Treasury Presentation to TBAC
Office of Debt Management

Fiscal Year 2018 Q4 Report
Table of Contents

2

I. Executive Summary p. 4

II. Fiscal

A. Quarterly Tax Receipts p. 6

B. Monthly Receipt Levels p. 7

C. Largest Outlays p. 8

D. Treasury Net Nonmarketable Borrowing p. 9

E. Cumulative Budget Deficits p. 10

F. Deficit and Borrowing Estimates p. 11

G. Budget Surplus/Deficit p. 12

H. Privately-Held Net Marketable Borrowing Outlook P. 13

III. Financing

A. Sources of Financing p. 16

B. OMB’s Projections of Net Borrowing from the Public p. 18

C. Interest Rate Assumptions p. 19

D. Projected Net Marketable Borrowing Assuming Future Issuance Remains Constant p. 20

IV. Portfolio Metrics

A. Historical Weighted Average Maturity of Marketable Debt Outstanding p. 23

B. Bills Outstanding as a Percent of Portfolio P. 24

C. Maturity Profile p. 25

V. Demand

A. Summary Statistics p. 28

B. Bid-to-Cover Ratios p. 29

C. Investor Class Awards at Auction p. 34

D. Primary Dealer Awards at Auction p. 38

E. Direct Bidder Awards at Auction p. 39

F. Foreign Awards at Auction p. 40
Section I:

Executive Summary

3
Receipts and Outlays

• During FY 2018, receipts totaled $3,329 billion (16.1% of GDP). Non-withheld income and SECA taxes were up $89 billion (15%) in FY 

2018, most of which occurred during April, when strong final payments were made for the 2017 (pre-tax cut) liability. Withheld 

income and FICA taxes were up $23 billion (1%) in FY 2018, reflecting growth in both employment and wages. Since February, when 

use of lower tax withholding rates was required, withheld income and FICA taxes have either declined or shown small increases. 

Mostly offsetting the increases, gross corporate taxes were $76 billion (22%) lower than last year, largely due to the corporate tax rate 

reduction and the expanded ability to immediately deduct the full value of equipment purchases. Corporate refunds were up $17

billion (39%) compared with last year. 

• During FY 2018, outlays totaled $4,108 billion (19.9% of GDP). After calendar adjustments, FY 2018 outlays were $183 billion (5%) 

higher than last year. Treasury outlays were $83 billion (15%) higher due primarily to increased interest on the public debt of $65 

billion (14%) and lower overall receipts from the GSEs. Homeland Security outlays were $18 billion (35%) higher due to increased 

payments for disaster relief. Education outlays were $48 billion (43%) lower due to differences in subsidy re-estimates during the 

year. Social Security Administration outlays were $43 billion (4%) higher due to increases in enrollment and the average benefit. 

Health and Human Services outlays were $41 billion (4%) higher due to increases in Medicare. Defense expenditures were up $36 

billion (6%) due to increased spending for military personnel, operations, maintenance, and procurement. 

Projected Net Marketable Borrowing (FY 2019) 

• Based on the quarterly borrowing estimate, Treasury’s Office of Fiscal Projections (OFP) currently estimates a net privately-held 

marketable borrowing need of $425 billion for Q1 FY 2019, with an end-of-December cash balance of $410 billion. For Q2 FY 2019, the 

net privately-held marketable borrowing need is projected to be $356 billion, with an end-of-March cash balance of $320 billion. 

Privately-held marketable borrowing excludes rollovers (auction “add-ons”) of Treasury securities held in the Federal Reserve’s 

System Open Market Account (SOMA), but includes financing required due to SOMA redemptions. 

• Recent deficit estimates contained in OMB’s “Mid-Session-Review, Fiscal Year 2019” (July 2018) in conjunction with SOMA 

redemptions suggest that Treasury auction sizes will need to rise over the next few years. CBO’s updated budget projections are not 

yet available. 

Demand for Treasury Securities

• Bid-to-cover ratios for all securities were largely stable over the last quarter.

• Foreign demand remained steady.

Highlights of Treasury’s November 2018 Quarterly Refunding Presentation

to the Treasury Borrowing Advisory Committee (TBAC)

4
Section II:

Fiscal

5
(50%)

(40%)

(30%)

(20%)

(10%)

0%

10%

20%

30%

40%

50%

60%

Sep-08

Dec-08

Mar-09

Jun-09

Sep-09

Dec-09

Mar-10

Jun-10

Sep-10

Dec-10

Mar-11

Jun-11

Sep-11

Dec-11

Mar-12

Jun-12

Sep-12

Dec-12

Mar-13

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

Mar-16

Jun-16

Sep-16

Dec-16

Mar-17

Jun-17

Sep-17

Dec-17

Mar-18

Jun-18

Sep-18

Year-over-Year % Change

Quarterly Tax Receipts

Corporate Taxes Non-Withheld Taxes (incl SECA) Withheld Taxes (incl FICA)

6

Source: United States Department of the Treasury 
7

Individual Income Taxes include withheld and non-withheld. Social Insurance Taxes include FICA, SECA, RRTA, UTF deposits, FUTA and 

RUIA. Other includes excise taxes, estate and gift taxes, customs duties and miscellaneous receipts. 

Source: United States Department of the Treasury 

0

20

40

60

80

100

120

140

160

Sep-08

Jan-09

May-09

Sep-09

Jan-10

May-10

Sep-10

Jan-11

May-11

Sep-11

Jan-12

May-12

Sep-12

Jan-13

May-13

Sep-13

Jan-14

May-14

Sep-14

Jan-15

May-15

Sep-15

Jan-16

May-16

Sep-16

Jan-17

May-17

Sep-17

Jan-18

May-18

Sep-18

$ bn

Monthly Receipt Levels

(12-Month Moving Average)

Individual Income Taxes Corporation Income Taxes Social Insurance Taxes Other
Source: United States Department of the Treasury 8

0

200

400

600

800

1,000

1,200

HHS

SSA

Treasury

Defense

VA

Agriculture

OPM

Transportation

Homeland Security

Education

Other Defense Civil

Labor

$ bn

Largest Outlays

Oct - Sep FY 2017 Oct - Sep FY 2018
9

Source: United States Department of the Treasury 

(40)

(30)

(20)

(10)

0

10

20

30

Q4-08Q1-09Q2-09Q3-09Q4-09Q1-10Q2-10Q3-10Q4-10Q1-11Q2-11Q3-11Q4-11Q1-12Q2-12Q3-12Q4-12Q1-13Q2-13Q3-13Q4-13Q1-14Q2-14Q3-14Q4-14Q1-15Q2-15Q3-15Q4-15Q1-16Q2-16Q3-16Q4-16Q1-17Q2-17Q3-17Q4-17Q1-18Q2-18Q3-18Q4-18

$ bn

Fiscal Quarter

Treasury Net Nonmarketable Borrowing

Foreign Series State and Local Govt. Series (SLGS) Savings Bonds
Source: United States Department of the Treasury 10

0

100

200

300

400

500

600

700

800

900

1,000

October

November

December

January

February

March

April

May

June

July

August

September

$ bn

Cumulative Budget Deficits by Fiscal Year

FY2016 FY2017 FY2018
11

FY 2019-2021 Deficits and Net Marketable Borrowing Estimates*, in $ billions

Primary Dealers1 OMB2 CBO3 CBO4

FY 2019 Deficit Estimate 1,000 1,086 955 981

FY 2020 Deficit Estimate 1,100 1,076 866 1,008

FY 2021 Deficit Estimate 1,200 1,010 945 1,123

FY 2019 Deficit Range 825-1,135

FY 2020 Deficit Range 1,000-1,250

FY 2021 Deficit Range 1,000-1,365

FY 2019 Privately-Held Net Marketable Borrowing Estimate 1,300

FY 2020 Privately-Held Net Marketable Borrowing Estimate 1,200

FY 2021 Privately-Held Net Marketable Borrowing Estimate 1,273

FY 2019 Privately-Held Net Marketable Borrowing Range 1,036-1,460

FY 2020 Privately-Held Net Marketable Borrowing Range 900-1,500

FY 2021 Privately-Held Net Marketable Borrowing Range 895-1,465

FY 2019 SOMA Redemption Estimate 286

FY 2020 SOMA Redemption Estimate 120

FY 2021 SOMA Redemption Estimate 0

FY 2019 Net Marketable Borrowing Estimate 1,014 1,186 1,049 1,074

FY 2020 Net Marketable Borrowing Estimate 1,080 1,164 924 1,065

FY 2021 Net Marketable Borrowing Estimate 1,273 1,097 993 1,171

Estimates as of: Oct-18 Jul-18 May-18 Apr-18

1Based on primary dealer feedback in October 2018. Estimates above are medians. 

2Table S-11 of OMB's "Mid-Session Review, Fiscal Year 2019," July 2018.

3Table 2 of CBO's "An Analysis of the President's 2019 Budget," May 2018.

4Table 4-4 of CBO's "The Budget and Economic Outlook: 2018 to 2028," April 2018 (current law).

*Privately-held marketable borrowing excludes rollovers (auction “add-ons”) of Treasury securities held in the

 Federal Reserve’s System Open Market Account (SOMA), but includes financing required due to SOMA redemptions.
(16%)

(14%)

(12%)

(10%)

(8%)

(6%)

(4%)

(2%)

0%

2%

(1,600)

(1,400)

(1,200)

(1,000)

(800)

(600)

(400)

(200)

0

200

20092010201120122013201420152016201720182019202020212022202320242025202620272028

% of GDP

$ bn

Fiscal Year

Budget Surplus/Deficit

Surplus/Deficit (LHS) Surplus/Deficit (RHS)

Projections are from OMB’s Table S-11 of “Mid-Session Review, Fiscal Year 2019,” July 2018.

OMB’s Projection

12
13

-$100

$0

$100

$200

$300

$400

$500

$600

Q1 Q2 Q3 Q4

$ bn

FY 16 FY 17 FY 18 FY 19 (projection-OFP)

*Privately-held marketable borrowing excludes rollovers (auction “add-ons”) of Treasury securities held in the 

Federal Reserve’s System Open Market Account (SOMA), but includes financing required due to SOMA 

redemptions.

Privately-Held Net Marketable Borrowing Outlook*
Section III:

Financing

14
15

Assumptions for Financing Section (pages 16 to 21)

• Portfolio and SOMA holdings as of 9/30/2018.

• Estimates assume an end date for SOMA capped redemptions at the end of CY2020. The assumption is 

based on the median case from “Statement Regarding the Annual Report on Open Market Operations 

during 2017,” Federal Reserve Bank of New York, April 2018.

• Estimates assume announced issuance sizes and patterns remain constant for nominal coupons, TIPS, 

and FRNs given changes made at the August 2018 refunding, while using a total of ~$2.24 trillion of 

bills outstanding. 

• The principal on the TIPS securities was accreted to each projection date based on market ZCIS levels 

as of 9/30/2018. 

• No attempt was made to account for future financing needs. 
16

*Privately-held marketable borrowing excludes rollovers (auction “add-ons”) of Treasury securities held in the Federal Reserve’s System 

Open Market Account (SOMA), but includes financing required due to SOMA redemptions.

