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Liquidity Coverage Ratio in a payments network
Richard Heuver and Ron Berndsen
De Nederlandsche Bank
15th Payment and Settlement System Simulation Seminar31 August - 1 September, 2017
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Overview
1 IntroductionBackgroundPossible Research questions
2 MethodologyApproachGeneration of Cascades
3 First results
4 Preliminary conclusions
5 Further Work
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Background
The Liquidity Coverage Ratio (LCR) requirement of the Basel III frameworkis aimed at making banks more resilient against liquidity shocks;
A bank should be able to fulfill its payment obligationsduring a 30-day stress period (LCR = 1)
Focus is on liquidity coverage of a single bank
FMI data is granular and contains network dimension
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Possible Research Questions
Can data from the LVPS add a network dimension to LCR?
What happens after a major participant becomes stressed (LCR < 1) ?
What is the impact of the size of the shortage (1 − σ) ?
How resilient are (the other) participants?How important is an additional buffer (1 + α) ?
Which participants cause most damage?
Which participants are most vulnerable?
What is the relationship with ECB and FSB lists of important institutions? 1
What effects can be observed during consecutive rounds ρ ?
What can be observed when generating a high frequency LCR?
...
1ECB internal list of Critical Particiants; FSB published lists of global systemically important institutions (G-SIBs) and insurers (G-SIIs).
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Methodology
Define 100 largest institutions (L100): 2
- ECB list of Critical Participants C- FSB list of Systemically Important Financial Institutions S- complete L100 based on value of outgoing payments and centrality
Define values for additional buffer α and shortage σ
For all banks calculate initial liquidity Buffer, assuming their LCR equals (1 + α)
Stress each of the L100 participants individually, by lowering their LCR to (1 − σ)
Start stress cascade for each bank, using payment network, applying decreased outflows
Each round ρ all other participants can also become stressed, in case (LCR < 1)
Store cascade calculations
Analyze results
2The terms ’institutions’, ’participants’ and ’banks’ all refer to the institutions participating in TARGET2 aggregated to the level of institution.
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Calculation of initial Liquidity Buffers using Payment Flows
The LCR requires a detailed runoff calculation using many balance sheet items
LCR = LiquidityBufferNetOutflow = LiquidityBuffer
Outflow−Inflow > 1
Important restriction;- Inflow may not exceed 0.75 of Outflow
Rewrite to: LCR = LiquidityBuffer(Outflow−0.75Outflow)
LiquidityBuffer = LCR 0.25 Outflow
Generate different Liquidity Buffers at LCR = 1 + α,
e.g. 1 + 0.05 , 1 + 0.10 , 1 + 0.20 , ...
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Generating a Stress Cascade in a payments network
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Example of an unfolding Stress Cascade
Each level in the graphrepresents a round in thecascade,starting at the top with level 0,and ending at the bottom withround ρ = 6
The stressed bank isrepresented by a square
Darkening of colors of nodesreflects LCR deteriorationa
Parameters: α = 0.05 ,σ = 0.50 , ρ = 6.
aColors: green, orange, brown and black were assignedusing LCR border values of 1.0, 0.70 and 0.50.
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Storage of Cascade Results
Initially: day , bank , inflow , outflow , liquidity buffer
Cascades: day , Source bank , round , Stressed bank , Affected bankActual inflow , - outflow , - LCR
Versions for levels of liquidity addition (α) and liquidity shortage (σ)
Enables generation of several statistics
Enables generation of a ”Network of LCR Deterioration”
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First results - Buffer size through time
Linear relationship betweenTotal Value and calculatedBuffers, which follows from
LCR = BufferNetOutflow i.e.
LCR = Buffer(Outflow−0.75Outflow)
Buffer = LCR 0.25 OutflowBuffer =
(1 + α) 0.25 Outflow
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First results - Example of an LCR Deterioration Network
Nodes:Size and darkening of colors reflectbanks’ outgoing strength a
i.e. the power to cause damage
Edges:Size and darkening of colors reflectvalue of damagei.e. path of destruction
aKleinberg’s hub centrality measure has been applied, which highly ranksnodes that have outgoing links to most central nodes.
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First Results - LCR Deterioration per round3
Percent value Number of Institutions
3Parameters used: addition α = 0.05 , shortage σ = 0.50 , nr of rounds ρ = 6.
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First Results - LCR Deterioration Caused and Suffered
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First Results - Relation between shortage and addition
As measured Fitted Grid
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(Very) Preliminary Conclusions
Liquidity Shortage σ seems to be the most important driver
Liquidity Addition α quickly lost, in the first rounds
Large institutions cause most damage
Small institutions on suffering side
LCR benefits from an added network dimension,at damaging side as well as at suffering side
...
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Challenges faced
Large amount of transactions (786 mln)
Aggregation from BIC code (2,500) to institution level (1,200) not present
Data Warehouse approach is necessary and time consuming
Storage of cascade results (566k) also needs database solution
Performance and storage of the environment has reached its limits
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Further Work
Data validation
Daily cascades on whole period (2008-2016)
Analysis of relation between addition α , shortage σ and rounds ρ
Analysis of deterioration network
...
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Thanks for your attention
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