Report to Congress 

Access to Capital and Market Liquidity 

As Directed by the Explanatory Statement to the 

Consolidated Appropriations Act, 2016 (P.L. 114-113) 

This is a report by the Staff of the Division of Economic and Risk Analysis of the 

U.S. Securities and Exchange Commission. The Commission has expressed no view 

regarding the analysis, findings, or conclusions contained herein. 

August 2017 
 

 

 

 

Executive Summary 

In December 2015,1

 Congress directed the Commission’s Division of Economic and Risk 

Analysis (DERA or we) to report on the impacts of the Dodd-Frank Act,2 especially the Volcker 

Rule, as well as other financial regulations, such as Basel III,3

 on: (1) access to capital for 

consumers, investors, and businesses; and (2) market liquidity, including U.S. Treasury and 

corporate debt markets.4 The Report responds to the Congressional directive. 

Quantifying the effects of the regulatory reforms is challenging for several reasons. Most 

notably, overlapping implementations make it difficult to isolate the effect of any single rule or 

requirement. When the post-implementation period of one reform coincides with a preimplementation period of another, there is no clear baseline against which to separately measure 

the potential economic impacts. This issue is particularly acute when market participants change 

their behaviors in anticipation of future rules, the content of which is frequently signaled in 

advance by the notice and comment rulemaking process. Thus, compliance may occur in 

advance of the effective dates. 

It is also possible that many of the observed changes in market participant behaviors 

would have occurred absent the reforms. In particular, the immediate effects of the financial 

crisis—including the failures of many institutions and business models—provided strong 

1

 Consolidated Appropriations Act, Pub. L. 114–113, H.R. 2029. 

2

 The Dodd–Frank Wall Street Reform and Consumer Protection Act, Pub.L. 111–203, H.R. 4173 (Dodd–Frank 

Act).

3

 Basel Committee on Banking Supervision “Basel III: A global regulatory framework for more resilient banks and 

banking systems,” Dec 2010, (Rev Jun 2011); “Basel III: The Liquidity Coverage Ratio and liquidity risk 

monitoring tools,” Jan 2013; “Basel III: the net stable funding ratio,” Oct 2014. For more, see 

http://www.bis.org/bcbs/basel3.htm

4

 Because the markets for single-name credit default swaps and investment funds may interact with the U.S. 

Treasury and corporate debt markets, this report also analyzes those two markets. 

2 
incentives to change market practices in ways that may have simultaneously shaped the ensuing 

reforms. 

Finally, post reform macroeconomic conditions, such as the economic recovery and the 

low interest rate environment, are different from those leading up to and right after the financial 

crisis. Therefore, it is difficult to quantify the benchmark levels of primary issuance and market 

liquidity that would have been observed following the financial crisis and absent the ensuing 

reforms. For example, some market participants have noted that evidence of liquidity 

deterioration can be found in the number of trades that have not occurred. However, such data 

are not available, so we are not able to explore this metric. 

Although the above factors significantly limit our ability to analyze whether specific 

regulatory reforms caused any particular changes, DERA’s analysis provides a comprehensive 

and detailed review of capital raising through primary issuance and secondary market liquidity 

over time and in ways that allow an assessment of whether observed trends could be consistent 

with the effects of regulatory reforms, or with one or more of the other potential explanations. 

Where possible, we highlight when multiple factors could be impacting trends in issuance and/or 

market liquidity in either amplifying or offsetting ways. We recognize that liquidity may interact 

with other market characteristics, such as informational efficiency and market stability. In the 

Report, we do not estimate the optimal amount of liquidity for corporate bond or Treasury 

markets, but document the evolution of different dimensions of liquidity over time and consider 

whether observed changes are consistent with a variety of proposed explanations. 

A distinguishing feature of DERA’s Report is that it includes a comprehensive 

assessment of a large body of recent research in addition to original analysis performed by 

DERA staff. To this end, the Report’s scope differs from other existing studies, and we focus on 

3 
 

 

primary securities issuance and secondary market liquidity across fixed income markets. For 

example, the U.S. Department of the Treasury recently issued a report that included policy 

recommendations, based on certain conclusions regarding secondary market liquidity.5 As this 

report uses a different methodology than that considered in the Treasury Report, the conclusions 

reached in this Report may differ from those stated in the Treasury Report. 

We note, however, that because some Basel III requirements are still being implemented, 

most available evidence on potential regulatory effects centers on the Dodd-Frank Act, including 

the Volcker Rule, and the JOBS Act. There is comparatively little research on the impacts of 

Basel III reforms. We nevertheless consider the regulatory timeline for Basel III reforms in 

addition to the implementation of the Dodd-Frank Act and JOBS Act, and document changes in 

issuance and market liquidity metrics over time, including all relevant dates. However, due to 

the lack of available evidence, we do not specifically address the effects of Basel III reforms in 

Part A and Part B.VI. 

Main Results 

Part A. Primary Issuance 

DERA analyzed primary issuance of debt, equity, and asset-backed securities (ABS). 

The total capital formation from the signing of the Dodd-Frank Act into law in 2010 through the 

end of 2016 is approximately $20.20 trillion, of which $8.8 trillion was raised through registered 

offerings, and $11.38 trillion was raised through unregistered offerings.6 We do not find that 

total primary market security issuance is lower after the enactment of the Dodd-Frank Act 

5 See U.S. Department of the Treasury, “A Financial System that Creates Economic Opportunities: Banks and Credit 

Unions” (June 2017) (hereinafter “Treasury Report”). 6 See Part A.II and Part A.III of the Report for the analysis of registered and unregistered offerings.
 

4 
 

 

 

(including during the implementation of the Volcker Rule) and during the implementation of 

Basel III, and it may have increased around the implementation of the JOBS Act.7 DERA has 

not attempted to establish a counterfactual level of primary securities issuance that would have 

been attained in the absence of the Dodd-Frank Act, Basel III, and other regulatory reforms. 

These results are also generally consistent with active issuance in strong macroeconomic 

conditions and a low interest rate environment. Some of the findings we discuss in the Report 

are as follows: 

	 Capital raised through initial public offerings (IPOs) ebbs and flows over time, reaching 

highs in 1999, 2007 and 2014, and lows in 2003, 2008, and 2016.8

 It is difficult to 

disentangle the many contributing factors that influence IPO dynamics. 

	 Recent years have seen an increase in the number of small company IPOs. IPOs with 

proceeds up to $30 million accounted for approximately 17% of the total number of IPOs 

in the period 2007-2011 and 22% in the period 2012-2016, following the passage of the 

JOBS Act in 2012.9

 In 2016, more than 75% of IPOs were classified as Emerging 

Growth Companies (EGCs) under Title I of the JOBS Act (Title I).10 

	 Private market issuance of debt and equity (unregistered offering activity) has increased 

substantially from $1.16 trillion in 2009 to $1.87 trillion in 2015, amounting to $1.68 

trillion in 2016.11 Amounts raised through exempt securities offerings of debt and equity 

for 2012 through 2016 combined exceeded amounts raised through registered offerings of 

7

 The Jumpstart Our Business Startups Act, Pub. L. 112-106, H.R. 3606 (JOBS Act). 8 See Part A.II. of the Report for the analysis of registered offerings.
 9 We note that IPO activity has experienced significant declines in 2015 and 2016, and explore this development in
 

Part A.II. 

10 See Proskauer (2017).
 11 See Part A.III of the Report for the analysis of exempt offering activity. 

5 
 

 

 

debt and equity over the same time period by approximately 26%. In comparison, the 

same figure for 2009 through 2011 was 21.6%. Amounts raised in reliance on the 

general solicitation rules under Title II of the JOBS Act, which were implemented in 

September 2013, remain low, representing only 3% of total amounts raised pursuant to 

Rule 506. 

	 Amendments to Regulation A12 initiated by Title IV of the JOBS Act were followed by a 

large increase in Regulation A offering activity over the initial 18 months post 

effectiveness, with 97 qualified offerings seeking to raise $1.8 billion (compared with 

about 14 qualified offerings seeking to raise approximately $163.3 million in a typical 

year during 2005-2016). Based on issuer reports of amounts raised filed during 2005­

2016, 56 issuers reported positive proceeds in Regulation A offerings, totaling 

approximately $314.6 million. Initial evidence on JOBS Act Title III crowdfunding 

activity suggests that some small pre-revenue growth firms are beginning to use 

crowdfunding as a securities offering method.13 

Part B. Market Liquidity 

Evidence for the impact of regulatory reforms on market liquidity is mixed, with different 

measures of market liquidity showing different trends. Moreover, many of the observed changes 

in these measures are consistent with the combined impacts of several factors besides new rules 

and regulations, including, among others, electronification of markets, changes in 

macroeconomic conditions, and post-crisis changes in dealer risk preferences that pre-date the 

12 Rel. No. 33-9741, Amendments to Regulation A (Mar. 25, 2015) 80 FR 21805 (Regulation A+ Adopting 

Release)

13 See Part A.III.G of the Report for initial evidence on crowdfunding activity. 

6 
 

 

 

 

passage of either the Dodd-Frank Act or Basel III. As noted above with respect to primary 

securities issuance, DERA has not attempted to estimate a counterfactual level of trading activity 

or average transaction costs in the absence of the recent regulatory reforms. 

	 In U.S. Treasury markets, we find no empirical evidence consistent with the hypothesis 

that liquidity has deteriorated after regulatory reforms. More specifically, there is no 

support for a causal link between the Volcker Rule and U.S. Treasury market liquidity 

conditions. Changes in Treasury market liquidity are unlikely to be directly attributable 

to the Volcker Rule because U.S. cash Treasuries are exempt from the Volcker Rule’s 

prohibitions on proprietary trading. 

	 In corporate bond markets, trading activity and average transaction costs have generally 

improved or remained flat. More corporate bond issues traded after regulatory changes 

than in any prior sample period. 14 In the post-regulatory period, we estimate that 

transaction costs have decreased (by 31 basis points (bps), to 55.4 bps round-trip) for 

smaller trade sizes ($20,000) and remain low for larger trade sizes relative to the precrisis period (estimated at 5.7 bps round-trip for trades of $5,000,000, compared to 5.8 

bps pre-crisis). 

14 See Table D.4, Panel B, which reports estimated half spreads for transactions of 5,000,000 (round-trip costs are 

double half-spreads). For the purposes of our transaction cost analysis, we split the sample into 6 sub-periods. We 

define January 2006 through June 2007 as the “Pre-crisis” sub-period. We designate July 2012 through May 2014 

as the “Regulatory” sub-period, and June 2014 through September 2016 as the “Post-regulatory” sub-period. As 

discussed in Part B.IV.C, our sample period cutoffs are aligned with existing research, such as Bessembinder et al. 

(2016) and Bao et al. (2016). See Part B.IV.C of the Report for a more detailed analysis. 

7 
 

 

 

 

- Although estimated transaction costs have decreased, corporate bond trading activity 

in recent years has also become somewhat more concentrated in less complex bonds 

and bonds with larger issue sizes.15 

- For some subgroups of corporate bonds, such as larger bonds (i.e., issue size greater 

than $500 million), certain investment grade bonds, younger bonds (i.e., less than 2 

years since issuance), and longer maturity bonds (i.e., original maturity longer than 20 

years), the estimated transaction costs for large trades are slightly higher than those in 

the pre-crisis period.16 

	 Dealers in the corporate bond markets have, in aggregate, reduced their capital 

commitment since the 2007 peak.17 This is consistent with the Volcker Rule and other 

reforms potentially reducing the liquidity provision in corporate bonds. It is also 

consistent with alternative explanations, such as an enhanced ability of dealers to manage 

corporate bond inventory, shorter dealer intermediation chains associated with 

electronification of bond markets, crisis-induced changes in dealer assessment of risks 

and returns of traditional market making, and the effects of a low interest rate 

environment. These alternative explanations are not mutually exclusive or necessarily 

fully independent of regulatory reforms, so distinguishing between these potential 

explanations from the market trends data is not possible. 

	 Although capital commitments have fallen, there has not been a commensurate decrease 

in the number of dealers participating in the market. We observe no notable changes in 

15 See Part B.IV.B.1a) and Part B.IV.C.1 for the analysis of trends in trading activity over time and for bonds with
 

different characteristics. 

16 See Part B.IV.B, Part B.IV.C.1 and Part B.IV.C.2 for more analysis of trends in trading activity and transaction
 

costs. 

17 See Part B.IV.B and Part B.IV.C.3 for more analysis of trends in dealer activity.
 

8 
 

the number of dealers providing liquidity per corporate bond issue over time, and we do 

not detect notable changes in trade sizes around regulatory reforms. 

	 The evidence on dealer activity in times of stress is mixed and varies with the definition 

of stress. 

- Some existing research suggests that during times of localized market stress under 

strong macroeconomic conditions there may be greater adverse price impacts 

from trading activity (a sign of deteriorating liquidity) associated with the decline 

in dealer capital commitments for a small subset of bonds. Other evidence on 

localized stress selling does not support the finding of a deterioration in liquidity 

around firm specific events. 

- Evidence from the crisis suggests that during times of severe market stress, 

dealers may not lean into the wind, but instead make larger cuts in inventory of 

bonds that are aggressively sold by their customers.18 Such evidence supports a 

finding that dealers decrease liquidity provision in times of severe market stress. 

	 Trading of corporate bonds on alternative trading systems (ATS) may partly account for 

the lower estimated average transaction costs for small trades and observed reductions in 

dealer capital commitments.19 

- Electronic trading may facilitate efficient management of dealer inventory and 

reduce counterparty search costs. In addition, electronic trading could enable 

customers to seek liquidity directly from other customers. 

18 See Part B.IV.B.2 and Part B.IV.B.3a) for a further discussion of liquidity conditions and dealer activity in times 

of stress. 

19 See Part B.IV.C.4 for our data analysis of ATS activity. 

9 
 

 

 

- The majority of interdealer trades enjoy positive price improvement, whereas 

small customer trades experience negative price improvement, on average. 

 Trading in single-name credit-default swaps (CDS) provides an alternative channel for 

investors to gain exposure to corporate bond credit risk. Some measures of CDS market 

liquidity—total number of participants transacting in a given reference entity and various 

trading activity metrics—have remained stable or point to improvements. Other 

measures show a reduction in activity: trade sizes have decreased, quoting activity has 

declined, and quoted spreads for the least liquid high yield underliers have risen. 

Interdealer trade activity has declined after 2010, but dealer-customer activity has 

remained stable.20 

20 See Part B.V.D for our analysis of activity in single-name CDS. 

10 
Table of Contents 

EXECUTIVE SUMMARY ........................................................................................................................... 2
 

INTRODUCTION....................................................................................................................................... 13
 

CONGRESSIONAL DIRECTIVE.....................................................................................................................13
 

BROAD ECONOMIC CONSIDERATIONS .......................................................................................................14
 

SUMMARY OF RESULTS .............................................................................................................................17
 

METHODOLOGICAL CONSIDERATIONS ......................................................................................................24
 

PART A. ACCESS TO CAPITAL: PRIMARY ISSUANCE ..................................................................28
 

I. INTRODUCTION ................................................................................................................................ 28
 

II. CAPITAL MARKETS FOR REGISTERED OFFERINGS ...........................................................................31
 

III. CAPITAL MARKETS FOR EXEMPT OFFERINGS ..................................................................................35
 

A. Introduction ................................................................................................................................ 35
 

B. General data on Regulation D and Rule 144A offerings............................................................38
 

C. Rule 506(c) of Regulation D....................................................................................................... 39
 

D. Investors in Regulation D Offerings...........................................................................................42
 

E. Regulation D Offering Fees ....................................................................................................... 44
 

F. Regulation A Activity Overview ................................................................................................. 47
 

G. Regulation Crowdfunding Activity Overview .............................................................................54
 

IV. THE ABS MARKET ..........................................................................................................................60
 

V. TABLES ON PRIMARY ISSUANCE ......................................................................................................64
 

PART B. MARKET LIQUIDITY.............................................................................................................. 69
 

I. INTRODUCTION ...................................................................................................................................... 69
 

II. EMPIRICAL MEASURES OF MARKET LIQUIDITY ....................................................................................70
 

Trading Activity ................................................................................................................................... 70
 

Transaction Costs ................................................................................................................................ 71
 

Liquidity Supply ................................................................................................................................... 72
 

Composite Measures............................................................................................................................ 73
 

III. TREASURIES ..................................................................................................................................... 74
 

A. Introduction ................................................................................................................................ 74
 

B. Determinants of Liquidity Changes............................................................................................77
 

C. Empirical Measures ................................................................................................................... 81
 

Funding Liquidity............................................................................................................................................ 82
 

D. Recent Evidence ......................................................................................................................... 83
 

E. Summary..................................................................................................................................... 92
 

IV. CORPORATE BONDS .........................................................................................................................93
 

A. Introduction ................................................................................................................................ 93
 

B. Existing Research ....................................................................................................................... 95
 

1. Dimensions of Liquidity ....................................................................................................................... 96
 

2. Liquidity Provision in Times of Stress ............................................................................................... 105
 

3. Drivers of Changes in Liquidity ......................................................................................................... 109
 

C. Data Analysis and Results........................................................................................................ 126
 

1. Trading Activity ........................................................................................................................................ 128
 

2. Transaction Costs...................................................................................................................................... 142
 

3. Dealer Activity.......................................................................................................................................... 161
 

4. Electronic Venues ..................................................................................................................................... 178
 

11 
 

D. Summary................................................................................................................................... 190
 

E. Tables on Corporate Bond Liquidity ........................................................................................191
 

V. SINGLE-NAME CREDIT DEFAULT SWAPS (CDS) ............................................................................207
 

A. Introduction .............................................................................................................................. 207
 

B. Limitations on Analysis of Causal Impacts ..............................................................................212
 

C. Spillovers between Bonds and Single-Name CDS ....................................................................214
 

D. Exposures, Participants and Liquidity .....................................................................................220
 

1. Volume of Activity............................................................................................................................. 220
 

2. Market Participants............................................................................................................................. 229
 

3. Transaction Activity ........................................................................................................................... 230
 

4. Quotes and Quoted Spreads................................................................................................................ 242
 

E. Summary................................................................................................................................... 247
 

F. Tables on Single-name CDS .....................................................................................................249
 

VI. FUNDS............................................................................................................................................ 253
 

A. Introduction .............................................................................................................................. 253
 

B. Evidence on Bond Liquidity from Bond Ownership .................................................................253
 

C. Limitations in Interpreting the Data.........................................................................................261
 

D. Summary................................................................................................................................... 263
 

REFERENCES .......................................................................................................................................... 265
 

APPENDICES ........................................................................................................................................... 274
 

Appendix A. Regulatory Reform Timeline......................................................................................... 274
 

Appendix B. Regulatory Reform Timeline: Security-Based Swap Markets ......................................278
 

Appendix C. Appendix to Access to Capital Analysis .......................................................................281
 

Regulation D Sample .................................................................................................................................... 281
 

Other Offerings............................................................................................................................................. 283
 

Total Capital Raised In Registered and Unregistered Offerings.................................................................... 284
 

Appendix D. Appendix to Market Liquidity Analysis: Corporate Bonds .........................................285
 

Data Filters and Estimation ........................................................................................................................... 285
 

Sample Composition ..................................................................................................................................... 292
 

Transaction Costs.......................................................................................................................................... 295
 

Dealer Activity.............................................................................................................................................. 302
 

12 
	 	

 

Introduction 

Congressional Directive

The Consolidated Appropriations Act, 2016 was signed into law on December 18, 

2015.21 The Explanatory Statement to the Appropriations Act22 directed the Division of 

Economic and Risk Analysis (DERA or we) of the U.S. Securities and Exchange Commission 

(Commission or SEC) to report to the Committees on Appropriations of the House and Senate, 

the Committee on Financial Services in the House and the Committee on Banking, Housing, and 

Urban Affairs in the Senate, on: 

the combined impacts that the Dodd-Frank Act--especially Section 619--and other 

financial regulations, such as Basel III, have had on: (1) access to capital for 

consumers, investors, and businesses, and (2) market liquidity, to include U.S. 

Treasury markets and corporate debt. DERA shall provide an update to the 

Committees on their work no later than August 1, 2016. 

This Report represents the considered views of staff in DERA, as informed by the 

processes described below, but the views expressed in this Report do not necessarily reflect those 

of the Commission or the individual Commissioners, or of staff of other Commission Offices or 

Divisions. 

To effectuate Congress’s direction, DERA studied (1) capital raising in the primary 

markets, and (2) secondary market liquidity. With regard to the first topic, DERA analyzed 

evidence on the evolution of the issuance of debt, equity, and ABS across registered and exempt 

offerings. With regard to the second topic, DERA analyzed market activity and liquidity in U.S. 

Government obligations (U.S. Treasuries or Treasuries) and corporate bonds, but also singlename CDS and investment companies, such as open-end mutual funds and exchange-traded 

21 Consolidated Appropriations Act, Pub. L. 114–113, H.R. 2029. 

22 161 Cong. Rec. H9693 (Dec. 17, 2015) (statement of Chairman of the House Committee on Appropriations) 

https://www.congress.gov/crec/2015/12/17/CREC-2015-12-17-pt2-PgH9693.pdf 

13 
	 	

 

 

funds (ETF), that invest in corporate bonds and Treasuries, for reasons we describe below. 

DERA considered the hypothesis that regulatory reform efforts, including the Volcker Rule and 

Basel III, as well as other regulations including those adopted by the Commission pursuant to the 

JOBS Act mandates, were impacting primary market activity, or secondary market liquidity, or 

both. DERA also considered whether factors other than regulatory reforms, including market 

structure changes,23 evolution of market participants’ preferences after the financial crisis, or 

aggregate macroeconomic conditions could be contributing to observed changes in the primary 

and secondary markets. 

Because DERA is not the first to analyze and assess these economic issues, the Report 

begins with a critical review of the results of the body of research to date. The Report then 

complements the findings from existing research with original analysis by DERA staff using 

market information obtained from SEC filings and other public data, subscription databases, and 

regulatory data feeds. 

Broad Economic Considerations

DERA has examined the evolution of the volume and structure of primary market 

issuances and secondary liquidity over time. The availability of different types and channels of 

primary issuance, such as registered and exempt issues of debt, equity, and ABS, impact how 

businesses access capital and influence the scope and riskiness of securities available to 

consumers, businesses, and investors. Five broad considerations inform our analysis. 

23 We recognize that these and other confounding phenomena are not necessarily mutually exclusive or fully 

exogenous. For instance, we cannot distinguish between bond market electronification arising out of technological 

advances, market response to the financial crisis, or market response to the regulations in the immediate aftermath of 

the financial crisis. 

14 
 

First, capital raising in primary markets and liquidity in secondary markets are 

inextricably intertwined. For example, liquid secondary markets that enable investors to exit 

large positions quickly and at low cost can facilitate primary market issuance and placement. At 

the same time, if high-risk issuers select into certain forms of issuance or if primary issuance 

becomes split across many different forms of issuance, secondary market liquidity can decrease. 

Most studies focus on either the issuance of new securities or the secondary market trading and 

liquidity. One of the distinguishing features of DERA’s analysis is a comprehensive and 

simultaneous exploration of both primary issuance and secondary liquidity issues across markets. 

Second, the existence of substitutes for exposure to credit risk may impact activity and 

liquidity in bond markets. Market participants seeking exposure to the credit risk of bond issuers 

can choose to transact directly in the market for corporate bonds or to trade in alternative credit 

risk products. The existence of single-name CDS and bond funds as alternative instruments for 

capital allocation and cross-market arbitrage may spill over into activity and liquidity in bond 

markets. As a result, it is important to consider how single-name CDS and fund liquidity may 

interact with activity in markets for Treasuries and corporate bonds. 

Third, liquidity is an important characteristic of a capital market and affects the ability of 

investors to execute trades of different sizes, quickly, and at low cost. We recognize that 

liquidity may interact with other market characteristics, such as informational efficiency of 

capital markets and market stability. The Dodd-Frank Act was intended, among others, to 

promote the financial stability of the United States.24 In the Report, we do not perform a costbenefit analysis of various regulatory changes, and do not estimate the optimal amount of 

securities issuance or liquidity in corporate bond or Treasury markets. Instead, we document the 

24 See Pub. L. No. 111-203, Preamble. 

15 
evolution in different dimensions of capital raising and market liquidity over time and consider 

whether observed changes are consistent with a variety of proposed explanations. 

Fourth, large sample evidence enables us to examine the issuance and trading activity of 

large groups of market participants using thousands, millions, and billions of observations. We 

explore cross-sectional heterogeneity in capital raising and trading activity by analyzing various 

subgroups of issuers and transactions, and report different distributional parameters (e.g., 

median, 25th, 75th, 99th percentiles, etc.). Statistics produced from large sample analysis may 

not always reflect the behaviors and experiences of market participants in smaller segments of 

the markets. Therefore, where we observe improvements in issuance or liquidity metrics, the 

findings do not necessarily imply that all market participants experienced such improvements, 

and vice versa. 

Finally, we recognize that the regulations that serve as the focus for this Report impose 

costs on certain groups of market participants. To the extent that such costs have a significant 

impact on the behavior of affected market participants, they may result in changes to issuance 

and liquidity. However, affected market participants may alter their business practices in 

response to regulatory impacts and unaffected market participants may change their activity in 

compensating ways, even as the regulations have their intended effect. These responses to 

regulation, along with other factors such as changing macroeconomic conditions may dampen 

observed changes in market indicators and weaken our ability to link changes in issuance and 

liquidity with regulatory reforms. 

The Report endeavors to analyze relevant research and data through the fourth quarter of 

2016, where available. Our analysis focuses on evidence to date, and we recognize various 

related studies may be ongoing. Evidence on the impacts of the implementation of Basel III on 

16 
	 	 	

 

bond liquidity is relatively scarce, possibly because elements of the Basel III regime are still 

being implemented in the United States, and this remains a fruitful area for future research. We 

also note that many of the studies discussed below are working papers, representing preliminary 

work that has not been fully vetted by the peer review process. 

Summary of Results

As a result of this analysis, DERA observes that the evidence as to the direction and 

magnitude of changes in primary issuance and secondary market liquidity after the financial 

crisis and ensuing regulatory reforms is mixed. The observed changes are consistent with a 

number of explanations, including the combined effect of various regulations, non-regulatory 

market structure changes, crisis-related changes to market participants’ preferences that pre-date 

the passage of regulatory reforms, and aggregate macroeconomic conditions (such as a low 

interest rate environment), among others. We also note that these explanations need not be 

mutually exclusive or independent. For instance, to the extent that market structure changes may 

have contributed to observed changes in liquidity metrics, we cannot assess whether such market 

structure developments occurred because of technological advances, as a result of the crisis, 

regulatory reforms, or some combination thereof. 

DERA’s analysis is focused on areas within the scope of the Commission’s jurisdiction 

as the primary securities regulator in the United States, i.e. having regulatory responsibilities that 

span both primary and secondary securities markets. 

Our analysis of primary issuance considers changes in the volume and structure of 

issuance of equity, debt, and ABS. This includes changes in IPOs, seasoned offerings, and 

17 
 

 

 

exempt offerings of debt and equity, including offerings under Regulation D and Regulation A.25 

We also present preliminary evidence on offerings pursuant to Regulation Crowdfunding,26 

which the Commission adopted as part of its implementation of the JOBS Act. In addition, ABS 

issuance supports a large volume of primary issuance, and we consider the evolution and 

structure of ABS issuance activity over time. 

We do not find that total primary market security issuance is lower after the enactment of 

the Dodd-Frank Act (including during the implementation of the Volcker Rule) and during the 

implementation of Basel III, and it may have increased around the implementation of the JOBS 

Act.27 DERA has not attempted to establish a counterfactual level of primary securities issuance 

that would have attained in the absence of the Dodd-Frank Act, Basel III, and other regulatory 

reforms. These observed increases are also generally consistent with active issuance in strong 

macroeconomic conditions and a low interest rate environment. 

With respect to primary issuance, capital raised through initial public offerings (IPOs) 

during the period of 1996 through 2016 reached highs in 1999, 2007 and 2014, and lows in 2003, 

2008, and 2016.28 The post-crisis dynamics of IPO issuance are consistent with several IPO 

waves observed during this time period. Capital raised through secondary equity offerings 

(SEOs) during the 1996-2016 sample period peaked in 2009, and in recent years has been 

exceeding pre-crisis issuance volume. Registered debt issuance has been growing during the 

sample period (1996 through 2016), reaching a peak in 2016. Regulation D offerings have more 

than doubled since 2009, the time for which data is available. Rule 144A offerings remaining 

25 See 17 CFR 230.500 through 230.508; Rel. No. 33-9415 (Jul. 10, 2013), Eliminating the Prohibition Against 

General Solicitation and General Advertising in Rule 506 and Rule 144A Offerings, 78 FR 44771; Rel. No. 33­

9741, Amendments to Regulation A (Mar. 25, 2015) (Regulation A+ Adopting Release) 80 FR 21805.

26 Rel. No. 33-9974, Crowdfunding (Oct. 30, 2015) 80 FR 71387 (Crowdfunding Adopting Release). 

27 The Jumpstart Our Business Startups Act, Pub. L. 112-106, H.R. 3606 (JOBS Act). 28 See Part A.II. for further analysis of registered offerings.
 

18 
 

stable post-crisis and comparable to pre-crisis levels. Although the volume of ABS issuance 

declined dramatically during the financial crisis and has been on the rise since, it is still a fraction 

of pre-crisis levels (our ABS sample spans 2005 through 2016). Further, we do not find 

evidence that costs of various types of offerings have changed notably during our sample period. 

Our study of liquidity in secondary markets emphasizes Treasury and corporate bond 

market liquidity. Following a large body of research that points to the importance of different 

measures of liquidity, we construct and examine a number of measures reflecting various facets 

of liquidity, provide a critical analysis of existing findings, and perform additional data analysis 

to supplement prior work. The analysis below explores different potential drivers of changes in 

liquidity, many of which may not be mutually exclusive or fully independent of regulatory 

effects. Our data analysis examines changes in secondary market liquidity metrics around 

regulatory reforms, the evolving role of some dealers from principal to agency trading,29 and 

evidence from electronic trading. 

With respect to Treasury markets, as we discuss below, U.S. cash Treasuries are exempt 

from the Volcker Rule’s prohibitions on proprietary trading. Therefore, changes in Treasury 

market liquidity are unlikely to be directly attributable to the Volcker Rule. Instead, the Volcker 

Rule may indirectly affect this market as a result of spillover effects from other markets.30 An 

analysis of evidence on a wide range of liquidity measures quantifying different dimensions of 

liquidity does not allow us to conclude that post-crisis regulations caused a reduction in Treasury 

market liquidity. None of the existing studies provides empirical support for a causal link 

29 Generally, dealers can trade as agents, matching customer buys to customer sells, or as principals, absorbing 

customer buys and customer sells into inventory and committing the requisite capital. See Li and Li (2017). 30 For instance, Treasury futures are not exempt, and the Volcker Rule could indirectly impact cash Treasuries 

through intermarket cash-futures basis trading. 

19 
 

 

 

between various regulations, and in particular the Volcker Rule, and changing Treasury market 

liquidity conditions. 

In our analysis of market liquidity in corporate bonds, we evaluate the evidence from 

existing research on the evolution of corporate bond market liquidity and find that some liquidity 

metrics have been reported to have improved, others have remained flat, and yet others have 

worsened after various regulations.31 While there is little consensus in existing work concerning 

the direction, causal attribution, and mechanisms behind observed changes, evidence suggests 

that in recent years dealers have been less likely to engage in risky principal transactions. In 

addition, dealers generally decrease liquidity provision in times of severe market stress, such as 

during the financial crisis.32 

With respect to the potential regulatory factors behind observed liquidity changes, there 

is a lack of agreement in research regarding the direction and magnitude of regulatory impacts. 

Moreover, studies with different measurement and empirical design often present different or 

conflicting conclusions.33 Most research does not find that post-trade transparency leads to a 

deterioration in bond market liquidity.34 Existing research on the role of electronic trading is 

limited,35 and there is competing evidence on the interplay between CDS and corporate bond 

market liquidity.36 In the sections that follow we critically assess these and other studies and 

discuss their implications. 

31 See Part B.IV.B.3a) for a discussion of evidence of changes in market liquidity around regulatory reforms. 32 See Part B.IV.B.2 and Part B.IV.B.3a) for a discussion of evidence on dealer provision of liquidity in normal
 

times and in times of stress before and after various regulatory reforms. 

33 See Part B.IV.B.3a) for a detailed analysis. 34 See Part B.IV.B.3c) for a discussion of existing research on post-trade transparency. 35 See Part B.IV.B.3b) for an analysis of existing findings on electronic trading.
 36 See Part B.V.C for a discussion of research on spillovers between single-name CDS and reference security
 

markets.
 

20 
 

 

 

Empirical analysis by DERA staff on corporate bond market liquidity conditions yields 

similarly mixed results. Following other studies, we segment the sample period into several subperiods, including the phase-in of the Trade Reporting And Compliance Engine (TRACE), precrisis, crisis, post-crisis, regulatory, and post-regulatory time periods.37 We present four main 

results. First, during the time periods after the crisis period we find an increase in the fraction of 

corporate bond issues with trades, higher levels of trading activity when traded, and greater par 

dollar volume traded compared to any other period except post-crisis. 

Second, we find that transaction costs have generally decreased or remained flat, with 

particularly strong declines for small trade sizes and relatively riskier investment grade (IG) 

bonds. However, because transaction costs can be estimated only for trades that occur, we are 

unable to observe how regulation and other market influencing factors may have had an effect on 

the ability of counterparties to find liquidity and successfully engage in a trade. In other words, 

trades sought but not executed cannot be empirically measured. Hence, interpreting the reported 

decline in transaction costs as an improvement in market liquidity requires consideration of the 

above evidence on changes in trading activity over time. 

Third, we consider changes in dealer activity as a proxy for changes in the availability of 

liquidity through market making activity. We find that the median number of dealers providing 

liquidity per corporate bond issue has remained stable across all sample periods. Moreover, we 

do not observe notable changes in the number of dealers providing liquidity per bond issue 

37 Our sample period cutoffs are aligned with Bessembinder et al. (2016) and Bao et al. (2016). We designate the 

January 2003 through December 2005 as the “TRACE Phase-in” sub-period. We define January 2006 through June 

2007 as the “Pre-crisis” sub-period. We designate July 2007 through April 2009 as the “Crisis” sub-period and May 

2009 through June 2012 as the “Post-crisis” sub-period. Lastly, we divide the rest of our sample into two additional 

sub-periods. We designate July 2012 through May 2014 as the “Regulatory” sub-period37 and June 2014 through 

September 2016 as the “Post-regulatory” sub-period. We discuss these issues in great detail in Part B.IV.C. 

21 
 

around the changes in regulation. Across sub-periods, more dealers trade in small trade sizes. In 

particular, while the fraction of large size (greater than $5,000,000) principal trades is similar 

across sub-periods, the portion of medium size ($100,000 - $1,000,000) principal trades is larger 

during the “Regulatory” and the “Post-regulatory” sub-periods. 

 Finally, we explore cross-sectional evidence on recent corporate bond quotation activity 

on ATS, which sheds light on the current use of electronic trading in corporate bonds. We 

document large cross-sectional heterogeneity in ATS activity for different types of bond issues. 

We find that electronification may be associated with lower trade sizes. We also find that the 

majority of interdealer trades enjoy positive price improvement, whereas small customer trades 

experience negative price improvement, on average. 

Overall, it is not clear that corporate bond market liquidity has deteriorated following the 

enactment of the Dodd-Frank Act, the implementation of the Volcker Rule, and the 

implementation of the Basel III reforms. As discussed below, the observed trends in market 

liquidity metrics are consistent with multiple alternative explanations. 

In addition to Treasury and corporate bond markets, we have considered changes in 

liquidity in other markets. Specifically, we explore: (1) potential diffusion of liquidity to singlename CDS, and (2) the evolution of fund liquidity. A recent CFA Institute survey addresses both 

as potential drivers of changes in secondary corporate bond market liquidity.38 Academic 

research also examines spillovers between single-name CDS and corporate bonds and the 

interaction between fund and bond liquidity.39 We consider whether these potential explanations 

may be consistent with observed changes in liquidity metrics. 

38 See CFA Institute (2016). 39 See Parts B.V and B.VI of this report. 

22 
 

 

The Report critically re-examines findings regarding the effect of the CDS market on 

bond liquidity. Existing literature finds evidence of two competing effects of trading activity in 

single-name CDS on bond liquidity. In particular, some evidence suggests that access to hedges 

of credit risk can enhance liquidity in riskier bond issues. Other evidence indicates that 

participants may be looking for liquidity across markets, and CDS liquidity may be crowding out 

bond market liquidity. That is, CDS is an alternative means by which market participants gain 

exposure to the credit risk of debt issuers.40 The analysis below documents changes in the 

volume of the market, participants, transaction and quotation activity in single-name CDS using 

public, commercial, and regulatory data.41 

We examine three groups of measures: (1) metrics of transaction activity, including 

notionals, market values, and participants by credit, industry, and tenor; (2) measures of singlename CDS trading activity using a regulatory feed, including interdealer and dealer-customer 

notionals, trade sizes, counts, and zero trading days; and (3) quotation activity, including number 

of quoted underliers, quotes per underlier, quoted spreads, etc., from a commercial database. We 

find that interdealer trade activity has declined after 2010, but dealer-customer activity has 

remained stable, a result that is consistent with two competing explanations: (1) a reduced 

ability by dealers to find liquidity on the interdealer market; and (2) greater efficiency in dealer 

intermediation chains matching buyers to sellers. Consistent with the latter, the number of 

participants transacting in a given underlier has remained relatively constant, the fraction of zero 

trading days has decreased, and the frequency of trading for active underliers has remained flat 

40 See Part B.V.C of this report. 41 See Part B.V.D of this report. 

23 
 

or elevated after the financial crisis and reforms that have been enacted to date.42 However, 

quoting activity has declined, and quoted spreads for the least liquid high yield underliers have 

risen. Overall, our time series and cross-sectional results are consistent with several alternative 

explanations which are not mutually exclusive. 

Lastly, the analysis that follows explores fund liquidity, which contributes to a holistic 

picture of changes in secondary market liquidity and potential structural changes in corporate 

bond and Treasury markets during the past decade. In particular, research suggests that liquidity 

can flow from bond markets into investment companies (such as mutual funds and ETFs), and 

vice versa. For instance, market participants seeking exposure to the credit risk of issuers may 

choose to allocate into bond funds instead of underlying bonds. In this way, bond funds, 

particularly open-end funds and ETFs, can serve as a substitute instrument that investors can use 

if and when the underlying bond markets are illiquid. We find that the evidence on the interplay 

between fund activity and bond liquidity is mixed, that fund ownership of corporate bonds has 

increased over this Report’s period of interest, and that fund activity could impact liquidity 

measures in underlying bonds in multiple ways. 

Methodological Considerations

DERA staff analyzed existing economic research and performed novel data analysis. 

Throughout this Report we discuss a number of data and experimental design issues that limit 

our ability to make causal determinations as to the effect of reforms on access to capital and 

42 As discussed in Part B.V.B, many of the substantive Title VII rules governing the single-name CDS market have 

been proposed but not adopted (e.g., capital and margin requirements for dealers and major participants, swap 

execution facilities), and compliance with many adopted rules is not yet required (e.g., dealer registration, crossborder activity, business conduct standards etc.). 

24 
market liquidity. In this section we introduce some of the broad methodological considerations 

and limitations of the analysis. 

As discussed above, the Report examines primary issuance and secondary market 

liquidity across many markets from the early 2000s through 2016. Any study of such scope and 

magnitude faces challenges, and two are most salient: baselining and identification. First, 

Dodd-Frank, Volcker, and Basel III reforms followed a historic financial crisis and the ensuing 

market rally. As a result, the pre-regulatory period includes the recession and the bull market 

that follows. Using this period as a baseline could bias estimates for many capital raising and 

liquidity metrics. To address this challenge, subject to data constraints and where practicable, 

we have extended our samples to the early/mid 2000s to capture a longer time period prior to the 

enactment of regulatory reforms. Where such analysis is not possible or practicable, we 

recognize the sensitivity of our conclusions to the selection of the baseline period. 

Similarly, regulatory reforms of interest coincided with a portfolio of other policies, 

market structure changes, demand shifts, and types of market participants. For instance, 

electronic trading, low interest rates, and market participants reevaluating the risks and returns of 

various business models in the aftermath of the financial crisis contaminate the comparison of 

liquidity measures before and after regulatory reforms of interest. As a result, the direct impact 

of regulatory reforms on liquidity in corporate bond and Treasury markets is likely confounded 

with the effects of these innovations and shocks. 

