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
1
Roadmap
 Payment system 101
 The Interbank Payment and
Settlement Simulator (IPSS)
 Demonstration & Experiment results
 Conclusions
2
Payment system 101
3
Payment System: DNS vs RTGS
Bank A
£10
£10
Liquidity
Bank D
Bank B
DNS
£0
RTGS
£ 40
£10
£10
Bank C
4
LVPS design issues
Two polar extremes:
- Deferred Net Settlement (DNS)
- Real Time Gross Settlement (RTGS)
DNS
Liquidity
Delay
Low
High
+
RTGS
High
Low
Hybrids
5
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 nonsynchronized payment flows.
6
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 B’s 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 :
1. Reduction of settlement risk
2. Improving efficiency of liquidity usage
3. Improving settlement speed (operational
risk)
10
What are agent-based simulations?
 Using a model to replicate alternative
realities
 Agent-based simulations allow us to
model these characteristics:
1. Heterogeneity
2. Strategies: rule of thumb or optimisation
3. 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)
13
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 N+1;
-impact of participation structure on risks.
15
Herfindahl Index
 measures the concentration of payment
activity:
HIPayments =


Bank i Payments

i  Total Value of Payments 


2
 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.
16
Herfindahl Index and Asymmetry
Equal Size Banks
(Proxied Data )
Herfindhal Index
1/14 ~ 0.071
Chaps Data
Herfindhal Index ~ 0.2
Bilateral
DNS
Lower Bound
(Multilateral
DNS)
£0
£0
£19.6 bn
£5.6 bn
Upper
Bound
£2.4 bn
£22.2 bn
Note that total value of payments is the same in all scenarios
17
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 α applied exogenously gives
liquidity ranging from LB to UB:
L(UB - (UB –LB)
 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*
1  i 

tE *  tR 
ln 
b  ab 
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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:
1. First in first out (FIFO)
2. Order by size (smallest first)
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Liquidity/Delay: JIT vs. OL
Time weighted delay vs. Liquidity
£25
Liquidity posted (billions)
£20
JIT (Delay by size)
£15
JIT (FIFO)
£10
£5
£0
0.00%
OL (FIFO)
1.00%
2.00%
OL (Delay by size)
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
10.00%
Percentage TW delay value
OL SIZE
OL FIFO
JIT SIZE
JIT FIFO
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Throughput in JIT vs. OL
Throughput: Cumulative value (%) of payments
made at any time.
Throughput in JIT vs OL
Percentage of payments made by value
100%
80%
60%
OL
40%
JIT
20%
0%
05:00
07:00
09:00
11:00
13:00
15:00
17:00
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:
Scenario
Chaps IID Real
Equal size banks
Failure big
bank (K)
Failure small
bank (F)
32,384
£94.2 bn
2,634
£1.0 bn
11,732
£21,1 bn
 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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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.
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