Financial Network Analysis

Simulation analysis and stress testing of payment networks – Session 1

Kimmo Soramäkikimmo@soramaki.net, www.fna.fi

Presentation at Payments System Oversight CourseCentral Bank of Bolivia, La Paz25 November 2011

Growing interest in networks

2003 20042003

“... need for new and fundamental understanding of the structure and dynamics of economic networks.”

“Meltdown modeling -Could agent-based computer models prevent another financial crisis?”

“Is network theory the best hope for regulating systemic risk?”

CFA Magazine, July 2009 Nature, August 2009 Science, July 2009

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

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.

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

Federal funds

Bech, 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

Visualising financial networks

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

http://www.bbc.co.uk/news/business-15748696

Eurozone debt web: Who owes what to whom?(BBC, 18 November 2011)

NETWORK THEORY

Financial Network Analysis

Biological Network Analysis

Graph & Matrix Theory

Social Network Analysis Network Science

Computer Science

Network theory

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

• Terminology– node/vertex – link/tie/edge/arc– directed vs undirected– weighed vs unweighted– graph + properties = network

• Algorithms/measures– Centrality– Flow– Community/pattern identification– Distance, shortest paths– Connectivity, clustering– Cascades, epidemic spreading

-> Financial interlinkages, bilateral positions, exposures

-> Systemical importantance

-> Liquidity

-> Contagion

4

1

2

3

-> Bank/banking group

Network basics

12

lattice complete random

Weighted random

Scale-free

“Prototypical” topologies

Ring

13

“Homophily”– “Birds of one feather flock together”, “herd

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

messenger)– The shortest path between any two nodes is very

short– Implications for contagion?

“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)

Pro

ba

bil

ity

(lo

g)

Fedwire degree distribution

Spread of obesity

Nicholas A. Christakis, James H. Fowler

New England Journal of Medicine 357 (4): 370–379 (26 July 2007)

Network analysis for Oversight

• Network maps: intuitive, provide a deeper understanding of thesystem 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 understandgradual 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

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

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 byother important nodes are more central, probability of a random process

17

Trajectory geodesic paths, paths, trails or walksTransmission parallel/serial duplication or transfer

Source: Borgatti (2004)

Centrality depends on network process

18

Components

Empirical networks often consist of a ‘Giant

Weakly Connected Component’

And several disconnected components

.. a ‘Giant In Component’ (which

flows into GSCC)

The GWCC has a ‘Giant Strongly Connected Component’ (each

node can reach each other)

.. and a ‘Giant Out Component’ (to

which GSCC flows)

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)

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.

Interactive visualization examples…

from www.fna.fi

Ring layout

Force-directed layout

Thank you

Kimmo Soramäki

kimmo@soramaki.netwww.fna.fi