Second, we are limited in our ability to make causal inferences. The prevalent 

econometric techniques used to test for causality, such as event study methodology, differencesin-differences estimation, instrumental variables, or regression discontinuity design, are not 

feasible for many of the analyses performed. As illustrated in Appendix A, the rules 

25 
implementing the Dodd-Frank Act and Basel III reforms were proposed, enacted, and adopted 

over several years, and in some cases have not yet been fully implemented as of this writing. 

Market participants widely anticipated and responded to many of these reforms years ahead of 

the compliance or effective dates across markets. As a result, event study evidence on the impact 

of reforms on transaction activity and dealer supply of liquidity is difficult to interpret. In 

addition, many regulatory reforms impacted large groups of participants at the same time, so we 

lack cleanly identified and otherwise comparable “treatment” and “control” groups of market 

participants. Absent well-defined treatment and control groups, the validity of differences-indifferences estimation is questionable. 

Where we cannot draw causal inferences, we engage in a comprehensive exploration of 

the current state and recent changes in primary issuance and secondary market liquidity and 

consider whether the observed trends and statistics are consistent with a variety of proposed 

explanations. 

Some additional limitations of the analysis include the following: 

	 The analysis of offerings relying on the JOBS Act provisions is qualified by small sample 

sizes and relatively short observation periods. Thus, it is unclear to what extent it can be 

extrapolated to future years or periods with different aggregate conditions. Medium- and 

long-term success of such placements remains an area for future study. 

	 We are cautious in using crisis or post-crisis figures as benchmarks for activity levels in 

normal times. Where the available data do not permit us to examine market activity for 

the early to mid-2000s period (e.g., single-name CDS), trend analysis is limited. 

	 The analysis of dealer balance sheets using Financial and Operational Combined Uniform 

Single (FOCUS) data is limited by the exclusion of Alternative Net Capital (ANC) filers, 

26 
owing to a lack of comparable data for those filers during our sample period. To the 

extent that ANC filers may have been more affected by regulations, our estimates of 

changes in activity may be conservative. 

	 Because of the unavailability of relevant data, we are unable to examine changes in 

dropped (unexecuted) orders, order splitting, and difficulty in executing large block 

orders on liquidity, among other things. 

The rest of the Report is structured as follows. Part A analyzes primary issuance of 

equity, debt, and ABS; and Part B evaluates changes in secondary market liquidity in Treasuries, 

corporate bonds, single-name CDS, and funds. 

27 
 

 

Part A. Access to Capital: Primary Issuance 

I. Introduction

In the United States, companies can use a wide variety of securities offerings to raise 

capital. Securities laws require that all offers and sales of securities be either registered with the 

SEC under the Securities Act of 1933 (the Securities Act) or conducted under an exemption from 

registration. When raising capital through the sale of securities to any potential investors in the 

public capital market, unless the transaction qualifies for an exemption from registration,43 the 

issuer must register the offer and sale of securities with the SEC, a process that is accompanied 

by extensive disclosure at the time of the offering and subsequent reporting (a “registered” 

offering). Alternatively, a company can raise capital by accessing the capital markets through a 

transaction exempt from registration (an “exempt” or “unregistered” offering). This path allows 

issuers to avoid certain regulatory burdens and the increased oversight that comes with a 

registered offering, with the intended effect of reducing issuance costs and the time required to 

raise new capital. Exempt offerings may be particularly attractive to smaller firms, for whom 

conducting registered offerings and becoming subject to reporting requirements may generally 

be too costly. However, because exempt offering alternatives require issuers to disclose less 

information and are accompanied by less oversight, they are generally subject to investor 

restrictions and/or offering limits. The investor protection provisions of the exemption claimed 

must be met to qualify for the exemption from registration. 

Companies that are willing to register their transactions can access the public equity 

market via registered equity offerings such as IPOs and SEOs, or the public debt market via 

registered debt offerings. Companies with or without a registered offering or a registered class 

43 See the discussion of Rule 506(c) of Regulation D, Regulation A and Regulation Crowdfunding below. 

28 
 

 

of securities can raise capital via exempt debt and equity offerings, such as Regulation D 

offerings or Rule 144A offerings. 

Recent changes to the securities laws made by the JOBS Act, which was enacted in 2012, 

and its implementing regulations, were designed to promote both registered and exempt 

offerings. Title I of the JOBS Act streamlined the registered offering process for a class of 

issuers called emerging growth companies (EGCs).44 Other provisions of the JOBS Act 

expanded options for exempt securities issuance: Title II required that the SEC permit general 

solicitation under Rule 506 of Regulation D and Rule 144A (subject to certain conditions); Title 

III required that the SEC create a new method of raising capital (crowdfunding); and Title IV 

required that the SEC update a little-used exempt offering provision (Regulation A). It is thus 

reasonable to expect that these changes may have important effects on the amount of capital 

being raised in exempt offerings. 

Since the JOBS Act is specifically focused on the primary capital markets, in this section 

we explore whether any developments in these markets may be linked to the JOBS Act. Where 

relevant, we also discuss whether observed changes can be linked to the Dodd-Frank Act. It is 

challenging to establish causality with respect to either regulation, however, because so many 

other factors could affect the primary capital markets—factors for which we cannot control. The 

JOBS Act was signed into law on April 5, 2012, and the several titles that comprise it went into 

44 The JOBS Act defines EGCs as an issuer with less than $1 billion in total annual gross revenues during its most 

recently completed fiscal year. If an issuer qualifies as an EGC on the first day of its fiscal year, it maintains that 

status until the earliest of (1) the last day of the fiscal year of the issuer during which it has total annual gross 

revenues of $1 billion or more; (2) the last day of its fiscal year following the fifth anniversary of the first sale of its 

common equity securities pursuant to an effective registration statement; (3) the date on which the issuer has, during 

the previous 3-year period, issued more than $1 billion in nonconvertible debt; or (4) the date on which the issuer is 

deemed to be a “large accelerated filer” (as defined in Exchange Act Rule 12b-2). See also Rel. No. 33-10332, 

Inflation Adjustments and Other Technical Amendments Under Titles I and III of the JOBS Act (Mar. 31, 2017), 

available at https://www.sec.gov/rules/final/2017/33-10332.pdf (raising the $1 billion limit to $1.07 billion to adjust 

for inflation) (JOBS Act Technical Amendments Release). 

29 
 

 

effect in the period from 2012 (Title I) through 2016 (Title III). Since these changes took effect 

over several years, it is difficult to pinpoint any causal relationship between the passage of either 

regulation as a whole or the implementation of different provisions, and developments in the 

capital markets. 

More generally, it is important to note that issuers will choose the type of offering that is 

optimal from their point of view in terms of costs and benefits. Those costs and benefits may 

depend on the current regulatory environment but will also depend on various other factors such 

as the general state of the economy, interest rate cycles, etc. For example, prior economic 

studies document the presence of hot and cold markets for registered equity offerings.45 These 

hot and cold markets are driven by macroeconomic factors, changes in the level of information 

asymmetry between investors and issuers, and changes in investor sentiment. It is also possible 

that registered and exempt capital markets will respond differently to these factors, and may 

function as either substitutes or complements. For example, it is possible that when registered 

markets are cold companies switch to exempt capital markets, and vice versa. Alternatively, a 

hot registered market could prompt companies to seek additional financing from exempt markets 

in preparation of future public offerings. 

Finally, while our analysis focuses on securities issuance, we recognize that bank lending 

is an important source of financing for issuers, and consumer lending is a valuable source of 

consumer access to capital. However, such lending falls within the statutory authority of 

banking regulators and the Consumer Financial Protection Bureau (CFPB), and the Commission 

is a primary regulator of capital markets. The analysis below, therefore, does not examine 

45 See, e.g., Lowry and Schwert (2002), Helwege and Liang (2004), Gao et al. (2013), and Arikan and Stulz (2016) 

30 
	 	 	 	 	

lending markets directly, but considers asset-backed securitization markets, which support a 

large volume of primary lending in an originate-to-distribute model. 

II. Capital Markets for Registered Offerings

One of the channels through which businesses can raise capital in the United States is a 

registered offering. In this section, we consider the issuance of equity and debt in the registered 

market, and document the evolution in the volume and structure of such issuance over time. 

Total registered issuance in the United States has increased steadily from 2011 through 2016, as 

shown in Figure C.1. It grew from $1.42 trillion in 2015 to $1.49 trillion in 2016. The period 

2013-2016 witnessed the largest registered issuance in the US for the last 11 years. 

Figure 1 plots capital raising in the IPO and SEO markets during the period 1996-2016. 

The data suggests that IPO activity reached highs in 1999, 2007 and 2014, and lows in 2003, 

2008, and 2016. 

Figure 1. Capital raising through registered equity offerings (in $ billions), 1996-2016 

0

50

100

150

200

250

$ Billions

Equity SEO Equity IPO

Source: DERA analysis 

31 
 

 

Figure 1 illustrates the cyclicality of issuance activity and shows the dynamics of IPO 

issuance through economic booms and busts. We cannot determine whether the Dodd-Frank Act, 

which was enacted in 2010, notably affected IPO activity since we are unable to identify 

mechanisms through which the Dodd-Frank Act would have impacted, positively or negatively, 

IPO activity. For example, several of the Dodd-Frank Act’s executive compensation provisions 

were not implemented in the sample period, and several other provisions of the Act did not apply 

to EGCs following the enactment of the JOBS Act. In contrast, as can be seen from Figure 1 the 

JOBS Act, which was enacted on April 5, 2012 and included provisions concerning IPOs and 

exempt offerings, may have had a positive effect on IPO activity. However, the observed effects 

are also generally consistent with higher issuance in strong macroeconomic conditions. 

Further, we also observe a decline in IPOs in 2015 and 2016. We recognize that certain 

provisions of the JOBS Act concerning various types of exempt offerings were implemented in 

2015 and 2016. However, given the overall size of those markets documented below, a shift in 

capital raising from traditional IPOs to Regulation A or crowdfunding offerings cannot explain 

the decline in IPO activity in recent years. At the same time, the 2015-2016 decline in IPOs is 

consistent with changes in investor demand, market saturation and the increased availability of 

private funding and other alternatives for exit. A recent industry analysis has also identified a 

market correction stemming from historically high market valuations, political and 

macroeconomic uncertainty, and the availability of private capital enabling firms to selectively 

time IPOs as potential contributing factors.46 Post-crisis evolution in IPO issuance is broadly 

consistent with historical patterns of IPO waves. 

46 Ernst & Young, May 2017, “Looking behind the declining number of public companies: An analysis of trends in 

US capital markets,” https://www.sec.gov/spotlight/investor-advisory-committee-2012/ey-an-analysis-of-trends-inthe-us-capital-markets.pdf 

32 
 

 

IPOs by EGCs may be becoming the prevailing form of issuance in some sectors. For 

example, Proskauer (2017) finds that 78% of IPO issuers in 2016 were EGCs, with a particularly 

high concentration of EGC IPOs in health care, telecommunications, energy and power, and 

financial service sectors (between 86% and 95%). At the same time, EGCs represented only 

17% and 43% of IPOs in industrial and consumer/retail sectors respectively. Proskauer (2017) 

also estimates that 90% of EGC issuers between 2013 and 2016 that disclosed testing-the-waters 

communications operated in the health care and telecommunications and media sectors. Issuers 

in those sectors often have shorter operating histories and lack revenue or net income, which is 

consistent with greater opacity and risk of EGC issuers. 

Title I of the JOBS Act was intended to make it less costly for EGCs to go public. 

Research by Dambra et al. (2015) finds that the number of IPOs, and especially those by small 

issuers, has increased notably over pre-JOBS Act levels. However, another study, Chaplinsky et 

al. (2016), finds no reduction of direct issuance costs, accounting, legal, or underwriting fees for 

EGCs, and observes an increase in indirect issuance costs in the form of underpricing.47 

Proskauer (2017) estimates average IPO expenses as a percentage of base deal for 2016 

placements, and finds that EGCs incur higher total IPO expenses (difference of 2.21%). 

However, unlike Dambra et al. (2015) and Chaplinsky et al. (2016), this statistic is descriptive 

and does not account for the potential selection of opaque and risky issuers into EGC status, or 

control for differences in EGC and non-EGC issuer growth, profitability, sector and other 

fundamentals.48 

47 Underpricing is typically defined as the percentage difference between the market closing price on the day of the 

initial public offering and the offer price at which underwriters sell shares to investors. 48 See Lowry et al. (2017) for a comprehensive overview of finance research on IPOs since 2000.
 

33 
More generally, it is difficult to disentangle the above effects of the JOBS Act from the 

effect of the general improvement in macroeconomic activity during this period. Prospective 

registered equity issuers are exposed to aggregate economic and industry-level risks. Negative 

macroeconomic shocks that reduce cash flows or increase the level of risk can reduce valuations 

making offerings appear less attractive to investors and leading to a decrease in the number of 

deals and/or deal sizes. Such aggregate trends similarly affect cycles in registered offering 

activity. Small issuers may be relatively more affected by downturns if their cash flows are 

lower and less diversified and if they lack sufficient collateral to obtain debt financing when their 

internal cash flow declines. 

Table 1 lists the number of offerings, the average and median offer size, and the average 

and median gross spreads for both IPOs and SEOs. Consistent with Figure 1, these statistics 

show that the average and median offering sizes for IPOs are close to what they were prior to the 

crisis. There has also been an increase in small company IPOs—IPOs with proceeds up to $30 

million were approximately 17 percent of the total number of IPOs in the period 2007-2011 and 

22 percent in the period 2012-2016. 

Figure 2 presents capital raising activity in the registered debt market for 2005-2016. 

Consistent with findings in other studies, the amount of funding obtained through the registered 

debt market on an annual basis is much larger than that obtained through the registered equity 

market. The dollar volume of registered debt appears to have increased in recent years, which 

may be a result of improving macroeconomic conditions and a low interest rate environment. As 

economic prospects for companies improve, companies tend to increase investment, increasing 

their demand for financing. Table 2 presents summary statistics for registered debt offerings and 

34 
	 	 	 	 	

shows that the average and median offer size of these offerings are much larger than those of 

IPOs and SEOs. 

Figure 2. Capital raising through registered debt offerings (in $ billions), 1996-2016 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

$ Billions

Public debt

Source: DERA analysis 

Next, we consider the cost of registered debt offerings. Because of the low level of 

interest rates, companies may be able to obtain debt financing at low cost. We find that the 

average and median gross spreads are notably lower for registered debt offerings. The gross 

spreads reported in Table 2 are similar to what prior economic studies have documented (Fang 

(2005)): issuers raising capital through registered bond issues pay commissions between 0.7% 

and 1.5% of the size of the offering. 

III. Capital Markets for Exempt Offerings

A. Introduction 

Having considered registered offerings, we now turn to the analysis of exempt issuance. 

Total primary unregistered issuance in the United States, which is the capital raised through all 

35 
 

 

the different types of unregistered offerings discussed below, has increased from 2010 through 

2016, as shown in Figure C.2. Most recently, it dropped from a peak of $1.87 trillion in 2015 to 

$1.68 trillion in 2016. During the period 2009-2016, total primary unregistered issuance has 

consistently outpaced total primary registered issuance. Amounts raised through exempt 

securities offerings of debt and equity for 2012 through 2016 combined exceeded amounts raised 

through registered offerings of debt and equity over the same time period by approximately 26%. 

In comparison, the same figure for 2009 through 2011 was 21.6%. 

This market is governed by several exemptions from registration, including those under 

Sections 4(a)(2), 3(b) and 3(a)(11) of the Securities Act. For example, Section 3(b) is the 

exemptive authority for Rule 504 under Regulation D as well as Regulation A.49 Other parts of 

the exempt market rely on “safe harbors”: rules and regulations that set forth specific conditions 

that, if satisfied, ensure compliance with an exemption from registration. For example, issuers 

can use Rule 506(b) of Regulation D, which is a non-exclusive safe harbor under Section 4(a)(2), 

Regulation S for offerings outside of the United States, and Rule 144A, for the resale of 

restricted securities to qualified institutional buyers. Finally, Rule 506(c) of Regulation D is a 

stand-alone exemption for unregistered sales to accredited investors by means of general 

solicitation. Bauguess et al. (2015) provide a comparative analysis of the characteristics of these 

and other offering exemptions and safe harbors. 

49 In 2015, Regulation A was amended to reflect the changes included in Title IV of the JOBS Act. Among the 

changes in Regulation A is an increase in the amount of capital that can be raised (from $5 million to $50 million) 

and preemption from state registration and review for certain offerings. See Rel. No. 33-9741, Amendments to 

Regulation A (Mar. 25, 2015) (Regulation A+ Adopting Release). Rule 504 was amended, effective January 20, 

2017, to increase the amount of capital that can be raised (from $1 million to $5 million) and to add the 

disqualification of certain bad actors. Rule 505 has been repealed effective May 22, 2017. See Rel. No. 33-10238 

Exemptions to Facilitate Intrastate and Regional Securities Offerings (Oct. 26, 2016) 81 FR 83494. We included the 

historical Rule 505 data in our analysis. 

36 
 

 

 

 

The importance of exempt capital markets as a source of financing in the economy is 

underscored by the fact that less than 0.02% of the estimated 28.8 million firms in the United 

States are currently exchange-listed firms.50 Moreover, there has been a steady and substantial 

decrease in the number of reporting companies in the United States.51 During this period, 

exempt offerings of securities have contributed substantially to capital formation in the U.S. 

economy, particularly for small and emerging companies that are often considered to be the 

engine for creating new jobs,52 driving innovation, and accelerating economic growth. Hence, 

exempt capital markets provide an important financing alternative for companies that, for various 

reasons, forego financing in the registered capital markets. 

Data for some of these exemptions are more readily available than for others. For 

example, because issuers relying on Section 4(a)(2) are not required to file any document with 

the Commission, offering information available in the commercial databases likely 

underestimates the amount of capital raised through this exemption. Similarly, the available data 

on Regulation D offerings could underestimate the true amount of capital raised through such 

offerings. While Regulation D requires the filing of a notice on Form D no later than 15 days 

after the first sale of securities, that filing is not a condition to the provision. Accordingly, it is 

50 See SBA, Office of Advocacy, United States Small Business Profile (2016), available at 

https://www.sba.gov/sites/default/files/advocacy/United_States.pdf. See also Barry Ritholtz, Where Have All the 

Public Companies Gone?, BLOOMBERG VIEW (Jun. 24, 2015). 51 The decline in the number of US-listed firms in 1996-2003 represents approximately 74% of the decline from 

1996 to 2016. See Ernst & Young, May 2017, “Looking behind the declining number of public companies: An 

analysis of trends in US capital markets,” https://www.sec.gov/spotlight/investor-advisory-committee-2012/ey-ananalysis-of-trends-in-the-us-capital-markets.pdf. Also see Doidge et al. (2017). 52 During the period 1998-2008, U. S. Small Business Administration estimates show that small businesses 

contributed almost 50% of U.S. non-farm GDP and accounted for 55% of U.S. employment, including 66% of all 

net new jobs since the 1970s. See Robert Longley, Top Ten Reasons to Love US Small Businesses THOUGHTCO, at 

https://www.thoughtco.com/reasons-to-love-us-small-businesses-3319899 (Sept. 4, 2015) (citing Office of 

Advocacy, Small Business Administration). See also Small Business Administration, Small Business Quarterly 

Bulletins, at https://www.sba.gov/advocacy/small-business-quarterly-bulletins. 

37 
 

 

 

 

possible that some issuers do not file a Form D for offerings under Regulation D.53 In addition, 

there is no requirement to file a Form D amendment reporting the total amount actually raised in 

the offering under Regulation D.54 Data used in the primary issuance analysis is described in 

more detail in Appendix C. 

B. General data on Regulation D and Rule 144A offerings 

Figure 3 shows the amounts raised in Regulation D offerings, given the data limitations 

discussed above, and Rule 144A offerings. The amount of capital raised through Regulation D 

offerings is much larger than that raised in the Rule 144A market. When combined, the capital 

raised through Regulation D and Rule 144A offerings in a year is consistently larger than the 

total capital raised via registered equity and debt offerings. Most Regulation D offerings (over 

66%) include equity securities; by contrast, in the Rule 144A market, the vast majority of issuers 

are financial institutions and over 99% of securities are debt securities. 

Table 3 provides summary statistics on the number of Regulation D offerings and the 

average and median offering amount.55 The number of Regulation D offerings is an order of 

magnitude larger than the number of registered debt and equity offerings. However, the average 

and median offer sizes of Regulation D offerings are much smaller than those of registered 

equity and debt offerings. 

53 Separate analysis by DERA staff of Form D filings by funds advised by registered investment advisers and 

broker-dealer members of the Financial Industry Regulatory Authority, Inc. (FINRA) suggests that Form D filings 

are not made for as much as 10% of unregistered offerings eligible to use the Rule 506(b) safe harbor under 

Regulation D.

54 See the General Instructions to Form D. An update to Form D may be required to reflect a change in the 

information previously filed, except that certain less substantial changes enumerated in Rule 502 of Regulation D 

(e.g., an increase in the offering amount of less than 10%) do not trigger the requirement to update the filing. If the 

requirement to update is triggered, current information must be provided for the entire form. 55 Due to data availability constraints, the analysis of Regulation D issuance covers 2009 through 2016 

38 
       

Figure 3. Capital raised through Regulation D and corporate Rule 144A offerings (in $ 

billions), 2005-2016 

$ Billions

1600

1400

1200

1000

800

600

400

200

0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
 

Regulation D offerings Corporate 144A offerings

Source: DERA analysis 

C. Rule 506(c) of Regulation D 

Title II of the JOBS Act directed the Commission to engage in rulemaking to permit 

general solicitation in Rule 506 offerings, provided that sales are made only to accredited 

investors and the issuer takes reasonable steps to verify purchasers’ accredited investor status. 

The Commission subsequently adopted Rule 506(c) of Regulation D, permitting the use of 

general solicitation and advertising subject to such conditions. From September 23, 2013, to 

December 31, 2016, a total of 5,374 issuers disclosed in their Forms D that they initiated 5,474 

new Rule 506(c) offerings (Table 4). During that period, almost $70.6 billion was reported 

raised in initial Form D filings. An additional $37.1 billion was reported to be raised in amended 

form D filings, some of which were originally initiated as Rule 506(b) offerings. During the 

same period, there were 65,772 new Rule 506(b) offerings that reported to raise $2,186.2 billion 

in initial Form D filings, and an additional $1,935.8 billion was reported to be raised in amended 

form D filings. Though large in absolute terms, issuances claiming the new Rule 506(c) 

39 
 

exemption have accounted for only 3% of the reported capital raised pursuant to Rule 506 since 

becoming effective in September 2013, through December 31, 2016. 

While the underlying motivation for permitting general solicitation was to boost capital 

formation through increased accessibility of certain issuers to accredited investors, the vast 

majority of Regulation D issuers continue to raise capital through Rule 506(b) offerings. Some 

have noted that the novelty of the Rule 506(c) provisions after decades of non-permissibility of 

general solicitation in Regulation D offerings may be one reason why Rule 506(b) continues to 

dominate the Regulation D market. In particular, issuers with pre-existing sources of financing 

or intermediation channels, or both, may not yet have a need for the new flexibility. Other 

issuers may become more comfortable with market practices as they develop over time, 

including, among other things, certainty over what constitutes general solicitation.56 There may 

also be concerns about the added burden or appropriate levels of verification of the accredited 

investor status of all purchasers.57 For instance, Warren (2015) indicates that investor privacy 

concerns regarding the disclosure of confidential financial information may be behind issuer 

reluctance to rely on Rule 506(c) provisions. Markets may develop more efficient means for 

verifying investor status over time. 

Regulatory uncertainty has also been identified as a possible explanation for the relatively 

low level of Rule 506(c) offerings. For example, certain pooled investment funds that need to 

comply with Commodity Futures Trading Commission (CFTC) regulations continued to be 

56 See, e.g., Keith Higgins, Director of the Division of Corporation Finance, U.S. Securities and Exchange 

Commission, Remarks before the 2014 Angel Capital Association Conference (Mar. 28, 2014) available at 

http://www.sec.gov/News/Speech/Detail/Speech/1370541320533. See also comments of Jean Peters, Board 

member, Angel Capital Association, at the 33rd Securities & Exchange Commission Government-Business Forum 

on Small Business Capital Formation (Nov. 20, 2014). 

57 See comments of Jean Peters, Board member, Angel Capital Association, at the 33rd Securities & Exchange 

Commission Government-Business Forum on Small Business Capital Formation (Nov. 20, 2014). 

40 
 

 

 

subject to the CFTC’s prohibition on advertising after September 23, 2013 (and at least until 

September 2014), and therefore could not use Rule 506(c).58 Further, the Commission’s 

proposed amendments to Regulation D and Form D at the time Rule 506(c) was adopted have 

elicited widely divergent views from commenters, with some commenters expressing the view 

that the “overhang” from the proposed (but never finalized nor withdrawn) rule has chilled use of 

the new exemption.59 

Additional analysis in Table 4 shows that the average amount reported sold in an initial 

Rule 506(c) offering ($13 million) is much smaller than the average amount reported sold in a 

Rule 506(b) offering ($26 million). The lower amounts reported to be raised at the date of initial 

filing may be because issuers that anticipated difficulties raising capital in a timely manner chose 

the Rule 506(c) market so that they would have an ability to advertise or generally solicit their 

offering to a broader audience of potential investors. It is also possible that some sophisticated 

investors perceive the election of the Rule 506(c) exemption as a signal that issuers anticipate 

difficulties in raising sufficient capital and consequently consider it a less attractive offering, 

which could also dissuade issuers from using the new exemption for their financing needs. 

Overall, it is not clear whether offerings under Rule 506(c) are indicative of new capital 

formation or a reallocation from other offering types. Consistent with the somewhat limited 

uptake of new Rule 506(c), we do not observe a notable migration of existing issuer capital 

raising activity from Rule 506(b) to Rule 506(c). In particular, only a small number of offerings 

58 See Letter from Gary Barnett, Director, Division of Swap and Intermediary Oversight, CFTC to Regina Thoele, 

National Futures Association, available at 

http://www.cftc.gov/idc/groups/public/@lrlettergeneral/documents/letter/14-116.pdf. 59 See Warren (2015). See also Keith Higgins, Director of the Division of Corporation Finance, U.S. Securities and 

Exchange Commission, Remarks before the 2014 Angel Capital Association Conference (Mar 28, 2014) available at 

https://www.sec.gov/news/speech/2014-spch032814kfh (noting that then-Chair White had stated publicly that 

issuers are not required to comply with the proposed rule, and that appropriate transition provisions would be 

considered for ongoing offerings if a final rule were adopted). 

41 
 

switched from relying on Rule 506(b) to Rule 506(c). From September 23, 2013 to December 

31, 2014, approximately 398 continuing Regulation D offerings switched their exemption to 

Rule 506(c).60 These “switched” offerings have reported capital raisings of $12 billion. There 

has been a similar movement in the number of “repeat” issuers (issuers that used to access the 

Regulation D market via Rule 506(b) offerings) that have switched their offering types from 

Rule 506(b) to subsequent Rule 506(c) offering: issuers that had a prior Regulation D offering 

initiated 447 new Rule 506(c) offerings. These issuers have reported capital raisings of $16.7 

billion. 

D. Investors in Regulation D Offerings 

Regulation D allows both accredited and non-accredited investors to participate in private 

offerings, with an unlimited number of non-accredited investors in Rule 504 offerings, while 

former Rule 50561 and Rule 506(b) offerings may include no more than 35 non-accredited 

investors. Only accredited investors can participate in Rule 506(c) offerings. On the basis of 

information collected from Form D filings, most participants in Regulation D offerings are 

accredited. For example, on average 9% of new offerings included non-accredited investors for 

the period 2009-2016 (Table 5). Offerings by financial issuers and real estate investment trusts 

(REITs) are more likely to have non-accredited investors (13% of offerings had at least one such 

investor during 2009-2016), while offerings by venture capital funds only rarely include nonaccredited investors (only 1% of offerings have at least one such investor). 

60 The transition guidance in the Rule 506(c) Adopting Release clarifies that only offerings initiated prior to 

September 23, 2013, can rely on the transition guidance to switch their exemption to Rule 506(c). See Rel. No. 33­

9415, Eliminating the Prohibition Against General Solicitation and General Advertising in Rule 506 and Rule 144A 

Offerings (July 10, 2013). 61 As noted above, the repeal of Rule 505 became effective May 22, 2017. See Rel. No. 33-10238, Exemptions to 

Facilitate Intrastate and Regional Securities Offerings (Oct. 26, 2016). 

42 
Aggregated Form D information also indicates that, on average, more than 260,000 

investors participated in Regulation D offerings for the period 2009-2016, of which more than 

103,000 participated in offerings by nonfinancial issuers, more than triple the number of 

investors that participated in offerings by hedge funds. However, the number of unique investors 

is likely less than 260,000. Because an investor can participate in more than one Regulation D 

offering, our aggregation likely overstates the actual number of unique investors, and we have no 

method of estimating the extent of overlap. The mean number of investors per offering (14) is 

substantially larger than the median (4), indicating the presence of a small number of offerings 

with a large number of investors. Much of this skew appears to be driven by financial offerings. 

For the period of 2009-2016, the median number of investors per offering varied between two 

and seven across all types of offerings. During the same period of time, the mean number of 

investors in nonfinancial offerings was nine, while the mean number of investors in financial, 

pooled investment funds (e.g., hedge funds, private equity funds, and venture capital funds) and 

REIT offerings varied from 15 to 25. 

Offerings involving non-accredited investors are typically smaller than those that do not 

involve non-accredited investors. This is evident in Table 6 below which shows that while the 

presence of non-accredited investors is large in former Rule 505 offerings (40%), where the 

number of non-accredited investors is limited to 35 and offering limit is $5 million, the 

proportion is much higher for offerings under Rule 504 (58%) that have access to an unlimited 

number of non-accredited investors but historically had an offer limit of $1 million. 

Interestingly, a notably lower percentage of Rule 506(b) offerings (8%), including those that 

have an offer size of up to $5 million, report selling or intending to sell to a non-accredited 

investor. The big difference between Rules 506(b) and (c) and other rules under Regulation D is 

43 
 

 

that the former have preemption from state registration and review.62 Rule 506(b) provisions, 

unlike Rule 504 or former Rule 505 provisions, also require the non-accredited investors to be 

“sophisticated.”63 Thus, while issuers may prefer to raise capital under Rule 506(b) because of 

the preemption of state registration and review, non-accredited investors who are also not 

sophisticated are thus unable to participate in a Rule 506(b) offering. 

E. Regulation D Offering Fees 

When an intermediary is used in a Regulation D offering, there is notable variation in the 

fees across each class of issuer. We calculate the total fee for an offering as the sum of 

commissions and finder’s fees, scaled by the offering amount. Information from Form D filings 

indicates that total fees are smallest for pooled investment funds and largest for nonfinancial 

issuers (Figure 4). Nonfinancial issuers paid on average about 6% in total fees for Regulation D 

offerings in 2009-2016. In comparison, a company going public pays an average gross spread of 

7% to its IPO underwriters, while a reporting company raising equity through a follow-on 

(seasoned) equity offering pays an average gross spread of about 5.4%. Issuers raising capital 

through registered bond issues pay commissions between 0.9% and 1.5% of the size of the 

offering. 

62 States retain the authority to investigate and bring enforcement actions for fraud, impose state notice filing 

requirements, and collect state fees. 63 Non-accredited investors in Rule 506(b) offerings must have sufficient knowledge and experience in financial and 

business matters to make them capable of evaluating the merits and risks of the prospective investment, or the issuer 

must reasonably believe immediately prior to making any sale that such purchaser comes within this description. See 

Rule 506(b)(2)(ii). 

44 
Figure 4. Total fees paid by type of Regulation D issuer: 2009-2016 

7.00%

6.00%

5.00%

4.00%

3.00%

2.00%

1.00%

0.00%

Hedge Fund Private Equity Other Venture Financial Real Estate Non‐Financial

Fund Investment Capital Fund Services (Operating)

Fund

Source: DERA analysis 

In contrast to operating issuers, hedge funds raising capital through Regulation D 

offerings and private equity funds paid about 1%. Brokers and finders are no more costly, on 

average, than the underwriters who charge fees for registered offerings, so fees do not provide an 

obvious reason for their relatively infrequent use in exempt offerings. 

The availability of general solicitation under new Rule 506(c) may have changed the role 

of intermediaries in these offerings relative to their role in the traditional offerings under 

Regulation D. During the period between September 23, 2013, and December 31, 2016, 

intermediary usage in Rule 506(c) offerings was dramatically higher among operating and 

financial issuers, compared to similar Rule 506(b) issuers (Figure 5). Overall, Rule 506(c) 

offerings exhibited a higher level of intermediary usage (33% of new offerings) than Rule 506(b) 

offerings (17% of new offerings). The higher usage of intermediaries in Rule 506(c) offerings 

may relate to issuers, and especially non-fund issuers, seeking to rely on outside entities, 

including third-party online platforms, to verify accredited investor status, a requirement for 

using general solicitation. 

45 
Figure 5. Role of intermediaries in Rule 506(c) market: September 23, 2013 - December 31, 

2016 

50% 12%

% of offerings with Intermediaries 40%

30%

20%

10%

0%

10%

8%

6%

4%

2%

0%

Commission

& Fees (% of total

amount sold)

Venture

Capital Fund

Hedge Fund Private Equity Other

Fund Investment

Fund

Financial

Services

Real Estate Non‐financial

506(b) Intermediary Usage 506(c) Intermediary Usage

506(b) fees 506(c) fees

Source: DERA analysis 

On average, Rule 506(c) offerings also pay higher fees than Rule 506(b) issuers. Figure 

5 shows that operating issuers paid almost 6.1% in fees in 506(c) offerings, relative to 5.3% paid 

by such non-fund, nonfinancial Rule 506(b) offerings. 

Figure 6 depicts the fees for different offering sizes, irrespective of issuer type or 

provision under Regulation D. Average total fees decrease with offering size (Figure 6). Unlike 

the gross spreads in registered offerings, the differences in commissions for Regulation D 

offerings of different sizes are large: the average commission paid by issuers engaging in 

offerings of up to $1 million (6.2% over the period 2009-2016) is more than three times higher 

than the average commission paid by issuers engaging in offerings of more than $50 million 

(1.9% over the period 2009-2016). These results are consistent with larger deals generating 

economies of scale for the involved intermediaries. Even so, the vast majority of the offerings 

are conducted without the use of a financial intermediary. 

46 
 

Figure 6. Total fees paid by size of Regulation D offering: 2009-2016 

7.0%

6.0%

5.0%

4.0%

3.0%

2.0%

1.0%

0.0%

$0 ‐ <1million $1 ‐ <5million $5 ‐ <10million $10 ‐ <50million Greater than

$50million

Source: DERA analysis 

Overall, capital formation through private placement of securities has increased 

substantially since the onset of the financial crisis. Amounts raised through exempt securities 

offerings have outpaced the level of capital formation through registered securities offerings 

during recent years. In Parts A.III.B through A.III.E, we have provided insights into a large 

segment of the exempt securities market—offerings conducted in reliance on Regulation D. 

Next, we turn to another type of exempt offerings, namely, offerings under Regulation A. 

F. Regulation A Activity Overview 

Regulation A is an exemption from registration for small offerings under Section 3(b)(2) 

of the Securities Act. Prior to June 19, 2015, it enabled issuers to raise up to $5 million in a 12­

month period (subsequently, up to $50 million). It is available to issuers organized, and with a 

principal place of business, in the United States or Canada. Historically, the Regulation A 

exemption was not available to Securities Exchange Act of 1934 (Exchange Act) reporting 

companies, investment companies (including business development companies), development 

47 
 

stage companies that have no specific business plan or purpose or have indicated that their 

business plan is to engage in a merger or acquisition with an unidentified company or companies, 

issuers of fractional undivided interests in oil or gas rights or a similar interest in other mineral 

rights. In addition to these existing categories of ineligible issuers, the newly amended 

Regulation A also excludes issuers that have not filed certain ongoing reports required by 

Regulation A, issuers subject to certain Section 12(j) orders by the Commission, and issuers 

subject to “bad actor” disqualification under Rule 262. 

Title IV of the JOBS Act added Section 3(b)(2) to the Securities Act, directing the 

Commission to adopt rules exempting from registration public offerings of up to $50 million 

annually. On December 18, 2013, the Commission proposed rules to implement Title IV of the 

JOBS Act by modernizing and expanding Regulation A and, on March 25, 2015, the 

Commission adopted final rules.64 The final rules expand Regulation A into two tiers: Tier 1, 

for securities offerings of up to $20 million (that tracks more closely “old” Regulation A); and 

Tier 2, for offerings of up to $50 million. Under the final rules, Tier 2 issuers are required to 

include audited financial statements in their offering documents and to file annual, semiannual, 

and current reports with the Commission. With the exception of securities listed on a national 

securities exchange, upon qualification, purchasers in Tier 2 offerings either must be accredited 

investors or are subject to specified limitations on their investment. The amendments to 

Regulation A took effect on June 19, 2015, and may have resulted in changes in the type of 

offerings and issuers in this market in potentially notable ways. 

64 See Regulation A+ Adopting Release. 

48 
 

We consider Regulation A offerings qualified by the Commission during 2005-2016.65 A 

little under half of offerings were identified as being by issuers in the financial sector. The most 

common jurisdiction of incorporation was Delaware. Figures 7 and 8 report the number of 

qualified offerings and amounts sought in qualified offerings between 2005 and 2016. 

Figure 7. Number of qualified Regulation A offerings, 2005-2016 

90

80

70

60

50

40

30

20

10

0

Source: DERA analysis 

In a typical year during 2005-2016, there were about 14 qualified offerings seeking to 

raise approximately $163.3 million. We note that there were substantially more issuers seeking 

to raise capital under Regulation A during June 19, 2015– December 31, 2016 (97 qualified 

offerings seeking to raise up to approximately $1.8 billion in the aggregate). We do not have 

complete information about amounts raised during that time. We estimate, on the basis of issuer 

65 Where relevant and where sufficient information was available, we focused on unique offerings and exclude postqualification amendments or new filings that are substantially similar to prior filings by the same issuer. We sought 

to maintain accuracy, but some measurement error is inevitable. For some offerings, offer amounts were not 

available in offering circulars. Offerings with missing information on amounts sought were excluded from the 

totals. 

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
 

49 
 

 

 

reports of amounts raised filed during 2005-2016 that are available to us, that 56 issuers reported 

positive proceeds in Regulation A offerings, totaling approximately $314.6 million.66 The total 

includes 27 issuers that reported in 2015-2016 an aggregate of approximately $238.6 million in 

proceeds raised in completed and ongoing offerings qualified under amended Regulation A. 

Figure 8. Aggregate amounts sought in qualified Regulation A offerings, 2005-201667 

1800

1600

1400

1200

1000

800

600

400

200

0

Source: DERA analysis 

The evolution of Regulation A offering activity during the period under consideration, 

2005-2016, was affected by several relevant market and institutional considerations. First, the 

Commission amended the Regulation A exemption in 2015 as part of implementing the mandate 

66 Some issuers filed reports of sales with zero proceeds. Additionally, incomplete information is available to us 

regarding the outcomes of Regulation A offerings during the examined period. Amounts raised are based on the 

reports of sales filed during the examined period and thus reflect a lag: they do not include amounts raised during 

the examined period that are reported at a later date, amounts raised in offerings in progress (unless the issuer has 

opted to report them during the examined period), or amounts raised and reported after the end of the examined 

period. For some offerings in the pre-amendment period, final reports of sales on Form 2-A were either not available 

or not accessible electronically from EDGAR or ThomsonOne. Thus, we cannot determine with certainty whether 

all of the offerings that qualified during the examined period have commenced or if any have been abandoned or 

terminated by issuers. 

67 These amounts are not inflation-adjusted. Since larger aggregate offering amounts were observed at the end of 

the examined period, expressing offering amounts in 2005 dollars had the effect of accentuating the differences 

between pre-amendment and post-amendment annual totals. 

