Designing Large Value Payment Systems: An Agent-based approach

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 YangBank of England

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

3. Payment system 101

4. Payment System: DNS vs RTGS Bank D

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

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. 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. 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. 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. 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)

11. 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

12. The Interbank Payment and Settlement Simulator (IPSS)

13. 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)

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. 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. 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. Herfindahl Index and Asymmetry Note that total value of payments is the same in all scenarios

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. 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. 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*

21. Demonstration

22. Experiment Results

23. 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)

24. Liquidity/Delay: JIT vs. OL

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

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.

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.