**An end-of-September 2018 cash balance of $385 billion versus a beginning-of-July 2018 cash balance of $333 billion. By keeping the cash 

balance constant, Treasury arrives at the net implied funding number. 

Net Bill Issuance 82 Security Gross Maturing Net Gross Maturing Net

Net Coupon Issuance 271 4-Week 690 650 40 2,460 2,405 55

Subtotal: Net Marketable Borrowing 353 13-Week 645 624 21 2,466 2,334 132

26-Week 567 564 3 2,154 1,905 249

Ending Cash Balance 385 52-Week 78 60 18 302 260 42

Beginning Cash Balance 333 CMBs 0 0 0 139 179 (40)

Subtotal: Change in Cash Balance 52 Bill Subtotal 1,980 1,898 82 7,521 7,083 438

Net Implied Funding for FY18 Q4** 301

Security Gross Maturing Net Gross Maturing Net

2-Year FRN 52 41 11 187 164 23

2-Year 105 78 27 358 234 124

3-Year 102 72 30 345 294 51

5-Year 109 53 56 420 398 22

7-Year 91 69 22 348 224 124

10-Year 71 14 57 267 66 201

30-Year 47 0 47 171 3 168

5-Year TIPS 14 0 14 44 53 (9)

10-Year TIPS 24 16 8 70 33 37

30-Year TIPS 0 0 0 17 0 17

Coupon Subtotal 615 344 271 2,227 1,469 758

Total 2,595 2,242 353 9,748 8,552 1,196

Sources of Privately-Held Financing in FY18 Q4*

Coupon Issuance Coupon Issuance

July - September 2018 July - September 2018 Fiscal Year-to-Date

Bill Issuance Bill Issuance

July - September 2018 Fiscal Year-to-Date
17

*Privately-held marketable borrowing excludes rollovers (auction “add-ons”) of Treasury securities held in the Federal Reserve’s System Open 

Market Account (SOMA), but includes financing required due to SOMA redemptions.

**Keeping announced issuance sizes and patterns constant for nominal coupons, TIPS, and FRNs based on changes made at the August 2018 

refunding. 

***Assumes an end-of-December 2018 cash balance of $410 billion versus a beginning-of-October 2018 cash balance of $385 billion.

Financing Estimates released by the Treasury can be found here: http://www.treasury.gov/resource-center/data-chart-center/quarterlyrefunding/Pages/Latest.aspx

Assuming Constant Coupon Issuance Sizes**

Treasury Announced Net Marketable Borrowing*** 425

Net Coupon Issuance 309

Implied Change in Bills 116

Security Gross Maturing Net Gross Maturing Net

2-Year FRN 53 41 12 53 41 12

2-Year 151 104 47 151 104 47

3-Year 108 72 36 108 72 36

5-Year 155 94 61 155 94 61

7-Year 124 91 33 124 91 33

10-Year 72 27 45 72 27 45

30-Year 48 3 45 48 3 45

5-Year TIPS 14 0 14 14 0 14

10-Year TIPS 11 0 11 11 0 11

30-Year TIPS 5 0 5 5 0 5

Coupon Subtotal 741 432 309 741 432 309

Coupon Issuance Coupon Issuance

Sources of Privately-Held Financing in FY19 Q1*

October - December 2018

October - December 2018 Fiscal Year-to-Date
18

OMB's projections of the change in debt held by the public (borrowing) are from Table S-11 of “Mid-Session Review, Fiscal Year 2019,” July 

2018. “Other” represents borrowing from the public to provide direct and guaranteed loans.

1,186 1,164

1,097 1,096

963

763

722

657

590 584

60%

65%

70%

75%

80%

85%

(500)

(300)

(100)

100

300

500

700

900

1,100

1,300

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028

% of GDP

$ bn

OMB's Projection of Borrowing from the Public

Primary Deficit (LHS) Net Interest (LHS)

Other (LHS) Debt Held by Public (RHS)

Debt Held by Public Net of Financial Assets (RHS)

The bubbles represent the total net marketable borrowing for that year

$ bn %

Primary Deficit 1,690 19

Net Interest 6,413 73

Other 719 8

Total 8,822 100

FY2019 - FY2028 Cumulative Total
19

OMB's economic assumption of the 10-Year Treasury Note rates are from Table 2 of OMB’s “Mid-Session Review, Fiscal Year 2019,” July 2018. 

CBO’s economic assumption of the 10-Year Treasury Note rates are from Table D-1 of CBO’s “The Budget and Economic Outlook: 2018 to 2028,” 

April 2018. The forward rates are the implied 10-Year Treasury Note rates on September 30, 2018. 

2.8

3.0

3.2

3.4

3.6

3.8

4.0

20182019202020212022202320242025202620272028

10-Year Treasury Note Rate, %

Interest Rate Assumptions: 10-Year Treasury Note

CBO Budget Outlook OMB MSR Implied Forward Rates as of 09/30/2018

10-Year Treasury Rate of 3.062% 

as of 09/30/2018
20

Projected Net Marketable Borrowing 

Assuming Future Issuance Remains Constant*

Treasury’s October 2018 primary dealer survey estimates can be found on page 11. OMB's projections of the change in debt held by the public 

are from Table S-11 of “Mid-Session Review, Fiscal Year 2019,” July 2018. CBO’s baseline budget projections of the change in debt held by the 

public are from Table 2 of “An Analysis of the President’s 2019 Budget,” May 2018. CBO’s current law budget projections of the change in debt 

held by the public are from Summary Table2 of “The Budget and Economic Outlook:2018 to 2028,“ April 2018. See table at the end of this 

section for details.

*Projections reflect capped SOMA Treasury redemptions up until the end of CY 2020. 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2019202020212022202320242025202620272028

$bn

Fiscal Year

Projected Net Marketable Borrowing CBO's "An Analysis of the President's 2019 Budget, " May 2018

OMB's FY 2019 Mid-Session Review, July 2018 PD Survey Marketable Borrowing Estimates, October 2018

CBO's "The Budget and Economic Outlook: 2018 to 2028," April 2018 (current law)
21

Historical Net Marketable Borrowing and Projected Net Borrowing 

Assuming Future Issuance Remains Constant, $ billions

Net borrowing capacity reflects capped SOMA redemptions up until the end of CY 2020. 

Treasury’s October 2018 primary dealer survey estimates can be found on page 11. OMB's projections of the change in debt held by the public 

are from Table S-11 of “Mid-Session Review, Fiscal Year 2019,” July 2018. CBO’s baseline budget projections of the change in debt held by the 

public are from Table 2 of CBO’s “An Analysis of the President’s Budget,” May 2018. 

Fiscal 

Year Bills 2/3/5 7/10/30 TIPS FRN

Historical/Projected 

Net Borrowing 

Capacity

OMB's FY 2019 MidSession Review

CBO's "An Analysis of 

the President's 2019 

Budget "

Primary Dealer 

Survey

2014 (119) (92) 669 88 123 669 

2015 (53) (282) 641 88 164 558 

2016 289 (82) 477 64 47 795 

2017 155 9 292 55 9 519 

2018 438 209 316 51 26 1,040 

2019 (0) 431 205 44 42 723 1,186 1,049 1,014 

2020 0 263 241 14 15 535 1,164 924 1,080 

2021 0 169 298 (2) (0) 465 1,097 993 1,273 

2022 0 106 323 (13) 3 418 1,096 1,085 

2023 0 148 199 (10) 5 342 963 1,018 

2024 0 (5) 282 (13) 1 265 763 911 

2025 0 (31) 261 (55) (2) 173 722 951 

2026 0 (29) 257 (47) (2) 180 657 952 

2027 0 (5) 233 (36) (3) 189 590 1,027 

2028 0 (13) 223 (64) 3 149 584 1,149 
Section IV:

Portfolio Metrics

22
23

40

45

50

55

60

65

70

75

80

198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018

Weighted Average Maturity (Months)

Calendar Year

Historical Weighted Average Maturity of Marketable 

Debt Outstanding

Historical Historical Average from 1980 to end of FY 2018 Q4

69.2 months on 

09/30/2018

59.8 months (Historical Average 

from 1980 to Present)
24

5

10

15

20

25

30

35

40

198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018

Percent

Calendar Year

Bills Outstanding as a Percent of Portfolio

Historical Average

14.7% on 9/30/2018

Historical Average = 23%
25

Recent Maturity Profile, $ billions

Recent Maturity Profile, percent

Date (0,1] (1,2] (2,3] (3,5] (5,7] (7,10] (10,30] Total (0,5]

Sep-11 2,620 1,334 980 1,541 1,070 1,053 1,017 9,616 6,476

Sep-12 2,951 1,373 1,104 1,811 1,214 1,108 1,181 10,742 7,239

Sep-13 2,939 1,523 1,242 1,965 1,454 1,136 1,331 11,590 7,669

Sep-14 2,935 1,739 1,319 2,207 1,440 1,113 1,528 12,281 8,199

Sep-15 3,097 1,775 1,335 2,382 1,478 1,121 1,654 12,841 8,589

Sep-16 3,423 1,828 1,538 2,406 1,501 1,151 1,800 13,648 9,195

Sep-17 3,631 2,027 1,504 2,433 1,466 1,180 1,946 14,188 9,596

Sep-18 4,299 2,076 1,603 2,472 1,531 1,209 2,077 15,268 10,450

Date (0,1] (1,2] (2,3] (3,5] (5,7] (7,10] (10,30] (0,3] (0,5]

Sep-11 27.2 13.9 10.2 16.0 11.1 10.9 10.6 51.3 67.3

Sep-12 27.5 12.8 10.3 16.9 11.3 10.3 11.0 50.5 67.4

Sep-13 25.4 13.1 10.7 17.0 12.5 9.8 11.5 49.2 66.2

Sep-14 23.9 14.2 10.7 18.0 11.7 9.1 12.4 48.8 66.8

Sep-15 24.1 13.8 10.4 18.5 11.5 8.7 12.9 48.3 66.9

Sep-16 25.1 13.4 11.3 17.6 11.0 8.4 13.2 49.7 67.4

Sep-17 25.6 14.3 10.6 17.1 10.3 8.3 13.7 50.5 67.6

Sep-18 28.2 13.6 10.5 16.2 10.0 7.9 13.6 52.3 68.4
26

20

30

40

50

60

70

80

198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018

Percent

Calendar Year

Treasury Maturity Profile History

Percent Maturing < 1 Year Percent Maturing <= 3 Years
Section V:

Demand

27
28

*Weighted averages of Competitive Awards. FRNs are reported on discount margin basis. 

**Approximated using prices at settlement and includes both Competitive and Non-Competitive Awards. For TIPS 10-year equivalent, a 

constant auction BEI is used as the inflation assumption.

