Quantitative Oversight of Financial Market Infrastructures

III Payment System Oversight Workshop
CEMLA – Central Bank of Guatemala
Guatemala City, 16-18 October 2013

Quantitative Oversight
of Financial Market Infrastructures
One Day Interactive Workshop

Dr. Kimmo Soramäki
Founder and CEO
Financial Network Analytics, www.fna.fi

Agenda
• Introduction and Background
• Elements of Quantitative Oversight
–
–
–
–
–
–

Network Maps
Relevant Metrics
Real-time Monitoring
Predictive Modeling
Stress Testing
Automation of Analysis

• Applying Quantitative Oversight

• Interactive Workshop using FNA
2

Quantitative Oversight
• The recent financial crisis prompted the need and created the
expectation for regulators to collect more data about the financial
system and to analyze it more efficiently
• At he same time, know-how and tools for analyzing large data sets so-called Big Data - have become more prevalent
• The continuing reduction in storage costs and increase in computing
power has meant that data stored economy wide is growing
exponentially. Regulatory data – especially in a data-intensive field
such as finance – is no exception
• The expectations set by the public and the opportunities created by
advances in data analytics will both necessitate and enable a more
quantitative approach to the Oversight of Financial Infrastructures
Demand – Supply - Expectations
3

Systemic Risk

News articles mentioning “systemic risk”, Source: trends.google.com

Not

“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.

Or

Domino effects, cascading failures, financial interlinkages,
… -> i.e. a process in the
financial network
4

Dragon King

Black Swan

(Sornette 2009)

(Taleb 2001, 2007)

vs.

Network Models
• The financial crisis brought to light the interconnected nature of
modern financial systems. Academia and policy-makers have
recently developed a stronger awareness of the need for new
analytical methods
• These new approaches often involve network models, which
naturally capture the interconnectedness of the financial system.
• In payment system oversight the links may be related to bilateral
payment flows, overnight lending relationships, or common
participation in different financial infrastructures.
• Payment system have led the research on financial networks as the
first area where data of the needed granularity has been available
from interbank payment systems operated by central banks.

6

Network Theory

Financial
Network Analysis
Social Network
Analysis

Network Science
NETWORK
THEORY

Graph & Matrix
Theory

Computer
Science
Biological
Network Analysis

Main Premise of Network Theory
Structure of links between nodes matters
• The properties and behavior of a node cannot be analyzed on the basis its
own properties and behavior alone.
• To understand the behavior of one node, one must analyze the behavior of
nodes that may be several links apart in the network.
• Financial contexts
– Trading networks, payment networks, exposure networks
– Networks of interconnected balance sheets
– Networks of asset dependencies

• Topics: Centrality, Communities, Layouts, Spreading and generation
processes, Path finding, etc.
8

Networks Brings us Beyond the Data Cube
For example:

Entities:
100 banks
Variables:
Liquidity, Opening Balance, …
Time:
Daily data
Links:
Bilateral payment flows

Links are the 4th dimension to data

Information on the links
allows us to develop better
models for banks' liquidity
situation in times of stress
9

Network 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, Markets, …
– 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), Langfield, Liu and
Ota (2012)

• More at www.fna.fi/blog

The Journal of Network Theory in Finance
• Financial institutions and markets are highly
interconnected, but only recently has literature begun
to emerge that maps these interconnections and
assesses their impact on financial risk and returns
• The Journal of Network Theory in Finance (JNTF) is an
interdisciplinary journal publishing academically
rigorous and practitioner-focused research on the
application of network theory in finance and related
fields
• If you have a paper you would like to submit to the
journal, or are interested in contributing in any other
way please contact Editor-in-Chief: Kimmo Soramaki –
kimmo@fna.fi

• For information on subscribing please contact Journals
Manager of Risk Journals: Jade Mitchell jade.mitchell@incisivemedia.com
11

Network Visualization
• Network layout
– The relative position of nodes
– Many algorithms are available

• Two (or 3) dimensions are available for visualization
– One variable in One dimension
– Many variables in Many dimensions

• All details must convey unique meaning
–
–
–
–

High data-ink ratio
No ‘chartjunk’
Correct dimensions
No duplication

(from Tufte 2001, Tufte’s Rules)

Intelligence Amplification
•

Intelligence Amplification vs Artificial
Intelligence
William Ross Ashby (1956) in ‘Introduction to
Cybernetics’

•

Visual Cortex is very developed

•

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

•

Game of Go (from China).
Computer programs only get to
human amateur level due to good
pattern recognition capabilities
needed in the game.

