When Do Operational Events Become a Systemic Concern: an - - PowerPoint PPT Presentation

▶
when do operational events become a systemic concern an
SMART_READER_LITE
LIVE PREVIEW

When Do Operational Events Become a Systemic Concern: an - - PowerPoint PPT Presentation

Sep 12, 2022 225 likes •451 views

When Do Operational Events Become a Systemic Concern: an Agent-Based Model of the Large Value Transfer System. Nicholas Labelle 10 February 2009 1 Introduction Basic assessment method of an outage. Value of payments - March 2008 25 20

slide-1
SLIDE 1

1

When Do Operational Events Become a Systemic Concern: an Agent-Based Model of the Large Value Transfer System.

Nicholas Labelle 10 February 2009

slide-2
SLIDE 2

2

Introduction

  • Basic assessment method of an outage.

Value of payments - March 2008

  • 5

10 15 20 25 0:30 1:30 2:30 3:30 4:30 5:30 6:30 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 Time Can$ billion Average Max. Min. Outage

slide-3
SLIDE 3

3

Outline

  • 1. discuss the analysis of payment systems
  • 2. demonstrate ABM application
  • 3. propose future improvements and applications
slide-4
SLIDE 4

4

  • 1. Agent-Based Model Contribution

1.1 Problems with Standard Simulation Approach

a) Fixed order of payment b) Cumbersome input data manipulation

slide-5
SLIDE 5

5

1.2 Solutions Offered by Agent-Based Modeling

a) Replicate the characteristics of the payment system b) Replicate assumed behavioural responses c) Simulate hypothetical outages

The outcome is then measured _

slide-6
SLIDE 6

6

  • 2. Demonstrate ABM Application

When do operational events become a systemic concern? 2.1 Payment System Features 2.2 Assumed Behaviour of Banks 2.3 Outage Simulation 2.4 Data 2.5 Parameterization 2.6 Preliminary Results

slide-7
SLIDE 7

7

2.1 Payment System Features: LVTS

1. LVTS Tranche 2 only 2. Bilateral Credit Limits (BCLs) 3. Bilateral and Multilateral Risk-Control Test 4. Central queue a) Release algorithm b) Jumbo queue algorithm c) Queue-expiry algorithm

slide-8
SLIDE 8

8

2.2 Assumed Behaviour of Banks

  • We take the BCLs as granted by the participants.
  • Unallocated collateral is significant in the LVTS.

Payments Processed Central queue Internal queue

slide-9
SLIDE 9

9

2.3 Outage Simulation

  • To simulate outages, the simulator intercepts and releases

payments at certain times.

slide-10
SLIDE 10

10

2.4 Data

  • Data on March and June 2008 is provided by the

Canadian Payments Association (CPA).

  • 2 files: payments and BCLs.
  • Distinction between payment submission times versus

processing times.

slide-11
SLIDE 11

11

2.5 Parameterization

35 – 60 – 120 minutes Period after which participants stop sending payments to the impacted participant until the outage ends. reaction 8:30 Beginning of the outage.

  • utstart

60 payments The maximum amount of payments a participant can send per minute from its internal queue through the LVTS. maxpaysec 1 to 9.5 hours Duration of the outage. duration Value Description Variable

slide-12
SLIDE 12

12

2.6 Preliminary Results A) Assumed Behavioural Response Validation

Context:

  • One day in March 2008, a large bank experienced a

partial outage from 7:24 to 13:37.

  • The CPA sent a notification at 8:11.
  • Participants held back payments at around 8:25.
  • We can compare the outage payment distribution

with simulation results.

slide-13
SLIDE 13

13

2.6 Preliminary Results A) Assumed Behavioural Response Validation

Value of payments - March 2008

  • 5

10 15 20 25 30 35 40 45 0:30 1:30 2:30 3:30 4:30 5:30 6:30 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 Time Can$ billion Outage Simulations Average Simulations max. Simulations min.

slide-14
SLIDE 14

14

2.6 Preliminary Results B) Concerns for Operational Outages

Context:

  • All the parameters were set as in the

parameterization table.