Security 

Type Term Stop Out Rate (%)*

Bid-toCover 

Ratio*

Competitive 

Awards 

($bn)

% Primary 

Dealer*

% 

Direct*

% 

Indirect*

NonCompetitive 

Awards ($bn)

SOMA 

"AddOns" ($bn)

10-Year 

Equivalent 

($bn)**

Bill 4-Week 1.926 2.8 676.8 54.4 9.2 36.4 13.2 0.0 6.1

Bill 13-Week 2.037 2.9 627.6 50.5 7.2 42.3 17.4 0.0 18.7

Bill 26-Week 2.191 3.0 550.0 45.4 4.6 50.0 17.0 0.0 32.8

Bill 52-Week 2.388 3.3 76.1 43.8 10.0 46.1 1.9 0.0 9.1

Coupon 2-Year 2.715 2.7 106.5 43.3 13.8 42.9 1.5 4.4 25.5

Coupon 3-Year 2.758 2.6 101.4 46.4 10.7 42.9 0.6 4.7 36.0

Coupon 5-Year 2.861 2.5 110.9 27.3 9.0 63.7 0.1 4.6 62.9

Coupon 7-Year 2.936 2.5 92.0 23.4 14.6 62.0 0.0 3.8 70.8

Coupon 10-Year 2.928 2.6 70.9 25.0 11.7 63.3 0.1 3.6 75.3

Coupon 30-Year 3.050 2.3 47.0 28.3 9.8 62.0 0.0 2.5 113.9

TIPS 5-Year 0.724 2.8 14.0 19.2 13.5 67.3 0.0 1.0 7.9

TIPS 10-Year 0.830 2.4 24.0 22.2 12.4 65.5 0.0 0.7 27.6

FRN 2-Year 0.047 2.9 51.9 42.9 7.6 49.5 0.1 2.2 0.0

Total Bills 2.056 2.9 1,930.5 50.1 7.3 42.6 49.5 0.0 66.7

Total Coupons 2.851 2.6 528.7 33.3 11.7 55.0 2.3 23.6 384.5

Total TIPS 0.791 2.5 37.9 21.1 12.8 66.1 0.1 1.7 35.5

Total FRN 0.047 2.9 51.9 42.9 7.6 49.5 0.1 2.2 0.0

Summary Statistics for Fiscal Year 2018 Q4 Auctions
29

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Dec-08

Mar-09

Jun-09

Oct-09

Jan-10

Apr-10

Jul-10

Nov-10

Feb-11

May-11

Aug-11

Dec-11

Mar-12

Jun-12

Sep-12

Jan-13

Apr-13

Jul-13

Oct-13

Feb-14

May-14

Aug-14

Nov-14

Mar-15

Jun-15

Sep-15

Dec-15

Apr-16

Jul-16

Oct-16

Jan-17

May-17

Aug-17

Nov-17

Feb-18

Jun-18

Sep-18

Bid-to-Cover Ratio

Bid-to-Cover Ratios for Treasury Bills

4-Week (13-week moving average) 13-Week (13-week moving average)

26-Week (13-week moving average) 52-Week (6-month moving average)
30

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Mar-16

Apr-16

May-16

Jun-16

Jul-16

Aug-16

Sep-16

Oct-16

Nov-16

Dec-16

Jan-17

Feb-17

Mar-17

Apr-17

May-17

Jun-17

Jul-17

Aug-17

Sep-17

Oct-17

Nov-17

Dec-17

Jan-18

Feb-18

Mar-18

Apr-18

May-18

Jun-18

Jul-18

Aug-18

Sep-18

Bid-to-Cover Ratio

Bid-to-Cover Ratios for FRNs

(6-Month Moving Average)
31

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Sep-13

Nov-13

Jan-14

Mar-14

May-14

Jul-14

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

Bid-to-Cover Ratio

Bid-to-Cover Ratios for 2-, 3-, and 5-Year Nominal Securities

(6-Month Moving Average)

2-Year 3-Year 5-Year
32

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Sep-13

Nov-13

Jan-14

Mar-14

May-14

Jul-14

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

Bid-to-Cover Ratio

Bid-to-Cover Ratios for 7-, 10-, and 30-Year Nominal Securities

(6-Month Moving Average)

7-Year 10-Year 30-Year
33

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Mar-08

Jun-08

Sep-08

Dec-08

Apr-09

Jul-09

Oct-09

Jan-10

May-10

Aug-10

Nov-10

Feb-11

Jun-11

Sep-11

Dec-11

Mar-12

Jul-12

Oct-12

Jan-13

Apr-13

Aug-13

Nov-13

Feb-14

May-14

Sep-14

Dec-14

Mar-15

Jun-15

Oct-15

Jan-16

Apr-16

Jul-16

Nov-16

Feb-17

May-17

Aug-17

Dec-17

Mar-18

Jun-18

Sep-18

Bid-to-Cover Ratio

Bid-to-Cover Ratios for TIPS

5-Year 10-Year (6-month moving average) Long-end (20- & 30-year)
34

Excludes SOMA add-ons. The “Other” category includes categories that are each less than 5%, which include Depository Institutions, Individuals, 

Pension and Insurance.

0%

5%

10%

15%

20%

25%

30%

35%

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

13-week moving average

Percent Awarded in Bill Auctions by Investor Class 

(13-Week Moving Average)

Other Dealers and Brokers Investment Funds Foreign and International Other
35

Excludes SOMA add-ons. The “Other” category includes categories that are each less than 5%, which include Depository Institutions, Individuals, 

Pension and Insurance.

0%

10%

20%

30%

40%

50%

60%

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

6-month moving average

Percent Awarded in 2-, 3-, and 5-Year Nominal Security 

Auctions by Investor Class (6-Month Moving Average)

Other Dealers and Brokers Investment Funds Foreign and International Other
36

Excludes SOMA add-ons. The “Other” category includes categories that are each less than 5%, which include Depository Institutions, Individuals, 

Pension and Insurance.

0%

10%

20%

30%

40%

50%

60%

70%

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

6-month moving average

Percent Awarded in 7-, 10-, 30-Year Nominal Security Auctions 

by Investor Class (6-Month Moving Average)

Other Dealers and Brokers Investment Funds Foreign and International Other
37

Excludes SOMA add-ons. The “Other” category includes categories that are each less than 5%, which include Depository Institutions, Individuals, 

Pension and Insurance.

0%

10%

20%

30%

40%

50%

60%

70%

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

6-month moving average

Percent Awarded in TIPS Auctions by Investor Class

(6-Month Moving Average)

Other Dealers and Brokers Investment Funds Foreign and International Other
38

Excludes SOMA add-ons. 

10%

20%

30%

40%

50%

60%

70%

80%

Mar-14

May-14

Jul-14

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

% of Total Competitive Amount Awarded

Primary Dealer Awards at Auction

4/13/26-Week (13-week moving average) 52-Week (6-month moving average)

2/3/5-Year (6-month moving average) 7/10/30-Year (6-month moving average)

TIPS (6-month moving average)
39

Excludes SOMA add-ons. 

0%

5%

10%

15%

20%

25%

Mar-14

May-14

Jul-14

Sep-14

Nov-14

Jan-15

Mar-15

May-15

Jul-15

Sep-15

Nov-15

Jan-16

Mar-16

May-16

Jul-16

Sep-16

Nov-16

Jan-17

Mar-17

May-17

Jul-17

Sep-17

Nov-17

Jan-18

Mar-18

May-18

Jul-18

Sep-18

% of Total Competitive Amount Awarded

Direct Bidder Awards at Auction

4/13/26-Week (13-week moving average) 52-Week (6-month moving average)

2/3/5 (6-month moving average) 7/10/30 (6-month moving average)

TIPS (6-month-moving average)
40

Foreign includes both private sector and official institutions.

0

10

20

30

40

50

60

70

80

90

100

Sep-16

Oct-16

Nov-16

Dec-16

Jan-17

Feb-17

Mar-17

Apr-17

May-17

Jun-17

Jul-17

Aug-17

Sep-17

Oct-17

Nov-17

Dec-17

Jan-18

Feb-18

Mar-18

Apr-18

May-18

Jun-18

Jul-18

Aug-18

Sep-18

$ bn

Total Foreign Awards of Treasuries at Auction, $ billions

Bills 2,3,5 7,10,30 TIPS FRN
Appendix

41
42 *Weighted averages of competitive awards.

**Approximated using prices at settlement and includes both competitive and non-competitive awards.

Issue Settle Date Stop Out 

Rate (%)*

Bid-toCover 

Ratio*

Competitive 

Awards ($bn)

% Primary 

Dealer* % Direct* % 

Indirect*

NonCompetitive 

Awards ($bn)

SOMA "Add 

Ons" ($bn)

