Designing Large Value Payment Systems: An Agentbased approach

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Designing Large Value Payment Systems An
Agent-based approach
10th Annual Workshop on Economic Heterogeneous
Interacting Agents (WEHIA 2005) - June 13-15,
2005 - University of Essex, UK

• 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

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

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Background
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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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Agent based vs. Analytical models

• Analytical models make simplifying assumptions
• equal size banks with equal size payments
• Agent based models can process and run data in
  real time and can simulate a system in model
  vérité to replicate its structural features and
  perform wind tunnel tests
• Nirvana of Agent based Computational Economics
  (ACE)
• Have agents respond autonomously and
  strategically to policy changes

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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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LVPS design issues

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

Hybrids
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Example DNS vs. RTGS
Bank D
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Logistics of liquidity posting

• Intraday liquidity can be obtained in two ways
  waiting for incoming payments or posting
  liquidity.
• 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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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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• The Interbank Payment and Settlement Simulator
  (IPSS)

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Related Research

• Bech and Soramaki, 2002 BoF-PSS1
• allow banks to post varying amounts of liquidity
  at opening
• assume payment arrival time is the time of
  submission
• evaluate delays at different level of liquidity

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Whats the difference with the BoF Simulator?

• We can handle stochastic simulations while the
  BoF simulator can only deal with deterministic
  simulations based on actual data.
• Stochastic simulations enable us to vary the
  statistical properties of interbank system in
  terms of the size, arrival time, and distribution
  of payments flows.
• We can model strategic behaviour of banks

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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
Note that total value of payments is the same in
all scenarios
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IPSS Strategies

• Open liquidity
• Just in Time
• No strategy (FIFO)
• Rule of Thumb (i.e. only small payments)
• Optimal Rule (minimization of cost)
• Different ordering of queues

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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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DemonstrationExperiment 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.

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Conclusion

• 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.

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Contact details

• IPSS website
• www.amadeo.name/ipss
• Email
• aalent_at_essex.ac.uk