Financial Networks

Dr. Kimmo SoramäkiFounder and CEOFNA, www.fna.fi

Center for Financial Studies at the Goethe UniversityPhD Mini-course Frankfurt, 25 January 2013

2

• 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

About the Course

3

Organization

Friday, 25 January, 16:00-19:001. Financial Cartography2. Introduction to Network Theory and FNA

Friday, 1 February, 16:00-19:003. Observing Network Structures4. Centrality and Systemic risk

Friday, 8 February, 16:00-19:005. Inferring Financial Networks6. Stress Testing Networks

4

Literature

• Blog at www.fna.fi/blog/

• Research Library at www.fna.fi/library/

• ~150 papers on financial networks

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 www.fna.fi/gettingstarted

Feel free to contact me at:kimmo@fna.fi

Financial Networks

1. Financial Cartography

Dr. Kimmo SoramäkiFounder and CEOFNA, www.fna.fi

Center for Financial Studies at the Goethe UniversityPhD Mini-course Frankfurt, 25 January 2013

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

8

We did not have maps …

9Eratosthenes' map of the known world 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

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 intuition 

Map by John Snow showing the clusters of cholera cases in the London epidemic of 1854

12

I. Mapping Systemic Risk

II. Mapping Financial Markets

13

Systemic risk ≠ systematic risk

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:

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), Physica A, Vol. 379, pp 317-333.See: www.fna.fi/papers/physa2007sbagb.pdf

M. Boss, H. Elsinger, M. Summer, S. Thurner, The network topology of the interbank market, Santa Fe Institute Working Paper 03-10-054, 2003.

15Minoiu, 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

More Maps

Cross-border bank lending

16

Exposure networks

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

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.

18

Network aspect is an unexplored dimension of data

Variables

Obs

erva

tions

Time

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

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

Promise of “Analytics based policy and regulation”, i.e. the application of computer technology, operations research, and statistics to support human decision making

(network is fictional)

Example: Oversight Monitor at Norges Bank

The monitor will allow the identification of systemically important banks and evaluation of the impact of bank failures on the system

21

II. Mapping Financial Markets

I. Mapping Systemic Risk

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

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

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

25

Corporate Bonds

CDS on Government Debt

FX Rates

Government Bond Yields

Stock Exchange Indices

2004-2007

-1

0

+1

Correlation

i) Heat Maps

t-2 t-1

t+1 t+2 t+3

2004-2007

Collapse of Lehman, t=month

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

1. Calculate (daily) asset returns2. Calculate pairwise Pearson correlations of

returns3. Convert correlations to distances4. Extract Minimum Spanning Tree (MST)

5. Visualize (as phylogenetic trees)

MST

28

Demo

Click here for interactive visualization

29

Correlation filtering

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, NETS, etc.

New graph, information-theory, economics & statistics -based models are being actively developed

PMFG

Influence Network

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)

31

iv) Sammon’s Projection

Iris Setosa

Iris Versicolor

Iris Virginica

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 oflower dimensionality. Example:

32

Demo

Click here for interactive visualization

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(Click here for interactive visualization)

Tail Sammon (click here for interactive visualization)

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

• Algorithms don’t fare well in periods of abrupt change, algorithms do not think outside the box

• Build intuition and mental maps, provide tools for trading strategies

Game of Go (from China).

Computer programs only get to human amateur level due to good pattern recognition capabilities needed in the game.

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

Blog, Library and Demos at www.fna.fi

Dr. Kimmo Soramäki kimmo@soramaki.netTwitter: soramaki