10-Year 

Equivalent 

($bn)*

4-Week 7/5/2018 1.860 2.45 34.2 75.4 6.5 18.1 0.8 0.0 0.3

4-Week 7/12/2018 1.850 3.16 34.1 58.6 8.8 32.5 0.9 0.0 0.3

4-Week 7/19/2018 1.880 2.91 44.2 59.7 10.5 29.8 0.8 0.0 0.4

4-Week 7/26/2018 1.880 2.84 54.2 45.6 12.6 41.8 0.8 0.0 0.5

4-Week 8/2/2018 1.910 2.70 64.1 55.3 7.6 37.1 0.9 0.0 0.6

4-Week 8/9/2018 1.905 2.65 69.1 47.8 10.2 42.0 0.9 0.0 0.6

4-Week 8/16/2018 1.910 2.66 69.1 55.4 8.4 36.2 0.9 0.0 0.6

4-Week 8/23/2018 1.910 2.80 69.1 51.2 7.3 41.5 0.9 0.0 0.6

4-Week 8/30/2018 1.930 2.77 64.0 59.7 10.2 30.1 1.0 0.0 0.6

4-Week 9/6/2018 1.970 2.68 53.8 57.4 6.4 36.3 1.2 0.0 0.5

4-Week 9/13/2018 1.975 2.97 43.9 49.0 11.5 39.5 1.1 0.0 0.4

4-Week 9/20/2018 2.020 3.38 38.8 39.2 8.6 52.2 1.2 0.0 0.4

4-Week 9/27/2018 2.080 3.05 38.1 60.9 12.0 27.1 1.9 0.0 0.4

13-Week 7/5/2018 1.940 2.62 47.1 52.1 4.1 43.9 0.9 0.0 1.4

13-Week 7/12/2018 1.945 2.85 47.1 47.8 7.3 44.9 0.9 0.0 1.4

13-Week 7/19/2018 1.980 2.76 49.6 42.8 10.5 46.7 1.4 0.0 1.5

13-Week 7/26/2018 1.970 2.92 49.1 42.6 11.8 45.6 1.9 0.0 1.5

13-Week 8/2/2018 2.000 2.87 49.6 57.6 6.6 35.8 1.4 0.0 1.5

13-Week 8/9/2018 2.010 2.54 49.7 51.8 9.7 38.5 1.3 0.0 1.5

13-Week 8/16/2018 2.030 2.83 49.6 52.5 6.6 40.9 1.4 0.0 1.5

13-Week 8/23/2018 2.035 2.96 49.8 50.6 5.7 43.7 1.2 0.0 1.5

13-Week 8/30/2018 2.080 2.90 49.0 53.3 5.6 41.1 2.0 0.0 1.5

13-Week 9/6/2018 2.095 2.88 47.0 50.8 4.9 44.2 1.0 0.0 1.4

13-Week 9/13/2018 2.110 3.02 47.0 56.8 5.6 37.7 1.0 0.0 1.4

13-Week 9/20/2018 2.125 2.94 46.7 49.4 4.4 46.2 1.3 0.0 1.4

13-Week 9/27/2018 2.180 3.01 46.1 48.4 10.6 41.0 1.9 0.0 1.4

26-Week 7/5/2018 2.085 2.83 40.8 47.6 3.9 48.5 1.2 0.0 2.4

26-Week 7/12/2018 2.100 2.78 40.7 53.3 4.1 42.6 1.3 0.0 2.4

26-Week 7/19/2018 2.140 2.98 43.7 44.7 3.2 52.2 1.3 0.0 2.6

26-Week 7/26/2018 2.140 2.90 43.2 46.6 12.0 41.4 1.8 0.0 2.6

26-Week 8/2/2018 2.160 3.14 43.9 45.8 4.8 49.4 1.1 0.0 2.6

26-Week 8/9/2018 2.180 2.66 43.7 42.2 4.4 53.4 1.3 0.0 2.6

26-Week 8/16/2018 2.180 3.25 43.7 32.3 3.7 64.1 1.3 0.0 2.6

26-Week 8/23/2018 2.185 3.11 43.9 45.1 3.3 51.5 1.1 0.0 2.6

26-Week 8/30/2018 2.210 3.06 43.3 49.4 3.8 46.8 1.7 0.0 2.6

26-Week 9/6/2018 2.240 3.03 41.0 47.9 3.2 48.9 1.0 0.0 2.4

26-Week 9/13/2018 2.265 3.14 41.0 46.3 4.0 49.7 1.0 0.0 2.4

26-Week 9/20/2018 2.290 3.09 40.9 51.9 3.0 45.1 1.1 0.0 2.4

26-Week 9/27/2018 2.320 3.28 40.3 37.8 6.0 56.2 1.7 0.0 2.5

52-Week 7/19/2018 2.335 3.03 25.4 47.8 10.0 42.2 0.6 0.0 3.0

52-Week 8/16/2018 2.365 3.21 25.3 50.7 12.7 36.6 0.7 0.0 3.1

52-Week 9/13/2018 2.465 3.76 25.4 33.0 7.4 59.6 0.6 0.0 3.0

Bills
43

*Weighted averages of competitive awards. FRNs are reported on discount margin basis. 

**Approximated using prices at settlement and includes both competitive and non-competitive awards. For TIPS’ 10-Year equivalent, a constant 

auction BEI is used as the inflation assumption.

Issue Settle Date Stop Out 

Rate (%)*

Bid-toCover 

Ratio*

Competitive 

Awards ($bn)

% Primary 

Dealer* % Direct* % 

Indirect*

NonCompetitive 

Awards ($bn)

SOMA "Add 

Ons" ($bn)

10-Year 

Equivalent 

($bn)*

2-Year 7/31/2018 2.657 2.92 34.5 40.7 14.3 45.0 0.5 1.9 8.5

2-Year 8/31/2018 2.655 2.89 35.5 42.5 13.7 43.8 0.5 2.5 8.7

2-Year 10/1/2018 2.829 2.44 36.5 46.6 13.4 40.0 0.5 0.0 8.4

3-Year 7/16/2018 2.685 2.51 32.8 51.3 9.1 39.6 0.2 0.1 11.2

3-Year 8/15/2018 2.765 2.65 33.8 45.2 12.1 42.7 0.2 4.6 13.1

3-Year 9/17/2018 2.821 2.68 34.8 43.0 10.7 46.3 0.2 0.0 11.7

5-Year 7/31/2018 2.815 2.61 36.0 24.1 8.7 67.2 0.0 2.0 20.9

5-Year 8/31/2018 2.765 2.49 36.9 24.7 9.0 66.2 0.1 2.6 21.4

5-Year 10/1/2018 2.997 2.39 38.0 32.9 9.2 57.9 0.0 0.0 20.6

7-Year 7/31/2018 2.930 2.49 30.0 23.4 12.0 64.6 0.0 1.6 23.6

7-Year 8/31/2018 2.844 2.65 31.0 21.5 19.0 59.5 0.0 2.2 24.4

7-Year 10/1/2018 3.034 2.45 31.0 25.3 12.8 62.0 0.0 0.0 22.8

10-Year 7/16/2018 2.859 2.57 22.0 24.5 10.5 65.0 0.0 0.1 22.0

10-Year 8/15/2018 2.960 2.55 26.0 27.5 11.3 61.3 0.0 3.5 30.3

10-Year 9/17/2018 2.957 2.58 23.0 22.6 13.4 63.9 0.0 0.0 23.0

30-Year 7/16/2018 2.958 2.34 14.0 27.8 10.3 61.9 0.0 0.1 32.2

30-Year 8/15/2018 3.090 2.27 18.0 29.7 8.0 62.2 0.0 2.4 47.6

30-Year 9/17/2018 3.088 2.34 15.0 27.0 11.3 61.7 0.0 0.0 34.2

2-Year FRN 7/31/2018 0.043 2.79 18.0 42.3 6.3 51.4 0.0 1.0 0.0

2-Year FRN 8/31/2018 0.047 2.94 17.0 51.9 8.5 39.5 0.0 1.2 0.0

2-Year FRN 9/28/2018 0.050 3.06 17.0 34.4 8.0 57.5 0.0 0.0 0.0

Nominal Coupons

Issue Settle Date Stop Out 

Rate (%)*

Bid-toCover 

Ratio*

Competitive 

Awards ($bn)

% Primary 

Dealer* % Direct* % Indirect*

NonCompetitive 

Awards ($bn)

SOMA "Add 

Ons" ($bn)

10-Year 

Equivalent 

($bn)*

5-Year TIPS 8/31/2018 0.724 2.78 14.0 19.2 13.5 67.3 0.0 1.0 7.9

10-Year TIPS 7/31/2018 0.762 2.22 13.0 22.6 15.3 62.1 0.0 0.7 15.5

10-Year TIPS 9/28/2018 0.910 2.57 11.0 21.7 8.9 69.5 0.0 0.0 12.1

TIPS
Please provide an update on efforts the Committee is making with regard to the 

development of issuance models, including any updated analysis or results and 

any revisions to or extensions of the modeling work that was presented in October 

2017, particularly the incorporation of TIPS into the model. Comment on the 

degree to which the updated modeling efforts can be used by Treasury as one 

input to help to inform potential its decisions regarding nominal coupon and TIPS 

issuance.

TBAC Charge: An Update on the TBAC Issuance 

Model – Incorporating TIPS

1
• This presentation extends the debt management model of Belton et al.1

to assess the optimal mix and maturity 

structure of nominal and inflation linked debt. While model outputs should not be (and are not being) used 

prescriptively, they do provide a number of insights on how TIPS contribute to U.S. debt costs and risks.

• Under the model’s structure, the debt service costs for TIPS issuance are generally lower than that of equivalent 

maturity nominal issuance because the risk premium required by investors as compensation for inflation risk 

exceeds what is required to compensate for liquidity risk.

- Five year TIPS seem to offer the greatest cost advantage; however, ten year TIPS offer an attractive cost / risk 

trade off. Minimum issuance sizes in the thirty year point are useful in maintaining a long-dated benchmark.

- Currently, the relative risk premium of nominal versus TIPS issuance appears lower than the longer run average. 

However, the dynamic optimal response function does not react sensitively to time varying inflation risk 

premium.

• TIPS issuance can reduce risk to the Treasury if kept to amounts that leave TIPS allocations as a moderate 

proportion of the debt stock.

- TIPS principal accretion flows through interest expense and introduces significant debt service volatility in any 

given period, even though this accretion does not represent an actual funding need in that period.

- Nevertheless, the negative correlation between CPI-U and U.S. primary deficits creates a significant 

diversification benefit for Treasury debt stock allocations containing TIPS. Assuming historical correlations hold 

going forward, total deficit volatility is reduced for TIPS allocations up to 13% of the debt stock. 

• In summary, when accounting for their relative cost and capacity for risk reduction, the model suggests that the 

level of TIPS outstanding could range from just a few percent of the outstanding debt stock (for a debt manager less 

averse to risk) to as much as 14% of the outstanding debt stock (for a more risk averse debt manager). Currently, 

TIPS make up 9% of the debt stock.

Executive Summary

2

1Belton, Dawsey, Greenlaw, Li, Ramaswamy, and Sack, “Optimizing the maturity structure of U.S. Treasury debt: A model-based framework”, The Hutchings Center on Fiscal and 

Monetary Policy, Brookings Institution (October 10, 2018). https://www.brookings.edu/research/optimizing-the-maturity-structure-of-u-s-treasury-debt/
• The existing debt optimization model (which does not contemplate TIPS) contains:

- A simulation module consisting of:

• A macroeconomic model for the unemployment gap, core PCE inflation, the Fed Funds target rate, the rate 

of change of real GDP, the potential rate of change of real GDP, and the equilibrium real rate of interest

• A model for the Treasury yield curve using expected Fed policy and term premium

• A fiscal model for the primary budget deficit

- A debt dynamics module that evolves current and future debt issuance

- An optimization module that identifies low cost strategies given risk appetite and constraints and can 

generate:

• Static optimizations (issuance fractions never change)

• Dynamic optimizations (issuance fractions depend on macro variables)

• In order for the original model to be extended, it needed to be re-implemented, and outputs cross-referenced 

with the original.

• In order to include TIPS, the re-implemented model had to be extended to include:

- Headline CPI in the macroeconomic model

- A model for the TIPS yield curve consistent with the existing model implementation, which involves a 

decomposition of term premium into inflation, real rate, and liquidity components

- The addition of TIPS to the debt dynamics module

- The inclusion of TIPS in the optimization module (both static and dynamic)

• By including TIPS, we aim to assess the optimal issuance allocation across nominal and inflation linked securities 

as well as optimal issuance points for each.

Review of Current Model and Extension

3
• TIPS investors need to be paid a risk premium for real rate risk (RRP), while investors in nominal Treasuries must be paid an extra risk premium 

for taking inflation risk (IRP). The sum of the IRP + RRP is the nominal term premium (TP), which was modeled by Belton et al.

Nominal yield = expected inflation + expected real yield + IRP + RRP

• In addition, a liquidity risk premium (LRP) for TIPS is necessary in order to provide a sensible yield decomposition of nominal and inflation-linked 

Treasuries into expected inflation, expected real yield, inflation risk premium, and real rate risk premium. 

TIPS yield = expected real yield + RRP + LRP

• Market-implied breakeven inflation, which is the difference between equal maturity Treasury and TIPS yields, leads to counter-intuitive results 

during periods of low market liquidity unless it is adjusted for LRP.

Breakeven Inflation = Nominal yield − TIPS yield = expected inflation + IRP − LRP

Term Premium Decomposition

We decompose TIPS breakevens by extending the model of AACM1to include 30Y yield curves2

1 Abrahams, Michael, Adrian, Tobias, Crump, Richard K., and Moench Emanuel, “Decomposing Real and Nominal Yield Curves”, Federal Reserve Bank of New York Stat Reports 

(February 2015). https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr570.pdf

2

In what follows, we refer to TBAC’s implementation of the AACM model as ARTS (Affine Real Term Structure) when including TIPS and ANTS (Affine Nominal Term Structure)when 

using only nominal Treasuries.

4

TBAC Implementation2

TBAC’s implementation of the term structure model shows 

consistency with the ACM model used in Belton et al. 
Inflation and Real Rate Risk Premiums

Inflation risk premium is modeled to vary with monetary policy; steady state behavior is based on recent averages

• The term premium is the sum of inflation risk premium 

and real rate risk premium.

• Output of the ARTS model shows more of the variation 

in TP can be explained with RRP. IRP is more steady.

• In our simulation module, we model IRP directly, and 

derive RRP as the difference between TP and IRP.

• The model for TP in our simulation module remains the 

same as in Belton et al.

• In the simulation module, we model expected real rates rt,τ

P

as 

the difference between expected nominal rates and expected 

inflation.

• We then write 5y and 10y IRP as affine functions of rt,τ

P

- rt

∗

.

• Slope coefficients are estimated from regressions of ARTS 

model outputs onto the above variables, and the constant 

term is chosen to set the long-term expected level of IRP.

• IRP for other maturities is obtained from IRP5 and IRP10 using 

historical regression of ARTS model outputs.

5

A

A
Liquidity Risk Premium

Model based estimates and market observables can be used to approximate TIPS liquidity risk premiums

6

Blue dotted line is 

our base case LRP

• The ARTS model uses TIPS yield curve fit errors and trade 

volume data to generate liquidity risk premiums for TIPS 

over the entire calibration window (1999-present).

• In the period for which there exists data on asset swap 

levels, the model based estimates are broadly similar (and 

in particular pick up the massive illiquidity during the 

financial crisis), but there are differences.

• Comparing the term structures, we see that the model 

tends to generate larger liquidity premiums for shorter 

dated TIPS than is observed in the asset swap market.

• We use model liquidity premiums as our base case for 

TIPS but also show results using asset swap levels instead 

(the differences are marginal).

A

B

C

A

B

C
Source: TBAC Source: TBAC

• The scatterplots below introduce metrics for the cost vs. risk visualization and optimization we will be using throughout this work. 

• In these simulations, cash needs are met every quarter entirely by issuance of a single security whose stock would, in the steady-state, 

finance the entire debt.

• The cost we look to minimize, on the vertical axis, is the average debt service cost (across all 2000 paths) at year 20 of our simulation. 

• The risk on the right graph is the standard deviation (across all 2000 paths) of the total deficit (primary deficit + funding cost), which 

we continue to use throughout what follows.

• However, on the left we also show standard deviation (across all 2000 paths) of the debt service cost, as a touchpoint back to Belton et 

al. The blue dots show results for nominals, in close agreement with previous work. 

• We are adding the red dots (TIPS), which for like tenor, are more volatile (shifted right), but also have lower cost (shifted lower), as 

holders of nominals must be compensated for the inflation risk premium.

Single Security Issuance Results for TIPS

Results show average debt service cost in year 20 vs two different measures of variance across the path population

7

A

A

B

B

C

C

D

D D
1yN 2yN 3yN 5yN 7yN 10yN 30yN 2yT 5yT 10yT 30yT

Average issuance rate 2.99 2.96 2.99 3.10 3.25 3.44 4.01 1.18 1.04 1.21 1.63

Average debt service / 

GDP

2.44 2.46 2.51 2.69 2.93 3.21 4.00 2.71 2.53 2.80 3.37

Standard deviation debt 

service/GDP 1.62 1.41 1.12 0.72 0.70 0.82 1.11 2.27 1.74 1.65 1.76

Standard deviation total 

deficit (%GDP) 2.32 2.15 2.09 2.11 2.11 2.14 2.29 2.67 2.43 2.31 2.38

Correlation funding cost, 

primary deficit (%GDP) (0.14) (0.18) (0.11) 0.12 0.14 0.11 0.10 (0.19) (0.11) (0.16) (0.15)

Single Security Issuance Results for TIPS

Results show additional cost and risk summary statistics for single-security strategies

• While TIPS are more volatile, they also have desirable 

correlation properties. 

• Since our primary risk metric is deficit volatility, where 

deficit = (funding cost + primary deficit), negative 

correlation between these two therefore lowers the 

volatility of the sum. 

• We see in the last row of the table that, for example, 5y 

TIPS show modest negative funding cost/ primary deficit 

correlation, while 5y nominals show small positive 

correlation.

8

A

A

C

C C

B

B

Source: TBAC

Source: TBAC
Issuance Kernels for Nominals and TIPS

Reduce the issuance profile to a base-case which meets funding needs and several kernels

TIPS kernel loadings 

subtract the base 

kernel and add TIPS

Nominal kernel 

loadings match those 

of the existing model

• One must be careful in specifying issuance kernels in terms of issuance, in order to take into account 

the implications for the steady-state debt distribution (see Appendix slides 21 and 22 for additional 

detail).

• Long-term issuance will pile up. For example, the baseline issuance kernel in Belton et al. would 

leave a large stock of original-issue 30y bonds after 20 years of issuance (5% of the quarterly 

issuance leads to 34% of the debt stock).

• The baseline TIPS kernel above is intended to replicate the current maturity distribution of TIPS after 

20 years.

9

Source: Belton et al. Source: TBAC
• In the plots above we display the effect of adding progressively more of each kernel to the baseline 

issuance (defined as one unit of nominal base kernel).

• The results of the “More Bills”, “More Belly”, and “More Bonds” kernels closely correspond with the 

results of the previous model.

• Adding more Baseline TIPS decreases cost.

Issuance Kernels for Nominals and TIPS

Frontier plots allow us to see the risk / cost contribution of each kernel

10

A

B

B B

A

Source: TBAC Source: TBAC
Issuance along the efficient frontier

Static Optimization of Kernels

Optimizing over kernel weights produces more realistic issuance strategies

• At top left, the efficient frontier comes from minimizing the 

objective:

cost + RA × risk

for different levels of risk aversion (RA).

• The risk aversion coefficient tells the optimizer what the relative 

importance of cost and risk are to Treasury. 

• The two extremes are RA = ∞ and RA = 0. When RA is large 

the optimizer focuses almost exclusively on risk reduction and if 

RA is small the optimizer puts more emphasis on cost reduction. 

• The optimizer solves for kernel weights constrained so that 

issuance proportions are non-negative.

• Gross issuance is zero for all but the base kernel. 

Plausible Range

11

Steady-state along 

the efficient frontier

A

A

RA RA

TIPS proportion is 12.5% at lower 

risk end of plausible range.

Source: TBAC

Source: TBAC Source: TBAC
Nominal allocations are 

consistent with Belton et al.

Cost Benefit From Issuing TIPS 

Optimal allocations include TIPS for a wide range of risk preferences

Plausible Range

12

TIPS improve the efficient frontier 

for static weighting of kernels

RA RA

TIPS proportion is 12.5% at lower 

risk end of plausible range.

Source: TBAC Source: TBAC

Source: TBAC Source: TBAC
Reassessing 5 Year TIPS Using Alternative LRP

5y TIPS look more attractive if the asset swap market is used to estimate the LRP instead of the ARTS model

• Lowering the liquidity premium for shorter dated 

TIPS makes them more attractive.

• Static kernel-based optimization shows a larger 

allocation to 5y and 10y TIPS in the range of 

plausible risk preferences, particularly for higher risk 

tolerances.

• The relative attractiveness of TIPS versus nominal 

Treasuries can be similarly shifted by changing 

assumptions for the long term average level of 

inflation risk premium.

13

A

A

B

B

A

Plausible Range

TIPS proportion ranges from 2.5% to 

14.25% over the plausible range.

Source: TBAC Source: TBAC

Source: TBAC
• While Treasury does not currently issue 2y TIPS, we explore their 

attractiveness in the model.

• Currently, 2y TIPS have a higher cost than 5y TIPS, and they are also 

significantly more volatile. 

• Taking our LRPs from asset swaps (ASW) drops and flattens the 2y5y LRP (page 6, dotted red line), lowering the cost for those two 

assets.

• We add 2y TIPS to our More Front TIPS kernel as 20% of issuance 

and run our model using ASW LRPs.

• Front TIPS issuance becomes optimal at lower levels of risk aversion 

when using ASW spreads for LRP, due to lower cost of the front-end 

TIPS.

• A small change in IRP term structure would be equivalent to a 

relative change in LRP. 14

A

A

B

B

C

C

Plausible Range

TIPS proportion ranges from 4.5% to 

14.33% over the plausible range.

Assessing 2 Year TIPS in the Model

Treasury doesn’t currently issue 2y TIPS; however, the model would provided ASW spreads are used for LRP 

Source: TBAC Source: TBAC

Source: TBAC
Less negative correlation makes TIPS less attractive

Examining Effects of IRP, CPI, Correlation

We vary the key drivers of relative cost and risk between TIPS and nominals

CPI vol is the main driver of extra TIPS volatility

IRP-LRP spread is a key driver of relative cost1

• In the cost vs. risk tradeoff of our objective, each component 

has one key variable which drives the relative attractiveness 

of TIPS compared to nominals.

• The TIPS cost advantage for the issuer comes from the IRPLRP spread. At top left, we show the effect of shifting the IRPLRP spread in parallel across all tenors.

• On the risk side, the TIPS disadvantage is driven by the 

volatility of CPI, which we model as a spread to PCE.

• The spread volatility is 1.7% and PCE vol is 0.79%, and the two 

are uncorrelated. At bottom left, we vary the spread vol.

• TIPS inflation indexation helps to lower total deficit vol, 

because inflation and primary deficit are negatively 

correlated.

15

A

A

B

B

1 0.3% change in IRP is roughly the 25/75 percentile range in our simulations (page 5)

C

C

Source: TBAC Source: TBAC

Source: TBAC
• We computed bootstrap t-stats for the optimal response function coefficients: 

• We generated 100 independent simulations, each consisting of 50 paths. 

• For each simulation we estimated the matrix of optimal response coefficients 

for a risk aversion parameter of 1.

• For each coefficient we computed the average and standard deviation across 

the 100 simulations.

• Finally we set the t-stat of each coefficient to be the ratio between its average 

and its standard deviation.

• T-stat results suggest that Deficit and Real2y might not be significant; however, TP10 

and IRP10 appear significant.

• Similar to Belton et al., we find that the model rotates out of the belly and into 

bills as TP10 increases.

• Additionally, as IRP increases, the model rotates out of bills and into the belly 

and TIPS.

16

T-stat

Dynamic Strategy Results 

Bootstrap results suggest a sparse set of macro economic variables (MEV) consisting of just IRP10 and TP10

Dynamic strategies improve 

significantly upon static strategies

A

Bootstrapped Optimal Response Coefficient T-stats

A

Source: TBAC Source: TBAC

Bootstrapped Optimal Response Coefficients

intercept TP10 Real2y Deficit IRP10

bills 7.7% 10.1% -2.3% 1.1% -3.4%

belly 8.0% -1.8% 0.4% -0.2% 0.6%

bonds -2.1% 0.5% -0.1% 0.0% -0.1%

tips 4.3% -0.6% 0.0% -0.3% 0.9%

frontTips 4.3% -0.6% 0.0% -0.3% 0.9%

Bootstrapped Optimal Response T-stats

intercept TP10 Real2y Deficit IRP10

bills 1.38 2.59 -0.23 0.16 -0.09

belly 1.72 -1.55 0.05 -0.23 -0.13

bonds -2.09 1.80 -0.29 0.13 0.04

tips 0.98 -0.93 0.02 0.13 0.79

frontTips 0.98 -0.93 0.02 0.13 0.79

Source: TBAC

The reaction function is fit to standardized MEVs; therefore, 

each column of coefficients above represents the effect of a 

one-sigma move in the corresponding MEV. 
• Consistent with the results of Belton et al., 

most of the fluctuation in issuance comes 

via bills and belly kernels.

