Presentation at Payments System Oversight Course at Central Bank of Bolivia, La Paz 25 November 2011

1. Presentation at Payments System Oversight Course
Central Bank of Bolivia, La Paz
25 November 2011
Financial Network Analysis
Simulation analysis and stress testing
of payment networks – Session 1
Kimmo Soramäki
kimmo@soramaki.net, www.fna.fi

2. Growing interest in networks
2003 2003 2004

3. “Is network theory the “Meltdown modeling - “... need for new and
best hope for regulating Could agent-based fundamental
systemic risk?” computer models prevent understanding of the
another financial crisis?” structure and dynamics of
economic networks.”
CFA Magazine, July 2009 Nature, August 2009 Science, July 2009

4. In the face of the crisis, we felt abandoned by conventional tools.
In the absence of clear guidance from existing analytical
frameworks, policy-makers had to place particular reliance on our
experience. Judgement and experience inevitably played a key role.
Jean-Claude Trichet, (Now former) President of the ECB,
Opening address at the ECB Central Banking Conference,
Frankfurt, 18 November 2010

5. Network maps
• Recent financial crisis brought to light the need to look at
links between financial institutions
• Networks are a natural way to visualize the financial system
• „Network thinking‟ widespread by regulators
• Mapping of the financial system
has only begun
Eratosthenes' map of the known
world, c.194 BC.

6. Visualising financial networks
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
Federal funds 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
Soramaki, K, M.L. Bech, J. Arnold, R.J. Glass and W.E. Beyeler (2007), “The topology of
interbank payment flows”, Physica A, Vol. 379, pp 317-333, 2007.
Fedwire

7. Europe's Web of Debt
(Bill Marsh / The New York Times, 1 May 2010)

8. Eurozone debt web: Who owes what to whom?
(BBC, 18 November 2011)
http://www.bbc.co.uk/news/business-15748696

9. Network theory
Financial
Network Analysis
Social Network
Network Science
Analysis
NETWORK
THEORY
Graph & Matrix Computer
Theory Science
Biological
Network Analysis

10. Main premise of network analysis:
the structure of the links between nodes matters
The properties and behaviour of a node cannot
be analysed on the basis its own properties and
behaviour alone.
To understand the behaviour of one node, one
must analyse the behaviour of nodes that may be
several links apart in the network.
Financial context: network of interconnected
balance sheets

11. Network basics
• Terminology
– node/vertex -> Bank/banking group
– 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 -> Systemical importantance
– Flow -> Liquidity
– Community/pattern identification
– Distance, shortest paths
– Connectivity, clustering
– Cascades, epidemic spreading -> Contagion

12. “Prototypical” topologies
lattice complete random
Ring Weighted random Scale-free 12

13. “Homophily”
– “Birds of one feather flock together”, “herd Spread of obesity
behaviour”
– Ideas, attributes, etc tend to cluster together
and enforce each other
– Examples: Some obvious (age, social
status), others less (obesity, happiness, divorces)
– How about: risk appetite, portfolio
decisions, etc.
“Small world phenomenon”
– “Six degrees of separation” (6.6 on MSN Nicholas A. Christakis, James H. Fowler
New England Journal of Medicine 357 (4): 370–379
messenger) (26 July 2007)
– The shortest path between any two nodes is
very short
Fedwire degree distribution
– Implications for contagion?
Probability (log)
“Robust yet fragile“, “Scale-free networks”
– “The removal of "small" nodes does not
alter the path structure of the remaining
nodes, and thus has no impact on the
overall network topology. “
Degree (log)
13

14. Network analysis for Oversight
• Network maps: intuitive, provide a deeper understanding of the
system via anomaly explanation and visualization
• Centrality metrics: such as Pagerank 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

15. 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…
– 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) …
• More at www.fna.fi/blog

16. Common centrality measures
Degree: number of links
Closeness: distance 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, probability of a random process

17. Centrality depends on
network process
Trajectory geodesic paths, paths, trails or walks
Transmission parallel/serial duplication or transfer
Source: Borgatti (2004)
17

18. Components
The GWCC has a ‘Giant
Strongly Connected
.. a ‘Giant In Component’ (each
And several
Component’ (which node can reach each
disconnected
flows into GSCC) other)
components
.. and a ‘Giant Out
Component’ (to
which GSCC flows)
Empirical networks
often consist of a ‘Giant
Weakly Connected
Component’ 18

19. Intelligence
• Can we algorithmically detect financial crisis? Payment data
as early warning tool?
• Financial crisis are different and rare. The patterns to be
recognized must be frequent enough for computers to learn
• Pattern recognition is hard for computers -> the best Go
programs only manage to reach an intermediate amateur
level
• A solution is to augment human intelligence
(in contrast to AI)
• Intelligence amplification (William Ross Ashby 1956)

20. Tools
• Pajek, Universty of Ljublana, Slovenia
– Focus on social network analysis of large networks
– pajek.imfm.si
• Gephi, Gephi Foundation, France
– Focus on graph visualisation “Like Photoshop for graphs”
– www.gephi.org
• FNA, Soramaki Networks, Finland
– Focus on Financial/Payment Networks, time-series, dashboards
– www.fna.fi
• Many others
– Cytoscape, Graphviz, Network Workbench, NodeXL, ORA, Tableau, Ucinet,
Visone, etc.

21. Interactive visualization examples…
from www.fna.fi

22. Ring layout

23. Force-directed layout

24. Thank you
Kimmo Soramäki
kimmo@soramaki.net
www.fna.fi