Financial Cartographyfor Payments and Markets

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

Seminar at CPSS SecretariatBank for International SettlementsBasel, 13 November 2013

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Agenda

SinkRank Algorithm for Identifying Systemically important Banks in Payment Systems

HeavyTails Forthcoming service for identifying signals from noise in market data

Systemic Risk in Payment Systems

• Credit risk has been virtually eliminated by system design (Real-Time Gross Settlement)

• Liquidity risk remains– “Congestion” – “Liquidity Dislocation”– together the "Disruption"

• Trigger may be– Operational/IT event– Liquidity event– Solvency event

• Time scale is intraday, spillovers possible

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Network 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

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

Soramäki and Cook (2012), “Algorithm for identifying systemically important banks in payment systems”

Distance to Sink

From B

From C

1

2

1

To A

From A

From CTo B

From A

From BTo C

(100%)

(100%)

(33.3%)

(66.6%)

• Soramäki and Cook (2013), "SinkRank: An Algorithm for Identifying Systemically Important Banks in Payment Systems"

• Payments can be modelled as random walks in the network. In this example we can calculate the following 'random walk distances':

SinkRank

• Measures how big of a “sink” a bank is in a payment system

• Based on theory of Absorbing Markov chains: average transfer distance to a node via (weighted) walks from other nodes

• Provides a baseline scenario of no behavioral changes by banks

• Allows also the identification of most vulnerable banks

Distance to Sink on sample unweighted networks

Calculation of Basic SinkRank

Transition Matrix P

where I is an m x m identity matrix (m = the number of absorbing states), S is a square (n - m) x (n - m) matrix (n = total number of states, so n - m = the number of non-absorbing states), 0 is a zero matrix and T is an (n - m) x m matrix

Fundamental Matrix Q

The i,jth entry of Q (qij) defines the number of times, starting in state i, a process is expected to visit state j before absorption

SinkRank

Starting nodes are indexed by i, and nodes visited en-route to sink by j

SinkRank

• SinkRank is the average distance to a node via (weighted) walks from other nodes

• We need an assumption on the distribution of liquidity in the network at time of failure

– Assume uniform -> unweighted average– Estimate distribution -> PageRank -weighted average– Use real distribution -> Real distribution are used as weights

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SinkRank: Example

Distribution SinkRankA 33.33% 0.67B 33.33% 0.75C 33.33% 0.40

A 37.5% 0.71B 37.5% 0.71C 25% 0.40

A 5% 0.95B 90% 0.75C 5% 0.34

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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

Distance from Sink vs Disruption

Highest disruption to banks whose liquidity is absorbed first (low Distance to Sink)

Relationship between Failure Distance and Disruption when the most central bank fails

Distance to Sink

SinkRank vs Disruption

Relationship between SinkRank and Disruption

Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)

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Stress Simulations Demo

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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

Dragon King (Sornette 2009)

Black Swan (Taleb 2001, 2007)

vs.

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

• Equity indices• FX• Commodities• Debt• Derivatives

…

Data

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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: Heatmaps can be misleading due to human color perception

Lets build some network approaches for visualizing correlations

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): "Obtain the taxonomy of a portfolio of stocks traded in a financial market by using the information of time series of stock prices only“

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

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FNA HeavyTails Demo

www.heavytails.com

The FNA Platform

FNA has developed a proprietary software platform that runs a wide range of applications (either cloud-based, via intranet, or on individual desktops) for financial data analysis and visualization.

The focus is on

• Providing unique analysis capabilities not available from any other solution vendors

• Automation of the analysis for ongoing reporting ad monitoring

The FNA Platform is operational and offers more than 200 functions for modeling, analysing and visualising complex financial data - ranging from graph theory to VaR models.

• FNA’s "secret sauce" is network analysis—algorithms and visualization

• Network approaches are the best way for modeling complex systems

• FNA leads the way in this new market segment

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Automation

• Research Project vs Ongoing Activity

• Automation of– Access data in real-time from database– Continuous calculation of analytics– Publishing and sharing of results– Alerts

• Benefits of automation– Organizational continuity– Analytics available when needed– Predictions ready when needed