Build intuition and mental maps, provide
tools for scenario thinking
13

“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

14

Elements of QO

1.
2.
3.
4.
5.
6.

Network Maps
Relevant Metrics
Real-time Monitoring
Predictive Modeling
Stress Testing
Automation

15

1. Network Maps
Fedwire Interbank
Payment Network, Fall
2001
Around 8000 banks, 66
banks comprise 75% of
value,25 banks completely
connected
Similar to other sociotechnological networks

Soramäki, Bech, Beyeler, Glass and Arnold
(2007), Physica A, Vol. 379, pp 317-333.
See: www.fna.fi/papers/physa2007sbagb.pdf

16

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

Cross-border bank lending

Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global
banking:1978-2009. IMF Working Paper WP/11/74.

17

Interactive Maps
Exploration of
data
and
Presentation of
information

18

2. Relevant Metrics
Starting point: BSBC Monitoring tools for intraday liquidity management
“It is envisaged that the introduction of monitoring tools for intraday
liquidity will lead to closer co-operation between banking supervisors and
the overseers in the monitoring of banks’ payment behaviour.“

A(i)
A(ii)
A(iii)
A(iv)

Daily maximum intraday liquidity usage
Available intraday liquidity at the start of the business day
Total payments
Time-specific obligations

B(i)

B(ii)

Value of customer payments made on behalf of correspondent
banking customers
Intraday credit lines extended to customers

C(i)

Intraday throughput
19

Network Metrics
Recently developed financial
system specific metrics:
•

Core-Periphery
–

•

DebtRank
–

•

Craig and von Peter 2010, Optimal
classification that matches theoretical
core-periphery model

Battiston et al, Science Reports
2012, Cascading failures -model

SinkRank
–

Soramäki and Cook, Kiel Economics
DP, 2012, Absorbing Markov chains

World's Ocean Currents
NASA Scientific Visualization Studio

20

Process in Payment Systems
Central bank
Payment system

4 Payment account

5 Payment account

is debited

Bi

is credited

Bj

6 Depositor account
3 Payment is settled

is credited

or queued

Qi

2 Depositor account

Bi > 0

Di

Bank i

Liquidity
Market

Dj

Qj > 0

Qj

Bank j

is debited

payment, if any, is
released

1

Agent instructs
bank to send a
payment

7 Queued

Productive Agent

Productive Agent
Beyeler, Glass, Bech and Soramäki
(2007), Physica A, 384-2, pp 693-718.

Payment
System
Instructions

Summed over
the
network, instructi
ons arrive at a
steady rate

Liquidity
When liquidity is high
payments are submitted
promptly and banks
process payments
independently of each
other

Time

Payments

Time

Payments

Instructions

(2007), Physica A, 384-2, pp 693-718.

Payment
System
Instructions

Liquidity

Time

Payments

Frequency

Time

Reducing liquidity leads to
episodes of congestion
when queues build, and
cascades of settlement
activity when incoming
payments allow banks to
work off queues. Payment
processing becomes
coupled across the
network

Payments

Cascade Length

Instructions

(2007), Physica A, 384-2, pp 693-718.

How to Calculate a Metric for Payment Flows
Depends on process that takes place in the network!
Trajectory
–
–
–
–

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

Transmission
– Parallel duplication
– Serial duplication
– Transfer

Source: Borgatti (2004)

SinkRank
•

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

•

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

SinkRank on unweighted
networks

3. Real-time Monitoring
Outlier Detection
Visual alerts
Visualizations can
highlight banks or their
links that are out of
normal bounds by
marking them with
different color or other
visual cues

E-mail/SMS alerts
In a real-time monitoring
environment the system
can sen e-mails if
aspects of the system
are out of normal
bounds
27

4. Predictive Modeling
• Predictive modeling is the process by which a model is
created to try to best predict the probability of an outcome

