No more tears without tiers? - Suomen Pankki · Outline 1.Introduction 2.Study design 3.Measures for liquidity management and risk 4.Results 5.Discussion 24 August 2015 Page 3 No

No more tears without tiers? Intraday indicators in tiered participation – Preliminary results

Jan Paulick

Disclaimer

The views expressed in the presentation are solely those of the author and

do not necessarily represent the views of Deutsche Bundesbank.

Work in progress, results are preliminary.

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No more tears without tiers?

Outline

1. Introduction

2.Study design

3.Measures for liquidity management and risk

4.Results

5.Discussion

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Outline

1. Introduction

2.Study design


4.Results

5.Discussion

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What is tiering?

−For different reasons banks chose to settle payments through a settlement

agent (regulation, institutional features, fee structures, banking system etc.)

−Settlement agents (direct participants) send and receive payments on

behalf of indirect participants

− If settlement agents and ultimate sender are not within the same banking

group it‘s called a tiered payment

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Why do we care?

−The Principles of Financial Market Infrastructures state that:

“An FMI should regularly review risks arising from tiered participation”

(Principle 19.4)

−Risk exposures include credit, liquidity and operational risk. These risks are

believed to be larger the more tiered participation a financial market

infrastructure exhibits

But:

−What can we say empirically about effects on liquidity and operational

efficiency?

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Outline

1. Introduction

2.Study design


4.Results

5.Discussion

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Study design

− How does tiered participation affect risk and liquidity management?

− Database on direct participant level

Filtering:

− Inclusion of operation type codes 1.1 (customer payments) and 1.2 (interbank payments)

− For robustness inclusion of ancillary system payments 3.x (this affects total running balance)

• Why? Exclusion of liquidity transfers, warehouse payments and central bank operations

− Inclusion of all BICs except CBs, AS and technical accounts

− Data from January 2013 to May 2015

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Study design

−Hierarchical information on Originator and Beneficiary from fields BIC52, BIC56, BIC57, BIC58, T_ASDEBT, T_ASCRED

−Swift Bank Directory+ data for consolidation (use of December 2012, December 2014 and March 2015 data, due to limited data availability)

−Use of Stata loops for data extraction and modification per day

−Total number of payments for analysis: 160 million

−Run time: two weeks

−Grouping of banks according to total payments (largest ~100 banks account for about 90 % of all payments), exclusion of banks with daily payments value of less than 500,000

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Payments volume (customer and interbank payments)

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Payments value (customer and interbank payments)

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What happened here?

−Break in BD+ data on group information, as actual data is only available for

three points in time

−Declining number of extra-group payments (but steady process)

−Could negatively affect significance of results, as intra-group payments

exhibit different dynamics

Second best solution:

−Controls with data on all payments from indirect participants and excluding

data further away from available data points

−Dummies for months and years

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Outline

1. Introduction

2.Study design


4.Results

5.Discussion

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Measures for liquidity management and risk

−Maximum liquidity need (MLN): calculated as maximum of running balance per day per direct participant (balance = payments sent minus payments received)

−Maximum liquidity need for funding payments can also be interpreted as: maximum exposure during the day / largest net debit position / minimum amount needed to make payments / liquidity provision to the system

−Liquidity usage (LU): total payments sent per direct participant

−Timing: Value weighted average timing of payments (daily)

−Payment delay indicators: processing time of all payments (including only final settlement status, accounting for earliest debit time), weighted by latest possible settlement

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A (random) walk down payment street….