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
 

50 
of Title IV of the JOBS Act. Second, compared to the size of the registered offerings and private 

placement markets, Regulation A experienced relatively limited use during the examined period, 

particularly prior to the effectiveness of the 2015 amendments. When the number of 

observations is small, statistical analysis lacks power and may be easily skewed by a few 

observations. Thus, any inference regarding potential trends or the set of issuers seeking to 

participate in this market must be approached with considerable caution. Third, to the extent that 

the Regulation A market serves as a smaller-scale alternative to a traditional IPO or SEO or as a 

method of financing merger and acquisition transactions, growth outlook—and, in turn, the 

extent of investor interest in a potential offering—at the economy and industry level can affect 

Regulation A activity. We discuss these considerations in greater detail below. 

The end of the examined period includes the months immediately following the 

effectiveness of Regulation A amendments. Although the period after the effectiveness of the 

amendments was relatively short, it was characterized by increased filing activity, with filers 

taking advantage of the expanded Regulation A provisions and seeking qualification of larger 

maximum offering amounts than under pre-amendment Regulation A. While it is too early to 

assess the extent to which Regulation A amendments will affect the amount of capital formation 

over the long run, based on the early indications available so far, aggregate filing counts and 

aggregate offer amounts and deal sizes have likely increased relative to the pre-amendment 

period. 

Furthermore, the 2015 Regulation A amendments contained certain provisions that may 

have ramifications for the development of a potential secondary market in Regulation A 

securities. Specifically, the creation of an ongoing reporting regime for issuers in Tier 2 

offerings may contribute to a reduction in information asymmetries between issuers and 

51 
 

 

investors and enable issuers to meet the requirements for having their securities quoted on one of 

the higher over-the-counter (OTC) market tiers, potentially resulting in improved liquidity for 

Regulation A investors. However, at this time, for the majority of issuers secondary trading in 

Regulation A securities is either at a nascent stage or nonexistent. 

Prior to the adoption of Regulation A amendments, analysis indicated that among the 

factors that could potentially be related to limited use of Regulation A were compliance with 

state securities review and qualification, a comparatively low $5 million offering limit, the 

timing and cost associated with filing an offering statement with the Commission for 

qualification, and the availability of other exemptions under the federal securities laws.68 

While Regulation A activity has exhibited a notable increase after the effectiveness of the 

Regulation A amendments, absolute levels of filing activity have remained low compared to 

other unregistered offerings such as Regulation D and Rule 144A, potentially consistent with 

issuers, service providers, and investors adjusting and learning about the new provisions. To the 

extent that the Regulation A amendments require an initial period of adjustment and/or confront 

market participants with a learning curve, the Regulation A offering activity may not be 

representative of the typical level of Regulation A offering activity expected after the market has 

gained experience with amended Regulation A. 

At the same time, it is possible that future Regulation A offering activity may remain 

modest. Issuers may continue to prefer Rule 506(b) offerings and private placements in reliance 

on Section 4(a)(2) because of their lower initial and ongoing compliance related cost and the 

absence of an offering limit. Issuers that elect to undertake an initial public offering may 

continue to favor the traditional IPO route and become Exchange Act reporting companies. 

68 See GAO, Factors that May Affect Trends in Regulation A Offerings, GAO-12-839 (July 2012), available at 

http://www.gao.gov/assets/600/592113.pdf (GAO Report). 

52 
Additionally, a lack of secondary market liquidity may discourage investors from participating in 

Regulation A offerings at valuations that the issuer finds attractive. 

Furthermore, investors may hesitate to invest in issuers about which the market has less 

information (known as “information asymmetry”) because of concerns about adverse selection 

(lower quality issuers choosing to raise financing). In many cases, a venture capital fund or a 

major underwriter, which may provide implicit certification of the prospects of a startup or small 

issuer, is not involved. Most Regulation A issuers are small companies without analyst coverage 

or notable institutional ownership, and both tiers of offerings subject issuers to disclosure 

requirements that are less extensive than those imposed on Exchange Act reporting companies. 

While institutions may be in a better position to analyze information about opaque issuers and 

overcome adverse selection, less sophisticated investors may have greater difficulty in evaluating 

the prospects of an issuer on the basis of offering materials alone. In addition, while venture 

capital and angel investors may be able to negotiate downside protection options for their 

investments in high-risk and high-information asymmetry companies, less sophisticated investors 

may have less bargaining power in negotiating offering terms. Having less information about the 

issuer may discourage such investors from investing or may lead investors only to invest in 

offerings at valuations below those that the issuer would find attractive. Finally, we note that 

there is not enough evidence at this time to determine whether the increase in capital formation 

following amendments to Regulation A has served as a complement to or a substitute for capital 

raising via other methods. 

Overall, early signs indicate that amended Regulation A may offer a potentially viable 

public offering on-ramp for smaller issuers as an alternative to a traditional registered IPO and 

offer either an alternative or a complement to other securities offering methods that are exempt 

53 
 

 

from Securities Act registration. Preliminary evidence suggests that there has been an increase 

in Regulation A offering activity in the initial period since effectiveness of the Regulation A 

amendments; different types of issuers have initiated offerings; and issuers participating in the 

offerings have generally sought to utilize the new provisions afforded by the 2015 amendments. 

Like other small-scale analyses, the findings of this analysis of the Regulation A market 

are qualified by the sample size and the relatively short observation period, thus, it is unclear to 

what extent it can be extrapolated to future years or periods with different aggregate conditions. 

The types of issuers that use this offering method in the future may differ from early adopters, 

and medium- and long-term outcomes of Regulation A issuers remain an area for future analysis. 

Further, questions remain regarding future offering outcomes and trends in intermediary 

involvement and secondary market liquidity as the Regulation A market continues to develop. 

G. Regulation Crowdfunding Activity Overview 

Title III of the JOBS Act (Title III) amended Section 4 of the Securities Act and created a 

new exemption from registration for Internet-based securities offerings of up to $1 million69 over 

a 12-month period. Title III was intended to help small and startup businesses conduct lowdollar capital raising on the Internet. It can be thought of as an Internet-based method` of raising 

seed financing from a broad, mostly retail investor base. Title III included a number of investor 

protection provisions, including investment limitations, issuer disclosure requirements, and a 

requirement to use regulated intermediaries. The SEC proposed securities-based crowdfunding 

rules on October 23, 2013, and adopted the final “Regulation Crowdfunding” rules on October 

69 On March 31, 2017, the Commission adopted amendments to increase the amount that issuers can raise through 

crowdfunding to adjust for inflation. The maximum aggregate offering amount increased to $1,070,000, and the new 

thresholds became effective on April 12, 2017. See JOBS Act Technical Amendments Release. 

54 
 

 

30, 2015, to implement the requirements of Title III.70 Issuers were able to use the new 

exemption beginning May 16, 2016, when Regulation Crowdfunding became effective.71 

While securities-based crowdfunding under Regulation Crowdfunding shares certain 

similarities with non-securities-based (lending-based, reward-based, donation-based, and royaltybased) crowdfunding, such as the ability of the public to participate and the use of an Internetbased platform to solicit backers, there are important distinctions. Non-securities-based 

crowdfunding campaigns do not involve a profit or revenue-sharing model and are generally not 

subject to regulation under federal securities laws. Although any crowdfunding backer may have 

nonpecuniary reasons for participating, backers in non-securities based crowdfunding campaigns 

are more likely to be guided by such motives. Overall, the differences in the legal framework, 

characteristics of fundraisers, and objectives of funders limit extrapolation from non-securities 

based crowdfunding to Title III crowdfunding. 

Regulation Crowdfunding established requirements for issuers and intermediaries seeking 

to participate in an Internet-based crowdfunding offering. The key provisions are summarized 

below:72 

 A given issuer is able to raise up to $1,070,000 across all crowdfunding offerings in a 12­

month period. An issuer must raise at least the target amount to receive funds. 

Crowdfunding securities are generally subject to resale limitations for one year. 

70 See Rel. No. 33-9470, Crowdfunding (Oct. 23, 2013) 78 FR 66427 (Crowdfunding Proposing Release); Rel. No. 

33-9974, Crowdfunding (Oct. 30, 2015) 80 FR 71387 (Crowdfunding Adopting Release). 71 Provisions related to funding portal registration became effective on January 29, 2016, to give funding portals 

additional time to undergo the SEC registration and FINRA membership process.

72 See Crowdfunding Adopting Release for details. In the JOBS Act Technical Amendments Release, the 

Commission revised the investment limits and other dollar amount thresholds in Regulation Crowdfunding to adjust
 

for inflation. The inflated-adjusted amounts are reflected below. 

55 
 The rules impose limits on the amount that an investor can invest in all Title III 

crowdfunding offerings over a 12-month period. Investors with both an annual income 

and net worth of at least $107,000 can invest up to 10% of the lesser of annual income or 

net worth, but an investor’s total investment across all Title III offerings may not exceed 

$107,000 in a 12-month period. Other investors can invest the greater of either $2,200 or 

5% of the lesser of their annual income or net worth. 

 Crowdfunding issuers are subject to disclosure requirements at the time of the offering 

(on Form C), during the offering’s progress and on completion of the offering (on Form 

C-U) and annually in the form of annual reporting requirements (on Form C-AR). 

Additionally, in offerings of over $107,000 in a 12-month period, financial statements 

must be reviewed by an independent accountant, and in offerings of over $535,000 in a 

12-month period (except the issuer’s first crowdfunding offering), financial statements 

must be audited. 

 Crowdfunding securities must be offered through an SEC-registered intermediary, either 

a broker-dealer or a funding portal, a new intermediary type established in Regulation 

Crowdfunding. These intermediaries must take measures to reduce the risk of fraud, 

make required disclosures about issuers available to the public, provide communication 

channels to permit discussion of offerings on the platform, disclose the compensation 

received by an intermediary, provide educational materials to investors, and comply with 

additional requirements related to investor commitments, notices to investors, and 

56 
 

 

 

maintenance and transmission of funds. Registered funding portals that participate in 

crowdfunding offerings may engage in a narrower set of activities than broker-dealers.73 

Given the offering limits, crowdfunding is primarily used by relatively small issuers, thus 

many of the economic considerations applicable to small and early-stage issuers apply to 

crowdfunding as well.74 The availability of solicitation over the Internet and sales to investors 

nationwide, irrespective of the location of the issuer (as long as the issuer is a U.S. issuer) 

distinguish Regulation Crowdfunding from intrastate securities-based crowdfunding regulated 

under state law that historically has relied on Securities Act Rules 147 or 504.75 The statistics 

reported in Table 7 are based on the analysis of data as reported in EDGAR filings of Forms C, 

C-U, and CFPORTAL through December 31, 2016, except as specified otherwise.76 For 

offerings that have been amended, the information is generally based on the latest amendment 

associated with that central index key (CIK) and file number as of the report date, except as 

specified. 

Between May 16, 2016, and December 31, 2016, excluding 24 withdrawn77 offerings and 

potential duplicate filings,78 there were 163 unique offerings by 156 issuers (including 7 issuers 

73 Among other things, a funding portal cannot offer investment advice or recommendations; solicit purchases, sales 

or offers to buy the securities offered or displayed on its platform; compensate employees, agents or other persons 

for solicitation or based on the sale of securities on its platform; and hold, manage, possess, or otherwise handle 

investor funds or securities. 

74 See Crowdfunding Adopting Release. 75 See Crowdfunding Adopting Release. To facilitate intrastate securities-based crowdfunding, the Commission 

recently amended Rule 147 and Rule 504 and adopted a new intrastate offering exemption as Rule 147A. See Rel. 

No. 33-10238, Exemptions to Facilitate Intrastate and Regional Securities Offerings (Oct. 26, 2016). Amendments 

to Rule 504 took effect on January 20, 2017. The amended Rule 147 and the new Rule 147A took effect on April 

20, 2017.

76 Data comes from the XML portion of Forms C and C-U and amendments to them. When we refer to offerings, 

we refer to initiated offerings that have not been withdrawn, unless specified otherwise. We analyze withdrawn 

offerings separately. When discussing completed offerings, we refer to offerings that have reported proceeds of at 

least the amount raised on Form C-U. Unless specified otherwise, offerings with Forms C-U filed without proceeds 

information reported are excluded from the analysis. 77 Withdrawn offerings include unique offerings for which a Form C-W was filed and a new Form C or amendment 

was not subsequently filed and offerings that used an intermediary that filed a Form CFPORTAL-W to withdraw its 

[Footnote continued on next page] 

57 
 

 

 

 

 

 

 

 

 

 

 

 

that filed for more than one crowdfunding offering).79 Of those, 28 offerings reported meeting 

their target amount on Form C-U as of December 31, 2016.80 Statistics are presented at the 

offering level. 

The median (average) offering between May 16, 2016, and December 31, 2016, targeted 

approximately $53,000 ($110,000). For almost all of these offerings, oversubscriptions up to a 

higher maximum were accepted (the median issuer set the maximum close to the $1 million limit 

in the final rules). Of the offerings that reported having raised at least the target amount, the 

median (average) amount reported raised was notably larger, at approximately $171,000 

($290,000).81 

Amounts reported raised are based on filings of Form C-U and generally pertain to 

completed and not ongoing offerings. Under the final rules, interim progress updates may be 

provided by the intermediary and are not required to be filed on EDGAR, but issuers must file 

Form C-U within 5 business days of reaching the target amount for offerings that do not accept 

amounts in excess of the target amount known as “oversubscriptions” and within 5 business days 

funding portal registration, even if the filings themselves had not been withdrawn. During this time period, 24 

offerings were classified as withdrawn. The remaining sample may contain additional withdrawn or abandoned 

offerings that have not been identified using these criteria (e.g., offerings that have reached the deadline date but 

have not been associated with filing activity or updates) and additional offerings may be classified as withdrawn at a 

future date (e.g., if the issuer withdraws an offering or if the portal through which it is offered withdraws its 

registration). Overall, since amounts sought may not be successfully raised, statistics on the amounts sought should 

be interpreted with caution. The actual ex post amount of capital formation in this market may be considerably 

lower than the amounts sought in offerings that had been initiated. 

78 Cases of multiple filings by the same filer on Form C with substantially similar offering information made within 

a short span of time are consolidated into a single offering. 79 See Ivanov and Knyazeva (2017). 80 In addition, there were four Forms C-U filed during January 1, 2017 - January 15, 2017, for offerings with 

deadline dates in 2016; for one of two offerings for which Forms C-U were filed before January 1, 2017, without 

proceeds information, proceeds information was obtained on the funding portal page. As of January 15, 2017, 

approximately $10 million in proceeds was reported raised for 33 offerings by issuers filing Forms C-U. No 

offerings have been withdrawn on Form C-W between January 1, 2017, and January 15, 2017. 

81 Unless specified otherwise, amounts raised are based on data through December 31, 2016. If data from note 80 

above is added, the median (average) raise becomes approximately $171,000 ($303,000), which illustrates the 

caveat that estimates obtained from a small sample can be highly sensitive to the addition of new data. 

58 
 

 

 

 

 

of reaching the offering’s deadline date for offerings that accept oversubscriptions.82 The 

offering characteristics in Table 8 show that a typical offering was due to close within 4-5 

months of initiation.83 With very few exceptions, issuers permitted oversubscriptions— 

investments above the target amount sought. Issuers took advantage of the flexibility afforded 

by Internet solicitation to raise as much capital as possible in a crowdfunding offering. 

As can be seen from Table 8 and Figure 9, the most popular security issued was common 

or preferred equity, accounting for 36% of offerings. Debt accounted for 20%, and there were 

various other security types, such as units, convertibles, “simple agreements for future equity,”84 

and others (including revenue sharing and membership / limited liability company (LLC) 

interests). 

Overall, initial evidence on the Title III crowdfunding market activity suggests that some 

small, pre-revenue growth firms are beginning to use crowdfunding as a securities offering 

method. However, the small sample limits the inference that can be drawn from statistics on 

issuer and offering characteristics, fees, and measures of offering activity. More time is needed 

to assess this market as it develops. 

82 See also Crowdfunding Adopting Release, at 71417. 83 Offering information is based on the most recent filing for each offering, except as specified otherwise. Offering 

duration (the number of months between the filing date and the deadline date) is based on the first filing. 84 “Simple agreements for future equity,” used in 26% of the offerings so far, were designed by a Silicon Valley 

startup accelerator for early-stage entrepreneurial ventures to provide their holders with option-like payoffs from a 

potential conversion into equity, contingent on a subsequent valuation event (such as a follow-on financing round or 

offering at a specified valuation) but no voting, dividend or coupon rights. Issuers may offer voting rights to larger 

investors. The conversion terms are based on whether the subsequent valuation event was at a higher than specified 

valuation. See Green, Joseph, and John Coyle, 2016, Crowdfunding and the not-so-safe SAFE, 102 Virginia Law 

Review Online 168, http://ssrn.com/abstract_id=2830213. But see Wroldsen, Jack, 2017, Crowdfunding investment 

contracts, 11 Virginia Law & Business Review (forthcoming), http://ssrn.com/abstract_id=2844771. 

59 
	 	 	

 

 

 

Figure 9. Distribution of crowdfunding security types 

Misc., 5%
 

Units, 7%
 

Equity, 36% 

"Simple 

Agreement for 

Future Equity", 

26% 

Convertible, 6% 

Debt, 20% 

Source: DERA analysis 

IV. The ABS Market

Asset-backed securitization (ABS) plays an important role in the creation of credit by 

increasing the amount of capital available for the origination of loans and other receivables 

through the transfer of those assets in exchange for new capital to other market participants.85 

Benefits of the securitization process may include reduced cost of credit and expanded access to 

credit for borrowers, ability to match risk profiles of securities to investors’ specific demands, 

and increased secondary market liquidity for loans and other receivables. In addition, the ABS 

market, although not a primary issue market, is nevertheless an important one because it supports 

a large volume of primary offerings issuance of underlying consumer and business loans and, 

thus, plays an important indirect role in capital raising.86 

85 See Rel. No. 34-73407, Credit Risk Retention (Oct. 22, 2014), 79 FR 77602 (Credit Risk Retention Adopting 

Release).

86 We make a distinction here between the primary market for the ABS as securities and the primary market for the 

underlying assets (loans, receivables, etc.). 

60 
$ Billion

Figures 10 and 11 show the dollar volume of both registered and Rule 144A ABS 

offerings for different types of underlying loans: Figure 10 for Residential Mortgage-Backed 

Securities (RMBS) and Figure 11 for ABS backed by all other types of collateral. 

Figure 10. Registered and Rule 144A RMBS offerings (in $ billions), 2005-2016
 800
 

700
 

600
 

500
 

400
 

300
 

200
 

100
 

0
 

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
 

Registered RMBS
 Rule144A RMBS

Source: DERA analysis 

The dollar volume of both types of offerings has decreased dramatically following the 

financial crisis. However, the issuance of registered RMBS almost stopped after 2008, whereas 

the issuance of registered ABS backed by other types of loans continued. We note that the 

market for private-label RMBS is distinct from other ABS markets because of the presence of 

Government Sponsored Enterprises (GSE), and its dynamics are more likely linked to the 

61 
 

 

 

 

       

dynamics of the underlying housing market rather than to any effects stemming from changes in 

the securities market.87 

Figure 11. Registered and Rule 144A non-RMBS ABS offerings (in $ billions), 2005-2016 

$ Billion

800

700

600

500

400

300

200

100

0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
 

Registered non‐RMBS ABS Rule144A non‐RMBS ABS

Source: DERA analysis 

For non-RMBS, we observe that the overall volume of ABS issuance has notably 

recovered since the trough of 2010. We also observe that the volume of private non-RMBS ABS 

offerings was slightly larger than the volume of registered ones following the financial crisis, a 

reversal from the pre-crisis period. Table 9 presents the number of ABS offerings, the average 

and median size of offerings, and the gross fees associated with offerings between 0.2% and 

0.4%. There is no discernible trend in the fees over the period under examination. 

87 See, e.g., Rel. No. 33-9638, Asset-Backed Securities Disclosure and Registration (Sept. 4, 2014) 79 FR at 57192 

(Regulation AB2 Adopting Release). (“In the RMBS market, private-label RMBS issuers encounter competitive 

pressure from government-sponsored enterprises, whose mortgage-backed securities are guaranteed and exempt 

from registration and reporting requirements. As private-label issuance has declined, issuance of agency RMBS has 

increased.”). 

62 
 

 

 

We note that recent rules specific to the ABS market went into effect at the end of or after 

the time period considered in the Report,88 and some rules (such as credit risk retention) affected 

both registered and private markets. Regulation AB2, which fully applies only to some types of 

registered ABS offerings, went into effect in stages, in November 2015 and in November 2016;89 

the Credit Risk Retention rule went into effect in December 2016 for most asset classes and 

applies to both registered and unregistered ABS.90 Thus, it is challenging to causally attribute 

the above trends to regulatory impacts. Further, while such trends may be generally consistent 

with regulatory impacts, they are also consistent with other explanations, such as a shift in 

investor risk tolerance and beliefs about the risks and returns of various ABS offerings following 

the financial crisis. 

88 As we noted in the Methodological Considerations section, it is possible that some market participants anticipated 

and responded to some of these reforms ahead of the compliance or effective dates. 89 Regulation AB2 shelf eligibility requirements other than asset-level disclosure and some other requirements went 

into effect in November 2015. The asset-level disclosure requirements went into effect in November 2016 for some 

types of ABS. Proposals to apply asset-level disclosure requirements to other types of ABS remain outstanding. See 

Regulation AB2 Adopting Release, 79 FR at 57184. 

90 See Credit Risk Retention Adopting Release, 79 FR at 77602. 

63 
	 	 	 	

 

 

V. Tables on Primary Issuance

Table 1. Number of offerings, offer size, and gross spreads for IPOs and SEOs, 2005-2016 

Number of Average offer Median offer Average gross Median gross 

Year offerings size size spread spread

 SEOs IPOs SEOs IPOs SEOs IPOs SEOs IPOs SEOs IPOs 

2005 477 236 $192 $159 $100 $105 4.3% 6.7% 4.7% 7.0% 

2006 486 246 $196 $188 $108 $104 4.2% 6.5% 4.6% 7.0% 

2007 439 298 $206 $210 $117 $114 4.3% 6.6% 4.5% 7.0% 

2008 280 53 $504 $195 $136 $126 4.1% 6.7% 4.2% 7.0% 

2009 858 69 $243 $311 $65 $139 4.8% 6.3% 4.8% 6.8% 

2010 861 200 $201 $203 $53 $90 4.8% 6.7% 5.0% 7.0% 

2011 641 201 $197 $187 $69 $81 4.5% 6.3% 4.6% 7.0% 

2012 738 206 $231 $217 $75 $77 4.5% 6.4% 4.5% 7.0% 

2013 920 283 $196 $202 $90 $99 4.5% 6.5% 4.5% 7.0% 

2014 811 347 $195 $257 $101 $90 4.6% 6.5% 4.8% 7.0% 

2015 767 218 $223 $164 $98 $80 4.6% 6.5% 5.0% 7.0% 

2016 702 119 $155 $90 $207 $80 4.3% 6.5% 4.5% 7.0% 

Source: DERA analysis 

Table 2. Number of offerings, offer size, and gross spreads for public debt offerings, 2005-

2016 

Number of Average Median Mean gross Median gross 

Year offerings offer size offer size spread spread 

2005 1,843 $406 $250 1.2% 1.0% 

2006 1,739 $557 $350 1.0% 0.7% 

2007 1,489 $662 $425 1.3% 1.0% 

2008 1,189 $740 $400 1.5% 1.2% 

2009 1,405 $734 $500 1.3% 0.8% 

2010 1,827 $448 $250 1.7% 1.5% 

2011 1,537 $528 $350 1.7% 1.3% 

2012 1,481 $661 $500 1.0% 0.7% 

2013 1,514 $698 $500 0.8% 0.7% 

2014 1,604 $695 $500 1.3% 0.8% 

2015 1,565 $775 $500 1.1% 0.7% 

2016 1,636 $810 $500 0.8% 0.7% 

Source: DERA analysis 

64 
 

 

Table 3. Capital raised through Regulation D and Regulation D/A (amended) offerings* 

Regulation D Regulation Mean Median 

filings D/A filings amount sold amount sold* 

Year (number) (number) ($ millions) ($ millions) 

2009 13,764 7,077 36 1.5 

2010 17,581 11,864 26 1.4 

2011 18,174 12,536 28 1.5 

2012 18,187 13,284 27 1.5 

2013 19,846 14,533 24 1.5 

2014 22,004 15,254 24 1.5 

2015 22,854 15,651 25 1.5 

2016 22,992 16,490 25 1.5 

Source: DERA analysis 

*Mean and median amount sold based on initial (new) Form D filings only. Total amount sold 

includes additional amounts raised and reported in amended filings, recorded at the time of the 

amendment. 

Table 4. Capital raised through Rule 506(c) and Rule 506(b) offerings: September 23, 2013 

- December 31, 2016 

Total 

Number of amount Mean Median Median offer 

Form D Amendment sold* amount sold amount sold size 

Rule Filings Filings ($ billions) ($ millions) ($ millions) ($ millions) 

506(c) 5,474 874 $108 $13 $0.7 $2.4 

506(b) 65,772 33,430 $4,122 $26 $1.6 $2.2 

All 506 71,146 34,304 $4,230 $25 $1.5 $2.3 

Regulation 

D** 73,556 50,491 $4,232 $24 $1.4 $2.0 

Source: DERA analysis 

* Total amount sold includes incremental amounts reported to be raised in amended filings 

(Form D/As). Mean and median amounts sold based on initial (new) Form D filings only. 

Median offer size is based on offerings that report their amount of offering. 

** Includes all four rules: Rules 504, 505, 506(b) and 506(c). 

65 
  

 

 

 

Table 5. Investors participating in Regulation D offerings: 2009-2016 

Total Number 

of Investors 

Mean 

Investors 

per 

Offering 

Median 

Investors per 

Offering 

Fraction of offerings 

with at least one 

non-accredited 

investor 

Hedge Funds 

Private Equity Funds 

Venture Capital 

Funds 

Other Investment 

Funds 

Financial Services 

Real Estate 

Nonfinancial Issuers 

All offerings 

32,684 

22,572 

7,428 

31,524 

12,220 

53,943 

103,194 

263,621 

17 

18 

15 

24 

15 

25 

9 

14 

2 

3 

4 

6 

4 

7 

4 

4 

7% 

4% 

1% 

5% 

13% 

13% 

10% 

9% 

Source: DERA analysis 

Table 6. Proportion of Regulation D offerings that sold or intend to sell to non-accredited 

investors91 

Rule 506(b) 

Offerings <=$5 All 506(b) 

Rule 504 Rule 505 million offerings 

2009 53% 39% 10% 10% 

2010 54% 41% 9% 8% 

2011 57% 43% 9% 8% 

2012 58% 44% 10% 8% 

2013 61% 41% 9% 8% 

2014 60% 36% 8% 7% 

2015 61% 43% 7% 6% 

2016 65% 41% 7% 6% 

2009-2016 58% 40% 8% 7% 

Source: DERA analysis 

91 Rule 506(c) offerings can sell only to accredited investors. 

66 
 

 

 

 

 

Table 7. Market size statistics (at the offering level) for crowdfunding offerings, 2016 

 Aggregate ($ million)92 Median ($ 000s) Mean ($ 000s) Observations 

Target amount 18.0 53 110 163 

Maximum amount93 101.1 999 647.5 163 

Amount raised 8.1 171 290 2894 

Source: DERA analysis 

Table 8. Offering characteristics for crowdfunding offerings, 2016 

Mean Median Observations 

Offering duration, in months 4.5 4.0 163 

%: Oversubscriptions accepted 98% 163 

Number of jurisdictions of solicitation 50.6 51.0 163 

%: Security type ‘equity’ 36% 163 

%: Security type ‘debt’ 20% 163 

%: Security type ‘other’ 44% 163 

Source: DERA analysis 

92 For issuers with multiple crowdfunding offerings, if the cumulative target or maximum offer amount is greater 

than $1 million, we cap it at $1 million for purposes of computing aggregate amounts offered. For example, if an 

issuer seeks up to $1 million in the first offering, raises $200,000, and seeks up to $800,000 in the second offering, 

we record the cumulative maximum amount sought by the issuer as $1 million.

93 Maximum amount is defined as the maximum amount as specified in Form C if the offering accepts 

oversubscriptions (investments above the target amount) and is defined as the target amount if the offering does not 

accept oversubscriptions. 

94 Excludes reports without information on the proceeds (for example, reports noting “end of offering” without 

indicating a dollar amount of proceeds). 

67 
Table 9. Number of offerings, offer size, and gross spreads for registered and private ABS 

offerings, 2005-2016 

Number of 

offerings Average ($ mil) Median ($ mil) Gross Fee 

Rule Rule Rule Rule 

Year Reg’d 144A Reg’d 144A Reg’d 144A Reg’d 144A 

2005 1,594 907 $910 $356 $734 $206 0.2% 0.3% 

2006 1,508 1,550 $959 $402 $796 $226 0.2% 0.3% 

2007 1,088 1,102 $964 $471 $700 $300 0.2% 0.2% 

2008 163 240 $863 $545 $710 $190 0.2% 0.3% 

2009 80 264 $1,068 $454 $946 $250 0.3% 0.4% 

2010 65 395 $785 $410 $772 $210 0.3% 0.4% 

2011 86 280 $871 $491 $877 $303 0.3% 0.4% 

2012 157 449 $1,001 $408 $1,026 $358 0.3% 0.4% 

2013 180 540 $964 $414 $995 $388 0.3% 0.3% 

2014 188 628 $966 $439 $1,000 $412 0.3% 0.3% 

2015 178 635 $901 $434 $951 $408 0.3% 0.2% 

2016 151 547 $924 $416 $894 $400 0.3% 0.3% 

Source: DERA analysis 

68 
		

Part B. Market Liquidity 

I. Introduction

As discussed above, primary issuance enables issuers to raise capital and impacts the 

types of securities available for capital allocation by businesses, investors, and consumers. 

Primary issuance characteristics can contribute to a reduction in secondary market liquidity if 

there is a high degree of concentration of risky issuers in certain issuance types or when primary 

issuance becomes fragmented. At the same time, the liquidity of issues in secondary markets 

determines whether investors are able to exit positions of different sizes quickly and without 

price impact, which may influence investor demand for securities in primary markets. As we 

noted earlier, the ability of market participants to exit their investments when secondary market 

liquidity is strong may facilitate primary issuance and increase the probability of placement 

success. 

Therefore, secondary liquidity is an important characteristic that influences capital 

markets. In this section we turn to the evolution of market liquidity. In addition to analyzing 

liquidity in Treasuries and corporate bonds, we consider developments in single-name CDS and 

funds to test whether spillovers and liquidity diffusion across markets may contribute to 

observed trends in Treasuries and corporates. The sections below discuss a variety of liquidity 

metrics in those markets. 

From the outset, we note that the analysis below considers the time series and crosssectional evolution of various liquidity metrics, and does not seek to estimate the optimal amount 

of liquidity under various market structures or stress scenarios. Similarly, we recognize that 

liquidity is only one attribute of a market, albeit an important one. We do not seek to establish 

69 
	 	 	 	 	 	

 

how liquidity should or should not be weighed against other market attributes, such as 

informational efficiency or market stability.95 

II. Empirical Measures of Market Liquidity

Broadly, liquidity refers to the ability to execute transactions of a required size in a short 

period of time with little price impact.96 A vast academic literature on market microstructure 

formulates different measures reflecting various dimensions of liquidity.97 Existing measures of 

liquidity can be grouped roughly into three categories: trading activity measures (e.g., trading 

volume, turnover, average trade size), transaction cost measures (e.g., bid-ask spread, price 

impact), and measures of liquidity supply (e.g., dealer inventory, order book depth). Because 

liquidity is priced, i.e., the ability to sell an asset quickly has value in and of itself,98 some of 

these measures reflect not only direct execution costs, but also the premium paid for more liquid 

assets. Some measures can be observed only at specific frequencies (e.g., daily), but many of 

these measures can be estimated with both daily and intraday data. 

Trading Activity 

Trading activity metrics generally reflect the overall amount of trading in a particular 

bond or market, and greater activity may reflect an increased ability of a participant to buy or sell 

95 For instance, there may be a tradeoff between informational efficiency and liquidity. When markets are opaque, 

market participants may be less willing to transact, dampening liquidity. At the same time, when market 

participants are highly informed about the value of an asset, prices accurately reflect existing information and little 

trading may occur. For example, Grossman and Stiglitz (1980) showed that informational efficiency reduces 

incentives of economic agents to expend resources to acquire information. Their seminal result is that, because 

information is costly, prices cannot perfectly reflect available information—if they did, those who spent resources to 

obtain it would receive no compensation. Therefore, they conclude that there is an equilibrium amount of 

“disequilibrium.”

96 See, e.g., Sarr and Lybek (2002) and Bank for International Settlements (2014). 97 These dimensions of liquidity are: (i) tightness, which refers to low transaction costs; (ii) immediacy, which refers 

to the speed with which orders can be executed; (iii) depth and breadth, which refer to the existence of abundant buy 

and sell orders at various price levels; and (iv) resiliency, that refers to the market’s ability to quickly correct order 

imbalances and move prices back to fundamental values if prices are disturbed. See Sarr and Lybek (2002). 98 See, e.g., Amihud et al. (2013) for a collection of academic articles that present theory and empirical evidence on 

the effect of liquidity on asset prices and how liquidity risk affects prices. 

70 
a bond. Trading volume and turnover directly measure trading activity in the market. If markets 

are liquid, then market participants should be able to exchange large volumes of traded assets 

either in absolute terms (trading volume) or in relation to the outstanding amounts of the assets 

(turnover). We note that both of these metrics are influenced by the changing trading needs of 

investors (e.g., trading volume might be elevated during times of high volatility), and by the 

changing supply of an asset (e.g., turnover does not take into account the share of a security’s 

supply held by long-term investors and not available for trading, which might change over time). 

Therefore, it may be difficult to disentangle liquidity supply and liquidity demand explanations 

of the evolution in trading volume and turnover. 

Average transaction size is sometimes used as a measure of liquidity—larger average 

trade size reflects participants’ ability and willingness to trade large quantities of assets with 

small price impact. However, with the advent of electronic markets, investors may be able to 

more efficiently split larger transactions into smaller amounts and rely on algorithms to optimize 

execution or to obscure their trading strategies. Thus, a transaction size metric might exhibit a 

secular downward trend because of structural changes in the market rather than any other effects. 

Transaction Costs 

Transaction costs and price impact of trades capture the price dimension of liquidity, with 

lower transaction costs and lower price impact generally suggesting improvements in this 

liquidity dimension. We note that empirical measures of transaction costs reflect trade execution 

costs and do not reflect untraded bonds. Therefore, it is important to interpret the transaction 

cost evidence in conjunction with the trading activity metrics above. 

A quoted bid-ask spread is a common transaction cost measure. It is calculated as the 

difference between buy and sell quoted prices and serves as a proxy for the direct cost of a 

71 
round-trip trade. A quoted bid-ask spread measures the tightness dimension of liquidity and the 

direct transaction costs of executing a trade. However, a quoted bid-ask spread does not take 

into account the quantity dimension of the order book. The quantities available at the bid or the 

ask price might be limited, despite a narrow quoted bid-ask spread and, thus, execution of large 

trades at these prices might not be possible. A closely related metric is the effective spread, 

which is a better proxy for true transaction costs. It is twice the difference between the execution 

price and the average of the bid and the ask price. In the absence of quote data, Roll (1984) 

suggests an estimator of the effective spread on the basis of the serial covariance of the change in 

price and shows that, under certain assumptions, this measure approximates transaction costs 

well. 

Other transaction cost measures are related to price impact, motivated by Kyle (1985), 

and are based on the estimated price changes per given net order flow. Price impact measures 

assess the change in a security’s price when a liquidity taker uses up some of the supplied 

liquidity. Lower price impact indicates better market resiliency and an ability to execute trades 

without adverse impact on prices. However, price impact does not measure direct transaction 

costs or market depth. 

Liquidity Supply 

Liquidity supply in fixed income markets can be represented by the total amount of bonds 

held by dealers in inventory. This is an ex post measure of whether dealers have been willing to 

take on inventory risk. It may be interpreted as an imperfect proxy of potential future ability of 

dealers to supply liquidity, but does not necessarily measure dealers’ willingness to provide 

liquidity. This measure also does not take into account the possibility that market participants 

other than dealers might substitute for dealers as liquidity providers. A reduction in the dealer 

72 
 

 

inventory levels may be offset by an increase in the inventory held by non-dealer liquidity 

providers. 

Similarly, visible quoted depth represents the amount of securities available for 

sale/purchase at the best bid/offer. Quoted depth measures the supply of liquidity by market 

participants as the volume that can be transacted at the quoted levels. Another measure of 

market depth, order book depth, is the amount of securities available for sale/purchase within a 

certain price range. We note, however, that neither measure of depth considers the cost 

dimension of liquidity, because they both focus on the quantity offered for purchase or sale 

conditional on price. 

Composite Measures 

Finally, some studies suggest composite measures that would present a single number to 

summarize multiple facets of liquidity.99 These composite measures typically rely on weighted 

averages or principal component analysis of several liquidity measures that quantify different 

dimensions of liquidity. However, such index measures may be difficult to interpret, and there is 

little consensus in academic and practitioner research on the use of aggregate liquidity metrics. 

99 See, e.g., Korajczyk and Sadka (2008) and Adrian et al. (2015) that use methods based on the principal component 

analysis for the U.S. equity markets and for the U.S. Treasury markets, respectively, Autorité des Marchés 

Financiers (2015) that uses weighted averages to measure liquidity of French bond market, and Financial Conduct 

Authority (2016) that uses a combination of both approaches to measure liquidity of the UK bond market. 

73 
 

 

 

III. Treasuries

A. Introduction 

U.S. Treasury securities are fixed income securities issued by the U.S. government, 

priced by most market participants as nearly default free.100 These securities play a unique role 

in financial markets because of their aggregate market size, level of trading activity, and backing 

by the U.S. Government. They serve as a critical element in the financing of the U.S. 

Government and the execution of U.S. monetary policy. U.S. Treasuries are used as a highquality investment, a store of value, and as a primary tool for interest rate risk management by 

not only investors, but also nation states. In addition, their yield is often used as the benchmark 

yield for pricing many other fixed income instruments.101 

The U.S. Treasury market is the largest government securities market in the world. As of 

September 30, 2016, there are $19.6 trillion of Treasuries securities outstanding, with $14.2 

trillion held by the public. Of publicly held debt, $13.6 trillion is tradable in financial markets.102 

Treasury securities make up approximately 33% of the entire U.S. fixed income market, which 

also includes municipal, mortgage-related, corporate, federal agency, money market, and assetbacked securities.103 

The U.S. Treasury market is also one of the most liquid and lowest transaction cost fixed 

income markets in the world.104 Treasury securities can be viewed as close cash substitutes 

because market participants can generally rely on the ability to convert Treasuries into cash 

100 Krishnamurthy and Vissing-Jorgensen (2012) even argue that the Treasury interest rates are lower that the “true” 

riskless interest rates because of the liquidity and convenience of Treasury securities. 101 See Fleming (2000) and Joint Economic Committee (2001). 102 See Monthly Statement of the Public Debt of the United States, September 30, 2016, U.S. Treasury at
 

https://www.treasurydirect.gov/govt/reports/pd/mspd/mspd.htm. 103 See SIFMA statistics at http://www.sifma.org/research/statistics.aspx. 104 See, e.g., Bank for International Settlements (2016). 

74 
 

 

rapidly and at low cost. Currently, trading in the U.S. Treasury market is more active than in all 

other U.S. fixed income markets combined. The average daily trading volume in the U.S. 

Treasury market in September 2016 was $499 billion and made up 64.6% of the entire U.S. bond 

markets trading volume.105 

Because of its importance in setting benchmarks for financial markets, its scale, and its 

trading activity, the liquidity of the secondary U.S. Treasury market is an important 

consideration for both market participants and regulators.106 In this section, we consider changes 

in U.S. Treasury market liquidity and whether any observed changes are consistent with the 

hypothesis that Dodd Frank, Volcker, and Basel III reforms, among other factors, had adverse 

effects on liquidity in this market. From the outset, we note that the Treasury market has 

evolved since the financial crisis of 2007–2009 in ways that may have influenced liquidity 

conditions. We summarize these changes below and discuss how they and other potential drivers 

may be changing liquidity conditions. 

