Financial Cartography at Bogazici University

Financial Cartography

Dr. Kimmo SoramäkiFounder and CEOFinancial Network Analyticswww.fna.fi

Boğaziçi University3rd February 2014

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Agenda

Mapping Interbank Payment Flows and Exposures

Soramäki, K. M.L. Bech, W.E. Beyeler, R.J. Glass and J. Arnold (2007). ‘The Topology of Interbank Payments’ Physica A, Vol. 379, pp 317-333.Soramäki, K. and S. Cook (2013). ‘Algorithm for Identifying Systemically important Banks in Payment Systems’. Economics E-Journal, Vol. 7.Langfield, S. and K. Soramaki (forthcoming). ‘Interbank Networks’. Journal of Computational Economics.

Asset Correlation Networks

Soramäki, K., S. Cook and A. Laubsch (forthcoming). ‘A Network-Based Method for Visual Identification of Systemic Risks’.

FNA Platform

Soramäki, K., S. Cook. (forthcoming) ‘Financial Network Analytics with FNA’. ISBN: 978-952-67505-1-4

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2003 20042003

Networks

Fedwire Interbank Payment NetworkFall 2001

Around 8000 banks, 66 banks comprise 75% of value,25 banks completely connected

Soramäki, Bech, Beyeler, Glass and Arnold (2007), Physica A, Vol. 379, pp 317-333.

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Fedwire – First Maps

NETWORK THEORY

Financial Network Analysis

Biological Network Analysis

Graph & Matrix Theory

Social Network Analysis Network Science

Computer Science

Network Theory

The behavior of a node cannot be understood on the basis its own properties alone.

To understand the behavior of one node, one must understand the behavior of nodes that may be several links apart in the network.

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Networks Brings us Beyond the Data Cube

Variables

Entiti

es

Time

For example:

Entities: 100 banks

Variables: Liquidity, Opening Balance, Collateral, …

Time: Daily data

Information on the links allows us to develop better models for banks' liquidity situation in times of stress

Link

sLinks:Bilateral payment flows

Links are the 4th dimension to data(Tesseract)

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“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

Systemic Risk

Or

8Minoiu, 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

Cross-border bank lending

More Network Maps

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 byother important nodes are more central, eg. Google’s PageRank

Centrality metrics aim to summarize some notion of importance

Common Centrality Metrics

How to Calculate a Metric for Payment Flows

Trajectory – Geodesic paths (shortest paths)– Any path (visit no node twice)– Trails (visit no link twice)– Walks (free movement)

Source: Borgatti (2004)

Transmission – Parallel duplication– Serial duplication – Transfer

Depends on process that takes place in the network!

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SinkRank Models Payment Flows

NASA’s model of ocean currents around the Caribbean

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Failure Scenario Normal Scenario

Network Simulation

Black node = can receive but cannot send (click to fail a node)

Green node = Liquidityavailable. Amount shown as node size.

Red node = No, liquidity. Queues build up. Number queued shown as node size.

Interactive demo at: www.fna.fi/demos/sofe/viz/simulation.html

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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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SinkRank in BoK-Wire+

Baek, Soramäki and Yoon (forthcoming). ‘Network Indicators for Monitoring Intraday Liquidity in BOK-Wire+ ‘

https://www.dropbox.com/s/rckmclzzstlmiht/Screenshot%202014-01-20%2010.32.39.png

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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

Pairwise correlations of daily returns on 35 global assets (ETFs), incl.

• Equity indices• FX• Commodities• Debt• Derivatives

One year of daily correlations with exponentially-weighted moving average (EWMA) estimate of the (daily) returns’ standard deviation.

…

Data in Example

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Data

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)

Significant Correlations

A and B are the same shade of gray

Right?

Color Perception

A and B are the same shade of gray

Color Perception

Problem: Heat maps can be misleading due to

human color perception

Correlation Network

Nodes are assets

Links are correlations:Red = negativeBlack = positive

Absence of link marks that asset is not significantly correlated

Correlation Network

Minimum Spanning Tree

Rosario Mantegna (1999) ‘Hierarchical Structure in Financial Markets’

We use the Minimum Spanning Tree (MST) of the network to filter signal from noise.

Hierarchical Structure in Financial Markets

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.

Phylogenetic Tree Layout

Bachmaier, Brandes, and Schlieper (2005). Drawing Phylogenetic Trees. Proceeding ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation, pp. 1110-1121

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

Mapping Returns and OutliersData Reduction + Adding Dimensions

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FNA HeavyTails

Interactive demo at: www.heavytails.com

Blog, Library and Demos at www.fna.fi

Dr. Kimmo Soramäki kimmo@soramaki.netTwitter: soramaki