,Turkey,63 Column Delimiter (,)
  69. 69. Tutorial I – Loading Networks into FNATry:loada -file sample-arcs.csv -preserve falseviz 69
  70. 70. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki