Post on 03-Jan-2016
Examples of Micropayments Data
James McAndrewsFederal Reserve Bank of New York
2008 World Congress on National Accounts and Economic Performance Measures for Nations
May 13, 2008The views expressed in this paper are those of the authors and do not necessarily represent the views of the Federal Reserve
Bank of New York or of the Federal Reserve System.
Payment Data
Central banks are usually operators of the large-value payment system.
Many important economic policy questions are involved, related to efficiency, risk, and equity.
Payment Data
In recent years, central bank economists have begun collecting disaggregated data on payment activity.
• Summary reports.• Hypothesis tests.
Payment Data
In this presentation, I’ll show two examples of using micropayments data using the Federal Reserve’s large-value payment system, Fedwire.
Changes in the Timing Distribution of Fedwire Funds
Transfers, Armantier, Arnold, McAndrews (2008)
Payment TimingA large literature has examined the economics of payment timing: Theory: Angelini (1999, 2000)Bech and Garratt (2003)Kahn, McAndrews, Roberds (2003)Mills and Nesmith (2007)Bech (2007)Empirical workMcAndrews and Rajan (2000)Becher, Galbiati, and Tudela (2007)
1998
2006
0.00
0.25
0.50
0.75
1.00
Perc
ent o
f Dai
ly Va
lue
of P
aym
ents
21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Hour
Notes: Mean daily percent of total payment value settled in each minute. Values exclude payments related to CHIPS, CLS, DTC and P&I payment funding.Sources: Federal Reserve Bank of New York, Authors' calculations.
Fedwire Funds Value Time Distribution, 1998 and 2006
Five stylized changes in the Fedwire Funds value time distribution
1998 to 2006
1.The distribution of value settled became more concentrated and less variable.
2.The highest peak of activity is later.
3. Three distinct peaks, 16:30, 17:00, and the third (the sharpest) at 17:11.
4.The 1998 distribution exhibits regular clock effects which virtually disappear in 2002.
5.The 2006 distribution shows higher activity at 08:00 and 08:30, and the 08:00 peak is only present in 2006.
Evolution of the value time distribution
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
21 23 1 3 5 7 9 11 13 15 17 21 23 1 3 5 7 9 11 13 15 17 21 23 1 3 5 7 9 11 13 15 17
21 23 1 3 5 7 9 11 13 15 17 21 23 1 3 5 7 9 11 13 15 17 21 23 1 3 5 7 9 11 13 15 17
21 23 1 3 5 7 9 11 13 15 17 21 23 1 3 5 7 9 11 13 15 17 21 23 1 3 5 7 9 11 13 15 17
1998 1999 2000
2001 2002 2003
2004 2005 2006Perc
ent o
f Tot
al D
aily
Valu
e
HourNotes: Mean daily percent of total payment value settled in each minute. Values exclude payments related to CHIPS, CLS, DTC and P&I payment funding.Sources: Federal Reserve Bank of New York, Authors' calculations.
Fedwire Funds Value Time Distributions by Year
These shifts in the value time distribution are statistically significant
25 96
-8
-6
-4
-2
0
2
4
6
8
Min
utes
diff
eren
ce b
etwe
en 2
006
and
1998
0 20 40 60 80 100Percentile of Time of Value
Notes: Minutes subtracted/added to each percentile until a Mann-Whitney rank-sum test is insignificant at 5% level.Sources: Federal Reserve Bank of New York, Authors' calculations.
Mann-Whitney U Test on Percentiles of Value Time, 1998 to 2006
Estimation strategy:reduced form regressions, one OLS
regression per percentile
• Reduced-form equation characterizing equilibria of coordination game.
• 100 independent OLS regressions• 2220 observations: business days between
March 1998 and December 2006.• Same set of independent variables in each
regression.
Variables are grouped into 5 categories
1) Value and Volume.
2) Federal Reserve Policies and Operations.
3) Settlement System Activities.
4) Other Control Variables.
5) Calendar Effects.
0
.02
.04
.06
.08
-.1
-.05
0
0
.05
.1
-.05
0
.05
.1
.15
0
500
1000
1500
0
.05
.1
.15
12 13 14 15 16 17 18 12 13 14 15 16 17 18 12 13 14 15 16 17 18
12 13 14 15 16 17 18 12 13 14 15 16 17 18 12 13 14 15 16 17 18
0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100
0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100
1: Inter-bank payment value 2: Customer payment value 3: Federal funds deliveries
4: Federal funds returns 5: Payments >= $10 6: Number of Payments
Coef
ficie
nt a
nd 9
5% C
I
PercentileNotes: Independent OLS regressions with Newey-West standard errors (max lag = 10), for the 2nd-99th percentile of value time. The color of the point indicates the signficance of the coefficient, Red = 1%, Green = 5%, Dark Gray = insig. The upper x-axis is the mean 2006 time for selected percentiles. Obs = 2220 for each regression.
Value and VolumeRegressions of the Fedwire Funds Value Time Percentiles
The role of settlement institutions on Fedwire Funds payment timing
18 Jan 2000CHIPS moves closing
to 17:00
22 Jan 2001CHIPS Intraday Finality
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Coincident with:DTC and CHIPS CHIPSDTC Neither
Notes: 7 days settling after 19:00 were excluded. Values exclude payments related to CHIPS, CLS, DTC and P&I payment funding.Sources: Federal Reserve Bank of New York, Authors' calculations.
Time of Peak 10 Mintues of Fedwire Value
Discussion
Why should Fedwire value move with CHIPS timing?
Four hypotheses:• Liquidity cascade• Customer credit cascade• Resolution of uncertainty• Focal point
Economic significance:Factors influencing the 1998 – 2006
change in the 75th Percentile
We “subtract out” the time trend, and ask how much can different variables explain of the later settlement.
• Volume and value—40 percent
• Changes in CHIPS—30-20=10 percent
• Increase in concentration—40 percent
Conclusions
Our results tend to support that the provision of liquidity, settlement institution activity, volume and value transferred, and industry structure are all influential in explaining changes in the timing of payment on Fedwire.
Segmentation in the U.S. Money Market: Fed funds and Eurodollars McAndrews, in progress
Close-up of Eurodollar-Fed funds interest rate spread in recent period
Values traded in the market segments
Values traded in the market segments: close-up of recent period
Predicted spreads from an EGARCH estimation
2002-2004 8/2007 –4/2008
Payments data
The studies examined are examples of work that seeks to test hypotheses with appropriate microdata about a previously unexamined part of the financial system.
Protocols need to be established to share these data with researchers outside of central banks.