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Maximum liquidity need Sent payments

Received payments

Randomization of settlement time

−Settlement time is randomized to compare results with random payment

instructions

−Differences to random settlement arise from structural payment dynamics

or behavioral patterns

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Relationship between Maximum liquidity need and tiering

−Using absolute measures biases results heavily due to multicollinearity:

• Direct participants with high maximum exposure typically send and receive more payments

• Participants sending and receiving more payments also send and receive more tiered payments

−Therefore, use of relative measures:

• Maximum liquidity need relative to value of payments

• Cost based liquidity provision

• Share of extra-group payments

Controls:

• Size of tiered payments

• Dummies for years and months

• Share of total payments

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Measures (continued)

−Cost based liquidity provision (difference of relative Maximum Liquidity

Need and relative Liquidity Usage) of bank i on day t

𝑀𝐿𝑁𝑖𝑡

𝑀𝐿𝑁𝑖𝑡 𝑛

𝑖=1

−𝐿𝑈𝑖

𝑡

𝐿𝑈𝑖𝑡 𝑛

𝑖=1

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Measures for liquidity management and risk

−Payments between 7 am and 6 pm to exclude night-time procedures

−Timing indicator:

𝑃𝑎𝑦𝑚𝑒𝑛𝑡𝑉𝑎𝑙𝑢𝑒𝑖 ∗ 𝑆𝑒𝑡𝑡𝑙𝑒𝑚𝑒𝑛𝑡𝑇𝑖𝑚𝑒𝑖𝑛𝑖=1

𝑃𝑎𝑦𝑚𝑒𝑛𝑡𝑉𝑎𝑙𝑢𝑒𝑖 𝑛𝑖=1

−Delay indicator:

𝑉𝑎𝑙𝑢𝑒𝑖 ∗ (𝑡2,𝑖 − 𝑡1,𝑖)𝑛𝑖=1

𝑉𝑎𝑙𝑢𝑒𝑖 ∗ (𝑡3,𝑖 − 𝑡1,𝑖)𝑛𝑖=1

Where:

t1,i is the time when payment i is available to be settled

t2,i is the actual settlement time of the payment i

t3,i is the end of day for the type of payment i, i.e. the latest possible settlement time

Source: Analysis of patterns in payment traffic and timing, Project report from the work stream “Behaviour and indicators”

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Outline

1. Introduction

2.Study design


4.Results

5.Discussion

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Cost based liquidity provision

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Cost based liquidity provision (randomized)

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MLN as share of total payments, OLS with time dummies

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Maximum liquidity needed / total payments

Large participants (1.1 and 1.2) Small participants (1.1 and 1.2)

Share of tiered payments -0.052 -0.186 0.093 -0.077

(7.52)*** (37.16)*** (18.09)*** (12.88)***

Size of tiered payments -0.004 -1.392 -0.050 0.014

(0.13) (1.89)* (1.49) (2.01)**

Share of total payments -1.201 -0.263 23.845 23.222

(29.21)*** (5.77)*** (23.54)*** (9.41)***

Constant 0.230 0.281 0.232 0.290

(57.19)*** (73.69)*** (57.83)*** (52.35)***

R2 0.01 0.04 0.02 0.01

N 39,471 38,886 61,794 31,446

* p<0.1; ** p<0.05; *** p<0.01

Randomized MLN as share of total payments, OLS with time dummies

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Maximum liquidity needed / total payments (randomized)


Share of tiered payments -0.027 -0.140 0.105 0.003

(4.00)*** (28.97)*** (20.49)*** (0.56)

Size of tiered payments -0.033 -1.634 -0.066 0.032

(0.99) (2.06)** (1.97)** (8.49)***

Share of total payments -1.061 0.129 25.060 28.656

(25.84)*** (2.83)*** (25.93)*** (11.41)***

Constant 0.206 0.227 0.225 0.258

(51.95)*** (60.68)*** (56.14)*** (46.70)***

R2 0.01 0.02 0.02 0.01

N 39,471 38,885 61,793 31,446

* p<0.1; ** p<0.05; *** p<0.01

Cost based liquidity provision (large participants), OLS with time dummies

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Cost based liquidity provision