First, regulators introduced, adopted, and made effective several new regulations that 

affected market participants—and, in particular, dealers. During and after the crisis, most major 

dealers became banks or bank holding companies and, thus, became subject to new enhanced 

capital and liquidity requirements and other applicable banking regulations. In particular, the 

new leverage ratio requirements of the Basel III framework were intended to constrain excess 

leverage and may have increased the costs of market making in U.S. Treasuries.107 Parts of the 

Dodd-Frank Act—in particular, the Volcker Rule—also directly affected dealers’ trading 

105 See SIFMA statistics at http://www.sifma.org/research/statistics.aspx. 106 The Treasury, the SEC, the Office of the Comptroller of the Currency (OCC), the Federal Reserve Board (FRB), 

and the Federal Deposit Insurance Corporation (FDIC), regulate different aspects of the cash Treasury market and 

many of its participants, while the CFTC regulates the futures markets, including the Treasury futures markets, and 

many of its participants. See Joint Staff Report (2015).
 107 See, e.g., Bank for International Settlements (2014). 

75 
 

activities in fixed income markets. Some market participants believe that these regulations have 

substantially changed dealers’ business models and practices, affecting dealers’ balance sheets 

and profitability and their ability to provide liquidity to fixed income markets.108 For instance, 

some have argued that Basel III minimum capital requirement and its lack of risk-weighting may 

potentially make capital intensive, low risk, low-margin and high-volume business, such as 

market-making in highly rated sovereign bonds and repo, less profitable and may, therefore, 

reduce its volume. For a more detailed regulatory reform timeline that covers other post-crisis 

regulations, see Appendix A. 

Second, the U.S. Treasury market structure has undergone substantial structural changes 

over the last decade. Most interdealer trading and a substantial share of institutional trading in 

Treasuries has migrated from voice networks to electronic marketplaces. According to 

Greenwich Associates (2015), 84% of institutional investors traded Government bonds 

electronically in 2015 compared to 61% in 2005. The growth of electronic trading has increased 

the speed of trading while reducing its cost. 

Third, the U.S. Treasury market recently experienced two major events that affected 

market liquidity: the 2013 Taper Tantrum, when remarks by then-Federal Reserve Chairman Ben 

Bernanke triggered a substantial selloff and a yield increase of over 100 basis points within a 10­

week period,109 and the October 15, 2014 event, when yields abruptly decreased and reverted 

within a span of 15 minutes without any clear catalyst. In response to the second event, staff 

from a group of regulators produced a joint report (Joint Staff Report (2015)) and hosted two 

conferences on Treasury market structure and liquidity in 2015 and 2016. 

108 See Bank for International Settlements (2014) and Bank for International Settlements (2016). 109 See, e.g., Silvia and Moehring. Case Study of the Taper Tantrum and Term Premiums, Wells Fargo Securities 

(Aug. 4, 2015). 

76 
 

 

 

We recognize that several recent regulatory initiatives aim to change some aspects of the 

Treasury market structure. For instance, the implementation of post-trade reporting of Treasury 

transactions for FINRA members and the corresponding expansion of TRACE reporting,110 the 

Federal Reserve’s transaction initiatives for banks announced in 2016,111 and other related 

reforms aimed at improving market monitoring and oversight by regulatory agencies may impact 

market participants’ behavior, market structure, participation, and liquidity metrics going 

forward. 

In the following sections, we review possible determinants of the changes in Treasury 

market liquidity and challenges in causal identification of the effect of individual factors, 

applicable empirical liquidity measures, and recent evidence on the evolution of secondary 

market liquidity in Treasuries. 

B. Determinants of Liquidity Changes 

Multiple factors potentially affect market liquidity, and in this section we consider a 

number of such factors. However, as discussed in the Broad Economic Considerations section of 

the Report above,112 our analysis of Treasury market liquidity faces challenges in establishing 

causality for any single driving factor that might affect Treasury market liquidity. Among other 

complications, we note that the precise timeline of various regulatory changes and market 

responses to each regulatory reform is ambiguous because of multiple other events and marketwide structural changes occurring over the same period of time. We also note that various 

110 Rel. No. 34-79116, Self-Regulatory Organizations; Financial Industry Regulatory Authority, Inc.; Notice of 

Filing of Amendment No. 1 and Order Granting Accelerated Approval of a Proposed Rule Change, as Modified by 

Amendment No. 1, Relating to the Reporting of Transactions in U.S. Treasury Securities to TRACE (Oct. 18, 2016) 

81 FR 73167 (Oct. 24, 2016). 

111 See Press Release, Board of Governors of the Federal Reserve System, Federal Reserve Board announces plans 

to enter negotiations with FINRA to potentially act as collection agent of U.S. Treasury securities secondary market 

transactions data (Oct. 21, 2016), available at 

https://www.federalreserve.gov/newsevents/press/other/20161021a.htm. 112 See Introduction to the report. 

77 
 

 

 

 

liquidity drivers may offset one another and dampen observed trends, which may lead us to 

observe that Treasury liquidity has not changed in response to regulatory changes in aggregate, 

whereas each change might have, in fact, differentially affected liquidity, with individual impacts 

in opposite directions. Such considerations further complicate causal identification. 

One of the most commonly cited factors behind changes in secondary market liquidity is 

the introduction of new regulations, in particular, the Dodd-Frank Act (especially, the Volcker 

Rule) and the Basel III framework post-crisis. These regulations were aimed at promoting 

financial stability.113 However, some commenters have argued that those regulations imposed 

higher costs of carrying inventory and restrictions on proprietary trading activities of banks, 

which may have reduced dealer provision of liquidity. Crucially, we note that U.S. Treasuries 

are exempt from the Volcker Rule’s prohibitions on proprietary trading. Therefore, the changes 

in Treasury market liquidity reported below are unlikely to be directly attributable to the Volcker 

Rule, though the Volcker Rule may indirectly affect them as a result of spillover effects from 

other markets.114 

In addition to the regulatory reforms of interest, the Treasury and other fixed income 

markets underwent a number of structural changes in recent years. The growth of electronic 

trading, an increasing share of proprietary trading firms in the Treasuries market, and the growth 

of bond ownership by large asset managers are all likely to have impacted Treasury liquidity. 

113 See Preamble, Dodd–Frank Wall Street Reform and Consumer Protection Act, Pub.L. 111–203, H.R. 4173. 114 For example, one of the related markets is the market for derivatives on Treasuries. The Volcker Rule does not 

provide an exemption for trading in derivatives on Treasury securities similar to the exemption for Treasuries 

(although other exemptions, e.g., the risk-mitigating hedging exemption, may be available for such trading). See Rel. 

No. BHCA-1, Prohibitions and Restrictions on Proprietary Trading and Certain Interests In, and Relationships 

with, Hedge Funds and Private Equity Funds (Dec. 10, 2013) 79 FR at 5639 (Volcker Rule Adopting Release). The 

lack of exemption for derivatives on U.S. Government obligations could affect liquidity in the underlying Treasury 

market in either direction. 

78 
 

 

 

 

For instance, the bond ownership share by mutual funds has risen notably post-crisis.115 

Traditionally, mutual funds have been liquidity seekers, and their increased ownership of bonds 

together with higher magnitude and volatility of fund flows116 may create higher and more 

volatile liquidity demand because of higher redemption risk. At the same time, increases in 

trading activity by proprietary trading firms might make them de facto liquidity providers.117 

Moreover, the financial crisis may have led to changes in dealer risk appetites, return 

expectations, and risk management practices, resulting in changes in observed liquidity 

conditions. A general decline in risk taking and changes in risk management practices combined 

may have contributed to reductions in dealer leverage independent of regulatory compliance 

effects. Adrian et al. (2017a) document dealer deleveraging and a reduction in balance sheets 

occurring during and in the immediate aftermath of the 2007-2008 financial crisis, before many 

of the regulations at issue were announced, proposed, or adopted. 

Three additional factors, including diffusion of liquidity to other markets, liquidity jumps, 

and liquidity bifurcation (diverging liquidity conditions in the same market for different 

subgroups of bonds), may further obstruct inference about causal effects of particular drivers of 

liquidity. These three factors, to date, have not been widely studied in the academic literature 

because of data limitations and difficulty in finding clear evidence to permit accurate causal 

attribution of changes in liquidity to changes in these factors. 

115 See Figure 4.5 in Joint Staff Report (2015), which shows that the share of Treasury securities held by mutual 

funds has increased in recent years, from under 4 percent in the years preceding the financial crisis to 6.7 percent at 

the end of March 2015. 

116 See Figure 4.6 in Joint Staff Report (2015), which shows that the magnitude and the volatility of Government 

bond fund flows has increased since 2008. 

117 See Joint Staff Report (2015). The Report notes, however, that proprietary trading firms often act as short-term 

liquidity providers and do not carry substantial inventory to respond to liquidity demands from other market 

participants. 

79 
 

First, in addition to the cash Treasury market, market participants have access to an active 

Treasury futures market.118 Thus, changes in Treasury market liquidity may reflect a diffusion of 

liquidity between the cash Treasury market and the Treasury futures market. For a complete 

picture of the liquidity conditions available to Treasury market investors, we need to consider the 

co-liquidity of the Treasury market with its derivatives market; and so we review available 

evidence in the following sections. We also recognize that liquidity in corporate bond and 

single-name CDS markets may interact with activity in Treasuries.119 Hence, for a cohesive 

understanding of changes in market liquidity conditions, it is important to consider liquidity in 

Treasuries in conjunction with liquidity in corporate bond and single-name CDS markets. 

Second, Adrian et al. (2015) introduce the concept of “liquidity risk” that describes the 

elevated probability of sudden spikes in illiquidity rather than changes in the overall liquidity 

level (similar to elevated “volatility of liquidity”). They argue that the recent increase in 

incidence of “flash” events in various financial markets can be described by a model of liquidity 

that has a slow-moving continuous level component and a jump component. However, jumps in 

liquidity have not been rigorously studied. If such liquidity risk is indeed present in the Treasury 

market, there are no main studies of its causes or drivers. If, as these researchers posit, changes 

in liquidity include a jump component, a factor may affect liquidity by changing the intensity and 

the size of the jump component without affecting the slow moving component of liquidity. For 

econometricians, estimating and making statistical inferences about models with jumps is 

generally more difficult and requires a longer time series of data. This, in turn, makes statistical 

inferences that rely on such models to identify changes in the level of liquidity difficult to make. 

118 See Joint Staff Report (2015), Tables 3.3 and 3.4.
 119 We explore changes in corporate bond and CDS markets liquidity later in Sections IV and V of Part B of this 

report.
 

80 
Third, liquidity conditions in different sectors of the same market may evolve in opposite 

directions (“liquidity bifurcation”). For example, a highly liquid electronic market for the most 

recently issued Treasuries of a particular maturity (“on-the-run” Treasuries) may attract 

participants seeking liquidity and low transaction costs who might otherwise seek to trade in the 

largely voice-based markets for Treasuries issued before the most recent issue (“off-the-run” 

Treasuries) and Treasury Inflation Protected Securities (TIPS), reducing liquidity in these 

markets. Therefore, looking at a single market-wide measure of liquidity might obscure more 

complicated substitution between market segments. 

In light of the above, it is difficult to disentangle multiple concurrent and correlated 

drivers of liquidity, limiting our ability to draw causal inference with respect to any individual 

determinant of liquidity. While we are unable to identify causal impacts, the analysis below 

sheds light on the evolution of transaction activity and liquidity in Treasuries and evaluates 

alternative explanations of observed changes. 

C. Empirical Measures 

Many of the measures of liquidity discussed in the introduction to Part B of this Report 

were originally proposed for use in particular markets, most commonly the equity market, and 

tested in those markets. Because of differences in market structure and data constraints, many 

traditional measures of liquidity are not readily applicable to the U.S. Treasury market. At the 

same time, unique features of the U.S. Treasury market give rise to several Treasury-specific 

liquidity measures that do not translate well to other markets. In this section, we review 

measures that are applicable to the U.S. Treasury market and that have been used in the literature 

to study its liquidity conditions, notably measures of funding liquidity (e.g., measures reflecting 

the ability to execute arbitrage opportunities, such as yield curve fitting error, on-the-run/off-the81 
	 	

 

 

run spread, or the yield spread between bonds issued by the Resolution Funding Corporation and 

those issued by the U.S. Treasury (RefCorp spread)). 

Funding Liquidity

Several liquidity measures are based on the ability to execute arbitrage opportunities and 

on the availability of funds to take advantage of such opportunities (here, referred to as “funding 

liquidity”). The U.S. Treasury market is unique in that it includes multiple securities with 

similar or identical cash flows and credit risk—the credit risk of the U.S. Government. The 

existence of multiple securities with similar cash flows allows participants to build arbitrage 

portfolios to take advantage of mispricing between two or more sets of securities. Often, 

however, such portfolios are characterized by different liquidity and the execution of arbitrage 

trades might require additional funding: divergence in prices of such portfolios can occur when 

there are limits to arbitrage, such as when arbitrageurs are capital constrained (have low funding 

liquidity). The price difference between arbitrage portfolios can be interpreted as a proxy for a 

market liquidity measure because good funding liquidity allows traders to provide market 

liquidity and vice versa; good market liquidity eases funding constraints of traders.120 

The first such measure, the yield curve fitting errors (Hu et al. (2013)), is the sum of 

squared residuals of observed Treasury yields from a fitted term structure model. These errors 

are evidence of unexploited profits and reflect liquidity constraints faced by arbitrage capital 

that, in the absence of funding constraints, should be able to eliminate deviations between 

observed Treasury yields and their expected yields, based on fundamentals, which are assumed 

to lie on the fitted term structure. 

120 See Brunnermeier and Pedersen (2009) for detailed arguments connecting funding liquidity and market liquidity. 

82 
 

 

The second arbitrage motivated measure, the on-the-run/off-the-run spread, is the 

difference between yields on an on-the-run Treasury and a synthetic off-the-run Treasury that is 

constructed from the off-the-run yield curve. This construction relies on the fact that a 5-year 

bond 3 years after its issuance becomes a 2-year bond that is similar to a newly issued 2-year 

bond up to a difference in coupon rates. Because both bonds have the same credit risk and cash 

flows, but their liquidity characteristics may be notably different, the yield differential between 

them is primarily attributed to the liquidity premium on the Treasury market. 

The third measure, the RefCorp spread, should reflect only differences in liquidity 

because both sets of bonds have the same credit risk.121 All three arbitrage-based measures of 

liquidity share the same limitations: they are indirect measures of market liquidity, and they do 

not directly assess the size, cost, or immediacy dimensions of liquidity. Those three measures 

capture funding liquidity, which might be affected by factors other than those that are relevant to 

market liquidity. 

D. Recent Evidence 

In this section, we assess the empirical evidence on the current state of Treasury market 

liquidity. First, we consider studies that examine the time series evolution of liquidity measures 

from a period before the crisis to a period after several regulatory reforms took place. The 

measures include bid-ask spreads, depth, price impact, trade size, yield curve fitting errors, 

RefCorp spread, on-the-run premia, return autocovariances, and turnover. Second, we discuss 

several studies that analyze Treasury liquidity during recent periods of market stress to better 

121 The Resolution Funding Corporation (RefСorp) is a government agency created by the Financial Institutions 

Reform, Recovery, and Enforcement Act of 1989. RefCorp bonds’ principal is fully collateralized by U.S. Treasury 

bonds and full payment of coupons is guaranteed by the U.S. Treasury. 

83 
understand the Treasury market’s resilience. Finally, we address the implications of liquidity in 

the interest-rate derivatives market for our findings about Treasury market liquidity. 

In a comprehensive study of fixed income liquidity, Adrian et al. (2017a) analyze a 

number of intraday and daily measures of U.S. Treasury market liquidity. The authors examine 

the three most actively traded Treasury securities—on-the-run 2-, 5- and 10- year notes—and 

construct the following market liquidity measures: (1) bid-ask spread, (2) order book depth, (3) 

price impact, (4) trade size, (5) yield curve fitting error, and (6) RefCorp spread. Measures (1) 

through (4) rely on intraday data, while (5) and (6) are constructed using daily data. As detailed 

below, most of these measures indicate that Treasury liquidity is within historical norms, and that 

post-crisis regulatory changes do not appear to have had a notable impact. 

Treasury bid-ask spreads, calculated as the difference between highest bid and the lowest 

ask, are presented in Figure 12. The Figure shows that bid-ask spreads were substantially higher 

during the crisis, suggesting that the cost dimension of liquidity was higher and liquidity lower 

during that period of market stress. However, bid-ask spreads have been low and stable since 

then, consistent with the view that this dimension of Treasury market liquidity is now back to 

pre-crisis levels. As discussed above, we recognize that bid-ask spreads do not fully reflect the 

supply of liquidity. Therefore, we turn to the evidence on other facets of liquidity. 

We present the time series of Treasury price impact, order book depth, and trade size in 

Figures 13 through 15. Price impact is motivated by Kyle (1985) and is calculated as the 

estimated price change per $100 million net order flow. Order book depth is the quantity of 

securities that is explicitly bid for or offered for sale at the best five bid and offer prices in the 

BrokerTec limit order book. Lower impact, more depth, and larger trade sizes are indicative of 

higher liquidity. 

84 
Figure 12. Treasury bid-ask spreads 

The chart plots the 21-day moving averages of average daily bid-ask spreads for on-the-run notes 

in the interdealer market. Price is per $100 par. Spreads are measured in 32nds of a point where 

a point equals one percent of par. Data is from BrokerTec. Source: Adrian et al. (2017a). 

Figure 13. Treasury price impact 

The chart plots the 4-week moving average of slope coefficients from weekly regressions of 5­

minute price changes (calculated using bid-ask midpoints) on 5-minute net order flow for on-therun notes. Price is per $100 par. Price impact is reported in 32nds of a point per $100 million of 

net order flow. Data is from BrokerTec. Source: Adrian et al. (2017a). 

85 
Figure 14. Treasury order book depth 

The chart plots the 21-day moving averages of average daily order book depth for on-the-run 

notes. Order book depth is summed across the top five levels of both sides of the order book and 

is reported in billions of dollars. Data is from BrokerTec. Source: Adrian et al. (2017a). 

Figures 13 through 15 show that these liquidity measures follow a similar pattern: 

liquidity dramatically deteriorated during the crisis, rebounded in the post-crisis period, and 

deteriorated again around other periods of market stress (May 2013 and October 2014). While 

price impact is now at a pre-crisis level, the current level of order book depth and average trade 

size paint a less clear picture. Both measures increased through 2013 but then began to decrease 

and are now at a relatively low level compared to the pre-crisis period. However, it is difficult to 

interpret this decline as evidence of a reduction in liquidity for several reasons. First, as 

discussed above, both measures are only partial indicators of liquidity. Second, depth estimated 

from the order book might be substantially lower or higher than true depth. For example, market 

participants may be willing to trade larger quantities than they quote, if market participants 

submit the same quote to different outlets with the intent to execute at one outlet and cancel the 

remaining quotes. Finally, declining trade sizes might simply reflect the increased presence of 

86 
high-frequency trading in the Treasury interdealer market and their preference for smaller trade 

sizes as documented in the Joint Staff Report (2015). 

Figure 15. Treasury trade size 

The chart plots the 21-day moving averages of average daily trade size in the interdealer market 

in millions of dollars for on-the-run notes. Data is from BrokerTec. Source: Adrian et al. 

(2017a). 

The two funding liquidity measures analyzed in Adrian et al. (2017a), namely yield curve 

fitting errors and RefCorp spreads, show no notable deterioration in liquidity over time. As 

discussed above, yield curve fitting errors (Hu et al. (2013)) reflect unexploited profits that may 

result from liquidity constraints in other markets, and the yield spread between bonds issued by 

the Resolution Funding Corporation and those issued by the U.S. Treasury (Longstaff (2004)) 

should reflect only differences in liquidity. Both measures substantially increased during the 

crisis, but fitting errors revert to levels similar to the pre-crisis period. RefCorp spreads, though 

lower than during the crisis, remain slightly elevated. This appears to be inconsistent with the 

adverse effects of regulatory reforms, such as the Volcker Rule or Basel III, on Treasury 

liquidity. Similar to the above caveat, we recognize that these measures reflect only one 

dimension of liquidity. 

87 
 

Trebbi and Xiao (2015) present a more systematic analysis of the effect of these reforms. 

The authors examine a sample of 1,124 Treasury bills, notes, and bonds traded between April 1, 

2005, and December 31, 2014. Using a statistical method that allows them to identify structural 

breaks in market liquidity without knowing the exact timing of such breaks, they look for 

structural breaks in four Treasury market liquidity measures: (1) yield curve fitting errors, (2) onthe-run premia, (3) return autocovariances, and (4) turnover.122 Trebbi and Xiao (2015) 

document that fitting errors, on-the-run premia, and return autocovariances show deterioration 

during the crisis, but then normalize, consistent with the evidence in Adrian et al. (2017a). They 

also find that turnover and return autocovariances show a small break in late 2011, close to the 

release of the first proposed Volcker Rule in November 2011, but the break is to a higher 

liquidity level. Only turnover appears to break to a lower liquidity level in 2008 (see Figure 16), 

but this may be an artifact of quantitative easing, which resulted in increased Treasury issuance 

and large Treasury holdings by the Federal Reserve in the aftermath of the crisis. Trebbi and 

Xiao (2015) conclude that post-crisis regulatory changes do not seem to have resulted in a 

statistically or economically significant deterioration in Treasury market liquidity. 

Finally, Adrian et al. (2015) construct a Treasury illiquidity index as the first principal 

component of the inverse of market depth, quoted bid-ask spread, and returns autocovariance 

(Roll (1984)). That is, the index captures a single factor that is responsible for most of the 

common variation in the three liquidity measures and therefore reflects both transaction costs 

and market depth in a composite measure of liquidity. As shown in Figure 17, the illiquidity 

index spikes substantially around the 2007-2008 financial crisis and then a few more times, 

though to a lesser extent, through the end of 2011. After that, the only notable increase in the 

122 See Part B.III.C above for the description of various liquidity measures unique to the Treasury market. 

88 
index occurs around the Taper Tantrum of 2013. For other post-2011 periods, the index remains 

at or below pre-crisis levels. 

Figure 16. Treasury (negative) turnover 

The chart plots the time series of negative turnover (blue line), defined as minus one times 

annualized monthly trading volume divided by amount outstanding, and its estimated mean (red 

line). The solid vertical line indicates the passage of the Dodd-Frank Act (July, 2010). The 

sample period is from April 2005 to December 2014. The data frequency is monthly. The grey 

area indicates recession. Source: Trebbi and Xiao (2015). 

Figure 17. Treasury illiquidity index 

The chart plots a Treasury illiquidity index that is calculated as the first principal component of 

daily (inverse) market depth, the quoted bid-ask spread, and Roll’s effective bid-ask spread for 

the on-the-run 10-year note. Roll’s measure uses 1-minute intraday returns to compute daily 

autocovariance; the daily depth averages intraday 1-minute order book depth at the innermost 

three tier. Data is from Bloomberg, BrokerTec, and the FRB. Source: Adrian, et al. (2015). 

To gain additional insight into market resilience after the crisis, several studies focus on 

Treasury liquidity during periods of market stress. Some market participants have argued that 

89 
 

 

regulation-induced dealer balance sheet constraints may have amplified the rise in yields and 

volatility,123 so several studies addressed this issue in two stress contexts. First, Adrian et al. 

(2013) and Adrian et al. (2017a) study Treasury market liquidity during the “Taper Tantrum” of 

2013. These studies show that less constrained dealers reduced their net positions during the 

stress period to a greater extent than more constrained dealers. The papers conclude that dealers’ 

unwillingness to engage in market making may have been driven by increased uncertainty and 

repricing of duration risk, but not by balance sheet constraints. 

A second incidence of Treasury market stress was the Flash Event of October 15, 2014, 

when yields rapidly fell and then rose within a short time interval. This event was extensively 

analyzed in the Joint Staff Report (2015). While the report does not identify the exact cause of 

the event, it establishes no direct link between the event and post-crisis regulatory changes. 

Importantly, it indicates some important structural changes in the Treasury market that may have 

an impact on liquidity. The report documents that the composition of participants in the 

Treasury market has changed substantially, with many market participants using electronic 

trading strategies and execution systems. As Table 10 indicates, principal trading firms (PTFs), 

which are principal investors that employ automated trading strategies, now account for more 

than half of the trading volume in the electronically brokered Treasury market. Furthermore, 

their trading activity is highly concentrated: the ten most active PTFs account for 93% of PTF 

trading volume, while the ten most active dealers account for only 77% of dealer trading volume. 

The report concludes that the growth of automated trading in the Treasury market introduces new 

challenges around operational risk, oversight and risk management, and market liquidity. 

123 See Matt Cameron & Lukas Baker, The great unwind: Buy-side fears impact of market-making constraints, RISK 

(July 30, 2013). 

90 
 

Table 10. Treasury market participants 

Bank/ Principal Trading 

Dealer Firm 

Number of Participants 45 40 

Volume 35% 56% 

Top 10 Volume Share 77% 93% 

Top 10 HHI 0.11 0.23 

The table presents market participant composition statistics for the 10-year, on-the-run Treasury 

during April 2 through April 17, 2014. HHI is Herfindahl-Hirschman Index. Data is from 

BrokerTec. Source: Joint Staff Report (2015), Tables 3.3 and 3.7. 

It is important to note that the assessment of Treasury market liquidity in this section 

focuses on the Treasury cash market. However, an investor looking to gain exposure to U.S. 

interest rates can do so in the derivatives market as well through synthetic positions in futures, 

options, or swaps. To the extent that liquidity in the cash and derivative markets is additive, our 

focus on liquidity measures from the cash market may underestimate overall Treasury liquidity. 

For instance, the price impact measures discussed above consider price change in relation to net 

order flow from the cash market only. According to the Joint Staff Report (2015), during April 2 

through April 17, 2014, average daily volume was $53 ($44) billion in the cash market compared 

to $132 ($248) billion in the futures market for Treasuries maturing in 5 (10) years. Because 

trading activity in the cash Treasury market is only a small fraction of total trading activity in 

Treasury-related interest rate markets, the true price impact of the aggregate order flow to all 

Treasury-related markets may be smaller than reported above, because some of that order flow 

might be absorbed by the derivatives market and, thus, might not be visible to observers and 

might not be accounted for in the cash market price impact calculations. Therefore, the 

estimated price impact might overstate the true price impact of the overall order flow on all 

related markets. 

91 
 

 

On the other hand, other dimensions of liquidity may not be additive across the cash and 

derivative markets. Dobrev and Schaumburg (2015) analyze quotes by high-frequency traders in 

the Treasury cash and futures markets and document that trades in the futures market are 

instantaneously followed by decreased depth in the cash market. This is consistent with 

contemporaneous quotes in each market reflecting more than the total quantity these traders 

intend to trade and suggests that Treasury depth may not be additive across the cash and 

derivative markets. Finally, as we noted earlier,124 differential impact of some regulations on the 

Treasury cash market and the Treasury derivatives market might change relative liquidity 

conditions of the two markets over time. 

E. Summary 

Overall, we find no consistent empirical evidence that Treasury market liquidity 

deteriorated as a result of post-crisis regulatory changes. The studies assessed in this section 

analyze a wide range of liquidity measures that quantify different dimensions of liquidity and use 

a variety of empirical methods to examine changes in these measures. None of these studies 

provide empirical support for a causal link between various regulations, and in particular, the 

Volcker Rule, and changing Treasury market liquidity conditions. 

124 See footnote 114 above. 

92 
IV. Corporate	 	 Bonds

A. Introduction 

As analyzed in Part A, the U.S. corporate bond market is one of the world’s largest 

sources of capital for companies. In addition, the corporate bond market provides valuable 

investment opportunities for market participants. Similar to Treasuries, corporate bonds are 

sensitive to interest rate fluctuations and provide investors with opportunities to gain exposure to 

interest rate risk. Further, corporate bonds enable market participants to gain exposure to the 

credit risk of individual issuers. In this Part we explore the liquidity of corporate bonds in the 

time series and in the cross section. We consider existing research as well as generate new 

estimates for a number of measures that reflect various aspects of liquidity. As discussed below, 

we find mixed evidence on the direction and magnitude of time series changes in corporate bond 

liquidity. While some metrics point to an improvement in liquidity conditions, others show no 

economically significant changes, yet others suggest liquidity may have declined. Crosssectional analysis further indicates that the evolution of liquidity has not been uniform across 

various groups of issuers and bonds. Lastly, we show that some of the observed changes in 

corporate bond liquidity appear to have occurred in the crisis and post-crisis periods, prior to 

regulatory action. 

Importantly, while some measures allow us to delineate liquidity supply from liquidity 

demand, other measures may reflect both, obscuring inference. As discussed in prior sections, 

we continue to recognize challenges to causal inference, including the prolonged regulatory 

timeline, market anticipation of regulatory changes, and a lack of cleanly identified and 

comparable control and treatment groups. As a result of these factors, we are unable to rely on 

common econometric techniques for testing causal impacts, and as discussed below, existing 

93 
research lacks consensus concerning the direction of and fundamental drivers behind the 

observed liquidity changes. 

Below we critically examine existing research on various aspects of corporate bond 

market liquidity over time and perform data analysis using commercially available and 

regulatory data. We first consider existing results (Part B.IV.B.1 and B.IV.B.2) and present our 

own analysis of various metrics (Part B.IV.C) of corporate bond liquidity, identifying those 

dimensions of liquidity that may have improved and those that may have deteriorated over time. 

Next, we discuss several alternative explanations for observed changes in liquidity metrics. As 

we noted above, the passage of Dodd-Frank reforms, including the Volcker Rule, and Basel III 

may have contributed to changes in corporate bond liquidity. However, it is also possible that 

rules requiring post-trade transparency have impacted bond liquidity, and that the financial crisis 

resulted in changes in risk preferences among dealers and customers. We review existing 

research on these potential alternative effects (Part B.IV.B.3). We also decompose the evolution 

in liquidity metrics into several time periods, including TRACE phase-in, pre-crisis, crisis, postcrisis, regulatory and post-regulatory time periods (defined in detail and presented in Part 

B.IV.C.). We report and discuss the timing of observed changes in liquidity measures 

throughout the analysis. 

Further, we consider the role of dealers and market structure in the provision of liquidity 

by various groups of participants. Corporate bond markets have traditionally been structured as 

OTC markets where dealers own or acquire the bonds to facilitate transactions with customers or 

other dealers. In this “principal” market, customers compensate dealers for supplying liquidity 

through the bid-ask spread or the “markup” as measured by the difference between a dealer’s 

purchase and sale price. This is in sharp contrast to an “agency” market where transactions are 

94 
brokered between two customers, and the intermediary charges commission for its service as an 

agent. 

This principal market structure requires dealers to acquire positions to facilitate 

transactions, and trading of bonds primarily occurs via bilateral transactions between a dealer 

and a customer or between two dealers. Dealers with adequate capital will often hold inventories 

of bonds to more readily facilitate customer buying and selling activity. Furthermore, without a 

central exchange, the corporate bond market is highly fragmented and pre-trade pricing 

information is not broadly disseminated. 

Parts B.IV.B.1 and B.IV.B.2 explore existing research on the role of dealer provision of 

liquidity before and after regulatory reforms and in times of market stress. We also discuss 

whether macroeconomic factors, such as the low interest rate environment, may influence dealer 

behavior by affecting the profitability of traditional dealer business models. Moreover, to the 

extent that electronic trading may impact the role of dealers and nondealers in the provision of 

liquidity, market structure may influence observed changes in liquidity metrics. Part B.IV.B.3 

explores existing research on the role of electronic trading, and Part B.IV.C.3 analyzes unique 

regulatory data on corporate bond activity on an ATS. Finally, we recognize that single-name 

CDS are an alternative credit market, and funds may serve as another tool for investors to gain 

exposure to corporates. We further explore these markets in Sections B.V and B.VI. 

B. Existing Research 

A large body of finance research is devoted to recent changes in corporate bond market 

liquidity. In this section, we consider existing research on overall trends in corporate bond 

liquidity as well as potential drivers behind observed changes in dealer and non-dealer provision 

of liquidity in recent years. The discussion below is organized as follows. 

95 
 

	 	 		

 

First, we assess existing research on various liquidity metrics and observe mixed results 

for different measures of liquidity, including some evidence that certain aspects of liquidity have 

remained stable or actually improved following regulatory reforms (e.g., transaction costs, 

turnover in less active bonds, daily trades of blocks in active bonds). Second, we distinguish 

between liquidity provision in normal times and in times of market stress, and we analyze the 

evidence on the role of dealers during these periods. Our specific focus is on whether dealers 

provide sufficient liquidity in times of market stress when demand for liquidity increases, or 

whether they become less willing to commit capital and engage in principal trading on stressful 

days. Finally, we turn to the evidence on potential drivers of changes in liquidity. Sections 

II.B.3a through II.B.3c explore and critically assess research on the impacts of regulations 

(Dodd-Frank, Volcker and Basel III), post-trade transparency, and electronic trading on various 

aspects of corporate bond liquidity.125 

1. Dimensions of Liquidity

In this section, we consider existing research on the evolution of different dimensions of 

corporate bond liquidity. As analyzed in Part A, debt issuance remains a vital source of capital 

for businesses, and primary issuance in the U.S. corporate bond market has grown tremendously 

since the early 2000s. Public debt issuance in the United States grew from about $0.8 trillion in 

2005 to about $1.3 trillion in 2016 (Figure 2). Against this backdrop of increasing primary 

issuance, we turn to the analysis of changes in secondary market corporate bond liquidity. 

125 While the analysis of existing research on corporate bond liquidity draws primarily from academic papers, our 

observations are largely consistent with existing regulatory and other related studies, including Mizrach (2015), 

Fender and Lewrick (2015), Adrian et al. (2017a), Bech et al. (2016), BIS (2014, 2015, 2016), IOSCO (2016), 

BlackRock (2014, 2016), PWC (2015), and CFA Institute (2016). 

96 
	 	 	

 

 

As noted in the introduction to Part B, liquidity broadly refers to the ability to execute 

transactions of a required size in a short period of time with little price impact. Existing research 

has developed a large number of liquidity measures that reflect different dimensions of liquidity. 

In this subsection, we consider the evidence on three broad groups of measures: trading activity 

(e.g., trading volume, turnover), transaction costs (e.g., Roll (1984), Schultz (2001), and 

Edwards, Harris, and Piwowar (2007) price impact measures),126 and measures of liquidity 

supply (e.g., dealer inventory, trade sizes). We also recognize that the evolution of liquidity has 

not impacted all bonds uniformly, and we explore both aggregate liquidity trends and crosssectional heterogeneity in liquidity metrics. 

a). Trading Activity

As discussed in Part B.II, trading activity metrics generally reflect the overall amount of 

trading in a particular bond or market. For example, greater volume or turnover point to more 

activity, and an increase in the number of block trades would suggest that market participants are 

able and willing to transact at larger trade sizes (blocks). 

Trends in different measures of trading activity in corporate bonds are mixed. Trading 

volume increased from about $5 trillion to about $8.3 trillion between 2003 and 2014, while the 

turnover ratio dropped only slightly from 1.2 in 2003 to 1.1 in 2014 (IOSCO, 2016). 

Importantly, changes in trading activity have not been universal across subsets of the bond 

126 Schultz (2001) estimates transaction costs by regressing the difference between the trade price and the reference 

price (contemporaneous bid quote) on a trade side dummy (variable that takes a value of one for buys and zero for 

sells); the average round-trip transaction cost is the regression coefficient on the trade side dummy. Edwards, Harris, 

and Piwowar (2007) present transaction cost estimation methodologies that are appropriate for corporate bond 

transaction data and the secondary trading market structure discussed in Part B.IV.A. Using data that identifies 

dealer-dealer trades and dealer-customer trades, the authors propose a measure of transactions costs (referred to as 

“EHP measure”) equal to the difference between an average adjusted customer buy price and an average adjusted 

customer sell price or twice the difference between a customer price and a dealer price. The EHP measure can be 

interpreted as imputed effective round-trip costs. As discussed in Part B.II, the Roll (1984) measure relies on 

estimating serial covariances of price changes. 

97 
 

market. Mizrach (2015) analyzes trading activity measures separately for the 1000 most actively 

traded bonds and for less actively traded bonds. As shown in Figure 18 below, the paper finds 

that the median turnover of the 1000 most active bonds declined from approximately 1.8% in 

2005 to approximately 1% in 2015, while the turnover of the less active bonds remained at 

approximately 0.16%. 

Figure 18. Median daily turnover (% of Issue) 

This figure plots the median daily turnover ratio in the most active 1,000 issues for each year 

(left scale) and the turnover in the less active bonds (right scale.) Source: Mizrach (2015) 

Moreover, the most active bonds are increasingly likely to include recently issued bonds 

as shown in Figure 19 below. From 2003 to 2007, bonds issued in the last 90 days comprised 

less than 20% of the 1000 most active bonds. Since 2011, such newly issued bonds represent 

about 45% of the most active bonds, which may be consistent with the rise in new issues in 

recent years. 

98 
 

Figure 19. Percentage of most actively traded bonds issued in the last 90 days 

This figure plots the average annual percentage of bonds among the most active 1,000 that were 

issued within the last 90 days on a rolling basis. Source: Mizrach (2015) 

Mizrach (2015) decomposes the number of block trades for the 1000 most active bonds 

by block size, as shown in Figure 20 below. The results appear to be mixed. On the one hand, 

the number of trades in blocks of $5 million to $10 million per day has declined by slightly more 

than 100 between 2003 and 2015. On the other hand, the number of trades for blocks of $10 

million or larger per day has actually increased slightly during the same period for the two larger 

size groups. 

At the same time, Mizrach (2015) finds that trade size for the 1000 most active issues 

decreased by approximately 35% between 2007 and 2013, and the proportion of total volume 

traded in large blocks (defined as blocks of $5 million or more) has decreased by almost 15%. In 

a similar vein, Bessembinder et al. (2016) define blocks using a $10 million trade size threshold 

and finds that block trade volume relative to aggregate volume declined from 27% in pre-crisis 

period to 22% in the regulatory period, while the corresponding average trade size decreased 

from $3.2 million to $1.8 million. 

99 
 

	 	

Figure 20. Number of block trades per day in active bonds 

This figure plots the number of trades between $5 and $10mln, between $10 and $25mln 

$25mln, and over $25mln in size in the most actively traded bonds in those size groups. Source: 

Mizrach (2015) 

b). Transaction Costs

Transaction costs reflect the price dimension of liquidity, and lower transaction costs 

would generally point to an improvement in that liquidity dimension, as discussed in Part B.II. 

Estimating corporate bond transaction costs requires a different estimation methodology from 

estimating transaction costs for equities because of important differences in data and market 

structure. Corporate bonds primarily trade in dealer-dominated, OTC market with limited price 

transparency. Unlike equities data, quotation data for corporate bonds are not publicly available. 

As a result, it is difficult to estimate transaction costs using quoted spread or quotation data. In 

addition, many corporate bonds are traded infrequently making transaction data scarce. 

Evidence on transaction costs and price impact tends to show that these measures have 

improved over time, with the exception of the financial crisis period, during which costs were 

100 
substantially higher compared to other periods. Schestag et al. (2016) calculate high frequency 

transaction costs measures, including Roll (1984), Schultz (2001), and Edwards, Harris, and 

Piwowar (2007) measures (Roll, Schultz, and EHP respectively) between 2004 and 2012, 

without breaking down the sample into sub-periods. As shown in Figure 21, transaction costs 

increased markedly during the financial crisis, but have since reverted back to below pre-crisis 

levels. 

Figure 21. Transaction costs (TC) 

This figure plots the time series of monthly high-frequency transaction cost measures calculated 

from intraday enhanced TRACE: Schultz (2001) and Edwards, Harris, and Piwowar (2007) 

measures use regressions to estimate costs from the difference between trade price and reference 

prices; relative difference between average customer buy and average customer sell (average bidask); round-trip transaction cost (Feldhütter (2012)); negative autocovariance of returns from 

trade prices (Roll (1984)); interquartile range of trade prices (e.g., Pu (2009)). Source: Schestag 

et al. (2016). 

When considering transaction costs in the corporate bond market, dealer dominance and 

low pre-trade transparency make it important to distinguish between costs on dealer-to-customer 

trades and dealer-to-dealer trades. Transaction costs on dealer-to-customer principal trades 

directly reflect customer trade execution costs. Bessembinder et al. (2016) estimate transaction 

101 
costs for different times, and defines five distinct sub-periods: (1) the TRACE phase-in period 

(1/2003-12/2005), (2) the pre-crisis period (1/2006-6/2007), (3) the crisis period (7/2007­

4/2009), (4) the post-crisis period (5/2009-6/2012), and (5) the “regulatory” period (7/2012­

5/2014), a period of heightened post-crisis regulatory activity starting in July 2012 when the 

Volcker Rule was originally scheduled to be effective. The paper reports that the average 

transaction cost in customer-to-dealer principal trades has evolved from 61 bps in the TRACE 

phase-in period to 52 bps in the pre-crisis period, 72 bps in the crisis period, 62 bps in the postcrisis period, and 42 bps in the regulatory period. Bessembinder et al. (2016) results suggest that 

liquidity may be higher along the cost dimension, reflecting lower transaction costs for 

customers buying from or selling to dealers, during the regulatory period compared with other 

sample periods. 

