Designing Large Value Payment Systems: An Agent-based approach

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Designing Large Value Payment Systems An
Agent-based approach

• Amadeo Alentorn CCFEA, University of Essex
• Sheri Markose Economics/CCFEA, University of
  Essex
• Stephen Millard Bank of England
• Jing Yang Bank of England

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Roadmap

• Payment system 101
• The Interbank Payment and Settlement Simulator
  (IPSS)
• Demonstration Experiment results
• Conclusions

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Payment system 101
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Payment System DNS vs RTGS
Liquidity
DNS 0
RTGS 40
Bank D
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LVPS design issues

• Two polar extremes
• Deferred Net Settlement (DNS)
• Real Time Gross Settlement (RTGS)

Liquidity Delay
DNS Low High
RTGS High Low

Hybrids
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Risk-efficiency trade off (I)

• RTGS avoids the situation where the failure of
  one bank may cause the failure of others due to
  the exposures accumulated throughout a day
• However, this reduction of settlement risk comes
  at a cost of an increased intraday liquidity
  needed to smooth the non-synchronized payment
  flows.

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Risk-efficiency trade off (II)

• Free Riding Problem
• Nash equilibrium à la Prisoner's Dilemma, where
  non-cooperation is the dominant strategy
• If liquidity is costly, but there are no delay
  costs, it is optimal at the individual bank level
  to delay until the end of the day.
• Free riding implies that no bank voluntarily
  post liquidity and one waits for incoming
  payments. All banks may only make payments with
  high priority costs.
• So hidden queues and gridlock occur, which can
  compromise the integrity of RTGS settlement
  capabilities.

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UK payment system CHAPS

• 13 direct members, and other banks have indirect
  access to CHAPS through correspondent
  relationship.
• Payments through the system average about ?175 bn
  per day (175 of UK annual GDP).
• CHAPS is a Real time gross settlement system
  (RTGS).
• Each direct member has an account at the BoE.
• Bank A ? ?X amount to Bank B Bank A
  instruct the BoE to transfer ?X to bank Bs
  account.

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Liquidity

• A bank may obtain liquidity needed to make
  payments in two ways.
• 1). Obtain liquidity directly by posting
  collateral with the Bank.
• 2). Obtain liquidity by receiving a payment from
  another bank.
• Total amount of liquidity in the system is
  determined by the amount of collateral the member
  banks post with the BoE.

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What are the design issues in a Large Value
Payment Systems (LVPS)?

• Three objectives
• Reduction of settlement risk
• Improving efficiency of liquidity usage
• Improving settlement speed (operational risk)

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What are agent-based simulations?

• Using a model to replicate alternative realities
• Agent-based simulations allow us to model these
  characteristics
• Heterogeneity
• Strategies rule of thumb or optimisation
• Adaptive learning

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• The Interbank Payment and Settlement Simulator
  (IPSS)

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What can IPSS do?1. Payments data and statistics

• Each payment has
• time of Request tR
• time of Execution tE
• Payment arrival at the banks can be
• Equal to tE from CHAPS data files (Chaps Real)
• IID Payments arrival arrival time is random
  subject to being earlier than tE. (CHAPS IID
  Real)
• Stochastic arrival time (Proxied Data)

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Upperbound Lowerbound liquidity

• Upper bound (UB) amount of liquidity that banks
  have to post on a just in time basis so that all
  payment requests are settled without delay. Note
  that the UB is not know ex-ante.
• Lower bound (LB) amount of liquidity that a
  payment system needs in order to settle all
  payments at the end of the day under DNS. It is
  calculated using a multilateral netting algorithm.

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What can IPSS do?2. Interbank structure

• Heterogeneous banks in terms of their size of
  payments and market share
• -tiering N1
• -impact of participation structure on risks.

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Herfindahl Index

• measures the concentration of payment activity
• In general, the Herfindahl Index will lie between
  0.5 and 1/n, where n is the number of banks.
• It will equal 1/n when payment activity is
  equally divided between the n banks.

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Herfindahl Index and Asymmetry
Bilateral DNS Lower Bound (Multilateral DNS) Upper Bound
Equal Size Banks (Proxied Data ) Herfindhal Index 1/14 0.071 0 0 2.4 bn
Chaps Data Herfindhal Index 0.2 19.6 bn 5.6 bn 22.2 bn
Note that total value of payments is the same in
all scenarios
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Liquidity posting

• Two ways of posting liquidity in RTGS
• Just in Time (JIT) raise liquidity whenever
  needed paying a fee to a central bank, like in
  FedWire US
• Open Liquidity (OL) obtain liquidity at the
  beginning of the day by posting collateral, like
  in CHAPS UK
• A good payment system should encourage
  participants to efficiently recycle the liquidity
  in the system.

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Open Liquidity

• Banks start the day by posting all liquidity
  upfront to the central bank. The factor a applied
  exogenously gives liquidity ranging from LB to
  UB
• In the benchmark OL case, IPSS simply applies the
  FIFO (first in first out) rule to incoming
  payment requests if it has cash. Otherwise, wait
  for incoming payments.
• Strategic behavior leading to payment delay or
  reordering of payments occurs only if the
  liquidity posted is below the upper bound UB.

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JIT Optimal rule of delay

• Minimization of total settlement cost, which
  consists of delay costs plus liquidity costs.
  Gives an optimal time for payment execution tE

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• Demonstration

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Experiment Results
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IPSS Experiments

• Open liquidity vs. Just in time liquidity
  (Optimal rule)
• Under two payment submission strategies
• First in first out (FIFO)
• Order by size (smallest first)

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Liquidity/Delay JIT vs. OL
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Throughput in JIT vs. OL
Throughput Cumulative value () of payments
made at any time.
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Failure analysis

• IPSS allows to simulate the failure of a bank,
  and to observe the effects. For example, under
  JIT
• Note that, because of the asymmetry of the UK
  banking system, a failure of a bank would have a
  very different effect, depending on the size of
  the failed bank.

Scenario Failure big bank (K) Failure small bank (F)
Chaps IID Real 32,384 94.2 bn 2,634 1.0 bn
Equal size banks 11,732 21,1 bn 11,732 21,1 bn
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summary

• We developed a useful payments simulator
• - able to handle stochastic simulation
• - able to handle strategic behaviour.
• The experiments we ran suggested that
  open-liquidity leads to less delay than
  just-in-time.
• Future work will covers adaptive learning by
  banks to play the treasury management game and
  their response to hybrid rules.