• TIPS issuance ranges from 1% to 7%, with an 

average of 4%. The steady state proportions 

range from 5% to 19%, with an average of 

13%.

• TP10 is equal to RRP10 + IRP10. Most of the 

variation in TP10 is coming from fluctuations 

in RRP10.

Dynamic Strategy Results1

Most of the variation in issuance patterns is caused by fluctuations in TP10

17

History of MEVs

Historical Optimal Issuance

TP10 is significantly 

more volatile than IRP10

1Back test uses a risk aversion parameter of 2 and imposes 0% lower bounds on issuance 

sizes.

A

A

B

B

C

C

B

Historical Optimal Steady State Debt Breakdown

Source: TBAC Source: TBAC

Source: TBAC
18

Case Study: Optimizing 2019 Issuance

We use the model to build an efficient frontier for issuance while maintaining minimum issuance sizes. We also 

study glide paths from 2018 issuance weights to the frontier. 

The second path (red) targets the 

elbow of the frontier to achieve a 

greater cost reduction with only a 

small increase in risk.

We illustrate two possible glide paths 

to the efficient frontier. The first 

(blue) attempts to maintain current 

levels of risk while reducing cost.

Source: TBAC

Debt Service vs Deficit vol Efficient Frontier
Issuance Proportion Through Time (%)

2018 2019 2020 2021 2022 2023

Debt Service 2.980 2.950 2.919 2.886 2.853 2.818

Stdev Deficit 1.996 1.995 1.995 1.997 2.002 2.009

Stdev Debt Service 0.734 0.758 0.787 0.821 0.860 0.904

Bills + FRN 53.7 53.1 52.6 52.0 51.4 50.9

2y Nominal 8.7 9.1 9.4 9.8 10.2 10.6

3y Nominal 8.2 9.4 10.7 11.9 13.2 14.4

5y Nominal 9.2 9.9 10.5 11.1 11.7 12.4

7y Nominal 7.7 6.7 5.7 4.7 3.7 2.8

10y Nominal 5.9 4.9 3.9 3.0 2.0 1.0

30y Nominal 3.9 3.6 3.3 3.0 2.7 2.4

TIPS 2.8 3.4 3.9 4.5 5.1 5.6

Issuance Proportion Through Time (%) Issuance Proportion Through Time (%)

2018 2019 2020 2021 2022 2023

Debt Service 2.980 2.962 2.942 2.921 2.898 2.874

Stdev Deficit 1.996 1.993 1.991 1.990 1.991 1.993

Stdev Debt Service 0.734 0.751 0.771 0.795 0.824 0.858

Bills + FRN 53.7 56.5 59.2 62.0 64.8 67.6

2y Nominal 8.7 8.2 7.6 7.1 6.6 6.1

3y Nominal 8.2 7.5 6.8 6.1 5.4 4.7

5y Nominal 9.2 8.6 7.9 7.3 6.6 6.0

7y Nominal 7.7 7.1 6.4 5.8 5.1 4.5

10y Nominal 5.9 5.6 5.3 4.9 4.6 4.3

30y Nominal 3.9 3.5 3.1 2.8 2.4 2.0

TIPS 2.8 3.2 3.6 4.0 4.5 4.9

• The blue glide path of issuance presented on the previous slide decreases cost while maintaining or reducing the 

level of risk associated with current issuance patterns. This path toward the frontier steadily increases 

allocations to bills and TIPS at the expense of the all other issues.

• The red glide path of issuance aims toward the elbow of the efficient frontier (a point with a good cost to risk 

tradeoff). With each step along the path, allocations to the belly and TIPS expand, while allocations to bills and 

the long end shrink.

• A blend of these two allocations could be used to move closer to the efficient frontier with relatively small 

absolute changes in issuance sizes.

19

Case Study: Two Glide Paths Toward Lower Cost Issuance

A

Source: TBAC

Issuance glide path: blue

Source: TBAC

A

Issuance glide path: red 

B

B
Limitations

Modeling Considerations

• This work represents one model with results that depend critically on model assumptions. 

TBAC does not drive recommendations off of one model, but instead takes into account a wide 

range of inputs on investor demand and market pricing.

• Results depend critically on the choice of risk measure (standard deviation of deficit versus 

debt service), and correlation between primary deficits and inflation.

• Results depend heavily on the ex-ante assessment of term premium and its decomposition into 

inflation, liquidity, and real risk premia.

• Results depend heavily on debt manager risk aversion.

Investor Demand Considerations

• TIPS trading volumes and turnover suggest that they are less liquid than nominal Treasuries and 

Conventional MBS. This may be due to the lack of an active derivatives / futures market. 

• TIPS are more complex than nominal Treasuries. 

• For tax purposes, TIPS are treated as original issue discount (OID) bonds, which means that 

increases in TIPS principal are taxable for the year in which they occur, rather than at maturity.

20
Conclusions and Recommendations

• The extension of the model of Belton et al. to incorporate TIPS demonstrates a cost and 

risk reduction for the issuance of TIPS in addition to nominals.

• Assuming historical correlations hold, total deficit volatility is reduced for TIPS 

allocations up to 13% of the debt stock (currently TIPS make up 9% of the debt 

stock).

• The optimal amount of TIPS to issue varies based on choice of risk metric, assessment of 

market risk premiums, and Treasury’s overall risk appetite.

• Given the diversification / correlation benefits, as well as the benefits of having 

benchmark issuance across the entire curve, continued issuance across the existing 

benchmark tenors (5y, 10y, 30y) is appropriate.

• The analysis of potential issuance of 2y TIPS illustrates that benefits here may be 

more limited, but further study is needed.

• The model finds that TIPS dependence on CPI causes them to behave like floating rate 

notes, and thus have many of the same risk characteristics as bills (both are relatively 

lower cost and higher volatility), but further study is needed.

• Overall, though further work is still needed, the model does correspond well with 

market intuition and provides a useful framework for future analysis of the tradeoffs 

involved in achieving a more optimal issuance allocation. 21
Appendix

22
See, for example, https://www.treasurydirect.gov/govt/reports/ir/IntExp022015.xls 

TIPS Interest Expense Accounting

• Treasury records the principal accrual of TIPS as an interest 

expense (or interest income) according to moves up (down) in 

CPURNSA

• We follow this treatment in our simulation block, resulting in 

the behavior shown above

• Treasury reporting for month of February 2015

• Uses CPURNSA change from mid-Nov to mid-Dec 2014

• 2014 Refs: mid Nov 236.792, mid Dec 235.4815

• Change – 0.5534%

• Treasury interest credit of $5.636Bn

• Implies outstanding TIPS notional of $5.636Bn/0.5534%

• Implies $1.02Tn TIPS outstanding in Feb2015

• Bloomberg DEBPINNT Index: $1.07Tn TIPS outstanding

23

B

A

C

C

D

D

A

B

Source: TBAC Source: TBAC
• Consider a hypothetical issuance split 50% : 50% 

between 1y Bills and 10y Notes. 

• In steady state, 100% of the outstanding stock of Bills 

turns over every year, but only 10% of the stock of 

10y Notes would be redeemed.

• The 50% : 50% issuance split leads to a 9% : 91% Bills 

/ Notes steady state distribution.

• The weighted average maturity of the steady state 

debt distribution is 4.6 years, which is more than 1⁄2 

the WAM of a 1Y + the WAM of a 10y (2.75 years).

24

Maturity Weighted Issuance

We focus on maturity weighted issuance because it more closely aligns with steady state portfolio metrics

A

B

Original Issue 10y Notes

Original Issue 1Y Bills

A

B

• Suppose Treasury can issue securities with maturities τ1, ... , τM . Denote by wm the fraction of each years debt issued in the m-th maturity 

with m=1

M wm = 1. 

• Assume that quarterly issuance is a constant one unit, and that the issuance fractions never change. Then after a long time, the total amount 

of outstanding debt which is an original-issue τm- maturity security is simply τmwm, because it takes τm years for each wm of debt issued to 

mature. The total stock of debt is simply D = m=1

M τmwm. 

• We can define the steady-state debt stock fractions 

w m =

τmwm

 n=1

M τnwn

,

which also sum to 1. The weighted average maturity of the debt stock can be computed as W =

1

2

 m=1

M w mτm. 

• The relationship can also be inverted, so that if one has a desired set of steady state debt stock fractions, one can find the required yearly 

issuance fractions as

wm =

w m/τm

 n=1

M w /τn

.

Source: TBAC
• As a point of reference, the current debt stock has 

approximately the same allocation of original issue bills and 30s 

(17% and 14% respectively).

• In terms of 2018 issuance, bills far outweigh bonds (54% and 

4% respectively).

• If 2018 issuance percentages are held constant, the steady 

state allocations will converge to 29% for bonds and 13.4% for 

bills.

• With increasing percentages of 30y issuance projected for 

2019, the steady state stock of 30y will be even higher (30%) 

and the bills slightly lower (12.8%).

• Based on 2018 maturity issuance in TIPS, the stock will fall 

through time, from the current level of 8.2% to 7.6%.

Steady State Based on Current/Projected Issuance

25

C

D

A

A

A

B

B

B

C

C

D

D

E

E

Source: TBAC, 

Bloomberg

Source: TBAC
Inflation Risk Premium Regression Details

We fit historical inflation risk premia from our 

implementation of the AACM model (ARTS) to the 

historical expected level of monetary policy 

accommodation:

IRPt,τ = ατ + βτ rt,τ

P − rt∗ + εt,τ

where 

• IRPt,τis the τ year inflation risk premium from our 

model

• rt,τ

P

(expected real rate) is the τ year nominal yield less 

the τ year ACM TP less expected inflation1

• rt

∗

is the neutral real rate of interest

• t is time through history 

1We calculated the historical time series of expected inflation using the method described in Belton et al.

26

• In our simulation block we model 5y and 10y IRP as an affine 

function of rt,τ

P

- rt

∗

, where both rates are in the block.

• We take the betas from the historical regression.

• We choose intercepts to match steady-state levels to their 

five-year averages in the ARTS model.

• Residual AR1 processes come from the historical regression 

IRPt,5 = 0.61 − 0.145 rt,5

P − rt

∗ + εt,5

IRPt,10 = 0.61 − 0.245 rt,10

P − rt

∗ + εt,10

where t is now the forward time of our simulation

Source: TBAC ARTS
Simulation Module Outputs

27
TBAC Charge

1

Please comment on developments regarding the transition away from LIBOR 

and toward the Secured Overnight Financing Rate (SOFR). How should 

market participants evaluate the risks of continued use of financial instruments 

linked to LIBOR? Summarize developments in SOFR derivative markets, the 

introduction of SOFR-linked issuance, and your expectations going forward.
LIBOR Exposure

2

Source: Member firm calculations, NYFRB, Second Report of The Alternative Reference Rates Committee, March 2018. 

