1. 1 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

2. 2 Roadmap  Payment system 101  The Interbank Payment and Settlement Simulator (IPSS)  Demonstration & Experiment results  Conclusions

3. 3 Payment system 101

4. 4 Payment System: DNS vs RTGS Bank C Bank B Bank A Liquidity DNS£ 0 RTGS£ 40 Bank D

5. 5 LVPS design issues Two polar extremes: -Deferred Net Settlement (DNS) -Real Time Gross Settlement (RTGS) LiquidityDelay DNSLowHigh RTGSHighLow Hybrids +

6. 6 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.

7. 7 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.

8. 8 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.

9. 9 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.

10. 10 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)

11. 11 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

12. 12 The Interbank Payment and Settlement Simulator (IPSS)

13. 13 What can IPSS do? 1. Payments data and statistics -Each payment has : -time of Request: t R -time of Execution: t E -Payment arrival at the banks can be: -Equal to t E from CHAPS data files (Chaps Real) -IID Payments arrival: arrival time is random subject to being earlier than t E. (CHAPS IID Real) -Stochastic arrival time (Proxied Data)

14. 14 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.

15. 15 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.

16. 16 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.

17. 17 Herfindahl Index and Asymmetry Bilateral DNS Lower Bound (Multilateral DNS) Upper Bound Equal Size Banks (Proxied Data ) Herfindhal Index 1/14 ~ 0.071 £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

18. 18 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.

19. 19 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:  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.

20. 20 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 t E *

21. 21 Demonstration

22. 22 Experiment Results

23. 23 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)

24. 24 Liquidity/Delay: JIT vs. OL

25. 25 Throughput in JIT vs. OL Throughput: Cumulative value (%) of payments made at any time.

26. 26 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. ScenarioFailure big bank (K) Failure small bank (F) Chaps IID Real32,384 £94.2 bn 2,634 £1.0 bn Equal size banks11,732 £21,1 bn

27. 27 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.