In addition, transaction costs vary based on credit quality, trade size, and issue size. 

Although Bessembinder et al. (2016) estimate the transaction costs of investment grade bonds of 

40 bps were lower than that of high-yield bonds of 46 bps in the regulatory period, this 

relationship was not stable in their sample. For example, during the crisis period, the transaction 

cost of investment grade bonds was higher than that of the high yield bonds (78 bps versus 52 

bps). To the extent that transaction costs reflect a dimension of liquidity, this suggests that 

investment grade bonds were more liquid than high yield bonds during the regulatory period, 

while the opposite was the case during the financial crisis. 

Existing literature also documents an inverse relationship between transaction costs and 

trade size across time (Bessembinder et al. (2016)). For example, small trades ($100,000 or 

lower) had estimated average transaction costs of 61 bps to 96 bps in different periods. At the 

same time, blocks of $5 million or larger traded at a cost of 17 bps to 32 bps during the sample 

102 
	 	 	

period. The inverse relationship between transaction costs and trade size continued to hold 

across all 5 sub-periods in their sample. This result is consistent with retail trades involving 

higher transaction costs. 

c). Liquidity Supply

When transaction costs are estimated for completed trades, they do not reflect difficulty 

in arranging trades or the incidence of trades that may be demanded, but not completed. We now 

consider existing research on the evolution of measures related to dealer balance sheets and 

inventory, which may reflect dealers’ role as liquidity providers and their managing of inventory. 

Bessembinder et al. (2016) show that dealers have shrunk their intraday capital 

commitment, measured as the absolute difference between their daily accumulated buy volume 

and sell volume. Specifically, they find that this measure declined from 2.29% relative to 

aggregate volume in the pre-crisis period to 1.74% in the crisis period, and to 0.97% in the 

regulatory period. Thus, dealers’ average intraday capital commitment dropped by 68% from the 

pre-crisis period to the regulatory period. Figure 22 shows a decline in intraday capital 

commitment for investment grade and high yield portfolios. We note that the decline in intraday 

capital commitment appears to have started in approximately mid-2008 and preceded the passage 

of post-crisis regulations. These results may be consistent with a reduction in dealers’ 

willingness to carry inventories post-crisis, or could reflect an increased ability of dealers to 

dispose of their bond inventories. 

In addition, the paper examines dealer capital commitment to facilitate block trading in 

various time periods. The paper defines block trades as transactions of at least $10 million, and 

constructs a block-trade offset measure, defined as the percentage of the block quantity reversed 

by the end of the day. The paper finds that this measure increased from 39.8% in the pre-crisis 

103 
period to 47.7% in the crisis period, and to 59.9% in the regulatory period. Thus, dealers’ blocktrade offset rose by 51% from the pre-crisis period to the regulatory period, which may be 

consistent with a reduction of dealer risk taking, regulatory impacts, or an enhanced ability of 

dealers to offset block trades. 

Figure 22. Intraday capital commitment 

Panel A. Intraday capital commitment / Aggregate volume (Top 75% Sample) 

Panel B. Intraday capital commitment / Total outstanding (Top 75% Sample) 

This figure plots the 6-month moving average intraday capital commitment for investment grade 

(solid blue) and high yield (dashed brown) portfolios. Source: Bessembinder et al. (2016). 

104 
	 	 	 	 	 		

Goldstein and Hotchkiss (2017) focus on the effect of expected illiquidity of a traded 

bond for dealer decision to take on inventory. They find that dealers take on less inventory risk 

when expected liquidity of a bond is lower, i.e., dealers are less likely to absorb a trade into 

inventory and are more likely to offset the transaction on the same day when a bond is less 

liquid. The paper also finds that the effect of expected bond illiquidity on spreads and on the 

likelihood of dealers offsetting the trade on the same day is particularly severe for the least 

actively traded bonds and for bonds with low credit quality. For instance, the paper documents 

that dealers offset approximately 75% of trades in the lowest rated, least actively traded bonds. 

At the same time, they estimate the figure at only 55% for the highest credit quality, most 

actively traded bonds. If dealers are less likely to absorb inactively traded bonds with low credit 

quality into inventory, such bonds can have lower roundtrip costs than higher quality actively 

traded bonds. This offers a potential explanation for the decline in transaction costs accompanied 

by decreases in dealer capital commitment. Namely, dealers may be less likely to engage in 

market making when bonds are less liquid, and lower transaction costs in observed trades may be 

a reflection of dealers choosing not to take certain groups of bonds into inventory. 

2. Liquidity Provision in Times of Stress

The evidence on the evolution in aggregate liquidity metrics and cross-sectional 

heterogeneity in such metrics presented so far suggests that some trading activity and transaction 

cost measures either have not deteriorated or have actually improved in recent years. At the 

same time, dealer willingness to provide liquidity may have decreased in recent years, as 

reflected in reduced dealer capital commitment. The above results do not distinguish, however, 

between dealer provision of liquidity in normal times and in times of market stress, when 

105 
liquidity is needed most. We now consider evidence on dealer provision of liquidity in times of 

market stress. 

Bessembinder et al. (2016) define times of market stress as days when customer-dealer 

trading volume exceeds the 6-month average of daily trading volume for that bond by more than 

two standard deviations. The authors focus on the activity of the single most active dealer, a 

choice that reflects an understanding that institutional customers may delegate execution of a 

large order to a single dealer. They find that the fraction of agency trading on stressful days is 

slightly higher in the regulatory period than in the benchmark period and that the proportion of 

stressful day activity completed on a principal basis but reversed before the end of the day (i.e. 

not absorbed into the dealers’ overnight inventory) increased. However, dealers’ willingness to 

commit capital on stressful days, measured by the proportion of stressful day activity that is 

absorbed into the dealers’ overnight inventory, remains at crisis period levels during the 

regulatory period. Figure 23 shows the evolution in stressful day activity where the dealer 

commits overnight capital. 

Figure 23. Dealer overnight capital commitment on stressful days 

This figure shows the fraction of trading activity where the dealer commits overnight capital on 

days of market stress for investment grade (solid blue) and high yield (dashed brown) portfolios. 

Source: Bessembinder et al. (2016). 

106 
Di Maggio et al. (2017) study the role of dealer networks and trading relationships in 

times of stress, using high volatility and financial crisis periods to proxy for stress periods. The 

paper finds that the role of stronger relationships and dealer centrality is particularly acute in 

times of stress. During stress periods, dealers provide less liquidity to clients and peripheral 

dealers. During the peak of the crisis, core dealers charged higher spreads to peripheral dealers 

and clients but lower spreads to dealers with whom they had strong ties. Further, during the 

Lehman collapse, dealers cut back their inventory more for bonds that clients were selling more 

aggressively. The paper concluded that dealers’ inability or unwillingness to provide liquidity 

partly contributes to market illiquidity during times of stress. We note that because of a lack of 

data on dealer inventories, the paper constructs dealer inventories from transaction data, which 

requires fairly strong assumptions on beginning-of-period positions (see more in Part B.IV.B.3). 

As a result, we recognize that these empirical estimates could be sensitive to such assumptions. 

In contrast, Choi and Shachar (2016) directly observe dealers’ corporate bond positions 

and find that dealers’ inventory increased sharply following the collapse of Lehman Brothers. 

They argue, therefore, that dealers supplied liquidity during the crisis, and that price declines 

during the crisis were concentrated in bonds with available CDS contracts and active basis 

trading. They argue that the unwinding of CDS-bond basis trading by hedge funds and other 

highly levered traders may have been the main culprit. However, we also recognize that the 

result is consistent with dealers being unable to unwind their positions. Further, the paper does 

not test whether dealers were providing liquidity primarily to customers or to other dealers (or 

both), and the authors did not consider the transaction costs and fees dealers may have charged 

for the provision of liquidity in stress times. 

107 
Goldstein and Hotchkiss (2017) find that dealers are more likely to act like brokers and 

are less likely to engage in market making when illiquidity increases. This result is especially 

important in explaining changes in dealer activity in times of stress when overall market 

illiquidity rises. The paper shows that dealers were less likely to take on inventory risk and were 

statistically and economically significantly more likely to offset trades on the same day during 

the financial crisis (second quarter of 2007 through second quarter of 2009). Further, this 

behavior persists across credit quality categories: in times of severe market stress when dealers 

are less willing to commit capital, even higher rated bonds may be traded conditional on dealers’ 

ability to offset the trade. The paper also finds that dealers continue to be somewhat more likely 

to engage in offsets post-crisis period relative to the pre-crisis period. 

Further research into the role of dealers during stressed times is warranted. Preliminary 

evidence, however, indicates that dealers may not always lean against the wind in times of severe 

market stress. We continue to recognize that potential reductions in dealer provision of liquidity 

may not adversely impact market liquidity if changes in market structure enable customers to 

seek liquidity from other customers. 

Now that we have considered existing research on the evolution of liquidity over time in 

normal times and in times of stress, we turn to the research on potential drivers to changes in 

liquidity. In the subsections that follow, we explore and critically assess research on the 

relationship between regulations, post-trade transparency, emergence of and liquidity in the 

single-name CDS market, electronic trading, and market structure on various aspects of 

corporate bond liquidity. 

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3. Drivers of Changes in Liquidity

a). Dodd‐Frank, Volcker Rule, and Basel III

An emerging body of academic literature addresses various effects of regulatory reforms 

at issue on corporate bond liquidity and delivers mixed results. In this section, we consider this 

evidence and explore how various aspects of empirical design may impact each study’s 

conclusions. 

First, one of two papers highlighted in the Treasury (2017) report127 is Bao et al. (2016). 

The paper uses TRACE data to study transactions in downgraded bonds relying on a differencesin-differences econometric test to produce three main results. First, the price impact for bonds 

newly downgraded from BBB to BB relative to other BB bonds increased after the 

implementation of the Volcker Rule, but is comparable to price impact measured during the 

financial crisis. Second, non-bank affiliated dealers take an increasingly higher proportion of 

dealer-customer trades relative to affiliated dealers, and bank affiliated dealers have increased 

the proportion of agency trades in their total volume (defined as offsetting a customer trade by 

another trade within one minute, following Harris (2015) and Bessembinder et al. (2016)). 

Third, to understand whether Basel III implementation and Comprehensive Capital Analysis and 

Review (CCAR) tests confounded their results, the authors identify subgroups of Volcker 

affected dealers that were constrained by CCAR and those that were not. They find that capital 

commitment has decreased for affected dealers that have neither failed nor conditionally passed 

CCAR tests. 

127 See U.S. Department of the Treasury, June 2017, “A Financial System that Creates Economic Opportunities: 

Banks and Credit Unions.” 

109 
Bao et al. (2016) measure capital commitment differently from Bessembinder et al. 

(2016), which may impact their results. As a general matter, optimal inventory levels for 

different dealers are not observable, and starting inventory levels are presumed to reflect dealer’s 

optimal inventory. Capital commitment is, then, measured as the accumulated buys net of 

accumulated sells relative to that starting inventory level (weighted over time). Bao et al. (2016) 

construct the measure over the course of a month, whereas Bessembinder et al. (2016) rely on a 

daily measure. This difference reflects varying assumptions about optimal inventory levels. 

Where the daily measure assumes that dealers begin each day with optimal levels of bond 

inventory, the monthly measure allows for inventory to continue to move away from optimal 

levels over multiple days and weeks. While the effect of this measurement difference on the 

paper’s conclusions is unclear, we recognize that assumptions regarding optimal inventory levels 

may impact estimates of dealer capital commitments. 

We also note that Bao et al. (2016) use the effective date of the Volcker Rule as the cutoff date to conduct their sub-period analyses. To the extent that markets anticipated the 

implementation of the Volcker Rule, this approach may mean that their estimates of impacts 

from the Volcker Rule are conservative and bias them against finding a significant result. At the 

same time, however, affected dealers that did not adjust to the reforms in the nearly 4 years prior 

to the effective date may be those with the highest costs of compliance and those that have 

under-reacted in the post-crisis and post-Dodd-Frank period, exhibiting the biggest reductions in 

capital commitments. Therefore, measuring their actions could lead the authors to overestimate 

the impact of the Volcker Rule. 

To attribute the reduction of dealer capital commitment to the effects of individual 

regulations, Bao et al. (2016) compare changes in market making for dealers affected by the 

110 
Volcker Rule, Basel III, and CCAR. They find that Volcker-affected dealers (including those 

not constrained by Basel III and CCAR) reduced market making during times of stress, while 

non-Volcker-affected dealers increased their liquidity provision during stress times. Bao et al. 

(2016) interpret these results to suggest that the Volcker Rule resulted in a net reduction in dealer 

provision of liquidity in times of stress. Since the liquidity provision declined both for Volckeraffected dealers constrained by Basel III and CCAR and those that were not, the paper concludes 

that the results are not consistent with the impacts of Basel III or CCAR. 

We note that the validity of such inference depends on whether the identifying 

assumptions behind difference-in-difference estimation are satisfied. Specifically, the two dealer 

groups must be following “parallel trends” in their market making activity prior to the shock. 

The Volcker Rule primarily affected bank-affiliated dealers, which carried large inventories 

during the crisis. As shown in our analysis of Financial and Operational Combined Uniform 

Single (FOCUS) data below, bank-affiliated dealers experienced large negative shocks to the 

profitability of their debt trading and reduced their inventory in the aftermath of the crisis and 

prior to the enactment of the Dodd-Frank Act and implementation of the Volcker Rule. At the 

same time, dealers that were not bank affiliates and not affected by the Volcker Rule carried 

smaller inventories during the crisis and did not experience precipitous declines in the 

profitability of trading during the crisis, suggesting a different trend in liquidity provision by 

these dealers. To the extent that the profitability of trading is an important determinant of dealer 

choice to engage in trading, this points to an alternative explanation: Volcker-affected dealers 

may have reassessed the risk-return tradeoff of providing liquidity in times of stress following 

the crisis and pulled back on liquidity provision during stressed days. To the extent that the 

parallel trends assumption for the two dealer groups may be violated, the validity of this 

111 
empirical methodology and resulting inference about causal impacts of the Volcker Rule and 

other regulations would be affected. 

Second, Dick-Nielsen and Rossi (2016) use a different empirical setting to examine the 

cost of immediacy for different bonds. The paper’s empirical design relies on the Barclay 

Capital Investment Grade Corporate Bond Index exclusion event as a quasi-natural experiment. 

When bonds are excluded from the Barclay Capital (formerly Lehman) Investment Grade 

corporate bond index, index tracking funds sell bonds to dealers and seek to execute these 

transactions quickly to minimize tracking error. Importantly, such bond trades are motivated by 

index exclusion rules and not information about bond fundamentals, meaning dealer bids are 

likely to reflect the price of immediacy and are less likely to be influenced by adverse selection 

costs of trading with more informed sellers. The paper introduces two main measures. First, the 

authors calculate the intertemporal bid-ask spreads, which are essentially the raw returns 

between the average dealer-purchase price and the average dealer-selling price on each day 

following the exclusion date. Second, they estimate abnormal dealer returns for the excluded 

bonds by subtracting the returns of a matched benchmark portfolio from the intertemporal bidask spreads. They find that the cost of immediacy has doubled for investment grade bonds, and 

more than tripled for high yield bonds between the pre-crisis (June 2002 – June 2007) and the 

post-crisis periods (January 2010 – December 2013; see detail in Figure 24 below.) They also 

find dealer inventories declined in the post-crisis period, and not during the crisis period itself, 

despite the intuition that dealers should have been more constrained in terms of their risk-bearing 

capacity during the crisis period. The authors conclude that lower capital commitment by 

dealers in the post-crisis period may be driven by regulatory reforms. However, we note that it is 

112 
 

 

 

also consistent with post-crisis changes in dealer risk aversion and the low interest rate 

environment during this period changing the profitability of traditional dealer business models. 

Figure 24. Cost of immediacy before and after the credit crisis (dealer level) 

(a) Maturity (EW) (b) Downgrade (EW) 

113 
 

 

 

(c) Maturity (VW1) (d) Downgrade (VW1) 

(e) Maturity (VW2) (f) Downgrade (VW1) 

Source: Dick-Nielsen and Rossi (2016) 

Third, Anderson and Stulz (2017) consider the evolution of liquidity metrics in normal 

times and in times of market stress during pre-crisis (2004-2006) and post-crisis (2010-2012; and 

2013-2014) periods. The paper finds that price-based liquidity metrics for small and large trades 

in 2013 through 2014 are generally better when compared against the pre-crisis 2004-2006 

levels. However, they document a reduction in turnover post-crisis, which may point to a 

decreased ability of market participants to execute trades. The paper further uses three different 

groups of stress events, including large swings in the Chicago Board Options Exchange 

Volatility Index (VIX),128 large increases in bond yields, and downgrades of investment grade 

bonds to high yield. They do not find statistically or economically significant deterioration in 

price impact around idiosyncratic stress events after the crisis compared to the pre-crisis period. 

The empirical approach in Anderson and Stulz (2017) differs from that in Bao et al. 

(2016) along several dimensions, which likely impacts the paper’s conclusions. Unlike the Bao 

et al. (2016) paper, which compared bonds newly downgraded to high yield with non128 The VIX is derived from the prices of options written on the S&P 500 index and represents a proxy for perceived 

financial market volatility. 

114 
downgraded high yield bonds, Anderson and Stulz (2017) compare downgraded bonds likely to 

trigger forced sales against other downgraded bonds unlikely to trigger such sales. Second, they 

control for potential differences in the information content of downgrades across economic 

cycles (e.g., pre-crisis compared to post-crisis), which may have confounded results in Bao et al. 

(2016). Finally, their pre-crisis period excludes the peak of the credit boom in 2007 and limits 

the subsample to 2004 through 2006, whereas Bao et al. (2016) define the pre-crisis period as 

January 1, 2006, through June 30, 2007. Anderson and Stulz (2017) find that the results in Bao 

et al. (2016) are sensitive to the choice of the pre-crisis period and that downgrades in the Bao et 

al. (2016) pre-crisis period do not appear to impact liquidity metrics, casting doubt on the 

robustness of that empirical design. 

In addition to bond downgrades, Anderson and Stulz (2017) use VIX and bond yield 

shocks as proxies for stress events. We note that such shocks, particularly within-sample VIX 

shocks, do not distinguish between shocks to liquidity supply and liquidity demand. Further, 

VIX shocks may not be indicative of dealer stress, particularly pre-crisis. This may lead the 

authors to overestimate liquidity provision in times of stress during the pre-crisis period, 

consistent with the paper’s finding of more adverse price impact of VIX shocks in the post-crisis 

period. 

Fourth, Adrian et al. (2017b) examine dealer liquidity provision using a methodology 

similar to Bao et al. (2016) and Bessembinder et al. (2016). Because Adrian et al. (2017b) do not 

focus on liquidity in stress scenarios, they are able to analyze a substantially larger sample 

(approximately 12,800 issues) compared with Bao et al. (2016)’s sample (687 issues by 218 

firms, including 45 firms in the “post-Dodd Frank” period and 55 firms in the “post-Volcker” 

period). The paper introduces three main sets of results. First, they find that bonds traded by 

115 
 

highly levered institutions and bank holding companies with small loan portfolio and higher 

trading revenues are less liquid. Second, more levered dealers and dealers with higher trading 

revenues have decreased transaction volume after the financial crisis. Third, such institutions 

have lower customer volume relative to dealer volume in the 2014-2016 “rule implementation” 

period. The paper interprets these results to suggest that more stringent leverage requirements 

and post-crisis regulatory reforms more generally may have reduced some dealers’ ability to 

provide liquidity. 

One interpretation of this evidence is that institutions most impacted by post-crisis 

regulations are less able to intermediate customer trades.129 However, three caveats are relevant 

for the interpretation of the results in Adrian et al. (2017b). First, we note that the paper splits 

the period after December 2009 into 2 sub-periods: the “Rule writing” and “Rule 

implementation” period. Unlike Bessembinder et al. (2016), and Bao et al. (2016), the paper 

does not have a “post-crisis” period per se. Given the regulatory timeline issues discussed 

above, it is difficult to disentangle regulation-driven changes in dealer behavior from post-crisis 

changes (due to, for instance, post-crisis and pre-regulation increase in risk aversion and 

updating of beliefs about the risks and returns of holding large inventories and traditional market 

making, evolving market structure, and a low interest rate environment). Second, the paper 

interprets changes to customer–dealer trading volume as evidence of dealer ability to 

intermediate trades. To the extent that dealers identified in the paper as more likely to be 

constrained have also suffered large losses on trading positions during the crisis, this evidence is 

also consistent with a decreased willingness of dealers to intermediate customer trades as a result 

of crisis-related changes to profitability of trading or risk aversion. Third, the paper focuses on 

129 See U.S. Department of the Treasury, June 2017, “A Financial System that Creates Economic Opportunities: 

Banks and Credit Unions.” 

116 
the activity of BHC affiliated dealers. To the extent that non-BHC affiliated dealers may have 

increased their activity, as suggested by Bao et al. (2016), the results in Adrian et al. (2017b) do 

not speak to the aggregate effect of regulatory changes on bond market liquidity. 

Fifth, Choi and Huh (2017) argue that the calculation of the bid-ask spread assumes that 

dealers provide liquidity to customers, which may underestimate actual transaction costs if 

liquidity is provided by customers. They find that a substantial amount of liquidity provision has 

moved from dealers to customers in the post-regulation period. Omitting trades in which 

customers provide liquidity and focusing only on trades in which customers demand liquidity, 

the authors estimate that spreads in recent years are higher compared to those in the pre-crisis 

period. In contrast, Bessembinder et al. (2016) focuses on dealer-to-customer principal trades 

and finds the average transaction cost, particularly for small trades (less than $100,000) and large 

trades (over $1,000,000), is lowest in the pre-crisis and regulation periods. The difference 

between these two results may stem from different proxies for transaction costs and the 

measurement of principal trading activity. 

As noted above, Bessembinder et al. (2016) find that overall transaction costs have 

declined over time, with a notable exception of the crisis period. This finding is consistent with 

Schestag et al. (2016) that examines a number of daily and intraday liquidity measures and finds 

that post-regulation transaction costs of bonds were at or below the pre-crisis level. They also 

find that intraday transaction cost measures are highly correlated. We note, however, that 

Schestag et al. (2016) uses daily bid-ask quotes from the Bloomberg Generic Composite Rate 

(BGN) and Composite Bloomberg Bond Trader (CBBT). The quotes from BGN are indicative 

rather than binding, and some of these quotes are stale. The paper acknowledges the 

shortcoming of BGN and suggests the use of executable quotes from CBBT, especially for the 

117 
transaction costs of institutional investors; however, the extent to which data sources may drive 

the paper’s empirical findings is unclear. 

Importantly, we note that existing studies on the impact of regulatory reforms on 

corporate bond liquidity impose time cutoffs identifying the “regulatory” period. However, as 

discussed above, regulatory reforms of interest were proposed, adopted, and have become 

effective over a prolonged period of time. For instance, the statutory Volcker Rule was first 

enacted on July 21, 2010, but final rules implementing the statutory Volcker Rule did not 

become effective until April 1, 2014, and compliance with the Rule’s proprietary trading 

restrictions was not required until July 21, 2015. The results of such analyses are, therefore, 

likely to be sensitive to the choice of the regulatory time period and may bias their results. 

To address this timing concern, and in contrast to the above studies of regulatory impacts, 

Trebbi and Xiao (2016) apply macroeconomic breakpoint methodologies (Bai and Perron, 1998, 

2003; Stock and Watson, 2011; Chen et al. 2014) to let the data determine the timing of 

structural breaks for a large set of liquidity measures from April 2005 to December 2014. The 

paper’s conclusions are consistent with Bessembinder et al. (2016) and Schestag et al. (2016). 

Specifically, the paper finds that transaction costs are not significantly worse in recent years 

compared to the pre-crisis period. 

One weakness of the breakpoint analysis of Trebbi and Xiao (2016) is that other 

contemporaneous policy measures, such as quantitative easing and bank bailouts, could have 

mitigated the impacts of the Volcker Rule, which may bias against finding deterioration in 

liquidity. The paper’s latent factor techniques require heuristic choices of certain parameters, 

and there are cases where these choices imply there are zero breaks in latent liquidity throughout 

the period of interest. Crucially, to cleanly identify the Volcker Rule effects, the authors need to 

118 
	 	 	

distinguish between bonds that were affected by the Volcker Rule and those that were not. 

Trebbi and Xiao (2016) assume that if a bond has at least one underwriter that is not subject to 

the Volcker Rule, the bond should be immune from liquidity effects, whereas bonds issued 

entirely by underwriters covered by the rule should experience Volcker-specific liquidity 

disruptions. If largely the same dealers make markets in affected and unaffected bonds, there 

may be spillovers from reduced liquidity in Volcker-affected bonds to Volcker unaffected bonds. 

This could impact the “control” group and bias the authors against finding statistically significant 

differences between two groups. The lack of significant liquidity disruptions in these results 

may simply reflect a lack of clear delineation between Volcker vs. non-Volcker affected bonds in 

this historical setting. 

b). Electronic Trading

As discussed above, dealer supply of liquidity may have declined post-crisis, as bank 

affiliated dealers reduced their capital commitment and shifted their business model from 

principal trading to agency trading post-crisis. However, prior sections presented mixed 

evidence concerning whether dealers actually provide liquidity in times of market stress and 

showed that many corporate bond liquidity measures have not deteriorated in the post-regulation 

period. This may point to an increasing role of alternative liquidity providers, such as non-bank 

dealers and institutional investors, and the emergence of electronic trading venues. We now turn 

to existing research on electronic trading in corporate bonds. 

Traditionally, corporate bonds traded via “voice” intermediation, though the fraction of 

buy-side participants trading corporate bonds on electronic venues may have increased in recent 

119 
 

years.130 Furthermore, the empirical results we present above indicate that corporate bond 

trading may be evolving toward a market structure where dealers increasingly engage in more 

agency or riskless principal based trading and carry less inventory. To the extent that electronic 

trading could facilitate agency or riskless principal based trading, it is important to understand 

various aspects of electronic trading for corporate bonds and how electronic trading is different 

from voice intermediated trading. For instance, one difference between voice intermediated and 

electronic trading is the broader (vs. bilateral) availability of or greater ease of access to pretrade information (e.g., quoted price and quoted size). 

Existing research on electronic trading venues is constrained by data availability. Most 

recently, Mizrach (2015) relies on survey data from Greenwich Associates (2015) to show that 

electronic platforms play an important role in facilitating trading of corporate bonds. The paper 

estimates that such electronic platforms were used for 80% of investment grade bonds and 43% 

of high yield bonds in 2014. Mizrach (2015) explains that, while most of these electronic 

platforms involve activities to facilitate trades, such as seeking or posting quotes, they do not 

provide direct electronic trading venues to corporate bond investors. Therefore, the paper 

suggests that fully automated electronic trading represents only 16% of volume weighted market 

share for investment grade bonds and 4% for high yield bonds. At the same time, when 

interpreting results that compare the performance of electronic trading venues to other trading 

methods, it is important to note that electronic trading venues may employ market mechanisms 

that are very different from traditional execution methods. 

Using electronic auction data from MarketAxess, Hendershott and Madhavan (2015) 

compare transaction costs of electronic and voice trades for 4.6 million customer-to-dealer 

130 See, e.g., Greenwich Associates (2015). 

120 
 

 

corporate bond trades between January 2010 and April 2011. In these auctions, investors query 

multiple dealers electronically, and dealers may respond with their bids in 5-20 minutes. At the 

end of each auction, investors either select the best quote to trade or choose not to trade at all.131 

The paper finds that electronic trading is concentrated in bonds we expect ex ante to be 

more liquid, but reduces transaction costs after accounting for endogenous venue selection, 

among other things. For instance, for the smallest trade size up to $100K,132 the average 

transaction cost is 22 bps for electronic trades and 87 bps for voice trades for investment grade 

bonds. For high yield bonds, the average transaction cost is 36 bps for electronic trades and 122 

bps for voice trades. Thus, investors transacting in amounts up to $100K, on average, can reduce 

transaction costs by more than 70% by trading electronically. The magnitude of the reduction in 

transaction costs from electronic trading is inversely related to trade size, and large institutional 

investors on average save slightly over 20% of the transaction costs using electronic trades. We 

note that even though small trade sizes save proportionally more transaction costs using 

electronic trades, they still incur higher absolute transaction costs when compared with the larger 

trade sizes. Thus, the inverse relationship between transaction cost and trade size is robust to 

different market structures and trading technologies. An important limitation of the analysis 

recognized by the authors is the lack of observability of phone searches not resulting in trades, 

which limits the authors’ ability to analyze unfilled orders under various mechanisms. 

Harris (2015) uses data for the period of December 15, 2014, to March 31,2015, and 

finds that retail investors incur notably higher transaction costs than institutional investors: the 

131 Bech et al. (2016) note that this kind of request-for-quote (RFQ) platform represents more than 95% of all 

electronic dealer-to-customer trades on multi-dealer platforms. 132 Small trade sizes are commonly assumed to be retail trades, as few retail trades have sizes over $100K. However, 

we recognize that institutions may engage in smaller trades for index rebalancing, to optimize transaction costs or
 

quality of execution. 

121 
paper estimates average transaction costs of 85 bps for retail, versus 52 bps for institutional 

trades. The paper also explores the incidence of trade-throughs (trades executed at a price above 

the best standing ask or at a price below the best standing bid), and riskless principal trades 

(RPTs), in which dealers offset a customer trade with an interdealer trade and carry no inventory 

risk (defined as pairs of sequentially adjacent trades of the same size for which one trade is a 

customer trade). Harris (2015) documents that the trade-through rate is 43%, the RPT rate is 

42%, and 41% of the trade-throughs are RPTs during the sample period (April 2014 through 

March 2015). The paper suggests that the prevalence of electronic bond trading may primarily 

benefit dealers and does not necessarily result in low trade-throughs for customers. It is 

important to note that most of these electronic trading platforms are used by dealers to facilitate 

trading, but not used as venues where customers can transact with each other directly. Overall, 

the paper concludes that electronic markets are an important liquidity source even in illiquid 

instruments, though the benefits are higher for more liquid bonds. The paper further suggests 

that the development of electronic crossing systems to directly match customers may serve as a 

path to centralized and continuous trading. 

One potential limitation of the analysis in Harris (2015) is that some of the quotes from 

Interactive Brokers (IB) are indicative quotes, which dealers might not honor. Harris (2015) 

recognizes this and notes that during the week ending September 10, 2015, IB obtained complete 

fills for 83% of its customers’ orders without cancellations. While this evidence is anecdotal, it 

may suggest that the quoted and indicated prices that IB records are largely actionable and the 

above estimates are meaningful. The paper concludes by proposing a pre-trade transparency 

initiative enabling the collection and dissemination of the National Best Bid and Offer (NBBO) 

122 
facility for the bond market and an eventual transition to a fully automated, electronic trading 

market for U.S. corporate bonds. 

Finally, Li and Li (2016) theoretically model principal and agency trading. In their 

model, agency trading by dealers is more likely to emerge in markets with high transparency, 

turnover, and costs of inventory. Dealer principal trading is more likely in opaque, 

decentralized, and illiquid markets. To the extent that the emerging electronic trading enhances 

transparency in bond markets, it is conducive to the shift from principal to agency trading by 

dealers. They also show that agency trading increases with intermediary holding cost and 

empirically document that shocks to broker-dealer leverage and dealer funding cost proxies 

increase the fraction of agency trading for high yield bonds. To the extent that the Volcker Rule 

may have increased inventory costs for bank-affiliated dealers, the paper’s evidence would 

predict a corresponding shift to agency trading. 

In sum, the growth in agency trading by dealers may be consistent with both the potential 

impacts of the Volcker Rule and the effects from the emergence of electronic trading. 

c). Post‐Trade Transparency

In addition to regulatory impacts, market structure changes, evolving dealer risk 

preferences, and liquidity demand shifts, it is possible that post-trade transparency may also have 

impacted bond liquidity and market quality. The effect of post-trade transparency on financial 

markets is an ongoing issue widely examined by researchers (Acharya et al. 2009; French et al., 

2010). The initiation of TRACE reporting for publicly traded bonds in 2003 and 2004 and 144A 

bonds in 2014 may have contributed to observed liquidity changes. As a result, we need to 

consider how post-trade transparency may have impacted bond liquidity, and whether observed 

changes in liquidity measures are consistent with the effects of post-trade transparency. 

123 
As we discuss in more detail below, existing research suggests that post-trade 

transparency generally reduces price dispersion for all bonds by mitigating information 

asymmetries among traders. However, the effects of post-trade transparency on transaction costs 

and trading activity are not universal. For most investment grade bonds, post-trade transparency 

clearly reduces transaction costs; but for thinly traded investment grade bonds and for high yield 

bonds, post-trade transparency reduces trading activity with little effect on transaction costs (e.g., 

Asquith et al. (2014)). 

Early studies of post-trade transparency generally concluded that TRACE reporting led to 

better market liquidity for corporate bonds (Bessembinder et al., 2006; Edwards et al., 2007; 

Goldstein et al., 2007). Bessembinder et al. (2006) use the dataset of the National Association of 

Insurance Commissions (NAIC) to examine the effect of post-trade transparency of 439 bonds 

phased in on July 1, 2002. They estimate a reduction in the imputed transaction cost of these 

transparent bonds of about 5 to 10 bps as well as a decline in the concentration ratio of the 

largest dealers. 

Edwards et al. (2007) use TRACE data for all trades from January 2003 to January 2005 

to examine the effect of post-trade transparency on a large set of bonds, phased in sequentially 

on March 1, 2003, April 14, 2003, and October 1, 2004. They find that post-trade transparency 

reduces the imputed transaction costs across all different trade sizes. Similarly, Goldstein et al. 

(2007) uses the TRACE dataset for all trades from July 8, 2002, to February 27, 2004 to examine 

the effect of post-trade transparency on 120 BBB-rated bonds (90 actively traded and 30 thinly 

traded), phased in on April 14, 2003. They find that post-trade transparency reduces the 

transaction cost of the actively traded bonds but has no statistically significant effect on thinly 

traded bonds. 

124 
Bessembinder et al. (2016) revisit this issue, using both the 2003-2004 initiation of 

transaction reporting for public bonds, phased in sequentially on March 1, 2003, April 14, 2003, 

and October 1, 2004, as well as the 2014 initiation for 144A bonds. They perform a differencesin-differences estimation and find that TRACE reporting for publicly traded bonds resulted in 

statistically significant increases in trading activity, mixed effects on intraday capital 

commitment, and no effect on market quality. Similar estimates around the 2014 transaction 

reporting change for 144A bonds in Bessembinder et al. (2016) show a lack of significance of 

TRACE reporting for all metrics except for a slight increase in trading activity. They also find 

lower declines in dealer capital commitment for TRACE-eligible bonds during the financial 

crisis. 

In a different context, Han et al. (2016) study the impact of public registration of 144A 

bonds. They find that increased transparency improves bid-ask spreads, particularly of bonds 

with higher ex ante information asymmetry. They also find that dealers reduce their net 

positions in 144A bonds after public registration of the bonds. This result is consistent with the 

observation in Li and Li (2016) that increased transparency is conducive to agency trading. 

Overall, the evidence suggests that post-trade transparency via TRACE reporting may improve 

market liquidity, or at least not hurt it. 

Asquith et al. (2014) use the historical TRACE dataset for the period of July 1, 2002 to 

December 31, 2006, to examine the effect of post-trade transparency of a large set of bonds, 

phased in sequentially on March 1, 2003, April 14, 2003, October 1, 2004, and February 7, 2005. 

Thus, unlike all previous studies, which focused on some selected TRACE phased-in period(s), 

Asquith et al. (2014) include all four major phase-in periods for public bonds, including 2,800 

high yield bonds, to examine the effect of post-trade transparency on trading activity and price 

125 
dispersion. As such, they argue that the earlier studies on the post-trade transparency 

(Bessembinder et al., 2006; Edwards et al., 2007; Goldstein et al., 2007) are essentially 

incomplete as they only apply to the specific subset of bonds affected in one or several phases of 

TRACE implementation, but not all bonds. The paper finds that post-trade transparency leads to 

an overall reduction in both trading activity, especially for the high yield bonds phased in last, 

indicating a deterioration in this dimension of liquidity. However, the paper also found that posttrade transparency reduced price dispersion for bonds in the high yield sample. 

Given their findings, Asquith et al. (2014) argue that transparency reduces information 

asymmetry for previously opaque high yield bonds as reflected in smaller price dispersion, 

which, in turn, lowers the spreads (or transaction costs) the dealers can charge. In response, 

dealers reduce their inventory positions, essential for market making in illiquid high yield bonds. 

This results in a decrease in the trading activity in high yield bonds. In light of this, the paper 

argues that the expansion of post-trade transparency into other similarly thinly traded over-thecounter securities, such as asset-backed securities, may adversely affect trading activity. To the 

extent that trading activity metrics reflect the ability and willingness of market participants to 

execute buy and sell trades, this may indicate a reduction in this dimension of liquidity. 

C. Data Analysis and Results 

Below we explore changes in corporate bond liquidity over time, and consider whether 

the observed effects may be consistent with regulatory changes, as well as other factors. We first 

examine changes in corporate bond trading activity and transaction costs over time. We then 

explore changes in the trading behavior of certain market participants, with a particular emphasis 

on changes in dealers’ trading activity over time. Finally, we discuss recent evidence about 

electronic trading and dealer quotations on ATS. 

126 
We recognize that data limitations constrain the scope of our empirical analysis of 

corporate bond liquidity. Because this analysis primarily relies on executed transaction data, it 

does not capture all dimensions of liquidity. For example, we are unable to examine changes in 

dropped (unexecuted) orders, order splitting, and difficulty in executing large block orders on 

liquidity, because order data for corporate bonds are generally not available for research. 

Conditional on data availability, this remains an open area for future research. 

In estimating the transaction costs of corporate bonds, we follow the transaction cost 

estimation methods presented in Edwards, Harris, and Piwowar (2007): the EHP measure. 

Furthermore, to understand how corporate bond transaction costs have changed over time, we 

divide our sample spanning January 2003 – September 2016 into six subsamples, using partitions 

by Bessembinder et al. (2016), which reflect regulatory action and shifts in corporate bond 

market structure. 

Our empirical results on trading activity and transaction costs are not consistent with the 

hypothesis that regulations reduced corporate bond market liquidity, consistent with prior 

studies. For example, we find that more corporate bonds traded in the “Regulatory” sub-period 

and in the “Post-regulatory” sub-period than in any other period in our sample. However, 

corporate bond trading activity in the “Regulatory” and the “Post-regulatory” sub-periods is 

more concentrated in liquid bonds with larger issue sizes. Further, we decompose trading 

activity and transaction costs by trade size. After July 2012, in general, we do not observe an 

increase in corporate bond transaction costs for larger trade size, but for some subgroup of 

corporate bonds, such as larger bonds (issue size greater than $500 million), certain investment 

grade bonds, younger bonds (less than 2 years since issuance), and longer maturity bonds 

(original maturity longer than 20 years), the estimated transaction costs of large trades are 

127 
	 	 	

slightly higher than those estimated pre-crisis. In addition, we observe a decrease in transaction 

costs for smaller trade sizes after the post-crisis period. Our transaction cost analysis shows that 

the average transaction costs for smaller trade sizes after July 2012 are lower than those prior to 

the crisis. 

Our analysis of dealer activity points to an evolving role of dealers in corporate bond 

markets, and some of the results are consistent with a reduction in dealer provision of liquidity 

during the post-regulatory period. However, our results are also consistent with greater ease in 

offloading inventory, changing economics of traditional dealer business models, post-crisis 

evolution in dealer risk taking and evolving macroeconomic conditions. Finally, we document 

large cross-sectional heterogeneity in ATS activity for different types of bond issues, and find 

that electronification may be associated with lower trade sizes. 

The rest of Part B.IV.C is organized as follows: Part B.IV.C.1 provides an overview of 

corporate bond sample data and discusses corporate bond trading activity across sub-periods and 

also by cross-sectional bond characteristics; Part B.IV.C.2 provides empirical results on average 

transaction costs across sub-periods; Part B.IV.C.3 provides evidence on changes in dealer 

activity; Part B.IV.C.4 provides evidence on current activity in electronic venues. 