● In July 2017 Andrew Bailey, the Chief Executive of the Financial Conduct Authority (FCA), announced a 

plan to no longer sustain LIBOR through the current mechanism, by which the FCA persuades or obliges 

panel banks to submit contributions to the benchmark, beyond the end of 2021

● The Alternative Reference Rates Committee (ARRC) chose SOFR to the be the standard overnight 

financing rate

– Following Andrew Bailey’s timeline the ARRC broadened its goals to help facilitate the transition of 

end-user cash products such as floating rate notes, CLOs, mortgages and consumer loads, etc

● ISDA has been leading an industry wide effort to implement robust fallbacks for derivative contracts 

referencing interbank offered rates (IBORs)

– ISDA launched a market-wide consultation on technical issues regarding the new benchmark fallbacks 

for derivative contracts that reference interbank offered rates (IBORs)

USD LIBOR-Related Notional Outstanding: 

2021 and Beyond

Breakdown of USD LIBOR Notional Outstanding 

Not Maturing by 2022 (ex Derivs.)
Taking Stock of LIBOR’s Broad and Ongoing Usage

3

Source: ARRC

● Over $200T in financial instruments currently reference LIBOR

● An estimated $36T notional of LIBOR-linked instruments will remain outstanding after 2021 assuming 

there are no new transactions referencing LIBOR

– Many new trades continue to reference LIBOR and the calculation does not consider replacement risk

– After this date, the FCA will no longer compel banks to provide LIBOR submissions

● Interest rate derivatives represent 

the largest portion of the notional 

outstanding beyond 2021, but 

LIBOR has a much broader asset 

class reach

● LIBOR remains an important 

reference rate as evidenced by 

new issue markets

● LIBOR transition plans have not 

meaningfully altered issuance 

behavior – many deals continue 

to reference LIBOR

LIBOR footprint by asset class
Critical Steps Towards LIBOR Transition Are Already Underway 

4

Source: ISDA. 

● Smooth functioning markets must exist for Alternative Reference Rates

– In the US, the NYFRB began publishing Secured Overnight Financing Rate (SOFR) in April 2018 

– CME launched trading in SOFR Futures (1-month and 3-month) on May 7, 2018 and clearing for OTC 

SOFR Swaps on October 1, 2018; LCH started clearing OTC SOFR swaps on July 16, 2018

– SOFR-linked issuance began in July 2018

– Increased SOFR-linked issuance will be another key driver towards building SOFR derivative liquidity

– There is a need for collaboration across jurisdictions

Alternative Reference Rates
What is SOFR?

5

● The Alternative Reference Rate Committee (ARRC) identified the Secured Overnight Financing Rate 

(SOFR) as its preferred rate

● SOFR is a transaction based rate, calculated from a broad universe of o/n UST repo activity. SOFR is 

based on three different repo segments:

– Tri-party US Treasury general collateral (GC) repo, cleared and settled by Bank of New York Mellon, 

excluding transactions with the Federal Reserve

– Tri-party US Treasury GC repo within the FICC GCF repo framework, where FICC acts as a central 

counterparty

– Bilateral Treasury repo transactions cleared through the FICC Delivery-versus-Payment (DVP) service 

Source: ARRC, New York Fed

0

100

200

300

400

500

600

700

800

900

Aug-14 Dec-14 Apr-15 Aug-15 Dec-15 Apr-16 Aug-16 Dec-16 Apr-17 Aug-17 Dec-17 Apr-18 Aug-18

$Billions SOFR= Tri-party +

cleared bilateral activity

Tri-party activity

OBFR Activity

Aggregate Volumes underlying select MMF rates
What drives SOFR?

6

Source: Bloomberg, NYFed

Note: T-bill in o/n equivalents are calculated as 1M T-bill yield – (1M FF OIS – O/N FF)

GCF/Tri-party repo spreads tend to reflect bidask of repo from dealer’s perspective

-10

0

10

20

30

40

50

Jan-13 Jan-14 Jan-15 Jan-16 Jan-17 Jan-18

GCF/TGCR spread (bp)

GCF/TGCR 3M avg

● SOFR moves with T-bill yields:

Higher T-bill issuance brought all o/n rates higher, including tri-party repo rates, as a competing asset 

for US MMFs

● SOFR moves with dealer B/S cost of repo:

Bilateral/GCF and BNYM Tri-party repo rates are linked as a bid-ask for dealers to intermediate repo 

between MMF and end-users (e.g. hedge funds). We have seen repo spreads widening from 2014-

2016 as LCR / SLR phased-in. Since then, we saw it tightening with more competitive repo 

intermediation post the US MMF reform

-0.5

0

0.5

1

1.5

2

2.5

Sep-14Dec-14

Mar-15

Jun-15

Sep-15Dec-15

Mar-16

Jun-16

Sep-16Dec-16

Mar-17

Jun-17

Sep-17Dec-17

Mar-18

Jun-18

Sep-18

% (o/n rates)

Tri-party UST

FF

IOER

RRP

GC Repo

T-bill in o/n equiv

Cheapening of T-bill brought all o/n rates higher...
How do SOFR and LIBOR differ?

7

● 3M LIBOR and o/n SOFR differ in two aspects: 

– SOFR is secured and LIBOR is unsecured. LIBOR is inherently bank-credit sensitive, pro-cyclical 

asset whereas SOFR is collateralized and largely cleared, hence a counter-cyclical asset

– 3M LIBOR is a term rate vs SOFR is an overnight rate. We find this difference to be more salient, 

as noted by volatility in 3M LIBOR / 3M FF OIS basis

● 3M LIBOR/OIS tend to widen on funding “stress” scenarios. This is not the case for SOFR

Source: Bloomberg

0

50

100

150

200

250

Aug-14 Aug-15 Aug-16 Aug-17 Aug-18 bp

SOFR FF LIBOR

0

50

100

150

200

250

Aug-14 Aug-15 Aug-16 Aug-17 Aug-18 bp

SOFR FF LIBOR

Overnight MMF rates tend to behave 

similarly on o/n basis ...

As well as in longer frames (3m 

rolling geometric average )...

However, behavior of OIS vs term 

rate such as 3M LIBOR can be quite 

different...

0

10

20

30

40

50

60

70

0

50

100

150

200

250

Aug-14 Aug-15 Aug-16 Aug-17 Aug-18

bp

3M LIBOR-OIS (rhs) 3M LIBOR 3M FF OIS
LIBOR/OIS tend to widen on funding shock episodes

8

● Since the crisis, LIBOR/OIS basis has experienced 4 widening episodes

– In 2010 and 2011/2012, European debt crisis intensified to put bank’s creditworthiness in question

– In 4Q2016, the basis widened on US MMF reform – which caused a pullback on 2a7 Prime funds to cause a 

demand shock in funding markets

– In 1Q2018, rapid T-bill issuance and shortening of WAM of repatriated cash after the tax reform led LOIS wider

Source: Bloomberg

Credit “shock” caused LOIS 

widening in prior years...

MMF reform “shock” caused LOIS 

widening in 2H2016...

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0

10

20

30

40

50

60

70

Jan-10 Jan-12 Jan-14 Jan-16 Jan-18

bp

tn

3M LOIS 2a7 Prime AUM (rhs)

Bill issuance & repatriation “shock” 

caused LOIS widening in 1H2018...

-350

-250

-150

-50

50

150

250

350

0

10

20

30

40

50

60

70

Jan-10 Jan-12 Jan-14 Jan-16 Jan-18

bpbn

3mo bill iss (rhs) 3M LOIS

0

50

100

150

200

250

300

350

0

10

20

30

40

50

60

70

Jan-10 Jan-12 Jan-14 Jan-16 Jan-18

CDS

bp

3M LOIS Euro Bank CDS (rhs)
Preparation is key and requires engagement across multiple stakeholders within every firm

9

Preparing for LIBOR Transition

Awareness

Assessment

Action Plan

Education is Critical -- from Main Street to Wall Street

• Households:

- Need to understand impact on mortgages, consumer loans, student loans

• Industry: 

- Collaboration is underway - Alternative Reference Rate Committee (ARRC) 

includes representation across industry groups

- Trade associations are promoting global coordination

Evaluate existing exposures to LIBOR

• Compress existing positions to simplify exposures where possible

Carefully review fallback methodologies:

• Assess inconsistencies or lack of sufficient fallbacks within existing agreements 

• Require stronger fallback language be incorporated into new LIBOR-referenced 

instruments

- ISDA consultation underway for derivatives

- ARRC consultation for cash products 

• Facilitate growth of SOFR markets and LIBOR/SOFR basis through more concrete 

fallback methodologies

Analyze Systems Readiness

The ARRC published a “Paced Transition Plan”, but all investors and market 

participants will need to make the appropriate preparations

Markets are highly interconnected - there will be implications both cross-asset and cross-currency
Paced Transition Milestones

2018 2019 2020 2021

Futures/OIS 

Infrastructure

April 3, 2018

New York Fed begins 

publishing SOFR rate

October 1, 2018

CME clears first 

OTC SOFR swaps 

with SOFR PAA

May 7, 2018

CME starts 

trading 1-Month 

and 3-Month 

SOFR Futures

The Paced Transition 

Plan Actions Taken

Begin trading 

Futures / 

uncleared OIS

Begin trading 

cleared OIS 

with FF PAA

CCPs offer 

choice of 

FF/SOFR PAA

CCPs no 

longer accept 

new trades 

with FF PAA

Creation of 

term 

reference rate

October 22, 2018

ICE launches 1-

Month and 3-Month 

SOFR Futures

Potential Difficulties

• Misalignment of PAA for derivatives between CCPs 

• Divergent payment conventions between SOFR FRN new 

issuance and derivatives

• Libor fallbacks that are not PV neutral will incentivize some 

market participants to continue to trade Libor derivatives

• Bespoke/regional Libor fallback triggers can create uncertainty

• Insufficient liquidity can prevent creation of term reference 

rates

End of 2021 onwards

FCA may not compel panel 

banks to contribute to LIBOR. 

