First lecture of a PhD level course on "Financial Networks" at Center for Financial Research at Goethe University, Frankfurt.

1. Center for Financial Studies at the Goethe University
PhD Mini-course
Frankfurt, 25 January 2013
Financial Networks
Dr. Kimmo Soramäki
Founder and CEO
FNA, www.fna.fi

2. About the Course
• Objective of the mini-course
To give an overview of how network theory can be applied in
financial regulation and risk management.
To show how to use FNA software to analyze financial networks
• Interdisciplinary approach
Combining methods from Graph
Theory, Economics, Finance, Statistics, Operations
Research, Computer Science, Bioinformatics, …
• Focus on empirical analysis and real-life applications
2

3. Organization
Friday, 25 January, 16:00-19:00
1. Financial Cartography
2. Introduction to Network Theory and FNA
Friday, 1 February, 16:00-19:00
3. Observing Network Structures
4. Centrality and Systemic risk
Friday, 8 February, 16:00-19:00
5. Inferring Financial Networks
6. Stress Testing Networks
3

4. Literature
• Blog at
www.fna.fi/blog/
• Research Library at
www.fna.fi/library/
• ~150 papers on financial
networks
4

5. Software
• Financial Network Analytics
–software available at
www.fna.fi/fna/
• Free to register and use
online
• All analysis and visualization
presented here are
developed with the software
• For getting started, see Feel free to contact me at:
www.fna.fi/gettingstarted kimmo@fna.fi
5

6. Center for Financial Studies at the Goethe University
PhD Mini-course
Frankfurt, 25 January 2013
Financial Networks
1. Financial Cartography
Dr. Kimmo Soramäki
Founder and CEO
FNA, www.fna.fi

7. “When the crisis came, the serious limitations of existing
economic and financial models immediately became apparent.
[...]
As a policy-maker during the crisis, I found the available
models of limited help. In fact, I would go further: in the face of
the crisis, we felt abandoned by conventional tools.”
in a Speech by Jean-Claude Trichet, President of the
European Central Bank, Frankfurt, 18 November 2010
7

8. We did not have maps …
8

9. Eratosthenes' map of the known world9
c. 194 BC

10. … but what are maps
“A set of points, lines, and areas
all defined both by position with
reference to a coordinate system
and by their non-spatial
attributes”
Data is encoded as
size, shape, value, texture or
pattern, color and orientation of
the points, lines and areas –
everything has a meaning Political map of Europe
10

11. … but what are maps (contd.)
Cartographer selects only
the information that is
essential to fulfill the
purpose of the map
Maps reduce
multidimensional data into
a two dimensional space
that is better understood by
humans
Maps are intelligence
amplification, they aid in
decision making and build Map by John Snow showing the clusters of cholera
cases in the London epidemic of 1854
intuition
11

12. I. Mapping II. Mapping
Systemic Risk Financial Markets
12

13. Systemic risk ≠ systematic risk
News articles mentioning “systemic risk”, Source: trends.google.com
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.
Not:
Domino effects, cascading failures, financial
interlinkages, … -> i.e. a process in the
financial network
13

14. First Maps Fedwire Interbank Payment
Network, Fall 2001
Around 8000 banks, 66 banks
comprise 75% of value,25 banks
completely connected
Similar to other socio-
technological networks
Soramäki, Bech, Beyeler, Glass and Arnold (2007), M. Boss, H. Elsinger, M. Summer, S. Thurner, The
Physica A, Vol. 379, pp 317-333. network topology of the interbank market, Santa
See: www.fna.fi/papers/physa2007sbagb.pdf Fe Institute Working Paper 03-
14
10-054, 2003.

15. More Maps
Federal funds
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
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
Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global 15
Cross-border bank lending banking:1978-2009. IMF Working Paper WP/11/74.

16. Exposure networks
Sam Langfield, Zijun Liu and Tomohiro Ota (2012). Presentation given at ETH
Conference 'Economics on the Move' on 14/09/12 16

17. Network Theory can be to Financial Maps
what Cartography is to Geographic Maps
Main premise of network theory:
Structure of links between nodes
matters
To understand the behavior of one
node, one must analyze the
behavior of nodes that may be
several links apart in the network
Topics:
Centrality, Communities, Layouts,
Spreading and generation
processes, Path finding, etc.
17

18. Network aspect is an unexplored
dimension of data
Variables
Observations
18

19. Centrality Measures for
Financial Systems
• Traditional
– Degree, Closeness, Betweenness
centrality, PageRank, etc.
• DebtRank
– Battiston et al, Science
Reports, 2012
– Feedback-centrality
– Solvency cascade
• SinkRank
– Soramäki and Cook, Kiel
Economics DP, 2012
– Transfer along walks
– Liquidity absorption
19