1. Trading Activity

In this section, we describe our data, sample construction, and report summary statistics 

on corporate bond trading activity across sub-periods and by cross-sectional bond characteristics. 

128 
	 	 	 	

 

a). Data and Sample

Our sample period spans from January 2003133 to September 2016.134 For corporate bond 

transaction data, we use a regulatory version of TRACE that includes a broker-dealer identifier 

(i.e., market participant ID (MPID)), unmasked trade sizes, an indicator that identifies whether a 

trade is buy or sell, a trade identifier that classifies whether a trade is an interdealer or a customer 

trade, a broker-dealer capacity indicator that reports whether a trade is a principal trade or an 

agency trade for a reporting broker-dealer, and a credit quality code for investment grade and 

high yield bonds. That version of TRACE includes corporate bond trades disseminated via 

TRACE to the public as well as trades reported to FINRA but not disseminated to the public. 

These data also include issuer and issue information for TRACE-eligible bond issues updated 

daily. We merge these transaction data with bond characteristics provided by the Mergent Fixed 

Income Securities Database (FISD). The FISD database includes issue date, issue size, maturity 

date, and various bond features (e.g., convertible, putable, redeemable, pay-in-kind, variable rate, 

sinking fund). 

We construct our corporate bond transaction data sample as follows. First, we exclude all 

primary market trades (i.e., “P1” trades in TRACE) from our analysis to isolate secondary 

market trades. Second, we drop all trades for Rule 144A issues because Rule 144A bonds are 

mostly traded by large institutions and the disclosure requirements for those bonds are lower. 

Third, we eliminate bonds that are not covered in the FISD. Appendix D.1 discusses sample 

filters and the potential effects of filtered observations on our liquidity study, Table D.1 reports 

133 TRACE reporting commenced on July 1, 2002. We exclude data from July to December 2002 because of 

potential reporting inaccuracies as market participants familiarize themselves with the TRACE system. 

134 The end date of our sample is September 19, 2016. 

129 
 

 

data filters and sample composition by sub-period, and Table D.2, Panels A and B show the 

effects from the FISD database coverage changes on trading activity for filtered observations. 

As mentioned above, we are interested in whether and how corporate bond activity and 

transaction costs have changed over time, and whether observed changes are associated with 

regulatory actions. To study corporate bond activity and transaction costs over time, we consider 

six sub-periods and compare metrics across those sub-periods. In defining appropriate subperiods, we consider two important factors that could have affected corporate bond market 

liquidity, mainly, changes in corporate bond market structure and regulations. The designation 

of sub-periods in Bessembinder et al. (2016) reflects changes in both corporate bond market 

structure (e.g., post-trade transparency) and regulations, and we follow their period definitions 

closely. As discussed in Part B.IV.B.3 above, existing research shows that post-trade 

transparency (i.e., TRACE reporting) is associated with the decrease in corporate bond 

transaction costs. The dissemination of corporate bond transaction prices via TRACE was 

phased in for publicly traded bonds by the end of 2005. We designate January 2003 through 

December 2005 as the “TRACE Phase-in” sub-period. We define January 2006 through June 

2007 as the “Pre-crisis” sub-period. We designate July 2007 through April 2009 as the “Crisis” 

sub-period and May 2009 through June 2012 as the “Post-crisis” sub-period. Lastly, we divide 

the rest of our sample into two additional sub-periods. We designate July 2012 through May 

2014 as the “Regulatory” sub-period135 and June 2014 through September 2016 as the “Postregulatory” sub-period. 

135 Our sample period cutoffs are aligned with Bessembinder et al. (2016) and are consistent with Bao et al. (2016), 

which uses the following cutoffs: pre-crisis 1/2006 – 6/2007; crisis 7/2007-4/2009, post-crisis 5/2009-7/2010, post 

Dodd-Frank 7/2010-3/2014, post-Volcker 4/2014-3/2016. We recognize that Dick-Nielsen and Rossi (2016) use a 

shorter post-crisis period of 2010 through 2013. Further, Anderson and Stulz (2017) use 2004-2006 as the pre-crisis 

period and 2010-2014 as the post-crisis period because of the constraints in the availability of Enhanced TRACE. 

[Footnote continued on next page] 

130 
 

 

 

As discussed in Broad Economic Considerations, it is often difficult to anticipate when 

regulatory activities should impact liquidity even with a clear regulatory timeline, and market 

participants may anticipate and change their behavior prior to the implementation of regulations. 

The Volcker Rule was originally set to take effect on July 21, 2012, with a 2-year phase-in 

period, and the FRB approved Basel III on July 2, 2013. Both dates fall in the “Regulatory” subperiod, but these regulatory changes may have been anticipated by market participants in the 

“Post-crisis” sub-period.136 Furthermore, we note that during the “Post-regulatory” sub-period, 

FINRA commenced the dissemination of Rule 144A bond trades to the public via TRACE in 

June 2014. We exclude Rule 144A bond trades from our main transaction costs analysis. 

However, we recognize that our cost estimates in the “Post-regulatory” sub-period may be 

contaminated by potential spillovers from the improved post-transparency in Rule 144A bonds. 

We provide an overview of our bond issuers and issues in our sample. Table 11137 

reports the distribution for the number of TRACE-eligible bond issues (i.e., CUSIPs) per issuer 

(i.e., firms) for each sub-period. The median issuer in our sample had two bond issues 

outstanding for any sub-period. The average number of bond issues per issuer was over 8 in all 

sub-periods, and it was the highest at around thirteen bond issues during the “Post-crisis” subperiod. The distribution for the number of TRACE-eligible bond issues per issuer was highly 

skewed. In any sub-period, firms in the 90th percentile of the distribution had more than 9 bond 

Given the availability of regulatory TRACE data through 2016 we use a full time series through 2016. Anderson 

and Stulz (2017) also argue that the inclusion of the credit boom during the first 6 months of 2007 may bias one to 

find a deterioration in liquidity post-crisis. Therefore, the selection of 1/2006 through 7/2007 as a “pre-crisis” subperiod is likely a conservative choice and may bias us to find reductions in liquidity post-crisis.

136 As presented in Appendix A, BCBS finalized Basel III capital framework in December 2010, which falls in the 

“Post-crisis” sub-period. 

137 For Table 11, we drop three days in our sample, July 3, 2014, August 29, 2014, and November 26, 2014, due to 

error in data feed that includes TRACE-eligible bond issues. 

131 
	 	 	 	 	

 

issues outstanding and firms in the 99th percentile had over 100 TRACE-eligible bonds 

outstanding. 

b). Full Sample Trading Activity

In this section,138 we examine various trading activity measures and study how they have 

changed over time. In examining trading activity, we consider the following measures: the 

number of bond issues traded, trade counts, and par volume. We find that a notably larger 

portion of TRACE-eligible bonds trades over more days during the “Regulatory” and the “Postregulatory” sub-periods compared to any earlier sub-periods. These findings are not consistent 

with the hypothesis that regulations reduced corporate bond market liquidity. 

Each sub-period in our sample is of a different length, so we normalize measures of 

trading activity by taking the average over multiple trading days. Specifically, we first construct 

daily trading activity measures and then compute the averages over each sub-period. Table 12139 

reports the sub-period averages of the following daily statistics: the number of issuers with 

TRACE-eligible corporate bonds, the number of TRACE-eligible bond issues, the number of 

trades, and par volume for each sub-period. From Column (1) in Table 12, we note that the 

average daily number of issuers with TRACE-eligible corporate bonds decreased during the 

“Regulatory” and the “Post-regulatory” sub-periods. During the “Post-crisis” sub-period, there 

were 4,837 issuers with TRACE-eligible bonds, whereas there are only 3,306 and 3,185 issuers 

with TRACE-eligible bonds outstanding during the “Regulatory” and the “Post-regulatory” subperiods, respectively. We observe similar changes over time for the average daily number of 

TRACE-eligible bond issues reported Column (3) of Table 12. 

138 For all figures and tables referenced in this section, we apply data filters in the trade reporting and the 

discretionary filters discussed in Appendix D.

139 For Table 12, we drop 3 days in our sample, July 3, 2014, August 29, 2014, and November 26, 2014, because of
 

errors in the data feed that includes TRACE-eligible bond issues. 

132 
 

 

 

Given the growth in public debt placements shown in Part A, this may be consistent with 

increases in redemptions. It is also consistent with changes in the sample composition due to 

changes in the FISD database coverage. However, as shown in Tables D.1 and D.2, TRACEeligible bonds with no coverage in the FISD database have notably larger average daily trade 

counts and par volumes during the “Post-crisis,” the “Regulatory,” and the “Post-regulatory” 

sub-periods than in earlier sub-periods. As a result, the exclusion of TRACE-eligible bonds with 

no coverage in the FISD database is less likely to bias our results towards finding better liquidity 

in the corporate bond market for the later sub-periods.140 

Column (4) of Table 12 reports the average daily percentage of TRACE-eligible bond 

issues with at least one trade on a given day. The average daily portion of TRACE-eligible 

bonds traded is the highest during the “Regulatory” sub-period and the second lowest at 10.5% 

for the “Post-crisis” sub-period. However, we note that the average daily number of trades 

(approximately 38,300 trades per day) during the “Post-crisis” sub-period is higher than in any 

other sub-periods. This is consistent with trading activity being concentrated in fewer corporate 

bonds during the “Post-crisis” sub-period. Lastly, by comparing Columns (2) and (4) for every 

sub-period, we note that the average daily portion of firms with at least one trade on a given day 

in any of their bonds is substantially larger than the average daily portion of bond issues with 

trades. This difference primarily reflects firms that have multiple bond issues outstanding but 

few bonds traded. 

Table 13141 Panels A and B present summary statistics on trading activity across the six 

sub-periods. Panel A of Table 13 reports the distribution for the portion of business days (in 

140 For an extended discussion of sample filters and sample composition, see Appendix D. 141 For Table 13, we drop 3 days in our sample, July 3, 2014, August 29, 2014, and November 26, 2014 because of
 

errors in the data feed that includes TRACE-eligible bond issues. 

133 
percentage) with at least one trade per TRACE-eligible bond issue during each sub-period. 

During the “Pre-crisis,” the “Crisis,” and the “Post-crisis” sub-periods, we observe that at least 

one-half of TRACE-eligible bonds do not trade (i.e., the median is 0%). Although one-half of 

bonds do not trade, approximately 5% of bonds trade on at least 78% of total business days 

during the “Post-crisis” sub-period. As mentioned above, this indicates that trading is 

concentrated in a small number of liquid corporate bonds during the “Post-crisis” sub-period. 

Furthermore, we note that a substantially larger portion of TRACE-eligible bonds trades on more 

days during the “Regulatory” and the “Post-regulatory” sub-period compared to any earlier subperiods, which is not consistent with the hypothesis that regulations reduced corporate bond 

market liquidity. At least 25% of TRACE-eligible bonds have at least one trade on over 20% of 

total business days during the “Regulatory” sub-period and the “Post-regulatory” sub-period 

whereas approximately 25% of TRACE-eligible bonds have at least one trade on less than 10% 

of total business days during the “Pre-crisis,” the “Crisis,” and the “Post-crisis” sub-period. 

Panel B of Table 13 presents the distribution for the portion of TRACE-eligible bond 

issues grouped by the number of trades per day for each sub-period. The average portion of 

TRACE-eligible bonds that do not trade on a given day ranges from approximately 79% to 91% 

across sub-periods. On average, the portion of bonds that do not trade is lower during the 

“Regulatory” and the “Post-regulatory” sub-period and higher during the “Crisis” and the “Postcrisis” sub-periods. Furthermore, during the “Regulatory” and the “Post-regulatory” sub-period, 

we estimate that a substantially larger portion of bonds trade at least once on a given day 

compared to other sub-periods. 

134 
	 	 	 	 	 	

 

c). Trading Activity and Cross‐Sectional Bond Characteristics

In this section,142 we examine how trading activity is related to cross-sectional bond 

characteristics, as well as how it has changed over time. Specifically, we consider issue size 

(measured in par value), credit quality, age (time since issuance), original maturity, and 

instrument complexity in our analysis. We find that throughout sub-periods, trading activity has 

been increasingly concentrated in bonds with issue size greater than $500 million. We also find 

that trading activity is concentrated in more recently issued bonds and bonds with maturity 

between 5 and 20 years across all sub-periods. Lastly, our analysis shows that trading activity is 

more concentrated in bonds with less complexity features during the “Regulatory” and the “Postregulatory” sub-periods compared to any earlier sub-periods. 

As above, we report average daily trading activity statistics to reduce the influence of 

differences in sub-period length. Table 14 Panels A through E report the number of TRACEeligible bond issues and the portion of TRACE-eligible bonds with trades across sub-periods by 

bond characteristic. Figures 25 Panels A through E show the distribution (in percentage) of bond 

issues, trades, and par volume grouped by bonds with similar characteristics across sub-periods. 

From Panel A of Table 14, the fraction of TRACE-eligible bonds with trades is substantially 

larger for corporate bonds with larger issue size (greater than $100 million) during the 

“Regulatory” and the “Post-regulatory” sub-periods compared to the sub-periods prior to the 

financial crisis. We also note that the portion of TRACE-eligible bonds with trades for small 

bonds (issue size smaller than $100 million) is slightly smaller during the “Regulatory” and the 

“Post-regulatory” sub-periods than earlier sub-periods prior to the “Crisis” sub-period. 

142 For all figures and tables referenced in this section, we apply data filters in the trade reporting and the 

discretionary filters discussed in Appendix D. 

135 
From Figure 25 Panel A, we observe that trade counts and par volume are highly 

concentrated in bonds with issue size greater than $500 million. For example, for the 

“Regulatory” sub-period, bonds with issue size greater than $500 million are approximately 42% 

of average daily traded bonds, 58% of average daily trades, and 74% of par volume, whereas 

bonds with issue size less than $100 million are 17% of average daily traded bonds, 9% of 

average daily trades, and 1% of average daily par volume. Furthermore, we note that the portion 

of average daily trades and par volume for bonds with issue size greater than $500 million has 

been increasing since the “Pre-crisis” sub-period. The average daily portions of traded bonds, 

trades, and par volume during the “TRACE Phase-in” and the “Pre-crisis” sub-periods are 

approximately 25%, 45%, and 58%, respectively, while the average daily portions of traded 

bonds, trades, and par volume during the “Post-regulatory” sub-period are approximately 48%, 

63%, and 77%, respectively. This indicates that trading activity has been increasingly more 

concentrated in bonds with large issue size throughout sub-periods, consistent with liquidity 

clustering in a subset of traded bonds. 

Panel B of Table 14 reports the number of TRACE-eligible bond issues and the portion 

of TRACE-eligible bonds with trades grouped by similar credit quality across sub-periods. 

Figure 25 Panel B shows the distribution of traded bonds, trades, and par volume grouped into 

four categories of credit quality. For our trading activity analysis on credit quality, we subdivide 

investment grade and high yield bond samples into two groups, bonds with a par volume 

weighted average price below $95 (out of $100 par) and bonds with a weighted average price 

above $95 on a given day. We consider bonds that trade below $95 to represent the segment of 

the corporate bond market with deteriorating credit quality. We recognize that this interpretation 

assumes that such bond groups are homogenous and are otherwise similar along other 

136 
dimensions relevant to bond pricing, such as interest rate risk. From Panel B of Table 14, we 

observe that the portion of TRACE-eligible bonds with trades is substantially smaller for both 

investment grade and high yield bonds with deteriorating credit quality (price below $95) than 

investment grade and high yield bonds with a price above $95 in any sub-period. Across subperiods, at most, 7% and 11% of investment grade and high yield bonds with a price below $95, 

respectively, are traded whereas at least 17% and 16% of investment grade and high yield bonds 

with a price above $95, respectively, are traded. Furthermore, although investment grade bonds 

with a price below $95 account for over 50% of all investment grade TRACE-eligible corporate 

bonds, only 1.9% of investment grade bonds with a price below $95 have trades during the 

“Post-crisis” sub-period. We also note that a substantially larger portion of investment grade 

bonds with a price above $95 are traded than any earlier sub-periods. 

Figure 25 Panel B shows that at least 64% of average daily trading activity is attributed to 

investment grade bonds. Furthermore, the average daily proportion of traded bonds, trades, and 

par volume for corporate bonds with deteriorating credit quality accounts for less than 20% 

across all sub-periods with the exception of the “Crisis” period. We observe that the average 

daily portions of traded bonds, trades, and par volume for bonds with deteriorating credit quality 

are higher during the “Crisis” period and are approximately 49%, 43%, and 47%, respectively. 

Panels C and D of Table 14 report the number of TRACE-eligible bond issues and the 

portion of TRACE-eligible bonds with trades grouped by similar bond age and original maturity, 

respectively, across sub-periods. Figure 25 Panel C and Panel D show the average daily 

distribution of bond issues, trades, and par volume grouped by bond age (time since issuance) 

and original maturity, respectively. In Panel C of Table 14, we observe that a larger portion of 

older bonds (bonds issued over 2 years ago) is traded during the “Regulatory” and the “Post137 
 

 

regulatory” sub-period than any earlier sub-periods. With the exception of the “Crisis” and 

“Post-crisis” sub-period, at least 12% of younger bonds with age less than 2 year have trades. In 

Figure 25 Panel C, we note that younger bonds (less than 2 years since issuance) account for 

approximately 50% of average daily par volume across all sub-periods and older bonds (greater 

than 5 years since issuance) account for less than 20% of average daily par volume across all 

sub-periods. 

In Panel D of Table 14, we note that a larger portion of longer original maturity bonds 

(original maturity longer than 5 years) are traded than short maturity bonds (original maturity 

less than 5 years) in any sub-period. At least 12% of longer original maturity bonds (original 

maturity longer than 5 years) have trades across any sub-periods. The portion of longer original 

maturity bonds (original maturity longer than 5 years) with trades is larger (at least 21%) during 

the “Regulatory” and the “Post-regulatory” sub-period than any earlier sub-periods. From Figure 

25 Panel D, we observe that bonds with maturity between 5 years and 20 years account for 

approximately 70% of trades and par volume across all sub-periods. Bonds with maturity of less 

than 2 years do not trade actively. These results indicate that trading activity is concentrated in 

more recently issued, younger bonds and bonds with maturity between 5 and 20 years. 

Using bond features included in the FISD, we construct an instrument complexity 

variable by aggregating the number of the following bond features: variable rate, nonstandard 

interest payment frequencies, pay-in-kind, redeemable, convertible, unconventional day count 

basis for accrued interests, and sinking fund.143 From Table 14 Panel E, in any sub-period, a 

substantially larger portion of corporate bonds with less complexity features (zero or one 

143 Edwards, Harris, and Piwowar (2007) consider a similar list of instrument complexity features: callable, putable, 

coupon types (fixed, floating, or variable), sinking fund, non-standard interest payment frequency, and nonstandard 

interest accrual basis. 

138 
 

 

complexity feature) are traded than corporate bonds with more complexity features (two or more 

complexity features). We note that the portion of TRACE-eligible bonds with less complexity 

features (zero or one complexity feature) is substantially larger than corporate bonds with more 

complexity features (two or more complexity features) in the recent sub-periods, the 

“Regulatory” and the “Post-regulatory” sub-period. 

Figure 25 Panel E reports the distribution of traded bonds, trades, and par volume across 

the number of complexity features. From Figure 25 Panel E, across all sub-periods, we observe 

that less than 20% of average daily traded bonds and trades have two or more complexity 

features. Furthermore, we note that during the “Regulatory” and the “Post-regulatory” subperiod, bonds with either zero or one complexity feature account for over 92% of average daily 

corporate bond trades and par volume showing that trading activity is more concentrated in 

bonds with fewer complexity features in recent years. 

Figure 25. Cross-sectional bond characteristics: Bond issues, trades, and par volume 

Panel A. Issue size distribution 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

139 
 

 

 

 

Panel B. Credit quality distribution 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel C. Age distribution 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

140 
 

 

 

Panel D. Maturity distribution 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel E. Complexity distribution 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory”

 Source: DERA analysis 

141 
	 	

	 	 	 	

 

2. Transaction Costs

In this section,144 we discuss our empirical results on average transaction costs for 

corporate bonds across sub-periods and also by cross-sectional bond characteristics. Following 

Edwards, Harris, and Piwowar (2007), we control for corporate bond market return, duration 

risk, and credit risk, which influence bond pricing, in estimating average transaction costs for 

customer trades. 

a). Full Sample Results

Our transaction cost analysis shows that there is a decrease in transaction costs for 

smaller trade size after the “Post-crisis” sub-period. We observe that the average transaction 

costs for smaller trade size during the “Regulatory” and the “Post-regulatory” sub-period are 

lower than those for smaller trade size during the “Pre-crisis” sub-period. Furthermore, we do 

not observe an increase in corporate bond transaction costs for larger trade size during the 

“Regulatory” and the “Post-regulatory” sub-period period. Rather, the average transaction costs 

for larger trade size during the “Regulatory” and the “Post-regulatory” sub-periods are similar to 

those during the “Pre-crisis” sub-period. These findings do not suggest that regulations reduced 

corporate bond market liquidity. 

As shown in Table D.3,145 some customer trades are reported as agency trades with zero 

commission. It is possible that those agency trades reflect transactions made on behalf of 

customer accounts that pay fees in lieu of commissions. If this is true, then for those agency 

trades, commissions may underestimate transaction costs. We observe that those zero 

commission agency trades are concentrated in smaller trade sizes throughout sub-periods. Thus, 

144 For all figures and tables referenced in this section, we apply data filters in the trade reporting, the discretionary, 

and the price-based filters discussed in Appendix D. 

145 Table D.3 in Appendix D reports the average daily agency trades for customer trades by trade size across subperiods. For Table D.3, we apply filters in the trade reporting and the discretionary filters discussed in Appendix D. 

142 
 

 

 

the inclusion of zero commission agency trades in estimating average transaction costs would 

result in biased (i.e., lower) transaction cost estimates for smaller trade size. We show our 

average transaction costs estimation results with and without zero commission agency trades in 

Figure 26 Panel A and Figure 26 Panel B, respectively; and tabulate the estimation results in 

Table 15 Panel A and Table 15 Panel B, respectively. For example, comparing Table 15 Panel A 

and Table 15 Panel B for the “Post-regulatory” sub-period, we note that the estimated average 

round-trip transaction costs for a trade size of $20,000 (smaller trade size) is approximately 0.15 

percentage points higher ([27.7 bps – 20.3 bps] × 2) when zero commission agency trades are 

excluded in estimation, whereas there is little difference in the estimated round-trip costs for a 

trade size of $1,000,000 (larger trade size) (0.045% (4.5 bps) when zero commission agency 

trades are excluded vs. 0.046% (4.6 bps) when zero commission agency trades are included). 

Therefore, it may be important to exclude zero commission agency trades in estimating average 

transaction costs and Table 15 Panel A146 and Figure 26 Panel A serve as our main transaction 

costs estimation results. 

146 Table D.4 Panels A through F in Appendix D report EHP transaction cost estimation results, including average 

and median transaction costs and the confidence interval for average transaction cost estimates for each sub-period. 

143 
 

 

 

Figure 26. EHP average transaction costs 

Panel A. Excluding zero commission agency trades 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel B. All trades 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory”

 Source: DERA analysis 

144 
 

 

We observe that the estimated average transaction costs for each sub-period decrease 

with trade size, which is consistent with findings in academic papers. For the “Post-regulatory” 

sub-period, the average round-trip transaction cost for a smaller trade size of $20,000 is 0.55% 

(27.7 bps × 2) of bond price, while a larger trade size of $1,000,000 is only 0.09% (4.5 bps × 2). 

This result may indicate that large traders have the market power to negotiate better prices than 

small traders or that broker-dealers price trades so that they can spread their fixed trading costs 

over volume, or both.147 

We examine whether and how average transaction costs have changed over time. Figure 

27 shows the estimated average round-trip transaction costs across six sub-periods for trade sizes 

of $20,000, $1,000,000, and $5,000,000, respectively. From Figure 27, we observe that, in 

general, the estimated average round-trip transaction costs increased during the “Crisis” subperiod and stayed elevated during the “Post-crisis” sub-period but have subsequently decreased 

throughout the “Regulatory” sub-period and the “Post-regulatory” sub-period. For instance, the 

estimated average round-trip transaction costs in Table 15 Panel A for a trade size of $1,000,000 

increased from 0.13% (6.4 bps × 2) of bond price during the “Pre-crisis” sub-period to 0.40% 

(19.9 bps × 2) during the “Crisis” sub-period but have subsequently decreased to 0.09% (4.5 bps 

× 2) during the “Post-regulatory” sub-period. Lastly, based on EHP measures, we do not observe 

an increase in corporate bond transaction costs during the “Regulatory” and the “Postregulatory” sub-periods. Rather, our empirical evidence is consistent with other studies on 

corporate bond liquidity (e.g., Mizrach (2015), Schestag (2016)), which conclude that corporate 

147 Edwards, Harris, and Piwowar (2007) find that the difference in market power between large and small traders in 

negotiating prices is more important than dealers’ fixed cost management in explaining the shape of the average 

transaction cost curve for corporate bonds. 

145 
 

bond transaction costs have decreased since the financial crisis, which is not consistent with the 

argument that regulations reduced corporate bond market liquidity. 

Figure 27. EHP average transaction costs by trade size 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory”
 

Source: DERA analysis 

 Furthermore, by comparing the estimated average transaction cost curves for the “Precrisis” and the “Post-regulatory” sub-period in Figure 26 Panel A, the estimated average 

transaction costs for the “Post-regulatory” period are lower for almost all trade sizes. We note, 

however, that the reduction in corporate bond transaction costs is more pronounced in smaller 

trade sizes. For example, by comparing the estimated average transaction costs for the “Precrisis” and the “Post-regulatory” sub-periods, the estimated average round-trip transaction cost 

for a smaller trade size of $20,000 is lower by 0.31 percentage points ([43.2 bps – 27.7 bps] × 2) 

in the “Post-regulatory” sub-period, while the estimated average transaction costs are similar for 

a larger trade size of $5,000,000 (the estimated average round-trip transaction cost of 0.059% 

146 
	 	

 

(2.9 bps × 2) in the “Pre-crisis” sub-period versus 0.057% (2.8 bps × 2) in the “Post-regulatory” 

sub-period). 

The sizable reduction in transaction costs for smaller trade size during the “Pre-crisis” 

sub-period could be attributable, in part, to enhancement in post-trade transparency with the 

introduction of TRACE reporting. Pricing information obtained via TRACE reporting could be 

valuable and result in lower transaction costs for small traders, such as retail investors, because 

they may otherwise lack alternative means to readily obtain useful pricing information on 

corporate bond transactions. In addition, the decrease in average transaction costs for smaller 

trade size after the “Post-crisis” sub-period may be attributable, in part, to the recent increase in 

electronic trading for corporate bonds and is inconsistent with the concern that regulations 

reduced corporate bond market liquidity. As discussed above, electronic trading could improve 

efficiency in managing inventory and searching for counterparties. In general, electronic trading 

may be more appropriate for executing smaller orders because the tradeoff between the cost of 

information leakage from displaying trading intentions and the reduction in search costs for 

finding a counterparty would be more favorable. Added efficiency in executing smaller orders 

via electronic trading would lower transaction costs for smaller corporate bond trades.148 We 

discuss summary statistics for corporate bond quotations and trades on electronic trading venues 

(i.e., ATS) in Part B.IV.C.3 below. 

b). Cross‐Sectional Results

In this section, we examine how average transaction costs are related to cross-sectional 

bond characteristics and how those have changed over time. Many academic papers examine 

148 Hendershott and Madhavan (2015) find that transaction costs for small trades on electronic venue are lower than 

those for “voice” intermediated trades. 

147 
 

 

 

how transaction costs are associated with various bond characteristics, and our cross-sectional 

subsample cuts are informed by well-established finance research.149 Literature shows that 

larger and newer bond issues have lower transaction costs.150 More complex bonds and a longer 

time-to-maturity are associated with higher transaction costs.151 In addition, some studies find 

that higher credit risk is linked to higher transaction costs.152 In this analysis, we consider five 

bond characteristics that the existing research associated with transaction costs: issue size, credit 

quality, age, original maturity, and complexity. For each bond characteristic, we estimate 

average transaction costs for bonds that are similar in characteristic by trade size over each subperiod. For ease of presentation, we show the estimated average transaction costs for the trade 

size of $1,000,000 and $5,000,000 for each bond characteristic,153 except for complexity. For 

complexity, we present the estimated average transaction costs for the trade size of $20,000 and 

$5,000,000 for the reasons discussed below.154 

For corporate bond trades with a trade size of $1,000,000, we find that, across all subperiods, the estimated average transaction costs for bonds with an issue size greater than $500 

million, bonds with a price above $95, bonds with an age of less than 2 years, bonds with an 

original maturity between 5 and 20 years, and bonds with zero or one complexity feature, are 

149 See Chakravarty and Sarkar (2003), Hong and Warga (2000), Harris and Piwowar (2006), and Edwards, Harris, 

and Piwowar (2007). 

150 See Chakravarty and Sarkar (2003) and Hong and Warga (2000). 151 See Harris and Piwowar (2006). 152 See Chakravarty and Sarkar (2003), Hong and Warga (2000), and Harris and Piwowar (2006). 153 We report the estimated average transaction costs for small (i.e., $20,000) size trades across sub-periods by bond 

characteristics (issue size, credit quality, age (time since issuance), original maturity, and complexity features in 

Appendix D. In general, for trades of size $20,000, our findings on the changes in the estimated average transaction 

costs across corporate bonds with different bond characteristics over sub-periods are qualitatively similar to the 

results for trades of size $1,000,000 discussed in this section. Furthermore, for small trades of size $20,000, in 

general, the estimated average transaction costs during the “Post-regulatory” sub-period across corporate bonds with 

different bond characteristics are lower than those in sub-periods prior to the “Crisis” sub-period. See also Figure 

D.1 Panel A through Panel D in Appendix D. 154 The transaction cost estimates for trade size of $1,000,000 for bonds with complexity features of two or more and 

zero or one are similar across sub-periods. See also Figure D.2 in Appendix D.
 

148 
lower than transaction costs for bonds with smaller issue sizes, a price below $95, other 

maturities and more than one complexity feature. However, we observe that the estimated 

average transaction costs for bonds with an issue size greater than $500 million are the highest 

during the “Crisis” sub-period compared to bonds with a smaller issue size. Furthermore, the 

average transaction cost estimates are higher for investment grade bonds with prices below $95 

compared to those for high yield bonds with prices below $95 during the “Crisis” and the “Postcrisis” sub-period. 

In general, we do not observe an increase in the estimated average transaction costs for 

large bond trades of size $5,000,000 during the “Regulatory” and the “Post-regulatory” subperiod. However, for large trade size of $5,000,000, the estimated average transaction cost 

estimates during the “Regulatory” and the “Post-regulatory” sub-period for corporate bonds with 

an issue size greater than $500 million, investment grade bonds with a price above $95, bonds 

younger than 2 years old, and bonds with an original maturity longer than 20 years are slightly 

higher than those in the sub-periods prior to the “Crisis” sub-period: the differences in the 

estimated average transaction costs are small (at most, 0.15 percentage points (15.2 bps)). We 

also acknowledge that our sample size in estimating average transaction costs for trades with 

trade size $5,000,000 is small because only a small portion of corporate bond trades have a large 

trade size of $5,000,000: from Table 17, across six sub-periods, the average daily portion of 

principal customer trades with trade size greater than $5,000,000 is at most, 6.8% (the “Precrisis” sub-period) and as small as 4.4% (the “Regulatory” sub-period). Estimating average 

transaction costs over a small sample of trades may result in less precise estimates. 

Figure 28 Panels A through E and Figure 29 Panels A through E plot the estimated crosssectional average transaction costs for each bond characteristic over six sub-periods for the trade 

149 
 

 

 

 

size of $1,000,000 and $5,000,000, respectively.155 Figure 28 Panel A compares the estimated 

average transaction costs for bonds with an issue size greater than $500 million, between $100 

million and $500 million and smaller than $100 million for the trade size of $1,000,000. For 

almost all sub-periods, the average cost estimates are smaller for bonds with an issue size greater 

than $500 million than bonds with an issue size between $100 million and $500 million, but the 

differences in the estimated average transaction costs are small ranging from 0.03 percentage 

points ([5.9 bps - 4.2 bps] × 2 during the “Post-regulatory” sub-period) to 0.09 percentage points 

([15.7 bps - 11.4 bps] × 2 during the “Post-crisis” sub-period). We observe that the estimated 

average cost is slightly higher for bonds with an issue size greater than $500 million during the 

“Crisis” sub-period.156 Our trading activity analysis above shows that bonds with an issue size 

greater than $500 million are the most actively traded bonds during the “Crisis” sub-period. This 

indicates that the most actively traded segment of the corporate bond market also has the highest 

average transaction costs during the “Crisis” sub-period. The estimated average transaction costs 

for bonds with an issue size smaller than $100 million vary notably across sub-periods. The 

average transaction costs for bonds with an issue size smaller than $100 million are lower than 

bonds with an issue size greater than $100 million during the “Pre-crisis,” the “Crisis,” and the 

“Post-regulatory” sub-period but higher than bonds with an issue size greater than $100 million 

during the “TRACE Phase-in,” the “Post-crisis,” and the “Regulatory” sub-period. 

155 Figure 28 Panel E reports the average transaction costs for the trade size of $20,000. 156 Higher estimated average transaction costs for corporate bonds with an issue size greater than $500 million than 

those of smaller bonds during the “Crisis” sub-period may be due to bonds issued by financial firms. During the 

“Crisis” sub-period, a larger portion of corporate bonds with an issue size greater than $500 million consists of 

bonds issued by financial firms compared to corporate bonds with an issue size between $100 million and $500 

million, and the estimated average transaction costs are higher for financial firms than for nonfinancial firms. 

150 
 

 

Figure 28. Cross-sectional EHP average transaction costs 

Panel A. By issue size ($1,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel B. By Credit quality ($1,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

151 
 

 

 

Panel C. By age ($1,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel D. By maturity ($1,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

152 
 

 

Panel E. By complexity ($20,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory”
 

Source: DERA analysis
 

Figure 29 Panel A shows the estimated average transaction costs for corporate bond 

trades of size $5,000,000 broken out by issue size. We observe that the estimated average 

transaction costs for larger trade size of $5,000,000 are generally lower than those for the trade 

size of $1,000,000 across sub-periods. For corporate bonds with issue size less than $500 

million, the estimated average transaction costs for the trade size of $5,000,000 during the 

“Regulatory” and the “Post-regulatory” sub-period are lower or similar to those in the subperiods prior to the “Crisis” sub-period (i.e., the “TRACE Phase-in” and the “Pre-crisis” subperiod). However, for corporate bonds with issue size greater than $500 million, we note that the 

estimated average transaction costs for trades with size $5,000,000 are slightly higher during the 

“Regulatory” and the “Post-regulatory” sub-period than those in the sub-periods prior to the 

153 
financial crisis; the differences in the estimated average transaction costs are small—at most, 

0.07 percentage points (6.8 bps). 

The estimated average transaction costs for investment grade and high yield bonds are 

shown in Figure 28 Panel B and Figure 29 Panel B. Overall, for corporate bond trades of the 

$1,000,000 size, transaction costs for both groups of bonds have declined in the “Regulatory” 

and the “Post-regulatory” sub-periods with the exception of investment grade bonds with a price 

below $95; however, the transaction cost difference for investment grade bonds with a price 

below $95 between the “Regulatory” and the “Post-regulatory” sub-periods is small, only an 

increase of 0.03 percentage points ([17.6 bps – 15.9 bps] × 2). We further subdivide investment 

grade and high yield bond samples into two groups, bonds with a par volume weighted average 

price below $95 (out of $100 par) and bonds with a par volume weighted average price above 

$95 over the given sub-period. We consider bonds with a par volume weighted average price 

below $95 to represent the segment of the corporate bond market with deteriorating credit 

quality, although we remain cognizant of potential confounding effects of other characteristics, 

such as interest rate risk exposure. In Figure 28 Panel B, we observe that the estimated average 

costs are similar for investment grade and high yield bonds with par volume weighted average 

price above $95 during the “Crisis,” the “Post-crisis,” and the “Regulatory,” and the “Postregulatory” sub-period. However, for both investment grade and high yield bonds, we observe 

substantially higher estimated average transaction costs for bonds with a par volume weighted 

average price below $95 than the estimated costs for bonds with a par volume weighted average 

price above $95 across all sub-periods. The differences in estimated average transaction costs 

for investment grade bonds with a par volume weighted average price above $95 and investment 

grade bonds with a par volume weighted average price below $95 during the “Crisis” and the 

154 
 

“Post-crisis” sub-period are substantially larger than other sub-periods. The difference between 

average transaction costs for investment grade bonds with a par volume weighted average price 

below $95 and investment grade bonds with a par volume weighted average price above $95 

ranges from 0.48 percentage points ([41.3 bps - 17.5 bps] × 2 during the “Crisis” sub-period)) to 

0.82 percentage points ([52.1 bps - 11.1 bps] × 2 during the “Post-crisis” sub-period). 

Subsequently, the differences in estimated average transaction costs for investment grade bonds 

with a par volume weighted average price above $95 and investment grade bonds with a par 

volume weighted average price below $95 narrowed to 0.19 percentage points ([15.9 bps - 6.5 

bps] × 2) during the “Regulatory” sub-period. These results are consistent with the argument 

that the risk of adverse selection widened effective spreads in bonds with deteriorating credit 

quality during the “Crisis” and the “Post-crisis” sub-period. 

We also note that the estimated average transaction cost for investment grade bonds with 

deteriorating credit quality (i.e., investment grade bonds with a par volume weighted average 

price below $95) is higher than for high yield bonds with a par volume weighted average price 

below $95 during the “Crisis” sub-period, and stays elevated during the “Post-crisis” sub-period. 

During the “Crisis” sub-period, investment grade bonds with deteriorating credit quality (i.e., 

investment grade bonds with a par volume weighted average price below $95) account for a 

substantially larger portion of trading activity compared to high yield bonds with deteriorating 

credit quality (i.e., high yield bonds with a price below $95). This is consistent with 

Bessembinder et al. (2016), which found that investment grade bonds have, on average, 

substantially higher block trading activity during the benchmark period but experienced the 

largest reduction in block trading during the crisis. 

155 
 

 

 

Figure 29. Cross-sectional EHP average transaction costs 

Panel A. By issue size ($5,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel B. By credit quality ($5,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

156 
 

 

Panel C. By age ($5,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel D. By maturity ($5,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

157 
Panel E. By complexity ($5,000,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory”
 

Source: DERA analysis 

Figure 29 Panel B shows the estimated average transaction costs for corporate bond 

trades with size $5,000,000 broken out by credit quality. For investment grade bonds with a par 

volume weighted average price below $95 and high yield corporate bonds, the estimated average 

transaction costs for trades with large size of $5,000,000 during the “Regulatory” and the “Postregulatory” sub-period are lower or similar to those in the sub-periods prior to the “Crisis” subperiod (i.e., the “TRACE Phase-in” and the “Pre-crisis” sub-period). On the other hand, for 

investment grade bonds with par volume weighted average price above $95, the estimated 

average transaction costs for trades with size $5,000,000 are slightly higher during the 

“Regulatory” and the “Post-regulatory” sub-period than those in the sub-periods prior to the 

financial crisis. However, the differences in the estimated average transaction costs are small— 

at most, 0.05 percentage points (5.2 bps). 

158 
Figure 28 Panel C and Figure 29 Panel C present results for bonds less than 2 years old, 

between 2 and 5 years old, and more than 5 years old for the trade size of $1,000,000 and 

$5,000,000, respectively. We observe that the estimated average transaction costs for bonds 

more than 5 years old are higher than younger bonds across all sub-periods. The estimated 

average transaction costs for bonds less than 2 years old and bonds between 2 and 5 years old are 

generally similar across all sub-periods except for during the “Post-crisis” sub-period. The 

estimated average transaction costs for bonds less than 2 years old is lower during the “Postcrisis” sub-period than the “Crisis” sub-period whereas the estimated average transaction costs 

for bonds between 2 and 5 years old remained high throughout the “Crisis” sub-period and 

“Post-crisis” sub-period. During the “Post-crisis” sub-period, for corporate bond trades with the 

trade size of $1,000,000, the estimated average transaction cost for bonds between 2 and 5 years 

old is higher by 0.17 percentage points ([17.7 bps – 9.2 bps] × 2) compared to bonds less than 2 

years old. 