• Given a distribution of liquidity among the banks in the
morning, how is it going to be in the afternoon?
– 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
28

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

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

Distance to Sink

Simulations
• Methodology to understand complex systems – systems that are large
with many interacting elements and or non-linearities (such as
payment systems)
• In contrast to traditional statistical models, which attempt to
find analytical solutions
• Usually a special purpose computer program is used that takes granular
inputs, applies the simulation rules and generates outputs
• Take into account second rounds effects, third round, …
• Inputs can be stochastic or deterministic. Behavior can be static, preprogrammed, evolving or co-learning

Application areas

Enhance
understanding of
system mechanics

Evaluate alternative
design features

Why
Simulate?
Stress testing and
liquidity needs
analysis

Platform for
communication
among stakeholders

Data Needs for Simulations
• Historical transaction data
– From interbank payment systems
– At minimum: date, time, sender, receiver, value
– More data on type of payment, economic
purpose, second tier (if any), type of
institution, etc. useful

• Artificial transaction data
– Based on aggregates (possible with Entropy
maximization methods)
– Based on a network model (defining bilateral flows)
– Assumptions
• Timing of payments
• Value distribution
• Correlations

– System stability (net flows over longer times)

Simulations are Difficult
Major challenge to make them
– tie in to recent/real-time data
– easier to carry out
– easier to understand

33

5. Stress Testing
• Scenario types
–
–
–
–

Historical
Probabilistic
Extreme but plausible market conditions
Worst-case

• Scenarios in BCBS document
– Own financial stress : a bank suffers, or is perceived to be suffering
from, a stress event
– Counterparty stress : a major counterparty suffers an intraday stress
event which prevents it from making payments
– Customer bank’s stress: a customer bank of a correspondent bank
suffers a stress event
– Market-wide credit or liquidity stress
34

Stress Simulations
Proper simulations need information on
payment flows between all banks –
feedback effects!
It is a Complex Adaptive System
A well set-up simulation environment
allows exploration of many different
stress scenarios
Large body of research and policy work
on various stress testing has been
carried out with data from interbank
payment systems
35

Bank Behavior
Payment Systems are “Complex Adaptive Systems”: Interaction of simple
events (one debit and one credit) creates complex overall behavior

A bank’s ability to settle payments (its liquidity risk) depends on its available
liquidity and other banks ability to settle payments, which depend …

Galbiati and Soramäki (2011), An Agent based Model of
Payment Systems. Journal of Economic Dynamics and
Control, Vol. 35, Iss. 6, pp 859-875

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.

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.
The platform represents more than six man-years
of work from highly experienced data scientists
and financial market professionals.

• 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

Automation
• End-to-end Automation of Analytics
– Speed, no set-up costs
– Organizational continuity

• Continuous Calculation
– Analytics available when needed
– Predictions ready when needed

• Security
– Only available to authorized people

• Integration
– Analysis available in different application and different formats
for online and print
38

Agenda

1.

Network Theory and Payment Networks
– Network concepts
– Network metrics

2.

Liquidity in Payment Networks
– Liquidity metrics
– Modeling liquidity

3.

Simulating Payment Networks
– Liquidity Saving Mechanisms
– Stress testing Payment Networks

4.

International
Remittances
network

Central Bank of
Guatemala
payment data

FNA Platform
40

Basics of Network Theory
• Constituents
– Nodes (vertices)
– Links (ties, edges or arcs)

• Links can be
– Directed vs undirected
– Weighed vs unweighted

• Graph + properties = Network

41

Graph Terminology

Trivial Graph

Complete graph, K4

Empty Graph

Complete graph, K7

Undirected Graph

Directed Graph

Graphs

Star

Lattice

Ring

44

Graphs

Random (Erdos-Renyi)

Scale-free (Barabasi-Albert)
Note: Scale free property assumes infinite size
45

Example Application Areas

Banks

Countries

Banks

Infrastructures

47

Example Application Areas
FNA HeavyTails

Node color indicates latest
daily return
- Green = positive
- Red = negative
Node size indicates
magnitude of return
Bright green and red
indicate an outlier return

49

Hands-on: Types of Layouts

Circle

Data, Remittances 2011

Force-Directed

Orbital

50

Common Centrality Metrics
Centrality metrics aim to summarize some notion of importance