Share of tiered payments -0.005 -0.020 0.000 -0.001

(23.85)*** (40.14)*** (4.33)*** (15.88)***

Size of tiered payments -0.001 -0.150 0.000 -0.000

(3.04)*** (2.05)** (2.02)** (1.85)*


(48.46)*** (8.17)*** (20.00)*** (8.36)***

Constant 0.004 0.010 -0.000 0.000

(17.35)*** (31.54)*** (7.55)*** (5.88)***

R2 0.33 0.08 0.26 0.18

N 39,471 38,886 61,794 31,446

* p<0.1; ** p<0.05; *** p<0.01

Cost based liquidity provision (large participants), OLS with time dummies

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Cost based liquidity provision (randomized)


Share of tiered payments -0.005 -0.010 0.000 0.000

(19.28)*** (31.82)*** (6.33)*** (0.40)

Size of tiered payments -0.001 -0.079 0.000 0.000

(3.10)*** (2.14)** (2.08)** (1.53)


(42.32)*** (31.96)*** (34.80)*** (4.99)***

Constant 0.003 0.006 -0.000 0.000

(15.24)*** (23.69)*** (10.46)*** (0.28)

R2 0.27 0.24 0.37 0.17

N 39,471 38,885 61,793 31,446

* p<0.1; ** p<0.05; *** p<0.01

Relationship between Maximum liquidity need and tiering

−Negative relationship between relative MLN/CBLP and share of tiered

payments

−But: Low explanatory power (especially across whole sample), potentially

omitted variable bias, model specification

−Size is more important than share of tiered payments, however tiering

variable is significant

−Effects are more pronounced for non-random specification and for large

participants

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One explanation: Timing of customer payments (Mean of largest participants)

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Timing of customer payments (Mean of largest participants)

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Timing of inter-bank payments (Mean of largest participants)

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Timing of inter-bank payments (Mean of largest participants)

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* p<0.1; ** p<.05; *** p<0.01

Delay of sent own and extra-group payments (inter-bank)

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Timing and delay of payments

−On average extra-group payments are sent later than received

−This could explain part of the negative relationship between maximum

exposure and the share of tiered payments

−Possibly stems from more discretion in when to settle extra-group payments

− No significant difference in delay of payments (but we do not know when

payments are received by settlement bank)

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Outline

1. Introduction

2.Study design


4.Results

5.Discussion

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Discussion

Relative to payments, MLN declines with higher share of tiered participation

Effect is more pronounced compared to random settlement of payments

−Direct participants have some discretion in timing of payments and conduct

active liquidity management

−However, findings are quite volatile across participants, calling for closer

investigation

−Level of tiering in TARGET2 is much lower compared to other payment

systems

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Discussion

In terms of risk and liquidity management there are ambivalent effects:

+Tiered payments have a negative effect on liquidity needed to settle

payments

+In terms of settlement speed, there are virtually no differences

Discretion in timing of payments might cause free-riding

Operational failure would still affect more participants

More tiering could improve efficient use of liquidity in the system

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Literature

Arculus, Hancock & Moran (2012), The Impact of Payment System Design on Tiering Incentives, Reserve Bank of Australia Research Dicussion Paper 2012-06.

Chapman, Chiu & Molico (2013), A Model of Tiered Settlement Networks, Journal of Money, Credit and Banking, Vol. 45, No. 2–3, pp. 327-347.

Denbee, Garratt & Zimmerman (2015), Variations in liquidity provision in real-time payment systems, Bank of England, Working Paper No. 513.

Lasaosa & Tudela (2008), Risks and efficiency gains of a tiered structure in large-value payments: a simulation approach, Bank of England Working Paper No. 337.

Finan & Sunderland (2013), Tiering in CHAPS, Bank of England, Quarterly Bulletin 2013 Q4.

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Thanks for your attention!

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Documents

No more tears without tiers? - Suomen Pankki · Outline 1.Introduction 2.Study design 3.Measures for liquidity management and risk 4.Results 5.Discussion 24 August 2015 Page 3 No