From Panel C of Figure 29, for bond trades with large trade size of $5,000,000, the 

estimated average transaction costs for corporate bonds more than 2 years old are similar to or 

lower during the two recent sub-periods (the “Regulatory” and the “Post-regulatory” sub-period) 

than those of the sub-periods prior to the financial crisis. For corporate bonds younger than 2 

years old, the estimated transaction costs during the “Regulatory” and the “Post-regulatory” subperiod are slightly higher than those in the sub-periods prior to the “Crisis” sub-period (i.e., the 

“TRACE Phase-in” and the “Pre-crisis” sub-period); but, the differences in the estimated average 

transaction costs are small— at most, 0.05 percentage points (4.8 bps). 

Figure 28 Panel D and Figure 29 Panel D plot the cost estimates for various bond original 

maturities old for the trade sizes of $1,000,000 and $5,000,000, respectively. Across all sub159 
periods, we find that the average transaction cost estimates increase monotonically in bond 

maturities for trades with size $1,000,000. However, from the trading activity results above, we 

observe that bonds with an original maturity between 5 and 20 years account for the largest 

portion of trading activity across all sub-periods. 

From Panel D of Figure 29, for bond trades with large trade size of $5,000,000, the 

estimated average transaction costs for corporate bonds with original maturity shorter than 20 

years are similar to or lower during the two recent sub-periods (the “Regulatory” and the “Postregulatory” sub-period) than those of the sub-periods prior to the financial crisis. On the other 

hand, for corporate bonds with original maturity longer than 20 years, the estimated transaction 

costs during the “Regulatory” and the “Post-regulatory” sub-period are higher than those in the 

sub-periods prior to the “Crisis” sub-period (i.e., the “TRACE Phase-in” and the “Pre-crisis” 

sub-period). However, the differences in the estimated average transaction costs are small—at 

most, 0.15 percentage points (15.2 bps). 

Having multiple complexity features on a bond (e.g., convertible, putable, redeemable, 

sinking fund) could render bond pricing more difficult or complicated for less sophisticated 

traders (e.g., retail investors), potentially resulting in a larger mark-up or mark-down on retail 

trades in more complex bonds. Thus, we focus our examination of how complexity features and 

average transaction costs are related on smaller trade sizes. Figure 28 Panel E plots the 

estimated average transaction costs for the number of different complexity features for the trade 

size of $20,000 (instead of for the trade size of $1,000,000). We observe that the transaction cost 

estimates are substantially higher for bonds with two or more complexity features than for bonds 

with fewer than two complexity features across sub-periods prior to the “Post-regulatory” sub160 
	 	 	

 

period.157 For instance, during the “Post-crisis” sub-period, the difference in the estimated costs 

for a smaller trade size of $20,000 was as large as 0.76 percentage points ([96.2 bps - 58.2 bps] × 

2). 

Figure 29 Panel E plots the estimated average transaction costs for the number of 

different complexity features for the trade size of $5,000,000. The estimated average transaction 

costs for trades of size $5,000,000 for bonds with two or more complexity features are lower 

than those for bonds with zero or one complexity feature during the most of sub-periods; 

however, the difference in the estimated average transaction costs are at most 0.05 percentage 

points (5 bps) for any sub-periods. 

3. Dealer Activity

Several factors may influence dealer provision of liquidity, including changes in dealer 

risk aversion after the financial crisis, changing profitability of traditional dealer business 

models, evolving market structure and disintermediation, as well as regulatory impacts. 

Although, as discussed above, it is difficult to establish causality among the aforementioned 

factors and changes in dealer capacity to provide liquidity, this section focuses on how dealer 

inventory, dealer risk taking, dealer profitability, and dealer trading activity have changed over 

time. We find that aggregate corporate bond inventory held by dealer firms is lower during the 

“Regulatory” and the “Post-regulatory” sub-periods compared to earlier sub-periods. This could 

indicate that dealers’ role as liquidity providers in the corporate bond market has diminished as a 

result of the regulations. This may also suggest that dealers have become more risk averse in 

recent sub-periods and could offload corporate bond inventory. Thus, inventory trends are not 

157 The transaction cost estimates for a larger trade size of $1,000,000 for bonds with complexity features of two or 

more and zero or one are similar across sub-periods. 

161 
	 	 	 	 	 	

 

definitive and need to be considered in conjunction with other characteristics of dealer activity. 

We further document trends in dealer profitability, risk, and trading activity, which may point to 

post-crisis changes in the economics of traditional dealer business models. 

a). Dealer Inventory, Risk Taking and Profitability158

Dealer inventory may reflect dealer capital commitment or capacity to provide liquidity. 

Below we examine how dealer corporate bond inventory has changed over time. To measure the 

level of dealer corporate bond inventory, we use the amount of corporate obligations held on the 

asset side of dealer firms’ balance sheets (i.e., corporate obligations inventory) obtained from 

FOCUS reports. We exclude dealer firms using the Alternative Net Capital (ANC) computation 

because such filers do not report a data item that is comparable to the corporate obligations 

inventory item reported by other dealer firms for the duration of our sample.159 Throughout our 

sample period, this filter excludes Goldman, Sachs & Co., Merrill Lynch, Pierce, Fenner & 

Smith Inc., Citigroup Global Markets Inc., Lehman Brothers Inc., Morgan Stanley & Co. LLC, 

J.P. Morgan Securities LLC (formerly Bear, Stearns & Co. Inc), J.P. Morgan Securities Inc., and 

Barclays Capital Inc. Therefore, we are unable to analyze the sample including both ANC and 

non-ANC filers. To the extent that ANC filers may have been more affected by regulations, we 

may underestimate the changes in activity around regulatory reforms. This evidence is, 

therefore, partial and suggestive and needs to be interpreted in conjunction with existing research 

on changes in the role of dealers in the corporate bond markets discussed above. Crucially, we 

note that our results on the evolution of aggregate dealer inventory and dealer inventory for 

dealers affiliated with bank holding companies (BHC) and dealers unaffiliated with BHC is 

158 For all figures and tables referenced in this section, we apply the FOCUS data filters discussed in Appendix D. 159 For ANC filers reporting ANC filings for at least one quarter during the sample period, we exclude these ANC 

filers for the duration of the sample period. 

162 
 

 

 

 

 

 

 

 

generally consistent with existing research, such as Bessembinder et al. (2016) and Bao et al. 

(2016). Our time period cutoffs for sub-periods remain substantially similar to prior sections and 

consistent with existing research.160 

Figure 30 shows the plot of quarterly figures for the aggregate corporate obligations 

inventory from the first quarter of 2003 to the third quarter of 2016 (55 quarters).161 Consistent 

with earlier findings, we observe a substantial build up in aggregate corporate obligations 

inventory during the “TRACE Phase-in” and the “Pre-crisis” sub-periods. The level of aggregate 

corporate obligations inventory peaked around $200 billion162 during the early part of the 

“Crisis” sub-period, but subsequently dropped to approximately $50 billion towards the end of 

the “Crisis” sub-period. During the “Regulatory” and the “Post-regulatory” sub-periods, the 

aggregate amount of corporate obligations inventory held by dealer firms remained at around 

$50 billion or less. The lower levels of aggregate corporate obligations inventory held by dealer 

firms during the “Regulatory” and the “Post-regulatory” sub-periods could suggest that dealers 

have reduced their liquidity provision in corporate bonds. However, this is also consistent with 

dealers becoming more risk averse following the financial crisis and being able to offset 

corporate bond inventory in recent sub-periods. 

160 Because we use quarterly data in FOCUS reports, the smallest time unit we use to subdivide our sample periods 

is a quarter: 1Q 2003 – 4Q 2005: “TRACE Phase-in”; 1Q 2006 – 2Q 2007: “Pre-crisis”; 3Q 2007 – 1Q 2009: 

“Crisis”; 2Q 2009 – 2Q 2012: “Post-crisis”; 3Q 2012 – 2Q 2014: “Regulatory”; 3Q 2014 – 3Q 2016: “Postregulatory.” 

161 We provide the underlying summary statistics for Figure 30 in Table D.5 Panel A in Appendix D. The table 

reports the number of dealer firms, the number of observations (i.e., dealer firm-quarter pairs), and the distributional 

statistics on the amount of corporate obligations inventory, excluding ANC filers, in each sub-period.

162 Figure D.3 in Appendix D shows the plot of aggregate corporate obligations inventory from the first quarter of 

1980 to the third quarter of 2016. The peak aggregate corporate obligations inventory of around $200 billion during 

the early part of the “Crisis” period represents the multi-decade peak, as can be verified in Figure D.3 in Appendix 

D. 

163 
 

 

 

 

Figure 30. Corporate obligations inventory, excluding ANC filers 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and 

spot commodities owned, at market value: corporate obligations. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phasein”; 1Q 2006 – 2Q 2007 (non-shaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 

(non-shaded) – “Post-crisis”; 3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – 

“Post-regulatory” 

Source: DERA analysis 

To the extent that regulatory reforms impacted dealers affiliated with BHCs and dealers 

unaffiliated with BHCs differently, it is important to consider the evolution of activity by these 

two groups of dealers. To examine whether and how BHC-affiliated dealers changed their 

inventory over time relative to unaffiliated dealers, we construct a constant dealer sample 

excluding dealers that changed affiliation status during the sample period. 

The use of a constant dealer sample enables us to consider the behavior of BHC-affiliated 

and unaffiliated dealers uncontaminated by entry and exit from the corporate bond market 

throughout our sample period. However, we recognize that the use of the constant dealer sample 

may affect our inference in several ways. For instance, if BHC-affiliated dealers most affected 

164 
 

 

by regulatory costs exit the market or drop their affiliation with the BHC after regulations, the 

constant dealer sample may underestimate the reduction in BHC-affiliated dealer activity around 

the Volcker Rule. In this scenario, we may be more likely to identify changes in behavior of less 

affected BHC-affiliated dealers and underestimate adverse impacts of regulations. However, 

BHC-affiliated dealers that are members of financial groups may have moved market making 

activity into a BHC-unaffiliated dealer entity. To the extent that such BHC-unaffiliated dealers 

entered or increased their activity in corporate bond markets following regulatory reforms, we 

may underestimate the true scope of market making by BHC-unaffiliated dealers after regulatory 

reforms and overestimate adverse impacts of regulations.163 

In constructing a constant dealer sample, we use the Federal Reserve System’s National 

Information Center164 to verify BHC affiliation status for dealer firms that submit FOCUS 

reports. We apply the following procedure. First, for each dealer firm that submitted FOCUS 

reports for every quarter between the first quarter of 2003 and the third quarter of 2016, we select 

dealer firms that do not change BHC status during our sample period. Second, for those selected 

dealer firms, we calculate the average of quarterly corporate obligations inventory for each year 

between 2003 and 2016.165 Third, we select those dealer firms that rank in the top 25, measured 

by yearly average of quarterly corporate obligations inventory at least in one of the years 

between 2003 and 2016 and keep them in our constant dealer sample for the duration of the 

sample period. Fourth, we split the selected dealer firms into two groups: BHC-affiliated dealer 

163 While Bao et al. (2016) find that non-BHC dealers stepped in to provide more liquidity in localized stress times 

after the reforms; however they still find an aggregate decline in measures of dealer liquidity supply during times of 

localized stress. 

164 For more information about the Federal Financial Institutions Examination Council (FFIEC), see 

https://www.ffiec.gov/nicpubweb/nicweb/NicHome.aspx. 165 The calculation for the average quarterly amount of corporate obligations inventory for 2016 includes only the 

first, the second, and the third quarters. 

165 
 

 

 

 

 

 

 

firms and dealer firms with no BHC affiliation. This process yields 48 constant dealer firms for 

our sample:166 30 bank-holding-dealer firms and 18 non-bank-holding dealer firms.167 

Panel A of Figure 31 shows plots for quarterly corporate obligations inventory level for 

BHC and non-BHC dealer firms in our constant dealer sample.168 We observe that the amount of 

corporate obligations inventory held with BHC-affiliated dealer firms is larger than that of BHCunaffiliated dealer firms. The difference in the amount of corporate obligations inventory is 

large and grows throughout the “TRACE Phase-in” and the “Pre-crisis” sub-periods. The 

difference peaks during the early part of the “Crisis” sub-period: at the peak, the difference in 

the size of corporate obligations inventory between dealer firms with BHC affiliation and dealer 

firms with no BHC affiliation is over $150 billion. However, the difference in the size of 

corporate obligations inventory level begins to shrink during the “Crisis” sub-period, and falls to 

about $20 billion during the “Post-regulatory” sub-period. 

To account for differences in dealer capacity among BHC-affiliated and BHCunaffiliated dealers, we normalize corporate obligations inventory level using data that reflects 

the size of dealer capacity (i.e., total capital and allowable subordinated liabilities).169 Then, we 

compute the average of the normalized figures for dealer firms with BHC affiliation and dealer 

166 Because the composition of the top 25 dealer firms is not necessarily the same for each year between 2003 and 

2016, the process of selecting dealer firms produced more than 25 dealer firms in the end. 167 Figure D.4 in Appendix D shows the share of corporate obligations inventory for dealer firms in our constant 

dealer sample in aggregate corporate obligations inventory for dealer firms that file FOCUS reports across subperiods. Dealer firms in our constant dealer sample account for a substantial portion, ranging between 59% and 

80%, of overall corporate obligations inventory across sub-periods. Repeating the procedure discussed above to add 

any number of dealer firms in the constant dealer sample beyond the top 25 dealer firms results in a gain of at most 

0.4% in the share of aggregate corporate obligations inventory in any quarter. The portion of aggregate inventory not 

captured in this analysis is accounted for by dealer firms that did not have data for all 55 quarters of our sample 

period and dealer firms that changed bank holding affiliation status at least once during our sample period.

168 We provide the underlying summary statistics for Panel A of Figure 31 in Table D.5 Panel B in Appendix D. The 

table reports the number of dealer firms, the number of observations (i.e., dealer firm-quarter pairs), and the 

distributional statistics on the amount of corporate obligations inventory by bank holding company affiliation for 

each sub-period. 

169 The size of the dealer balance sheet is regulatorily constrained by the amount of dealer capital and allowable 

subordinated liabilities. 

166 
 

firms with no BHC affiliation. Panel B of Figure 31 shows the normalized corporate obligations 

inventory level for BHC-affiliated and BHC-unaffiliated dealers. In Panel B of Figure 31, we no 

longer observe the large build up in corporate obligations inventory for BHC-affiliated dealer 

firms during the “TRACE Phase-in” and the “Pre-crisis” sub-periods as in Panel A of Figure 31. 

This indicates that the large buildup in corporate obligations inventory during the “TRACE 

Phase-in” and the “Pre-crisis” sub-periods was accompanied by a large increase in the overall 

capacity of BHC dealers (with capacity measured with total capital and allowable subordinated 

liabilities). At the same time, we observe an increase in the normalized corporate obligations 

inventory for BHC-unaffiliated dealer firms during the “TRACE Phase-in” and the “Pre-crisis” 

sub-periods. Both BHC-affiliated and BHC-unaffiliated dealer firms reduced their normalized 

corporate obligations inventory during the “Crisis” period. Furthermore, we observe an increase 

in normalized corporate obligations inventory for BHC-affiliated dealers during the “Regulatory” 

period, with a subsequent decrease during the “Post-regulatory” sub-period. 

167 
 

 

 

 

 

Figure 31. Corporate obligations inventory by affiliation. Top 25 constant dealer sample 

Panel A. Total inventory 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and 

spot commodities owned, at market value: corporate obligations. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phasein”; 1Q 2006 – 2Q 2007 (non-shaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 

(non-shaded) – “Post-crisis”; 3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – 

“Post-regulatory” 

168 
 

 

 

 

Panel B. Normalized inventory 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and 

spot commodities owned, at market value: corporate obligations – divided by Line Item 3530 – total capital and 

allowable subordinated liabilities. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phase-in”; 1Q 2006 – 2Q 2007 (nonshaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 (non-shaded) – “Post-crisis”; 

3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – “Post-regulatory” 

Source: DERA analysis 

We also examine the riskiness of dealers’ securities positions, measured as haircuts on 

corporate obligations obtained from FOCUS reports. Holding market value of securities 

constant, larger haircuts would correspond to riskier securities. We note that this measure does 

not distinguish between the activities creating the risk. For example, the data does not 

distinguish between securities held in inventory for the purposes of market making from risk 

related to proprietary positions. Therefore, this measure may be broadly indicative of overall 

riskiness of dealer positions as opposed to the risk of any particular type of position. Figure 32 

169 
 

 

 

 

 

 

 

plots the quarterly aggregate figures for haircuts on corporate obligations from the first quarter of 

2003 to the third quarter of 2016.170 

Figure 32. Haircuts, excluding ANC filers 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3710 – haircuts on 

securities: corporate obligations. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phase-in”; 1Q 2006 – 2Q 2007 (nonshaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 (non-shaded) – “Post-crisis”; 

3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – “Post-regulatory” 

Source: DERA analysis 

Panel A of Figure 33 shows the plots of quarterly haircuts on corporate obligations for 

dealer firms affiliated with BHC and dealer firms without BHC affiliation in our constant dealer 

sample.171 In Figure 32, we observe a substantial build up in aggregate haircuts for dealer firms 

throughout the “TRACE Phase-in” and the “Pre-crisis” sub-periods, peaking during the early 

part of the “Crisis” sub-period. This is similar to the pattern that we observe for aggregate 

170 We provide the underlying summary statistics for Figure 32 in Table D.6 Panel A in Appendix D. The table 

reports the number of dealer firms, the number of observations (i.e., dealer firm-quarter pairs), and the distributional 

statistics on the amount of haircuts, excluding ANC filers, in each sub-period.

171 We provide the underlying summary statistics for Panel A of Figure 33 in Table D.6 Panel B in Appendix D. The 

table reports the number of dealer firms, the number of observations (i.e., dealer firm-quarter pairs), and the 

distributional statistics on the amount of haircuts by bank holding company affiliation for each sub-period. 

170 
corporate obligations inventory. Subsequently, aggregate haircuts on corporate obligations on 

dealer firms’ balance sheets decreased and reached their lowest level during the “Postregulatory” sub-period in our sample period. From Panel A of Figure 33, we observe that the 

large runup and the subsequent decrease in aggregate haircuts on corporate obligations was 

primarily driven by BHC-affiliated dealers in our constant dealer sample. 

Next, we scale haircuts on corporate obligations by the total securities and spot 

commodities owned at market values and compute the average of the scaled figures for dealer 

firms with and without a BHC affiliation. Where trends in unscaled haircuts may be driven by 

the riskiness of securities, the size of the balance sheet, or both, scaled haircuts control for the 

size of the dealer’s book. Thus, normalized haircuts reflect the riskiness of corporate obligations 

on a dealer’s balance sheet, after accounting for the size of the aggregate exposure. Panel B of 

Figure 33 shows the plots for normalized quarterly haircuts on corporate obligations for BHCaffiliated and BHC-unaffiliated dealers in our constant dealer sample. Unlike in the plots for 

unadjusted haircuts on corporate obligations in Panel A of Figure 33, we do not observe an 

increase in normalized haircuts for BHC-affiliated dealers during the “TRACE Phase-in” and the 

“Pre-crisis” sub-periods. We do observe an increase in normalized haircuts on corporate 

obligations during the “Regulatory” and the “Post-regulatory” sub-periods for BHC-affiliated 

dealers, indicating that these dealer firms may be taking higher risk on corporate obligations 

following the Volcker Rule and other regulatory changes. This could suggest that BHCaffiliated dealers increased their role as liquidity providers in riskier corporate bond issues during 

the two recent sub-periods. At the same time, this could also reflect BHC-affiliated dealers’ 

inability to offload risky issues from their inventory or a desire to further manage credit risk 

exposures on their balance sheets. 

171 
 

 

 

Figure 33. Haircuts by affiliation. Top 25 constant dealer sample 

Panel A. Total haircuts
 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3710 – haircuts on 

securities: corporate obligations. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phase-in”; 1Q 2006 – 2Q 2007 (nonshaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 (non-shaded) – “Post-crisis”; 

3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – “Post-regulatory” 

172 
 

 

 

 

 

 

 

 

Panel B. Normalized haircuts 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3710 – haircuts on 

securities: corporate obligations – divided by Line Item 850 – total securities and spot commodities owned, at 

market value. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phase-in”; 1Q 2006 – 2Q 2007 (non-shaded) – “Pre-crisis”; 

3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 (non-shaded) – “Post-crisis”; 3Q 2012 – 2Q 2014 

(shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – “Post-regulatory” 

Source: DERA analysis 

Finally, we turn to the evolution in dealer profitability. To measure the profitability of 

dealer firms, we use gains or losses on securities trading accounts from trading debt securities for 

dealer firms (FOCUS reports).172 Figure 34 plots the quarterly aggregate figures for gains or 

losses from trading in debt securities from the first quarter of 2003 to the third quarter of 2016.173 

Figure 35 shows the plots of quarterly gains or losses from trading debt securities for BHCaffiliated and BHC-unaffiliated dealers in our constant dealer sample.174 In Figure 34, we 

172 FOCUS reports do not break out gains or losses on securities trading accounts from trading corporate debt 

securities for dealer firms. 

173 We provide the underlying summary statistics for Figure 34 in Table D.7 Panel A in Appendix D. The table 

reports the number of dealer firms, the number of observations (i.e., dealer firm-quarter pairs), and the distributional 

statistics on the amount of gains or losses from trading in debt securities, excluding ANC filers, in each sub-period. 174 We provide the underlying summary statistics for Figure 35 in Table D.7 Panel B in Appendix D. The table 

reports the number of dealer firms, the number of observations (i.e., dealer firm-quarter pairs), and the distributional 

[Footnote continued on next page] 

173 
 

 

 

 

observe large aggregate losses from trading debt securities for dealers during the “Crisis” period. 

We also observe smaller fluctuations in gains or losses from trading debt securities during the 

“Regulatory” and the “Post-regulatory” sub-periods. From Figure 35, we note that BHCaffiliated dealers were the main contributors to large aggregate losses from trading debt 

securities during the “Crisis” sub-period. 

Figure 34. Gains or losses from trading in debt securities, excluding ANC filers 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3944 – gains or losses 

on firm securities trading accounts from trading in debt securities. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phasein”; 1Q 2006 – 2Q 2007 (non-shaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 

(non-shaded) – “Post-crisis”; 3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – 

“Post-regulatory” 

Source: DERA analysis 

statistics on the amount of gains or losses from trading in debt securities by bank holding company affiliation for 

each sub-period. 

174 
 

 

Figure 35. Gains or losses from trading in debt securities by affiliation. Top 25 constant 

dealer sample 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3944 – gains or losses 

on firm securities trading accounts from trading in debt securities. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phasein”; 1Q 2006 – 2Q 2007 (non-shaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 

(non-shaded) – “Post-crisis”; 3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – 

“Post-regulatory”

 Source: DERA analysis 

Overall, our analysis supports the evolving role of dealers in corporate bond markets, but 

we are unable to attribute the observed effects to a particular causal factor. In conjunction with 

the above results on dealer activity, this evidence is consistent with BHC-affiliated dealers 

experiencing crisis-related shocks to the profitability of fixed income market making. Aggregate 

macroeconomic conditions, including lower interest rates, after the financial crisis may have also 

impacted dealer profitability, consistent with our results on aggregate dealer profitability. 

Whether low interest rate and other macro shocks would have differential impacts on BHCaffiliated and BHC-unaffiliated dealer firms is unclear; however, the results are not inconsistent 

with macroeconomic effects on the risks and profitability of principal trading and carrying large 

inventory. Finally, differences in trends for BHC-affiliated and BHC-unaffiliated dealer firms 

175 
	 	 	 	

 

 

limit our ability to rely on a differences-in-differences analysis to cleanly identify the causal 

impact of regulations. 

b). Dealer Trading Activity

In this section,175 we examine how dealer trading activity has changed across sub-periods. 

We do not observe large distributional changes in the number of dealers providing liquidity per 

corporate bond issue over time. Further, as shown below, we do not detect large changes in trade 

size distribution during the “Regulatory” and the “Post-regulatory” sub-period compared to 

earlier sub-periods. 

We study dealer activity by examining the number of broker-dealers trading each 

corporate bond issue and principal versus agency trades broken out by trade size over six subperiods. As mentioned above, the regulatory version of TRACE includes broker-dealer identifier 

(i.e., MPID). We identify each dealer by its MPID. Table 16176 reports the distribution for the 

number of dealers reporting trades per corporate bond issue on each trading day averaged over 

each sub-period. We also report in Table 16 distributional statistics for overall, interdealer, and 

customer trades broken out by trade size. We observe that the number of dealers reporting trades 

varies notably across bond issues for any sub-period. Across sub-periods, the median number of 

dealers providing liquidity per corporate bond issue is 2 or 3 and corporate bonds in the 99th 

percentile have over 16 distinct dealers reporting trades. We do not observe large change in the 

number of dealers providing liquidity per corporate bond issues around changes in regulation. 

Furthermore, across sub-periods, we note that on average, more dealers trade in a small trade size 

(less than $100,000) than in larger trade sizes. 

175 For all tables referenced in this section, we apply data filters in the trade reporting and the discretionary filters 

discussed in Appendix D.

176 In Table 16, for each interdealer trade, we count both dealers reporting the trade.
 

176 
 

 

 

The regulatory version of TRACE includes a broker-dealer capacity code that indicates 

whether a reporting dealer traded on principal or agency basis for each trade. Table 17177 reports 

the number of customer trades and par volume per day averaged over each sub-period. In Table 

17, we also break out the number of customer trades and par volume by dealer capacity and the 

portion of principal and agency customer trades across trade size for each sub-period. Across 

sub-periods, we observe that only a small portion of customer trades are reported as agency 

trades. The portion of customer trades executed on an agency basis has been generally 

increasing across six sub-periods: approximately 20% of customer trades are reported as agency 

trades in the “Post-regulatory” sub-period whereas only 10% of customer trades are executed on 

agency basis in the “TRACE Phase-in” sub-period. However, we note that the par volume from 

agency customer trades is, at most, 3.5% of total customer trade par volume in any sub-period. 

A large portion of principal trades has a smaller trade size: over 58% of principal trades have a 

trade size of less than $100,000 in any sub-period. For agency trades, approximately 90% of 

trades are concentrated in smaller trade sizes (less than $100,000 in recent sub-periods, 

specifically the “Regulatory” and the “Post-regulatory” sub-period). We note that the portion of 

principal trades with smaller trade size is larger during the “Crisis” and the “Post-crisis” subperiod. Furthermore, the portion of block size (greater than $5,000,000) principal trades is 

177 Table D.8 Panel A through Panel E in Appendix D report the distribution of customer principal trades by trade 

size across five bond characteristics (issue size, credit quality, age (time since issuance), original maturity, and 

complexity features) over six sub-periods. We observe that the portion of customer principal trades with larger trade 

size (larger than $5,000,000) is higher for corporate bonds with issue size greater than $500 million for any subperiod compared to those of corporate bonds with smaller issue size. For investment grade corporate bonds, we 

observe that the portion of medium and large trades (trade size range in $100,000 - $1,000,000 and $1,000,000 - 

$5,000,000) is slightly higher and the portion of small trades (trade size less than $100,000) is somewhat lower 

during the recent sub-periods, the “Regulatory” and the “Post-regulatory” sub-period, than those in earlier subperiods. On the contrary, for high yield bonds, a larger portion of trades have smaller trade size (trade size less than 

$100,000) in the sub-periods after the “Crisis” sub-period. Furthermore, we note that the portion of customer 

principal trades with larger trade size (larger than $1,000,000) is higher for younger corporate bonds (age less than 2 

years) for any sub-period compared to those of corporate bonds older than 2 years. 

177 
	 	 	

 

 

 

 

similar across sub-periods and the portion of medium size ($100,000 - $1,000,000) principal 

trades is larger during the “Regulatory” and the “Post-regulatory” sub-period. 

4. Electronic Venues

Electronic trading may facilitate the management of dealer inventory and reduce 

counterparty search costs. As referenced above, the usage of electronic venues in corporate bond 

trading may have become more prevalent in recent years. ATS178 represent one type of 

electronic venue that hosts corporate bond trading. In this section, we characterize dealer 

quotations on ATS, the types of corporate bonds with dealer quotations on ATS, corporate bond 

trades on ATS, and the amount of pre-trade information (e.g., price quotes) disseminated on 

ATS.179 Further, we use dealer quotations on ATS to estimate corporate bond transaction costs. 

We find that corporate bonds with any dealer quotations on ATS are larger issues. Corporate 

bonds with both dealer bids and offers on a given day are even larger issues, with a median issue 

size of around $380 million. Further, ATS trades are small in size, with over 90% of ATS 

corporate bond trades having a trade size of less than $100,000. During our sample period, ATS 

host 37% of interdealer trades, but ATS trades account for only 7% of total par volume for 

interdealer trades. Furthermore, during our sample period, at least 50% of customer trades are 

executed outside of the best quoted spreads—an effect primarily driven by small trades (trade 

178 Generally, an ATS is defined as any organization, association, person, group of persons, or system that meets the 

definition of “exchange” within the meaning of Section 3(a)(1) of the Exchange Act and Rule 3b-16 thereunder, but 

does not: (a) set rules governing the conduct of subscribers other than the conduct of such subscribers trading on the 

ATS; or (b) discipline subscribers other than by exclusion from trading. An ATS that complies with Regulation 

ATS, which includes, among other things, registering as a broker-dealer and filing a Form ATS with the 

Commission, is not required to register as a national securities exchange under Section 5 of the Exchange Act. 179 We do not analyze corporate bond quotations and trades on the New York Stock Exchange. During our sample 

period, we estimate that approximately 1,300 corporate bond issues are quoted and approximately 400 corporate 

bond trades are executed on the NYSE. Our ATS quotations and trades data is more comprehensive: from Panel A 

of Table 18, over 16,000 TRACE-eligible bonds are quoted on ATS during our sample period. In addition, from 

Table 20, we observe that ATS host over 193,000 interdealer trades (approximately 37% of interdealer trades 

reported to TRACE) during our sample period. 

178 
	 	 	 	 	 	 	

 

 

 

size of less than $100,000). As discussed below, the sample period for this analysis is relatively 

short and does not enable us to analyze changes in corporate bond liquidity through the time 

series. However, these results suggest there may be notable cross-sectional heterogeneity in 

ATS activity for different types of bond issues and indicate that electronification may be 

associated with lower trade sizes. 

a). Descriptive Statistics for Dealer Quotations on ATS180

First, we consider corporate bond quotes, using quotation data between August 1, 2014 

and November 28, 2014 (84 business days)181 collected from KCG and TMC in support of 

corporate bond trading. Our quotation data include the following information: bond identifier 

(CUSIP), quoted price, quoted size, an indicator that specifies whether a quote is a bid or an 

offer, a broker-dealer identifier (MPID), date of quotation, time stamps for quote messages, and 

messages (e.g., quote cancellation and modification). We include the following summary 

statistics on corporate bond quotations: the number of dealer quotations, the number of bond 

issues (CUSIPs) with dealer quotations, the number of days with dealer quotations, quoted time 

length, and quote size. 

We identify dealers that post quotations on ATS using MPIDs. We count the number of 

dealer quotations in the following way: on a given day, we count the number of unique MPIDCUSIP pairs. Panel A of Table 18 reports the number of dealer quotations and the number of 

bonds quoted broken out by quoted side, offer, or bid. We observe that there are approximately 

180 For all tables referenced in this section, we apply data filters in the trade reporting and the discretionary filters 

discussed in Appendix D.

181 In analyzing dealer quotations on ATS, we drop 2 days in our sample, August 29, 2014 and November 26, 2014, 

because of an error in the data feed that includes TRACE-eligible bond issues. Thus, Tables 18, 19, and 23 do not 

include August 29, 2014, and November 26, 2014.
 

179 
 

3.2 million dealer offer quotations for 13,740 distinct bond issues182 (CUSIPs) over 84 business 

days from August 1 to November 28, 2014. Furthermore, we observe a substantially larger 

number of dealer bid quotations than dealer offer quotations: there are 16,461 distinct bond 

issues (CUSIPs) with over 6.1 million dealer bid quotations. 

Panel B of Table 18 shows the distribution for the number of TRACE-eligible bond 

issues (CUSIPs) per day, the portion of TRACE-eligible bond issues (CUSIPs) quoted per day, 

the number of dealer quotations per day, and the number of days quoted per bond issue (CUSIP). 

The median number of TRACE-eligible bond issues per day is approximately 28,000. On an 

average day, over 50% of TRACE-eligible bond issues have dealer bid quotations. On the other 

hand, approximately 35% of TRACE-eligible bond issues have dealer offer quotations on a given 

day. We observe both dealer bid and offer quotations on a given day for approximately 35% of 

TRACE-eligible bond issues. The median number of dealer quotations (bid or offer) is over 

116,000 per day. The median number of dealer bid and offer quotations per day is over 75,000 

and 39,000, respectively, and on a given day, we observe twice as many dealer bid quotations as 

dealer offer quotations. We include only TRACE-eligible bond issues with at least one dealer 

quotation during the sample period in deriving the distribution for the number of days with 

dealer quotation per bond issue (CUSIP) in Panel B of Table 18. We observe that at least 75% of 

TRACE-eligible bond issues quoted during the sample period have either dealer bid or offer 

quotation on at least 82 out of 84 business days. 

Panel C of Table 18 shows the distribution of duration for which a dealer quotes a bond 

issue per day (i.e., quote duration) and quote size. ATS business hours for our sample are from 8 

a.m. to 5:15 p.m., for a total length of time of 9.25 hours. We observe that the median for quote 

182 As in earlier sections, we exclude Rule 144A corporate securities from our analysis. 

180 
	 	 	 	 	 	 	 	

 

duration for dealer bid or offer quotation is 9.25 hours per day, so the majority of dealers quote 

for an entire trading day. We also note that 90% of dealer quotations are posted for at least 8 

hours per day. From the lower section of Table 18 Panel C, the median quote size is larger for 

dealer bid than offer quotations ($250,000 versus $130,000). 

b). Types of Corporate Bonds Quoted on ATS183

Now we turn to the analysis of the types of corporate bonds that have dealer quotations 

and also examine the types of corporate bonds with two-sided (both bid and offer vs. only oneside, bid or offer) dealer quotations on a given day. To this end, we examine the cross-sectional 

bond characteristics of corporate bonds quoted on ATS. As above, we consider the following 

five bond characteristics: issue size, credit quality, age (time since issuance), original maturity, 

and complexity features. 

As can be seen in Table 19, quotation activity is concentrated in bond issues with larger 

issue size, investment grade, longer original maturity, and less complexity features. On a given 

day, we note that the average issue size ($358.2 million) for corporate bonds quoted on ATS is 

larger than the average issue size ($221.3 million) of TRACE-eligible bond issues. Furthermore, 

the average issue size ($358.2 million) for corporate bonds quoted on ATS is substantially larger 

than the average issue size ($56.9 million) for corporate bonds that are not quoted on ATS on a 

given day. Further, at least 75% of corporate bonds that have dealer quotations on ATS have an 

investment grade credit rating, whereas at least 50% of corporate bonds that are not quoted on 

ATS on a given day are high yield bonds. 

183 For all tables referenced in this section, we apply data filters in the trade reporting and the discretionary filters 

discussed in Appendix D. 

181 
While issues quoted on ATS have longer original maturity than those not quoted on ATS, 

we note little difference in the average age (time since issuance) between the two groups of 

corporate bonds (although the median age for corporate bonds that are quoted is slightly older 

than those not quoted on ATS (3.3 years for corporate bonds quoted vs. 1.7 years for corporate 

bonds not quoted), the difference in the median age is small). We observe that the average 

original maturity (14.3 years) of corporate bonds quoted is longer than that (8.5 years) of 

corporate bonds not quoted on ATS on a given day. 

Corporate bonds quoted on ATS are less likely to be complex. From Table 19, at least 

10% of corporate bonds quoted have two or more complexity features whereas at least 25% of 

corporate bonds that are not quoted on ATS on a given day have two or more complexity 

features. Table 19 also reports distributional statistics on bond characteristics for corporate 

bonds with two-sided (both bid and offer) dealer quotations and for corporate bonds with dealer 

quotation only on one side (only bid or only offer) on a given day. 

Finally, we explore cross-sectional characteristics of corporate bonds with one-sided and 

two-sided dealer quotes. We observe little difference in the median and average values across 

bond characteristics on a given day for the two groups, with the exception of issue size. The 

average issue size for corporate bonds with two-sided dealer quotations on a given day is $518.6 

million while the average issue size for corporate bonds with dealer quotation only on one side is 

$71.5 million. 

182 
	 	 	 	 	

 

c). Corporate Bond Trades on ATS184

In examining corporate bond trades on ATS, we use regulatory TRACE. The ATS trade 

indicator in the regulatory version of TRACE enables us to distinguish corporate bond trades 

executed on ATS from non-ATS trades. The sample period for our analysis on ATS trades is 

different from the sample period for our analysis of dealer quotations on ATS because the 

inclusion of the ATS trade indicator in the regulatory version of TRACE commenced on August 

1, 2016, limiting the sample period for our analysis to August 1, 2016, to September 19, 2016. 

We first examine trade size and the types of corporate bonds that trade on ATS. We find 

that a large fraction of ATS trades are interdealer trades, but they represent a small share of 

interdealer par volume. Table 20 reports the distribution of trade counts and par volume across 

different trade sizes for ATS interdealer trades and non-ATS interdealer trades. We exclude 

customer trades from our analysis of ATS trades because there are few customer trades executed 

on ATS. From Table 20, we note that ATS host 37.5% of all interdealer trades during the 

sample period. However, the par volume share for ATS is less than 7% of total interdealer trade 

par volume. Further, ATS trades are smaller than non-ATS trades. Over 92% of interdealer 

trades executed on ATS are small trades (less than $100,000 in size), whereas 69% of non-ATS 

interdealer trades have a trade size of less than $100,000. 

In examining the types of corporate bonds traded on ATS, as above, we consider the 

following five bond characteristics: issue size, credit quality, age, original maturity, and 

complexity features. Table 21 shows the distributional statistics of bond characteristics for 

interdealer trades executed on ATS and non-ATS interdealer trades. We observe little difference 

184 For all tables referenced in this section, we apply data filters in the trade reporting and the discretionary filters 

discussed in Appendix D. 

183 
	 	

 

 

 

 

in the median and average values across bond characteristics for ATS interdealer trades and nonATS interdealer trades. 

d). Pre‐trade Information185

We examine the amount of pre-trade information (e.g., price quotes) disseminated on 

ATS. To this end, we use dealer quotation data from August 1, 2014, to November 28, 2014, 

and customer trades from the regulatory version of TRACE for the same period. In general, 

dealers have access to dealer quotations. However, institutional (e.g., small institutional 

investors) and individual investors may not have access to dealer quotations. Our analysis 

addresses whether a dealer quote (i.e., price quote) is available and observable prior to the trade. 

We match quoted prices with trades to evaluate whether the quoted price had been available 

around the time of trade execution. In matching quoted prices with trades, we require quoted 

price to stand for at least 30 minutes prior to the time of trade execution to ensure that dealers 

have had the time to observe the quoted price.186 

Table 22187 reports the number of customer trades with pre-trade information (i.e., 

standing quoted price) at the time of trade execution and the distributional statistics for five bond 

characteristics (issue size, credit quality, age (time since issuance), original maturity, and 

complexity features) for corporate bonds with pre-trade information (i.e., standing quoted price). 

185 For all tables referenced in this section, we apply data filters in the trade reporting and the discretionary filters 

discussed in Appendix D.

186 We repeat this analysis requiring quoted price to stand for at least 2 seconds (instead of 30 minutes). The results 

are tabulated in Table D.9 in Appendix D. The findings in Table D.9 in Appendix D are similar to those in Table 22.

187 Our analysis reported in Table 22 includes only corporate bond trades between 8:30 a.m. and 5:15 p.m., because 

we require quoted price to stand for at least 30 minutes prior to the time of trade execution (the earliest time a dealer 

can post a quotation is 8:00 a.m., and the latest time that a dealer can post a quotation on an ATS is 5:15 p.m.). The 

total number of customer trades between August 1, 2014, and November 28, 2014, is 1,709,298. Of the 1,709,298 

customer trades, 54,581 (approximately 3.2% of 1,709,298) customer trades are executed either before 8:30 a.m. or 

after 5:15 p.m. 

184 
		 	 	 	 	 	 	

 

	 	

The number of customer trades (not reported in Table 22) between 8:30 a.m. and 5:15 p.m. from 

August 1, 2014, to November 28, 2014, is approximately 1.7 million. 