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 by
other important nodes are more central, visiting
probability of a random process

Degree

Degree

In-Degree

Out-Degree

Degree Distribution

The topology of interbank
payment flows. Soramäki et
al. Physica A: Statistical
Mechanics and its
Applications 379 (1), 317-333
53

Paths, Trails, Walks

A Walk is any free movement along the links
A Trail is a walk where a given link is visited only once
A Path is a walk where a given node is visited only once
A Geodesic Path is the Shortest Path
A Cycle is a path starting and ending to the same vertex

Strongly Connected Graph
Starting from any red
node allows one to
reach any other red
node with a walk along
the links.
Depending which black
node one begins from,
one can either reach
other black nodes or red
nodes

Closeness
• The Farness of a node is defined
as the sum of its distances to all
other nodes
• The Closeness of a node is
defined as the inverse of the
farness

• Needs a connected graph (or
component)
• Directed/undirected
• Weighed/un-weighted

57

Betweenness Centrality
• Measures the number of shortest paths going through a vertex or an arc
• Algorithm
– Calculate shortest paths
between each pair of nodes
– Determine the fraction of shortest
paths that pass through the node
in question
– Sum this fraction over all pairs of
nodes

• Directed/undirected; Weighed/unweighted

Freeman, Linton (1977). "A set of measures of centrality
based upon betweenness". Sociometry 40: 35–4

58

Eigenvector Centrality
Problem: Eigenvector Centrality can be (meaningfully) calculated only for
“Giant Strongly Connected Component” (GSCC)

Solution: PageRank

PageRank
• Algorithm used by Google to rank web pages. Random surfer model.

• Solves the problem of dead-ends with a “Damping factor”
to modify the adjacency matrix (S)
– Gi,j= Si,j

• Effectively allowing the random process out of
dead-ends (dangling nodes), but at the cost of
introducing error
• Effect of
–

Centrality of each node is 1/N

–

Eigenvector Centrality

– Commonly

is used

which is used

Hands-on: Centrality

Exploring centrality
of countries in
Remittances 2011
network

61

Agenda

1.

– Network metrics

2.


3.


4.

International
Remittances
network

Central Bank of
Guatemala
payment data

FNA Platform
62

Standard Reporting
• System turnover (value, volume)
–
–
–
–
–

Daily
Monthly peak/low/average
Yearly total
Unsettled payments
Distribution

•

– Payments value over day
– Intraday pattern/throughput
(value/volume)
– Delays due to lack of liquidity

•

Technical
– Processing times
– Settlement mode (by algorithm)

• Individual payments
– Average/min/max value
– Value distribution
– Payment type breakdown
(interbank, ancillary, cb
operations, etc)
– Priority (urgent, normal)
– Breakdown by bank

Intraday statistics

•

Static information
–
–
–
–

•

Number/types of participants
Opening/closing balances
Intraday credit limits
Bilateral limits

Incident reports
63

BCBS Monitoring tools
Starting point: BCBS Monitoring tools for intraday liquidity management
“It is envisaged that the introduction of monitoring tools for intraday
liquidity will lead to closer co-operation between banking supervisors and
the overseers in the monitoring of banks’ payment behaviour.“

A(i)
A(ii)
A(iii)
A(iv)

Daily maximum intraday liquidity usage
Available intraday liquidity at the start of the business day
Total payments
Time-specific obligations

B(i)

B(ii)

Value of customer payments made on behalf of correspondent
banking customers
Intraday credit lines extended to customers

C(i)

Intraday throughput
64

(i) Daily maximum liquidity requirement usage

65

Systemic Risk in Payment Systems
• Credit risk has been virtually eliminated by
system design (real-time gross settlement)
• Liquidity risk remains
– “Congestion”
– “Liquidity Dislocation”

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

• Time scale is intraday, spillovers possible

Distance to Sink
Absorbing Markov Chains give distances:

(66.6%)

(100%)

To B

1

From C

To A

From B

2

From A

From C
(33.3%)

To C

From A
From B

(100%)

1

SinkRank
SinkRank is the average distance of a unit of liquidity to the sink.
The actual liquidity distribution can be used in calculating SinkRank
Uniform
(A,B,C: 33.3% )
C

