This document summarizes a seminar given by Dr. Kimmo Soramäki on identifying systemically important banks in payment systems and signals in market data. It discusses using the SinkRank algorithm to model liquidity flows in payment networks and identify banks that absorb liquidity from the system. It also discusses using correlation networks and minimum spanning trees to visualize correlations between financial assets and identify outliers. The seminar demonstrated FNA's software platform and HeavyTails service for automating such financial network analysis.

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. Agenda
SinkRank
Algorithm for Identifying Systemically important
Banks in Payment Systems
HeavyTails
Forthcoming service for identifying signals from
noise in market data
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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. 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
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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. 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. SinkRank Models Payment Flows
Soramäki and Cook (2012), “Algorithm for identifying
systemically important banks in payment systems”
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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%)
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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. 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. 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. 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
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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
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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. SinkRank vs Disruption
Relationship between
SinkRank and Disruption
Highest disruption by
banks who absorb
liquidity quickly from the
system (low SinkRank)

16. Stress Simulations Demo
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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
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18. Dragon King
Black Swan
(Sornette 2009)
(Taleb 2001, 2007)
vs.

19. Data
…
Pairwise correlations of daily
returns on 35 global assets
(ETFs), incl.
•
•
•
•
•
Equity indices
FX
Commodities
Debt
Derivatives

20. Data
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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. Color Perception
A and B are the same
shade of gray
Right?

23. Color Perception
A and B are the same
shade of gray

24. Correlation Network
Problem:
Heatmaps can be
misleading due to
human color
perception
Lets build some
network approaches
for visualizing
correlations

25. Correlation Network
Nodes are assets
Links are correlations:
Red = negative
Black = positive
Absence of link marks
that asset is not
significantly correlated

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. 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. 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. FNA HeavyTails Demo
www.heavytails.com
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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. 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
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