Potential discontinuation of 

LIBOR

March 5, 2018

ARRC released 

Second Report

July 16, 2018

LCH commences 

clearing of SOFR OIS 

and basis swaps with 

FF PAA

10
Libor- vs SOFR-linked Liabilities – Banks’ Perspective (i)

11

Suitability for Financial Institutions and Other Borrowers

● Likely suitable for majority of floating rate borrowers seeking exposure to secured funding rate

● Potential operational/system challenges, particularly for smaller institutions and if compounding 

becomes standard

Risks

● Persistent limited liquidity in cash and derivatives 

markets

– Inadequate investor pool for new issuances and 

secondary trading

– Restricted ability to perform dynamic ALM

– Long-dated callable issuances may require 

references to illiquid/long-end parts of the SOFR 

curve

● May not match performance of Libor-based assets

● Limited ability to hedge general bank funding risk 

due to secured nature of SOFR 

Benefits

● SOFR issuances may provide greater 

transparency for investors (clearer delineation of 

credit risk)

● Can closely match performance of certain 

secured investments

● Potentially represents new balance sheet 

management tool in combination with Libor 

instruments

● Certain investors may see additional utility in 

SOFR-based investments – potentially offsetting 

premium demanded by other investors
Libor- vs SOFR-linked Liabilities – Banks’ Perspective (ii)

12

Considerations following the Crisis of 2007 and Outlook

● Banks generally responded to increased regulation and improved liquidity risk management with the 

extension of maturity profiles of unsecured borrowings

● Post-crisis shifts towards deposit funding (commercial/demand deposits) increased the relative size of 

certain short- to medium- duration liabilities

Overall the exposure to funding spread resets of liabilities arguably has been reduced

● However banks still have to manage funding spread risks due to spread duration gaps between 

assets with longer re-pricing cycles and shorter-dated liabilities – exposing banks to a sudden 

widening of sector credit spreads

There is still a need for ALM instruments whose performance is linked to unsecured bank credit 

spread such as Libor

● On the other hand unsecured inter-bank lending volumes have collapsed since the crisis resulting in 

Libor being less representative of actual bank funding costs 

Will the banking industry require new hedging instruments based on unsecured benchmarks? 
SOFR can allow GSE Issuers to diversify out of LIBOR

13

● FHLBs are the second largest issuer of USD FRNs as of end of Q3 2018, after the US Treasury. SOFR 

floaters would allow FHLBs to diversify their LIBOR exposure upon the cessation/fallback risk

– FHLBs may have been issuing more floaters over the years as (1) increase in Government-only fund 

AUM post the US MMF reform increased demand for GSE papers and (2) hiking cycle made floaters 

more attractive to fixed from the investors without derivative access

● SOFR FRN issuance by GSEs is the natural starting place to test and develop the demand base for cash 

SOFR products, as end-users often won’t require derivative markets

– Fannie Mae have issued the most SOFR FRNs so far ($11bn)

– The survey notes that investors are likely to be more receptive to SOFR FRNs issued by GSEs

Source: Bloomberg

FHLBs have increased LIBOR FRN issuance after 

the US money market reform

FHLBs are the second largest issuer of 

USD FRNs (as the end of Q32018)
Managing LIBOR Risk

14

● Market participants establish risk metrics for 

active management of net exposure to Libor

● Active banking and public sector outreach to 

amend existing contracts

● ISDA protocol amendment approach with 

limited optionality

● Dealers and FMUs support development of 

liquid SOFR derivative term markets

● ISDA/ARRC may recommend marketneutral fallback language in derivative and 

cash markets

● ARRC regulatory advocacy for no action 

relief and exemptions 

● Bank-wide “Libor offices” contribute to 

global and cross-product coordination to 

align trigger language; exposure 

management by product 

● Industry coordination with CCPs to align 

market conventions and cessation triggers

Risks of existing Libor contracts without fallbacks

● Inadequate legacy fallback language increases risk 

of litigation

● Partial adoption of new fallbacks

Risks of Libor references with fallbacks

● SOFR market not sufficiently developed at time of 

cessation could lead to market disruption

● Fallback rate calculation causes valuation impact 

upon cessation

● Accounting/Tax/Margin/Clearing impact from Libor 

cessation and fallback adoption could lead to 

litigation risk and liquidity risk

● Regional or product specific trigger events lead to 

partial cessation increasing market fragmentation

Risks of new SOFR contracts

● Sluggish adoption of SOFR as new standard

● Insufficient liquidity in longer tenors

Mitigation Effort
An appropriate “fallback methodology” must be established

Permanent cessation of LIBOR is generally not consistently 

contemplated in documentation

Contract language is not standardized across corporates, 

mortgages, FRNs, and loans, raising risk of fragmentation

• For example: “in the event of LIBOR cessation...”

“...the security can change to a fixed rate based off the last setting”

“...the security converts to a fixed instrument based on the first setting”

“...the issuer, in its sole discretion, can name a successor rate”

• In some cases there is no fallback mentioned at all

15

Critical Steps Towards LIBOR Transition Are Already Underway

Fallback Methodologies

Source: ISDA

Recent ISDA survey highlights fallback provisions as a key 

concern in the event LIBOR is permanently discontinued

A “fallback methodology” should:

• Define what constitutes LIBOR cessation event

• Outline a methodology to capture the spread between LIBOR and SOFR

• Methodologies should be consistent across asset classes to mitigate market disruption and fragmentation

ISDA and ARRC are undertaking industry-wide consultations with numerous methodologies being considered for various products

• Upon update of ISDA definitions, new LIBOR derivatives would reflect the final fallback methodology

• New fallback language will not necessarily apply to legacy products, but ISDA contemplates a protocol approach to amend legacy derivatives

• ARRC Guiding Principles for More Robust LIBOR Fallback Contract Language in Cash Products: 

Shift from discretion to specificity; Consistency between asset classes; Feasibility and fairness of implementation; Rate, spread and term structure adoption

Ultimately, clarity on the selected methodology will create a path forward and introduce potential for more active trading of basis 

swaps between these markets

Market disruption is a risk if LIBOR prematurely ceases publication
Defining major risks and market implications

16

● Inconsistent legal interpretations could lead to contract frustration

● Inconsistent fallback language and calculation methodology could drive market fragmentation and asset 

hedge misalignments

● Breadth of jurisdictional oversight, if not aligned, could drive market fragmentation

● Inconsistent accounting / tax implications could factor into fallback adoption

● Market participants could use economic impact from fallback to drive protocol adoption decisions

Market participants are working to define major risks

● Rotation from LIBOR based derivatives to OIS, or SOFR and a move from IRS instruments to Treasury futures

● Reduction of the CCP delta mismatch could shift the CME/LCH basis

● Market pricing of LIBOR forwards will also be a function of the selected fallback approach

Potential market implications of a LIBOR cessation

● For derivatives: firms can estimate their exposure by quantifying their LIBOR projection risk, in dv01 

terms, under different fallback scenarios

● For cash products: the notional amount for instruments referencing LIBOR can be analyzed under 

different fallback scenarios

How firms can quantify LIBOR cessation risk
LIBOR fallback process

17

Source: Bloomberg

● Two step process to apply SOFR as LIBOR replacement:

– Term adjustment: Transform SOFR, which is an o/n rate, to a term rate

– Spread/Credit adjustment: Apply a spread on top of the SOFR rate to take into account LIBOR’s credit 

premium component

● Potential term adjustment methodologies: Spot o/n SOFR, Convexity adjusted o/n SOFR, Compounded 

in arrears, and Compounded in advance

● Potential spread adjustment methodologies: Forward curve, Historical mean/median, Spot spread

We benchmark term adjustment methodologies to 

3m OIS forwards

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

0y 2y 4y 6y 8y 10y 12y 14y 16y 18y 20y 22y 24y 26y 28y

%

Forward start

3m OIS forwards

3m LIBOR forwards

LIBOR - OIS 

spread

Spread adjustment captures the basis between 

LIBOR and the risk free rate
Market implications from LIBOR fallback

18

Source: Bloomberg

● Choosing the historical mean approach as fallback would likely flatten the LIBOR-OIS basis swap curve 

– This is especially true for 5s30s and 10s30s

● The probability that the historical mean approach will be implemented, on a cessation of LIBOR, can be 

implied from 30y FRA/OIS spreads

● We can approximate the fair value of 30y FRA/OIS to be ~23bp assuming:

– Historical mean approach is used in the fallback process with a 10y window

– A 20% chance of LIBOR discontinuation each year from 2021-2025

LIBOR cessation estimates can be derived from 

current 30y FRA/OIS levels FRA/OIS will likely flatten with the historic approach
• Over $10 billion in SOFR 

floaters have been issued

• Investors should read the 

fine print: 

- Compounding 

differences can occur 

between deals

- LIBOR and SOFR can be 

expected to behave 

differently in different 

market environments

19

SOFR Adoption

Trading Begins

Source: Fannie Mae

CCPs Launch 

Futures and Swaps

Fannie Mae issues first 

SOFR-linked debt deal

Broader adoption of 

SOFR-linked issuance is 

gaining momentum

Maturities Amount Pricing

6-month $2.5B SOFR + 8 bps

12-month $2.0B SOFR + 12 bps

18-month $1.5B SOFR + 16 bps

Total $6.0B

• Fannie Mae successfully 

issued a three-tranche, $6B 

SOFR debt transaction on 

July 26, 2018

• The deal was met by 

demand from a broad and 

diverse investor base

SOFR Product Development Timeline

Jun 

22

2017

Jul 26

2017

Q4

2017

May 7

2018

Jul 16

2018

Oct 1

2018

Oct 22

2018

SOFR endorsed 

by ARRC

CME announces 

development of 

SOFR futures

Industry outreach 

on contract 

design validation

CME SOFR 

futures launch

LCH launches 

cleared OTC 

SOFR swaps

CME launches 

cleared OTC 

SOFR swaps

ICE SOFR futures 

launch

Issue Date Issuer Notional ($M) Tenor 

(years)

07/30/18 Fannie Mae $6,000 1.5, 0.5, 1.0

08/21/18 World Bank $1,000 2.0

08/21/18 Credit Suisse AG/NY $100 0.5

08/28/18 Barclays $525 0.25

09/07/18 MetLife $1,000 2.0

09/20/18 Triborough Bridge & Tunnel $107.28 13.5

09/21/18 Wells Fargo $1,000 1.5

09/25/18 Wells Fargo $125 1.0

10/05/18 Credit Suisse $1,056 0.5, 1.0

10/19/18 JP Morgan $800 2.0

10/24/18 Toyota $500 0.25

10/30/18 Fannie Mae $5,000 0.5, 1.0, 1.5

10/31/18 L-Bank (SSA in Germany) $12 1

Total $17,225
We have identified several areas of further development that we expect would help build activity and 

liquidity

20

SOFR Adoption

Going Forward

Market Structure Developments

Yield Curve

• Build liquidity beyond 2 years

• Build out of the long-dated SOFR 

curve - this will require issuer / 

derivative market participation

CCPs • SOFR-based PAA

Options/

Swaptions

• Developed options market on 

SOFR futures

• Eventual growth of SOFR 

swaptions

Bilateral 

agreements

• Thoughts on other ways bilateral 

counterparties can choose to 

incorporate SOFR discounting 

Issuers • Treasury should evaluate issuing 

FRNs off SOFR

Official Sector Guidance

Regulators

• Could provide relief on central 

clearing mandate for legacy LIBOR 

positions

• Could assess ways to encourage 

banks to move away from using 

LIBOR

• Consideration of effects across 

jurisdictions given global nature of 

the swaps market

LIBOR 

Oversight

• Guidance on conditions under 

which LIBOR will no longer be 

representative (or produced at all)
SOFR Survey to Short-end investors

21

Source: Bloomberg

● We performed a front-end survey around SOFR FRN issuance in September 2018

– 100 respondents covered 2a7 funds (government and prime), non-2a7 money market funds such 

as offshore, security lenders and corporate treasurers

● Key results: 

– LIBOR cessation risk: 

50% chance of cessation beyond 2021(25% of the respondents) 

– SOFR FRNs would be considered over LIBOR:

for cheaper levels (27%), and for diversification of floating benchmarks (24%)

– LIBOR FRNs are still attractive over SOFR :

given the better liquidity in cash markets (18%) and derivative markets (16%) and volatility of the 

underlying rate (16%)

– Preferred issuers of SOFR FRNs:

GSEs (25%), Financials (22%) and US Treasury (15%) would be more receptive

– SOFR FRNs would take up significant portion (more than a quarter) of their FRN portfolio: 

beyond 2021 (38%)