A

“Real”
(A: 5% B: 90% C:5%)
C

PageRank
(A: 37.5% B: 37.5% C:25%)
C

B

A

Note: Node sizes scale with 1/SinkRank

B

A

B

Core-Periphery Structure
•

Craig and von Peter (2010)

•

Interbank markets are tiered in a
Core-Periphery structure
The algorithm determines the optimal
set of core banks that achieves the
best structural match between
observed structure and perfectly
tiered structure

•

•
•

•

Core banks are connected to each
other.
Periphery banks are not connected to
other periphery banks.
Core banks are connected to (some)
periphery banks.
Ben Craig and Goetz von Peter (2010). Interbank tiering
and money center banks, BIS Working Papers No 322.
71

How good is it? Experiments:
• Design issues
– Real vs artificial networks?
– Real vs simulated failures?
– How to measure disruption?

• Approach taken
1.
2.
3.
4.

Create artificial data with close resemblance to the US Fedwire system
(BA-type, Soramäki et al 2007)
Simulate failure of a bank: the bank can only receive but not send any
payments for the whole day
Measure “Liquidity Dislocation” and “Congestion” by non-failing banks
Correlate 3. (the “Disruption”) with SinkRank of the failing bank

SinkRank vs Disruption
Relationship between
SinkRank and Disruption

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

Hands-on: Systemically Important Banks

74

3. Real-time Monitoring
Outlier Detection
Visual alerts
Visualizations can
highlight banks or their
links that are out of
normal bounds by
marking them with
different color or other
visual cues

E-mail/SMS alerts
In a real-time monitoring
environment the system
can sen e-mails if
aspects of the system
are out of normal
bounds
75

4. Predictive Modeling
• Predictive modeling is the process by which a model is
created to try to best predict the probability of an outcome

• Given a distribution of liquidity among the banks in the
morning, how is it going to be in the afternoon?
– 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
76

Agenda
1.

Introduction

2.

Payment Networks
– Network metrics

3.

International
Remittances
network


4.

Simulating Payment Systems
–
–

5.

Liquidity Saving Mechanisms
Stress Testing Payment Networks

Central Bank of
Guatemala
payment data

FNA Platform
78

What are simulations?
• Methodology to understand complex systems – systems that are large
with many interacting elements and or non-linearities (such as payment
systems)
• In contrast to traditional statistical models, which attempt to
find analytical solutions
• Usually a special purpose computer program is used that takes granular
inputs, applies the simulation rules and generates outputs
• Take into account second rounds effects, third round, …
• Inputs can be stochastic or deterministic. Behavior can be static, preprogrammed, evolving or co-learning

Simulations vs analytical models
• Simulations (e.g. Koponen-Soramaki 1998, Leinonen, ed.
2005, work at FRB, ECB, BoC, BoJ, BoE) have so far not
endogenized bank behaviour
– behaviour has been assumed to remain unchanged in spite of other
changes in the system
– or to change in a predetermined manner
– due to the use of actual data, difficult to generalize

• Game theoretic models (e.g. Angelini 1998, Kobayakawa 1997,
Bech-Garratt 2003) need to make many simplifying
assumptions
– on settlement process / payoffs
– topology of interactions
– do not give quantitative answers
80

Short history of LVPS simulations
•

1997 : Bank of Finland
– Evaluate liquidity needs of banks when Finland’s RTGS system was joined with TARGET
– See Koponen-Soramaki (1998) “Liquidity needs in a modern interbank payment systems:

•

2000 : Bank of Japan and FRBNY
– Test features for BoJ-Net/Fedwire

•

2001 - : CLS approval process and ongoing oversight
– Test CLS risk management
– Evaluate settlement’ members capacity for pay-ins
– Understand how the system works

•

Since: Bank of Canada, Banque de France, Nederlandsche Bank, Norges
Bank, TARGET2, and many others

•

2010 - : Bank of England new CHAPS
– Evaluate alternative liquidity saving mechanisms
– Use as platform for discussions with banks
– Denby-McLafferty (2012) “Liquidity Saving in CHAPS: A Simulation Study”

