Financial Cartography for Payments and Markets

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Slides from a economist seminar at the Bank for International Settlements, Basel

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Financial Cartography for Payments and Markets

  1. 1. Seminar at CPSS Secretariat Bank for International Settlements Basel, 13 November 2013 Financial Cartography for Payments and Markets Dr. Kimmo Soramäki Founder and CEO Financial Network Analytics www.fna.fi
  2. 2. Agenda SinkRank Algorithm for Identifying Systemically important Banks in Payment Systems HeavyTails Forthcoming service for identifying signals from noise in market data 2
  3. 3. Systemic Risk in Payment Systems • Credit risk has been virtually eliminated by system design (Real-Time Gross Settlement) • Liquidity risk remains – “Congestion” – “Liquidity Dislocation” – together the "Disruption" • Trigger may be – Operational/IT event – Liquidity event – Solvency event • Time scale is intraday, spillovers possible
  4. 4. 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 4
  5. 5. 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, eg. Google’s PageRank
  6. 6. 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)
  7. 7. SinkRank Models Payment Flows Soramäki and Cook (2012), “Algorithm for identifying systemically important banks in payment systems” 7
  8. 8. Distance to Sink • • Soramäki and Cook (2013), "SinkRank: An Algorithm for Identifying Systemically Important Banks in Payment Systems" Payments can be modelled as random walks in the network. In this example we can calculate the following 'random walk 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
  9. 9. SinkRank • 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 Distance to Sink on sample unweighted networks
  10. 10. Calculation of Basic SinkRank Transition Matrix P where I is an m x m identity matrix (m = the number of absorbing states), S is a square (n - m) x (n - m) matrix (n = total number of states, so n - m = the number of non-absorbing states), 0 is a zero matrix and T is an (n - m) x m matrix Fundamental Matrix Q The i,jth entry of Q (qij) defines the number of times, starting in state i, a process is expected to visit state j before absorption SinkRank Starting nodes are indexed by i, and nodes visited en-route to sink by j
  11. 11. SinkRank • SinkRank is the average distance to a node via (weighted) walks from other nodes • We need an assumption on the distribution of liquidity in the network at time of failure – Assume uniform -> unweighted average – Estimate distribution -> PageRank -weighted average – Use real distribution -> Real distribution are used as weights
  12. 12. SinkRank: Example A B C Distribution SinkRank 33.33% 0.67 33.33% 0.75 33.33% 0.40 A B C 37.5% 37.5% 25% 0.71 0.71 0.40 A B C 5% 90% 5% 0.95 0.75 0.34 12
  13. 13. Predictive Modeling • Predictive modeling is the process by which a model is created to try to best predict the probability of an outcome • For example: Given a distribution of liquidity among the banks at noon, how is it going to be at 5pm? – 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 13
  14. 14. 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
  15. 15. SinkRank vs Disruption Relationship between SinkRank and Disruption Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
  16. 16. Stress Simulations Demo 16
  17. 17. Market Signals • Markets are a great information processing device that create vast amounts of data useful for trading, risk management and financial stability analysis • Main signals: asset returns, volatilities and correlations • There is no easy way to monitor large numbers of assets and their dependencies -> Correlation Maps 17
  18. 18. Dragon King Black Swan (Sornette 2009) (Taleb 2001, 2007) vs.
  19. 19. Data … Pairwise correlations of daily returns on 35 global assets (ETFs), incl. • • • • • Equity indices FX Commodities Debt Derivatives
  20. 20. Data 20
  21. 21. Significant Correlations Common method to visualize large correlation matrices is via heat maps Keep statistically significant correlations with 95% confidence level Carry out 'Multiple comparison' correction -> Expected error rate <5% All correlations (last 100 days) Statistically significant correlations (last 100 days)
  22. 22. Color Perception A and B are the same shade of gray Right?
  23. 23. Color Perception A and B are the same shade of gray
  24. 24. Correlation Network Problem: Heatmaps can be misleading due to human color perception Lets build some network approaches for visualizing correlations
  25. 25. Correlation Network Nodes are assets Links are correlations: Red = negative Black = positive Absence of link marks that asset is not significantly correlated
  26. 26. Minimum Spanning Tree Hierarchical Structure in Financial Markets Rosario Mantegna (1999): "Obtain the taxonomy of a portfolio of stocks traded in a financial market by using the information of time series of stock prices only“ We use the Minimum Spanning Tree (MST) of the network to filter signal from noise.
  27. 27. Phylogenetic Tree Layout We lay out the assets by their hierarchical structure using Minimum Spanning Tree of the asset network. Shorter links indicate higher correlations. Longer links indicate lower correlations. Bachmaier, Brandes, and Schlieper (2005). Drawing Phylogenetic Trees. Proceeding ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation, pp. 1110-1121
  28. 28. Data Reduction Mapping Returns and Outliers Network layout allows for the display of multiple dimensions of the same data set on a single map: Node color indicates latest daily return - Green = positive - Red = negative Node size indicates magnitude of return Bright green and red indicate an outlier return
  29. 29. FNA HeavyTails Demo www.heavytails.com 29
  30. 30. 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 • Automation of the analysis for ongoing reporting ad monitoring 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. • 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
  31. 31. Automation • Research Project vs Ongoing Activity • Automation of – – – – Access data in real-time from database Continuous calculation of analytics Publishing and sharing of results Alerts • Benefits of automation – Organizational continuity – Analytics available when needed – Predictions ready when needed 31

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