  • The month of June 2008 was chosen:

21 business days, 672 simulation runs.

  • The outage starts at 8:30.
  • The impacted participant is Bank 1.

Warning:

  • Results depend on the assumed behaviour.
slide-15
SLIDE 15

15

2.6 Preliminary Results Can the payment system settle?

Proportion of Unsettled Transaction Volume

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 16:30 17:00 17:30 18:00 Outage end time Mean Reaction 35 Mean Reaction 60 Mean Reaction 120

  • Max. Reaction 35
  • Min. Reaction 35
slide-16
SLIDE 16

16

2.6 Preliminary Results How many delays and costs does the outage entail?

Average Intraday Queue Value

5 10 15 20 25 30 35 40 45 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30

Outage end time Can$ billions

Mean Reaction 35 Mean Reaction 60 Mean Reaction 120

  • Max. Reaction 35
  • Min. Reaction 35
slide-17
SLIDE 17

17

Network Average Intraday Queue Value

0.0 0.5 1.0 1.5 2.0 2.5 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30

Outage end time Can$ billion

Mean Reaction 35 Mean Reaction 60 Mean Reaction 120

  • Max. Reaction 120
  • Min. Reaction 120

2.6 Preliminary Results How many delays and costs does the outage entail?

slide-18
SLIDE 18

18

2.6 Preliminary Results

How many delays and costs does the outage entail? Average Network Delay Indicators (Reaction 35 min.)

slide-19
SLIDE 19

19

  • 3. Improvements and Applications

1. Improvements:

  • better behavioural rules that are empirically and/or

theoretically founded;

  • methods to validate these assumptions;
  • search and develop better metrics.

2. Other applications:

  • change in parameter (SWP, BCL%, central queue);
  • change in assumed behaviour;
  • multi-operational outages;
  • risk assessment of periods and participants;
  • interaction with other payment systems.
slide-20
SLIDE 20

20

Conclusion

  • ABM: a black box that gives the end-results of our

assumptions about participant behaviour.

  • ABM might make our oversight approach more

quantitative and empirical.

  • Possible preliminary implications:
  • 1. confidence in the system robustness;
  • 2. proactive approach on certain payment system rules

related to participants’ reaction to outages;

  • 3. efficient ways to manage payments during outages.
slide-21
SLIDE 21

21

References

Arciero, L., C. Biancotti, L. D’Aurizio and C. Impenna. 2008. “Exploring agent-based methods for the analysis of payment systems: A crisis model for StarLogo TNG.” Bank of Italy Working Paper No. 686. Arjani, N. 2006. “Examining the Trade-Off between Settlement Delay and Intraday Liquidity in Canada’s LVTS: A Simulation Approach.” Bank of Canada Working Paper No. 2006-20. Arjani, N. and D. McVanel. 2006. “A Primer on Canada’s Large Value Transfer System.” Bank of Canada. <http://www.bankofcanada.ca/en/financial/lvts_neville.pdf> (5 January 2009). Bank for International Settlements. 2003. “A glossary of terms used in payments and settlement systems.”

  • March. <http://www.bis.org/publ/cpss00b.pdf?noframes=1> (5 January 2009).

Belisle, C. 2005. “Event Study of LVTS Participants in Situations of Partial Outages.” Bank of Canada. Canadian Payments Association. <http://www.cdnpay.ca/> (5 January 2009). Galbiati, M. and K. Soramäki. “An agent-based model of payment systems.” Bank of England Working Paper No. 352. Lefebvre, S. and K. McPhail. 2003. “Pannes du STPGV: note explicative.” Bank of Canada. FN-03-036. Leinonen, H. and K. Soramaki. 1999. “Optimizing Liquidity Usage and Settlement Speed in Payment Systems.” Bank of Finland Discussion Paper No. 16/99. McPhail, Kim and A. Vakos. 2003. “Excess Collateral in the LVTS: How Much is Too Much?” Bank of Canada Working Paper No. 2003-36.