Tools
• Bof-PSS2
–
–
–
–

Bank of Finland, 1997- (BoF-PSS1)
RTGS, RRGS, Net, many optimization methods
www.bof.fi/sc/bof-pss
Free, Support & Training available, Annual workshop

• FNA
– Financial Network Analytics Ltd. (UK), 2009– RTGS, RRGS, many optimization methods,
visual exploration of results, network analysis
– www.fna.fi
– Free online, License, Support & Training available

• Proprietary tools or general purpose programs
– Matlab, SAS, Excel, …

Hands-on: Simulation on LSM

Register at www.fna.fi
83

Framework – Liquidity Optimization

Settlement speed

END OF DAY

IMMEDIATE
MIN

Liquidity Used

HIGH

Source: Koponen-Soramäki (1997). Intraday liquidity needs in a modern interbank payment
system - a Simulation Approach , Bank of Finland Studies in Economics and Finance 14.
84

Framework – RTGS

Instructions

RTGS

Settlement

Queue

RTGS

Settlement

Instructions
Queue
Optimization
85

Framework – LSM

Time critical
payments

RTGS Stream

Settlement

Queue
Instructions
Optimization
LSM Stream
Non-time
critical
payments
86

Optimization
What? For example:

How?

• I have 20m and a payment of
30m to settle

• Splitting (eg. CLS)

• I have a 30m payment to B,
but only 20m on account. B
has a 10m payment to me

• Bilateral offsetting (eg.
CHAPS)

• I have no money, a payment
of 10m to B, B has a payment
of 10m to C, C has a payment
of 10m to me

• Multilateral netting (eg. CLS)
• Partial netting (e.g. Kronos)

10

10

10

30
10

10

20

10

10

10
10

87

Optimization
• What is being optimized?
– Minimize idle liquidity
– Maximize use of queued incoming payments

• Five main methods
–
–
–
–

Splitting
Ordering/Bypassing
Bilateral netting (offsetting)
Multilateral netting (offsetting, circles processing, gridlock
resolution)
– New payments
88

Bilateral offsetting with new payments

Bilaterally offset
liquidity needs

New payments aimed at
minimizing multilateral
positions

Bilaterally offset, reduced
liquidity needs

Source: www.lmrkts.com89

Issues
• What LSM mechanisms to use? LSM simulations
(have a long tradition and) are standard now
• Some options may have legal ambiguity (status of
split payments) or issues with netting agent in
multilateral netting
• Banks need to learn to co-use the LSM features. “If
you build it they will come” is not likely to work
90

Stress Testing
• Scenario models
–
–
–
–

Historical
Probabilistic
Extreme but plausible market conditions
Worst-case

• Scenarios in BCBS document
–
–
–
–

Own financial stress
Counterparty stress
Customer stress
Market wide credit or liquidity stress

• Predictive modeling
91

Stress Simulations

Proper simulations need information on payment flows
between all banks – feedback effects!
It is a Complex adaptive system

A well set-up simulation environment allows exploration
of many different stress scenarios
Large body of research and policy work on various stress
testing has been carried out with data from interbank
payment systems
92

FNA Payment Simulator
• Allow testing of Liquidity saving mechanisms
–
–
–
–
–

Queuing (FIFO + priorities + bypass)
Bilateral limits, overdraft limits, opening balances
Two-stream operation
Bilateral offsetting (first, fifo, best)
Queue optimization (Bech-Soramaki)

• Allow simulation of Stress scenarios, such as the scenarios in BCBS
document “Monitoring Indicators for Intraday Liquidity Management”
–
–
–
–

(i) Own financial stress
(ii) Counterparty stress
(iii) Customer stress
(iv) Market wide credit or liquidity stress

• Any functionality can be implemented in a custom Payment Simulator application
93

Hands-on: Stress Simulations

Register at www.fna.fi
94

Interactive Workshop Agenda

1.

– Network metrics

2.


3.


4.

International
Remittances
network

Central Bank of
Guatemala
payment data

FNA Platform
95

Hands-on: FNA Platform

http://www.fna.fi

96

Thank you
Blog, library and demos are available at www.fna.fi

Dr. Kimmo Soramäki
kimmo@soramaki.net
Twitter: soramaki

97

Quantitative Oversight of Financial Market Infrastructures

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