Title: Designing Large Value Payment Systems: An Agentbased approach
1Designing 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
2Roadmap
- Background
- The Interbank Payment and Settlement Simulator
(IPSS) - Demonstration Experiment results
- Conclusions
3Background
4What 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
5Agent 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
6What 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)
7LVPS design issues
- Two polar extremes
- Deferred Net Settlement (DNS)
- Real Time Gross Settlement (RTGS)
Hybrids
8Example DNS vs. RTGS
Bank D
9Logistics 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.
10Risk-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.
11Risk-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.
12- The Interbank Payment and Settlement Simulator
(IPSS) -
13Related 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
14Whats 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
15What 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)
16Upperbound 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.
17What 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.
18Herfindahl 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.
19Herfindahl Index and Asymmetry
Note that total value of payments is the same in
all scenarios
20IPSS 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
21Open 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.
22JIT 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
23DemonstrationExperiment Results
24IPSS 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)
25Liquidity/Delay JIT vs. OL
26Throughput in JIT vs. OL
Throughput Cumulative value () of payments
made at any time.
27Failure 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.
28Conclusion
- 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.
29Contact details
- IPSS website
- www.amadeo.name/ipss
- Email
- aalent_at_essex.ac.uk