20. Where are we today?
Regulatory response to recent financial crisis
was to strengthen macro-prudential
supervision with mandates for more
regulatory data
“Big data” and “Complex Data”-> Challenge
to understand, utilize and operationalize the
data
(network is fictional)
Promise of “Analytics based policy and
regulation”, i.e. the application of computer
technology, operations research, and Example: Oversight Monitor at Norges Bank
statistics to support human decision making
The monitor will allow the identification of
systemically important banks and evaluation of
the impact of bank failures on the system
20

21. I. Mapping II. Mapping
Systemic Risk Financial Markets
21

22. Outline
Purpose of the maps
– Identify price driving themes and market
dynamics
– Reduce complexity
– Spot anomalies
– Build intuition
The maps: Heat Maps, Trees, Networks
and Sammon‟s Projections
Based on asset correlations or tail
dependence
These methods are showcased for
visualizing markets around the collapse
of Lehman brothers
22

23. The Case
Lehman was the fourth largest investment bank in the US (behind
Goldman Sachs, Morgan Stanley, and Merrill Lynch) with 26.000
employees
At bankruptcy Lehman had $750 billion debt and $639 billion assets
Collapse was due to losses in subprime holdings and inability to find
funding due to extreme market conditions
Is seen as a divisive point in the 2007-2009 financial crisis
We create 3 visualization of a 5 month period around the failure (15
September 2008) from asset price data
23

24. The Data
Pairwise correlations of
return on 141 global
assets in 5 asset classes
9870 data points per
time interval
5 intervals, 2 months
before and 3 months
after Lehman collapse
24

25. i) Heat Maps
2004-2007
Corporate
Bonds
CDS on
Government
Debt
FX Rates
Government
Bond Yields
Correlation
-1
Stock
Exchange 0
Indices
+1
25

26. Collapse of Lehman, t=month
2004-2007 t-2 t-1
t+1 t+2 t+3

27. ii) Asset Trees
Originally proposed by Rosario Mantegna in 1999
Used currently by some major financial institutions
for market analysis and portfolio optimization and
visualization
Methodology in a nutshell MST
1. Calculate (daily) asset returns
2. Calculate pairwise Pearson correlations of returns
3. Convert correlations to distances
4. Extract Minimum Spanning Tree (MST)
5. Visualize (as phylogenetic trees)
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28. Demo
Click here for interactive visualization 28

29. Correlation filtering PMFG
Balance between too much and too little
information
One of many methods to create networks
from correlation/distance matrices
– PMFGs, Partial Correlation Networks,
Influence Networks, Granger Causality, Influence Network
NETS, etc.
New graph, information-theory, economics
& statistics -based models are being
actively developed
29

30. iii) NETS
• Network Estimation for Time-
Series
• Forthcoming paper by Barigozzi
and Brownlees
• Estimates an unknown network
structure from multivariate data
• Captures both comtemporenous
and serial dependence (partial
correlations and lead/lag effects)
30

31. iv) Sammon‟s Projection
Proposed by John W. Sammon in IEEE Transactions on Computers 18: 401–409
(1969)
A nonlinear projection method to map a
high dimensional space onto a space of
lower dimensionality. Example:
Iris Setosa
Iris Versicolor
Iris Virginica
31

32. Demo
Click here for interactive visualization 32

33. Tail dependence
• Correlation is a linear dependence. The same visual maps can be extended
to non-linear dependences.
• Joint work with Firamis (Jochen Papenbrock) and RC Banken (Frank
Schmielewski), see www.extreme-value-theory.com
• Instead of correlation, links and positions measure similarity of distances to
tail losses
Tail Tree Tail Sammon
(Click here for interactive visualization) (click here for interactive visualization) 33

34. Intelligence Amplification
• Intelligence Amplification vs Artificial
Intelligence
William Ross Ashby (1956) in „Introduction to
Cybernetics‟
• Technology, products and practices
change constantly, market knowledge is
essential
Game of Go (from China).
• Algorithms don‟t fare well in periods of
Computer programs only get to
abrupt change, algorithms do not think human amateur level due to good
outside the box pattern recognition capabilities
needed in the game.
• Build intuition and mental
maps, provide tools for trading
strategies
34

35. “In the absence of clear guidance from existing analytical
frameworks, policy-makers had to place particular reliance on
our experience. Judgment and experience inevitably played a
key role.”
in a Speech by Jean-Claude Trichet, President of the
European Central Bank, Frankfurt, 18 November 2010
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36. Blog, Library and Demos at www.fna.fi
Dr. Kimmo Soramäki
kimmo@soramaki.net
Twitter: soramaki