financial cartography at bogazici university
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Financial Cartography
Dr. Kimmo SoramäkiFounder and CEOFinancial Network Analyticswww.fna.fi
Boğaziçi University3rd February 2014
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Agenda
Mapping Interbank Payment Flows and Exposures
Soramäki, K. M.L. Bech, W.E. Beyeler, R.J. Glass and J. Arnold (2007). ‘The Topology of Interbank Payments’ Physica A, Vol. 379, pp 317-333.Soramäki, K. and S. Cook (2013). ‘Algorithm for Identifying Systemically important Banks in Payment Systems’. Economics E-Journal, Vol. 7.Langfield, S. and K. Soramaki (forthcoming). ‘Interbank Networks’. Journal of Computational Economics.
Asset Correlation Networks
Soramäki, K., S. Cook and A. Laubsch (forthcoming). ‘A Network-Based Method for Visual Identification of Systemic Risks’.
FNA Platform
Soramäki, K., S. Cook. (forthcoming) ‘Financial Network Analytics with FNA’. ISBN: 978-952-67505-1-4
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2003 20042003
Networks
Fedwire Interbank Payment NetworkFall 2001
Around 8000 banks, 66 banks comprise 75% of value,25 banks completely connected
Soramäki, Bech, Beyeler, Glass and Arnold (2007), Physica A, Vol. 379, pp 317-333.
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Fedwire – First Maps
NETWORK THEORY
Financial Network Analysis
Biological Network Analysis
Graph & Matrix Theory
Social Network Analysis Network Science
Computer Science
Network Theory
The behavior of a node cannot be understood on the basis its own properties alone.
To understand the behavior of one node, one must understand the behavior of nodes that may be several links apart in the network.
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Networks Brings us Beyond the Data Cube
Variables
Entiti
es
Time
For example:
Entities: 100 banks
Variables: Liquidity, Opening Balance, Collateral, …
Time: Daily data
Information on the links allows us to develop better models for banks' liquidity situation in times of stress
Link
sLinks:Bilateral payment flows
Links are the 4th dimension to data(Tesseract)
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“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.
Domino effects, cascading failures, financial interlinkages, … -> i.e. a process in the financial network
News articles mentioning “systemic risk”, Source: trends.google.com
Not
Systemic Risk
Or
8Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global banking:1978-2009. IMF Working Paper WP/11/74.
Federal fundsBech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986.
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
Italian 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
Unsecured Sterling money market
Cross-border bank lending
More Network Maps
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, eg. Google’s PageRank
Centrality metrics aim to summarize some notion of importance
Common Centrality Metrics
How to Calculate a Metric for Payment Flows
Trajectory – Geodesic paths (shortest paths)– Any path (visit no node twice)– Trails (visit no link twice)– Walks (free movement)
Source: Borgatti (2004)
Transmission – Parallel duplication– Serial duplication – Transfer
Depends on process that takes place in the network!
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SinkRank Models Payment Flows
NASA’s model of ocean currents around the Caribbean
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Failure Scenario Normal Scenario
Network Simulation
Black node = can receive but cannot send (click to fail a node)
Green node = Liquidityavailable. Amount shown as node size.
Red node = No, liquidity. Queues build up. Number queued shown as node size.
Interactive demo at: www.fna.fi/demos/sofe/viz/simulation.html
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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
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SinkRank in BoK-Wire+
Baek, Soramäki and Yoon (forthcoming). ‘Network Indicators for Monitoring Intraday Liquidity in BOK-Wire+ ‘
https://www.dropbox.com/s/rckmclzzstlmiht/Screenshot%202014-01-20%2010.32.39.png
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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
Pairwise correlations of daily returns on 35 global assets (ETFs), incl.
• Equity indices• FX• Commodities• Debt• Derivatives
One year of daily correlations with exponentially-weighted moving average (EWMA) estimate of the (daily) returns’ standard deviation.
…
Data in Example
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Data
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)
Significant Correlations
A and B are the same shade of gray
Right?
Color Perception
A and B are the same shade of gray
Color Perception
Problem: Heat maps can be misleading due to
human color perception
Correlation Network
Nodes are assets
Links are correlations:Red = negativeBlack = positive
Absence of link marks that asset is not significantly correlated
Correlation Network
Minimum Spanning Tree
Rosario Mantegna (1999) ‘Hierarchical Structure in Financial Markets’
We use the Minimum Spanning Tree (MST) of the network to filter signal from noise.
Hierarchical Structure in Financial Markets
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.
Phylogenetic Tree Layout
Bachmaier, Brandes, and Schlieper (2005). Drawing Phylogenetic Trees. Proceeding ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation, pp. 1110-1121
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
Mapping Returns and OutliersData Reduction + Adding Dimensions
Blog, Library and Demos at www.fna.fi
Dr. Kimmo Soramäki kimmo@soramaki.netTwitter: soramaki
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