We find that most customer trades in our sample had pre-trade information and that 

bonds with pre-trade information tend to be larger issues and less complex bonds. From Table 

22, we note that a large portion of customer trades had pre-trade information (i.e., standing 

quoted price): over 90% of customer trades had standing quoted price at the time of trade 

execution during the sample period. The median and average issue size for corporate bonds that 

had pre-trade information (i.e., standing quoted price) is approximately $750 million and $1 

billion, respectively, which are notably larger than those for corporate bonds with no pre-trade 

information. We observe that most of the corporate bonds with pre-trade information (i.e., 

standing quoted price) have zero or one complexity feature. Furthermore, at least 25% of 

customer trades that had pre-trade information (i.e., standing quoted price) are high yield bonds. 

e). Transaction Costs: Quoted Spread, Effective Spread, and Price

Improvement188

Finally, we turn to the analysis of transaction costs using dealer quotations. We examine 

the following three transaction cost measures constructed using dealer quotations on ATS and 

corporate bond trades: time-weighted quoted spread, effective spread, and price improvement to 

quoted spread. We study transaction cost differences across corporate bonds by examining the 

distribution of time-weighted quoted spreads189 for corporate bonds with different bond 

188 For all tables referenced in this section, we apply data filters in the trade reporting, the discretionary, and the 

price-based filters discussed in Appendix D. 

189 The definition of quoted spread is as follows: 

ܽ݁݀ ݐ ܾ݁ݏ

ൌ ݁ܽ݀ݎ݌ݏ ݁݀ݐ݋ݑݍ

[Footnote continued on next page] 

݌ݎ݁ܿ݅ ܾ݀݅ ݈݁ܽ݁݀	ݎ ݐ ܾ݁ݏ െ ݌ݎ݁ܿ݅ ݋݂݂݁	ݎ ݈݁	ݎ

ݐ݅݊݋݌݉݅݀-

185 
 

 

 

 

 

characteristics. In constructing daily time-weighted quoted spreads, for each corporate bond 

issue, we use the best continuously quoted bid and offer price between 8 a.m. and 5:15 p.m. 

weighted by price quote duration.190 As above, we consider five bond characteristics: issue size, 

credit quality, age (time since issuance), original maturity, and complexity features. 

Table 23191 reports the distributional statistics of time-weighted quoted spreads for 

corporate bonds across five bond characteristics for the period between August 1, 2014, and 

November 28, 2014. We observe that the median time-weighted quoted spread decreases 

monotonically with increases in issue size. The median time-weighted quoted spread is 0.4% 

(39.7 bps) for corporate bonds with issue size greater than $500 million, whereas the median 

time-weighted quoted spread is 1.23% (123.2 bps) for bonds with issue size less than $100 

million. We note that the median time-weighted quoted spread for investment grade corporate 

bonds is 0.43 percentage points lower than the median time-weighted quoted spread for high 

yield corporate bonds (65.5 bps versus 108.4 bps). The median time-weighted quoted spread is 

smaller for corporate bonds younger than 5 years compared to that of bonds older than 5 years. 

Corporate bonds with an original maturity of less than 2 years have a substantially larger median 

time-weighted quoted spread, 2.1% (209.4 bps), than those with an original maturity longer than 

2 years.192 For corporate bonds with an original maturity longer than 2 years, the median timewhere mid-point is the average of best dealer offer price and best dealer bid price. To express quoted spread in basis 

points, we multiply the quantity by 10,000. 

190 In computing time-weighted quoted spread, we do not include cases where only one side of the market is quoted. 

Furthermore, we drop cases for locked and crossed markets in computing time-weighted quoted spread.

191 The average time-weighted quoted spread of 172.9 bps reported in Table 23 is somewhat smaller than the 

average quoted spread of 260 bps reported in Harris (2015). This difference may be due to the differences in data 

source, bond sample selection, sample period, and computational methods. 

192 Large median time-weighted quoted spreads for corporate bonds with an original maturity less than 2 years is 

primarily due to large time-weighted quoted spreads for corporate bonds with two or more complexity features. For 

corporate bonds with an original maturity of less than 2 years, the median time-weighted quoted spread for bonds 

with zero or one complexity feature is only 0.2 % (20.3 bps) while the median time-weighted quoted spread for 

bonds with two or more complexity features is 4.01% (401.1 bps). 

186 
 

	 	 	

 

 

 

weighted quoted spread increases monotonically with original maturity. Furthermore, corporate 

bonds with zero or one complexity feature have a substantially smaller time-weighted quoted 

spread than corporate bonds with two or more complexity features (67.6 bps versus 144.8 bps). 

Effective spread193 measures the transaction cost of a round-trip trade. We match the best 

two-sided quoted prices available around the time of a trade execution. To ensure that quoted 

price was available at the time of trade execution, we require two-sided quote prices to stand for 

at least 1 second before the time of trade execution, because corporate bond trades in the 

regulatory version of TRACE are time-stamped only to the second. Table 24194,195 shows the 

distribution of effective spreads broken out by interdealer and customer trades and across 

different trade size for customer trades. The average and median effective spread for customer 

trades is larger by 0.67 percentage points (76.6 bps versus 143.1 bps) and 0.24 percentage points 

(42.0 bps versus 65.9 bps), respectively, than those for interdealer trades. The median and 

average effective spread for smaller customer trades with trade size less than $100,000 are 

substantially larger than those for trades with trade size greater than $100,000. Furthermore, we 

observe that customer trades with trade size larger than $5,000,000 have the smallest effective 

spreads. 

193 The definition of effective spread as follows: 

ቈ ൈ 2 ൌ ݁ܽ݀ݎ݌ݏ ݂݂݁݁ݒ݅ݐ݁ܿ

ܦ ൈ ሻݐ݅݊݋݌‐݉݅݀ െ ݅ܿ݁ݎ݌ሺ

቉ ݐ݅݊݋݌‐݉݅݀

where mid-point is the average of best dealer offer price and best dealer bid price. D = 1 if trade is buyer initiated 

(i.e., price ≥ mid-point) and D = -1 if trade is seller initiated (i.e., price < mid-point). To express effective spread in 

basis points, we multiply the quantity by 10,000. 

194 For corporate bond trades in Table 24, we apply all filters in the trade reporting filters, the discretionary filters, 

and the price-based filters discussed in Appendix D. Furthermore, we exclude all trades executed before 8 a.m. or 

after 5:15 p.m.

195 The average values for effective spread reported in Table 24 are qualitatively similar to those reported in Harris 

(2015). The differences in the average values for effective spreads between ours and Harris (2015) may be due to the 

differences in data source, bond sample selection, sample period, and computational methods. 

187 
 

 

	 	

	 	

Table 25196 reports the distributional statistics of effective spreads across five bond 

characteristics (issue size, credit quality, age (time since issuance), original maturity, and 

complexity features). We observe that the average and median effective spread decreases 

monotonically with issue size. The median effective spread for corporate bonds with an issue 

size greater than $500 million is 0.38% (37.5 bps) while the median effective spread for bonds 

with an issue size less than $100 million is 1.26% (125.5 bps). The median effective spread for 

investment grade corporate bonds is 0.5 percentage points lower than the median effective spread 

for high yield corporate bonds (38.4 bps versus 87.9 bps). Although the median effective spread 

is increasing monotonically with age (time since issuance), we note that the differences in 

effective spreads are small. For corporate bonds with original maturity longer than 2 years, the 

median effective spread increases monotonically with original maturity years. Furthermore, the 

median effective spread for corporate bonds with zero or one complexity feature is 0.64 

percentage points smaller than that of corporate bonds with two or more complexity features 

(50.4 bps versus 114.8 bps). 

Finally, we compute price improvement197 for a corporate bond trade using the best twosided quoted prices available at the time of trade execution. The procedure of matching trades 

with the best two-sided quoted prices in computing price improvement is the same as the one 

196 For corporate bond trades in Table 25, we apply all filters in the trade reporting filters, the discretionary filters, 

and the price-based filters discussed in Appendix D. Furthermore, we exclude all trades executed before 8 A.M. or 

after 5:15 P.M. 

197 The definition of price improvement as follows. For buyer initiated trades (i.e.,	price ≥	mid‐point, where midpoint is the average of best dealer offer price and best dealer bid price),

݌ݎ ݋݂݂݁	ݎ ݁	ݎ

ݐ݅݊݋݌‐݉݅݀

For seller initiated trades (i.e.,	price <	mid‐point,	where	mid-point is the average of best dealer offer price and best 

ܿ	݁ ݌ݎ݅ െ ݁ܿ݅ ݈ܽ݁݀ ݐ ܾ݁ݏ

ൈ	 2 ൤	ൌ ݐ݁݉݁݊ݒ݋ݎ݌݅݉ ݅ܿ݁ݎ݌

݅ܿ݁ݎ݌ ܾ݅݀ ݎ݈݀݁ܽ݁ ݐݏܾ݁ െ ݅ܿ݁ݎ݌ ൨	

ݐ݅݊݋݌‐݉݅݀

dealer bid price), 

ൈ	 2 ൌ ൤	ݐ݁݉݁݊ݒ݋ݎ݌݅݉ ݅ܿ݁ݎ݌

൨

To express price improvement in basis points, we multiply the quantity by 10,000. 

188 
 

used to compute effective spreads above. Price improvement of 0 indicates that the trade was 

executed at the quoted price. Negative price improvement measure indicates that the trade was 

executed outside of the best two-sided quote prices, whereas positive price improvement 

indicates that the trade was executed inside of the best quoted spread. Table 26198 Panel A shows 

the distribution of price improvement to the quoted price broken out by interdealer and customer 

trades and across different trade size for customer trades. Both average and median price 

improvement for customer trades are negative, indicating that the majority of customer trades are 

executed outside of the best quoted prices, whereas the average and median price improvement 

for interdealer trades are positive, indicating that the majority of interdealer trades are executed 

inside of the best quoted spread. The difference in median price improvement between customer 

and interdealer trades is 0.17 percentage point (0.0 bps – (-17.0 bps)). The negative median price 

improvement for customer trades may be driven by small trades. The average and median price 

improvement for customer trades with trade size smaller than $100,000 is negative, -1.18%(­

117.7 bps) and -0.49% (-48.6 bps), respectively, whereas the median and average price 

improvement for larger customer trades (trade size larger than $1,000,000) are positive. We note 

that at least 75% of customer trades with a trade size smaller than $100,000 are executed outside 

of the best quoted spreads (i.e., negative price improvement). Based on the average and median 

price improvement figures, customer trades with a trade size between $1,000,000 and $5,000,000 

receive the largest price improvement. 

Panel B of Table 26 reports the distribution of price improvement scaled by the quoted 

spread at the time of trade execution broken out by interdealer and customer trades and across 

198 For corporate bond trades in Table 26, we apply all filters in the trade reporting filters, the discretionary filters, 

and the price-based filters discussed in Appendix D. Furthermore, we exclude all trades executed before 8 a.m. or 

after 5:15 p.m. 

189 
different trade sizes for customer trades. The upper bound for scaled price improvement is one 

(for trades executed at mid-point). The value of zero for scaled price improvement indicates that 

the trade is executed at the quoted price. As above, a negative value for scaled price 

improvement indicates that a trade is executed outside of the best quoted spread. The median 

scaled price improvement for small (trade size less than $100,000) customer trade is -1.33 

indicating that the median mark-up to the best quoted price for small customer trade is 133% of 

quoted spread. The median mark-up is zero (i.e., traded at quoted price) for customer trade with 

a size between $100,000 and $1,000,000. The median scaled price improvement for a larger 

customer trade with a size between $1,000,000 and $5,000,000 and greater than $5,000,000 is 

0.18 and 0.15, respectively: this indicates that price improvement to the best quoted price at the 

time of trade execution is 18% and 15% of the quoted spread, respectively. 

D. Summary 

Overall, empirical evidence on the direction, magnitude, and factors behind observed 

changes in corporate bond market liquidity is mixed. While some measures of liquidity, such as 

dealer capital commitment and principal trading have declined, other measures of liquidity, such 

as trading activity and transaction cost metrics have improved. As discussed above, observed 

changes point to an evolving role of dealers in corporate bond markets, reduced transaction costs 

and an increased importance of disintermediated trading. The observed effects are consistent 

with a number of alternative explanations, as discussed above. 

190 
E. Tables on Corporate Bond Liquidity 

Table 11. Bond issues per issuer (number of CUSIPs per firm) 

Number of bond issues per issuer 

1st 10th 25th 75th 90th 99th 

Mean percentile percentile percentile Median percentile percentile percentile 

"TRACE Phase-in" period Sub-period 1 8.6 1 1 1 2 4 10 115 

"Pre-crisis" period Sub-period 2 9.4 1 1 1 2 4 9 136 

"Crisis" period Sub-period 3 10.6 1 1 1 2 4 10 151 

"Post-crisis" period Sub-period 4 13.2 1 1 1 2 4 11 151 

"Regulatory" period Sub-period 5 10.5 1 1 1 2 4 10 116 

"Post-regulatory" period Sub-period 6 11.3 1 1 1 2 4 11 135 

Source: DERA analysis 

Table 12. Bond issues, trades, and par volume 

"TRACE Phase-in" period Sub-period 1 

"Pre-crisis" period Sub-period 2 

"Crisis" period Sub-period 3 

"Post-crisis" period Sub-period 4 

"Regulatory" period Sub-period 5 

"Post-regulatory" period Sub-period 6 

Source: DERA analysis 

(1) (2) (3) (4) (5) (6) 

Number of TRACE eligible Number of TRACE eligible 

TRACE eligible issuers TRACE eligible bond issues Number of 

issuers with trades bond issues with trades trades Par volume 

(firms) (%) (CUSIPs) (%) (counts) (in $billion) 

4,306 31.1 30,466 14.6 22,470 16.2 

4,547 29.5 36,576 12.5 19,799 15.9 

4,694 25.5 44,640 9.2 25,761 14.5 4,837 30.5 55,385 10.5 38,306 17.8 3,306 46.4 27,016 20.8 37,351 17.6 

3,185 49.0 29,561 20.0 37,836 19.7 
Table 13. Trading activity 

Panel A. Portion of days with trades per bond issue 

5th 10th 25th 

Panel B. Distribution of bond issues by the number of daily trades 

"TRACE Phase-in" period Sub-period 1 

"Pre-crisis" period Sub-period 2 

"Crisis" period Sub-period 3 

"Post-crisis" period Sub-period 4 

"Regulatory" period Sub-period 5 

"Post-regulatory" period Sub-period 6 

Trades per day Mean 

1st 

percentile 

10th 

percentile 

Portion of bond issues (%) 

25th 

percentile Median 

75th 

percentile 

90th 

percentile 

9

per

"TRACE Phase-in" period Sub-period 1 

0 trade 

1 - 3 trades 4 - 9 trades 

10+ trades 

85.4 

9.4 3.5 1.7 

81.8 

4.4 1.0 0.3 

83.1 

8.5 2.9 1.1 

84.1 85.2 

8.9 9.5 3.3 3.6 1.4 1.7 

86.3 10.0 

3.9 1.9 

87.3 10.4 

4.2 2.3 

"Pre-crisis" period Sub-period 2 

0 trade 

1 - 3 trades 4 - 9 trades 

10+ trades 

87.5 

8.3 3.0 1.2 

85.4 

5.8 1.5 0.4 

86.1 

7.6 2.5 0.9 

86.6 87.3 

8.0 8.4 2.8 3.0 1.1 1.2 

88.0 

8.7 3.3 1.4 

88.9 

9.0 3.5 1.5 

"Crisis" period Sub-period 3 

0 trade 

1 - 3 trades 4 - 9 trades 

10+ trades 

90.8 

5.5 2.4 1.3 

88.2 

2.6 0.9 0.2 

89.1 

4.4 2.0 0.9 

90.0 90.8 

4.7 5.5 2.2 2.4 1.1 1.2 

91.4 

6.4 2.6 1.6 

92.2 

7.0 2.7 2.0 

"Post-crisis" period Sub-period 4 

0 trade 

1 - 3 trades 4 - 9 trades 

10+ trades 

89.5 

5.4 3.1 2.0 

77.5 

2.3 0.9 0.3 

79.6 

4.0 2.3 1.3 

89.8 90.7 

4.4 4.7 2.5 2.8 1.5 1.8 

91.6 

5.2 3.1 2.1 

92.3 

10.6 

6.1 3.5 

"Regulatory" period Sub-period 5 

0 trade 

1 - 3 trades 4 - 9 trades 

10+ trades 

79.2 11.0 

6.3 3.5 

76.1 

6.1 2.1 0.6 

77.3 10.4 

5.6 2.9 

78.0 78.7 10.8 11.1 

6.1 6.4 3.3 3.7 

79.7 11.4 

6.7 4.0 

81.0 11.7 

7.0 4.2 

"Post-regulatory" period Sub-period 6 

0 trade 

1 - 3 trades 4 - 9 trades 

10+ trades 

80.0 10.3 

6.2 3.5 

77.3 

6.1 2.3 0.5 

78.3 

9.1 5.7 2.7 

79.0 79.6 

9.6 10.4 6.1 6.4 3.1 3.5 

80.4 11.1 

6.7 4.0 

81.4 11.5 

6.9 4.4 

Portion of days with trades per bond issue (%) 

75th 90th 95th 

Mean percentile percentile percentile Median percentile percentile percentile 

13.2 0 0 0 2.3 15.6 45.1 68.9 10.8 0 0 0 0 9.3 38.9 64.8 

9.2 0 0 0 0 6.3 31.0 60.0 

10.0 0 0 0 0 3.7 39.6 77.5 

18.6 0 0 0 2.1 25.2 73.3 91.3 

17.6 0 0 0 1.3 22.7 71.9 90.3 

Source: DERA analysis 
 

Table 14. Bond issues and trades by bond characteristics 

Panel A. Issue size 

Number of 

TRACE eligible 

bond issues 

(CUSIPs) 

TRACE eligible 

bond issues 

with trades 

(%) 

"TRACE Phase-in" period Sub-period 1 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

18,263 

10,504 

1,699 

6.7 

21.5 

56.1 

"Pre-crisis" period Sub-period 2 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

23,441 

10,843 

2,293 

5.9 

19.0 

48.8 

"Crisis" period Sub-period 3 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

30,041 

11,603 

2,996 

3.7 

14.7 

43.2 

"Post-crisis" period Sub-period 4 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

39,028 

12,436 

3,921 

3.5 

18.6 

48.9 

"Regulatory" period Sub-period 5 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

16,690 

7,044 

3,281 

5.7 

32.8 

71.9 

"Post-regulatory" period Sub-period 6 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

18,770 

6,908 

3,883 

3.9 

34.1 

72.7 

193 
 

Panel B. Credit quality 

Number of 

TRACE eligible 

bond issues 

(CUSIPs) 

TRACE eligible 

bond issues 

with trades 

(%) 

"TRACE Phase-in" period Sub-period 1 

Investment Grade, price below 95 

Investment Grade, price above 95 

High Yield, price below 95 

High Yield, price above 95 

7,949 

15,334 

3,621 

2,648 

3.8 

20.4 

10.6 

26.7 

"Pre-crisis" period Sub-period 2 

Investment Grade, price below 95 

Investment Grade, price above 95 

High Yield, price below 95 

High Yield, price above 95 

13,087 

12,522 

6,014 

3,450 

3.6 

20.3 

10.9 

26.6 

"Crisis" period Sub-period 3 

Investment Grade, price below 95 

Investment Grade, price above 95 

High Yield, price below 95 

High Yield, price above 95 

20,701 

10,469 

11,814 

1,657 

5.6 

17.1 

7.5 

16.8 

"Post-crisis" period Sub-period 4 

Investment Grade, price below 95 

Investment Grade, price above 95 

High Yield, price below 95 

High Yield, price above 95 

18,278 

14,567 

18,539 

4,001 

1.9 

25.1 

4.3 

21.1 

"Regulatory" period Sub-period 5 

Investment Grade, price below 95 

Investment Grade, price above 95 

High Yield, price below 95 

High Yield, price above 95 

3,843 

12,361 

5,190 

5,622 

2.1 

33.7 

3.7 

21.3 

"Post-regulatory" period Sub-period 6 

Investment Grade, price below 95 

Investment Grade, price above 95 

High Yield, price below 95 

High Yield, price above 95 

3,629 

12,364 

7,298 

6,270 

6.1 

34.6 

6.0 

15.5 

194 
 

 

 

 

 

 

 

 

 

 

 

 

Panel C. Age 

Number of 

TRACE eligible 

bond issues 

(CUSIPs) 

TRACE eligible 

bond issues 

with trades 

(%) 

"TRACE Phase-in" period Sub-period 1 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

10,205 

7,922 

12,275 

18.3 

17.9 

9.6 

"Pre-crisis" period Sub-period 2 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

11,158 

12,346 

12,442 

12.3 

17.2 

8.8 

"Crisis" period Sub-period 3 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

13,517 

14,590 

16,491 

8.9 

11.3 

7.5 

"Post-crisis" period Sub-period 4 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

12,620 

16,970 

25,759 

13.0 

12.2 

8.5 

"Regulatory" period Sub-period 5 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

10,639 

6,355 

10,016 

19.1 

28.9 

17.6 

"Post-regulatory" period Sub-period 6 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

11,877 

8,709 

8,963 

16.5 

26.1 

18.7 

Panel D. Maturity 

Number of 

TRACE eligible 

bond issues 

(CUSIPs) 

TRACE eligible 

bond issues 

with trades 

(%) 

"TRACE Phase-in" period Sub-period 1 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

1,322 

4,820 

19,544 

4,713 

1.5 

9.5 

16.1 

17.4 

"Pre-crisis" period Sub-period 2 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

2,597 

5,946 

21,760 

5,637 

0.6 

6.5 

15.2 

15.3 

"Crisis" period Sub-period 3 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

7,620 

7,433 

23,388 

6,150 

0.3 

3.6 

12.5 

14.4 

"Post-crisis" period Sub-period 4 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

14,162 

8,875 

25,748 

6,558 

0.4 

5.2 

14.7 

19.7 

"Regulatory" period Sub-period 5 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

2,215 

3,757 

15,268 

5,765 

1.4 

12.2 

25.5 

21.8 

"Post-regulatory" period Sub-period 6 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

2,196 

5,304 

16,202 

5,834 

1.1 

9.6 

25.0 

22.7 

195 
Panel E. Complexity 

Number of 

TRACE eligible 

bond issues 

(CUSIPs) 

TRACE eligible 

bond issues 

with trades 

(%) 

"TRACE Phase-in" period Sub-period 1 Complexity feature 0 or 1 

Complexity features 2 or more 

21,491 

8,975 

17.1 

7.8 

"Pre-crisis" period Sub-period 2 Complexity feature 0 or 1 

Complexity features 2 or more 

25,045 

11,531 

14.6 

7.1 

"Crisis" period Sub-period 3 Complexity feature 0 or 1 

Complexity features 2 or more 

33,091 

11,549 

10.2 

5.8 

"Post-crisis" period Sub-period 4 Complexity feature 0 or 1 

Complexity features 2 or more 

42,331 

13,055 

11.7 

6.1 

"Regulatory" period Sub-period 5 Complexity feature 0 or 1 

Complexity features 2 or more 

19,731 

7,285 

25.2 

7.7 

"Post-regulatory" period Sub-period 6 Complexity feature 0 or 1 

Complexity features 2 or more 

21,207 

8,354 

25.3 

5.2 

Source: DERA analysis 

196 
Table 15. EHP average transaction costs 

Panel A. Excludes zero commission customer agency trades 

"TRACE Phase-in" period "Pre-crisis" period "Crisis" period "Post-crisis" period "Regulatory" period "Post-r

Sub-period 1 Sub-period 2 Sub-period 3 Sub-period 4 Sub-period 5 S

Trade size Effective half-spread Effective half-spread Effective half-spread Effective half-spread Effective half-spread Effec

(in thousand) (bps) (bps) (bps) (bps) (bps) 

5 82.8 58.3 77.8 67.8 43.5 

10 76.5 52.5 70.4 65.5 43.0 

20 66.2 43.2 62.4 60.4 39.8 

50 48.3 30.2 51.2 47.3 30.5 

100 35.4 22.8 42.6 36.2 22.8 

200 25.2 16.7 34.8 27.0 16.7 

500 14.9 10.2 25.7 17.9 11.0 

1000 9.2 6.4 19.9 12.6 7.7 

2000 5.6 4.1 15.5 8.4 5.3 

5000 3.2 2.9 12.1 5.8 4.4 

10000 5.3 4.0 11.3 6.3 5.9 

Panel B. All trades 

"TRACE Phase-in" period "Pre-crisis" period "Crisis" period "Post-crisis" period "Regulatory" period "Post-r

Sub-period 1 Sub-period 2 Sub-period 3 Sub-period 4 Sub-period 5 S

Trade size Effective half-spread Effective half-spread Effective half-spread Effective half-spread Effective half-spread Effec

(in thousand) (bps) (bps) (bps) (bps) (bps) 

5 66.9 45.7 59.5 49.1 27.7 

10 64.0 42.6 55.0 50.1 29.5 20 55.9 35.5 49.4 47.5 28.5 50 41.4 25.6 41.9 38.7 23.5 

100 31.1 19.9 36.5 31.0 18.7 

200 22.8 15.0 31.5 24.3 14.7 

500 14.2 9.7 24.8 17.3 10.5 

1000 9.3 6.5 20.2 13.0 8.0 

2000 6.1 4.4 16.2 9.3 5.9 5000 3.6 3.0 12.3 6.2 4.6 

10000 4.8 3.5 10.8 5.8 5.3 

Source: DERA analysis 
Table 16. Number of broker-dealers (MPIDs) per bond issue 

Daily number of broker-dealers per bond issue 

1st 10th 90th 

Mean percentile percentile Median percentile 

Overall 3.4 1 1 2 7 

Interdealer 3.5 2 2 2 6 

"TRACE Phase-in" period Sub-period 1 Customer <$100,000 $100,000 - $1,000,000 2.0 1.5 1 1 1 1 1 1 4 3 

(trade size) $1,000,000 - $5,000,000 1.5 1 1 1 3 

>$5,000,000 1.4 1 1 1 2 

Overall 3.2 1 1 2 7 

Interdealer 3.3 2 2 2 6 

"Pre-crisis" period Sub-period 2 Customer <$100,000 $100,000 - $1,000,000 1.8 1.4 1 1 1 1 1 1 4 2 

(trade size) $1,000,000 - $5,000,000 1.4 1 1 1 2 

>$5,000,000 1.3 1 1 1 2 

Overall 3.7 1 1 2 8 

Interdealer 3.7 2 2 3 7 

"Crisis" period Sub-period 3 Customer <$100,000 $100,000 - $1,000,000 2.2 1.5 1 1 1 1 1 1 5 3 

(trade size) $1,000,000 - $5,000,000 1.4 1 1 1 2 

>$5,000,000 1.3 1 1 1 2 

Overall 4.5 1 1 3 10 

Interdealer 4.1 2 2 3 8 

"Post-crisis" period Sub-period 4 Customer <$100,000 $100,000 - $1,000,000 2.4 1.6 1 1 1 1 1 1 5 3 

(trade size) $1,000,000 - $5,000,000 1.4 1 1 1 2 

>$5,000,000 1.3 1 1 1 2 

Overall 4.3 1 1 3 9 

Interdealer 4.0 2 2 3 7 

"Regulatory" period Sub-period 5 Customer <$100,000 $100,000 - $1,000,000 2.2 1.6 1 1 1 1 1 1 5 3 

(trade size) $1,000,000 - $5,000,000 1.4 1 1 1 2 

>$5,000,000 1.3 1 1 1 2 

Overall 4.2 1 1 3 9 

Interdealer 3.8 2 2 3 7 

"Post-regulatory" period Sub-period 6 Customer <$100,000 $100,000 - $1,000,000 2.2 1.5 1 1 1 1 1 1 4 3 

(trade size) $1,000,000 - $5,000,000 1.4 1 1 1 2 

>$5,000,000 1.2 1 1 1 2 

Source: DERA analysis 
Table 17. Number of trades and par volume by trade size 

Principal Agency Principal Agency 

Customer trade count / Par volume ($billion) 14,500 1,621 12.3 0.4 

Portion (%) 89.9 10.1 97.0 3.0 

Trade size (%) (%) (%) (%) 

<$100,000 65.4 84.9 1.8 7.6 

$100,000 - $1,000,000 15.7 10.7 5.8 14.3 

$1,000,000 - $5,000,000 13.6 3.1 32.0 27.0 

>$5,000,000 5.3 1.3 60.5 51.1 

Customer trade count / Par volume ($billion) 11,418 1,614 11.8 0.3 

Portion (%) 87.6 12.4 97.5 2.5 

Trade size (%) (%) (%) (%) 

<$100,000 62.6 87.5 1.4 10.6 

$100,000 - $1,000,000 16.2 9.8 4.9 15.9 

$1,000,000 - $5,000,000 14.4 1.9 29.0 22.2 

>$5,000,000 6.8 0.9 64.7 51.3 

Customer trade count / Par volume ($billion) 13,737 2,495 11.2 0.2 

Portion (%) 84.6 15.4 98.0 2.0 

Trade size (%) (%) (%) (%) 

<$100,000 66.8 92.4 1.9 23.8 

$100,000 - $1,000,000 15.5 6.2 5.7 19.3 

$1,000,000 - $5,000,000 12.1 0.9 29.3 19.1 

>$5,000,000 5.7 0.5 63.0 37.8 

Customer trade count / Par volume ($billion) 18,926 3,405 13.3 0.2 

Portion (%) 84.8 15.3 98.3 1.7 

Trade size (%) (%) (%) (%) 

<$100,000 67.2 92.4 2.3 23.7 

$100,000 - $1,000,000 17.9 6.3 8.0 24.0 

$1,000,000 - $5,000,000 10.6 1.1 31.3 27.1 

>$5,000,000 4.3 0.2 58.5 25.2 

Customer trade count / Par volume ($billion) 18,213 4,120 13.4 0.4 

Portion (%) 81.6 18.5 96.9 3.1 

Trade size (%) (%) (%) (%) 

<$100,000 62.1 90.3 2.2 15.8 

$100,000 - $1,000,000 21.8 7.7 9.6 20.6 

$1,000,000 - $5,000,000 11.8 1.6 33.5 29.7 

>$5,000,000 4.4 0.4 54.8 33.9 

Customer trade count / Par volume ($billion) 17,924 4,468 14.7 0.5 

Portion (%) 80.1 20.0 96.5 3.5 

Trade size (%) (%) (%) (%) 

<$100,000 58.1 88.7 1.9 13.4 

$100,000 - $1,000,000 23.9 8.7 9.5 20.7 

$1,000,000 - $5,000,000 13.1 2.1 33.5 32.6 

>$5,000,000 4.9 0.5 55.2 33.4 

Trade count Par volume 

Dealer capacity 

"Post-regulatory" period 

"Crisis" period 

"Post-crisis" period 

"Regulatory" period 

"TRACE Phase-in" period 

"Pre-crisis" period 

Source: DERA analysis 

199 
Table 18. Descriptive statistics for dealer quotations on ATS 

Panel A. Number of dealer quotations and corporate bond issues quoted 

Quote (bid or offer) 9.4 16,717 

Bid 6.1 16,461 

Offer 3.2 13,740 

Number of Number of 

Quotations TRACE eligible bond issues 

(in million) (CUSIPs) 

Panel B. Number of dealer quotations per day, bond issues quoted per day, and number of days quoted pe

Daily distribution 

5th 10th 25th 75th 90th 

Mean percentile percentile percentile Median percentile percentile 

Number of TRACE eligible bonds (CUSIPs) 28,073 27,788 27,801 27,855 28,076 28,251 28,357 

(%) (%) (%) (%) (%) (%) (%) 

TRACE eligible bonds with bid 53.9 53.0 53.2 53.6 54.1 54.5 54.6 

TRACE eligible bonds with offer 35.7 35.1 35.3 35.5 35.7 36.1 36.4 

TRACE eligible bonds with both bid & offer 35.0 34.5 34.6 34.9 35.1 35.4 35.6 

Number of quotes 114,097.6 103,551 105,509 113,646 116,022 119,892 122,159 

Bid 74,748 65,341 67,009 72,892 75,776 80,284 81,822 

Offer 39,349 36,732 37,075 38,736 39,942 41,173 41,815 

Number of days quoted per bond (CUSIP) 75.2 18 55 82 82 82 82 

Bid 75.3 23 57 81 82 82 82 

Offer 59.8 3 8 35 80 82 82 

Panel C. Quote duration and quote size 

Distribution 

5th 10th 25th 75th 90th 95th 

Mean percentile percentile percentile Median percentile percentile percentile 

Quote duration (in hour) 

Bid 

Offer 

Both bid & offer 

9.1 8.9 

8.8 

8.49 7.38 

6.96 

8.89 8.58 

8.41 

9.20 9.25 9.00 9.25 

8.95 9.25 

9.25 9.25 

9.25 

9.25 9.25 

9.25 

9.25 9.25 

9.25 

Quote size Bid NM 25 50 100 250 250 300 500 

(in thousand) Offer NM 6 14 65 130 250 500 825 

Source: DERA analysis 
Table 19. Bond characteristics of corporate bonds quoted on ATS 

Distribution 

5th 10th 25th 75th 

Mean percentile percentile percentile Median percentile 

Overall TRACE eligible bonds 221.3 0.3 0.7 2.0 10.0 300 

Issue size (in $million) 

Not quoted 

Quoted 

Quoted One-side Two-side 

56.9 

358.2 

71.5 

518.6 

0.2 

0.6 

0.3 

2.9 

0.4 

1.2 

0.5 

7.0 

1.1 

5.4 

1.3 

142 

3.1 

200 

4.5 

385 

13.7 

500 

30 

700 

Overall TRACE eligible bonds NM HY HY HY IG IG 

Not quoted NM HY HY HY HY IG 

Credit quality Quoted NM HY HY IG IG IG 

Quoted One-side Two-side NM NM HY HY HY IG IG IG IG IG IG IG 

Overall TRACE eligible bonds 4.9 0.2 0.4 1.0 2.6 7.0 

Age (in years) 

Not quoted 

Quoted Quoted One-side Two-side 

4.6 5.1 5.4 5.0 

0.1 0.4 0.4 0.4 

0.2 0.7 0.6 0.7 

0.7 1.6 1.5 1.7 

1.7 3.3 3.1 3.4 

6.8 7.1 7.9 6.8 

Overall TRACE eligible bonds 11.7 1.5 2.0 5.1 9.5 15.2 

Maturity (in years) 

Not quoted 

Quoted Quoted One-side Two-side 

8.5 

14.3 13.7 14.6 

1.1 

4.1 4.0 4.1 

1.5 

5.1 5.1 5.1 

2.0 

7.1 7.1 7.2 

5.1 

10.2 10.2 10.2 

10.2 

20.3 18.3 20.3 

Overall T

RA

C

E eligible bonds 

NM 0 0 0 0 1 

Not quoted 

NM 0 0 0 0 1 

Complexity 

Quoted 

NM 0 0 0 0 0 

Quoted 

One-side 

T

wo-side 

NM 

NM 

0 0 

0 0 

0 0 

0 0 

1 0 

Note: The value 0 for the complexity variable indicates that a corporate bond issue has zero or one complexity feature and the value 1 findicates that a corporate bond issue has two or more complexity features. Source: DERA analysis 
Table 20. Corporate bond trades on ATS and trade size 

Number of Par volume of 

Interdealer trades Interdealer trades (in $billion) 

Overall ATS Non-ATS Overall ATS Non-ATS 

Total 515,817 193,562 322,255 93.4 6.5 87.0 

Portion (%) 37.5 62.5 6.9 93.1 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 77.7 92.2 69.0 9.0 52.0 5.7 

$100,000 - $1,000,000 17.3 7.7 23.1 27.2 42.1 26.0 

$1,000,000 - $5,000,000 4.5 0.1 7.1 43.1 5.8 45.9 

>$5,000,000 0.5 0.0 0.8 20.8 0.2 22.3 

Source: DERA analysis 

Table 21. Bond characteristics for corporate bond trades on ATS 

Distribution 

5th 10th 25th 75th 90th 95th 

Mean percentile percentile percentile Median percentile percentile percent

Issue size (in $million) 

Interdealer trades 

Non-ATS 

ATS 

1028.9 

1066.1 

967.0 

34.8 

45.0 27.9 

250 

250 250 

400 

425 400 

750 

750 700 

1,300 

1,400 1,250 

2,250 

2,250 2,000 

2,750 

2,750 2,500 

Interdealer trades NM HY HY IG IG IG IG IG 

Credit quality Non-ATS NM HY HY IG IG IG IG IG 

ATS NM HY HY IG IG IG IG IG 

Age (in years) 

Interdealer trades 

Non-ATS 

ATS 

4.9 

4.7 5.2 

0.7 

0.7 0.8 

1.2 

1.1 1.3 

2.1 

2.0 2.2 

3.7 

3.5 3.9 

6.0 

5.9 6.2 

9.6 

9.4 9.9 

14.8 

14.1 17.1 

Maturity (in years) 

Interdealer trades 

Non-ATS 

ATS 

13.2 

13.3 13.0 

4.7 

4.4 5.1 

5.1 

5.1 5.1 

7.2 

7.2 7.3 

10.2 

10.2 10.2 

11.2 

11.8 11.0 

30.5 

30.5 30.4 

30.5 

30.5 30.5 

Interdealer trades NM 0 0 0 0 0 0 0 

Complexity 

Non-A

TS NM 0 0 0 0 0 0 0 

A

TS NM 0 0 0 0 0 0 0 

Note: The value 0 for the complexity variable indicates that a corporate bond issue has zero or one complexity feature and the value 1 findicates that a corporate bond issue has two or more complexity features. Source: DERA analysis 
Table 22. Amount of pre-trade information 

Number of 

Trades Mean 

5th 

percentile 

10th 

percentile 

Distribution 

25th 

percentile Median 

75th 

percentile 

90th 

percentile 

95th 

percentile 

Issue size (in $million) 

Without quote 

With quote 

91,920 

1,562,797 

416.5 

1,052.5 

1.6 

69.0 

3.3 

250 

22.0 

450 

253 

750 

500 

1,339 

1,025 

2,250 

1,500 

2,750 

Credit quality Without quote With quote 91,916 1,562,786 NM NM HY HY HY HY HY HY HY IG IG IG IG IG IG IG 

Age (in years) 

Without quote 

With quote 

91,907 

1,562,767 

1.9 3.8 

0.0 0.2 

0.0 0.4 

0.1 1.3 

0.9 2.6 

2.3 4.8 

4.9 8.1 

8.2 

11.4 

Maturity (in years) 

Without quote 

With quote 

91,379 

1,561,571 

10.3 12.1 

1.7 4.4 

3.0 5.0 

4.3 6.6 

7.0 

10.0 

12.0 10.2 

28.9 30.0 

30.0 30.1 

Complexity Without quote With quote 91,920 1,562,797 NM NM 0 0 0 0 0 0 0 0 1 0 1 0 1 1 

Note: The value 0 for the complexity variable indicates that a corporate bond issue has zero or one complexity feature and the value 1 foindicates that a corporate bond issue has two or more complexity features. 

Source: DERA analysis 
Table 23. Transaction costs: Quoted spread and bond characteristics 

Mean 

Distribution of time-weighted quoted spread (in bps) 

5th 10th 25th 75th 90th 

percentile percentile percentile Median percentile percentil

Overall 

Time-weighted quoted spread 172.9 12.2 18.2 35.4 73.7 145.6 275.8 

By bond characteristics 

Issue size 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

248.1 190.0 

94.7 

26.3 37.9 67.4 123.2 218.3 394.0 17.1 24.4 43.9 84.3 158.5 297.0 

8.1 11.5 20.3 39.7 76.8 133.0 

Credit quality Investment grade High yield 115.3 408.1 11.1 16.3 30.9 65.5 131.0 240.4 27.5 37.9 62.0 108.4 214.2 480.5 

Age 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

129.1 

139.6 247.8 

12.2 18.0 34.9 72.6 142.1 267.8 

11.9 17.6 34.3 69.7 131.1 235.1 12.6 19.1 37.1 80.7 167.1 325.6 

Maturity 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

242.0 

113.1 158.4 219.8 

5.3 5.7 23.1 209.4 426.7 494.0 

7.7 10.7 17.6 31.7 70.9 161.6 

11.1 16.3 30.0 58.7 113.6 214.1 34.3 48.4 79.3 130.4 227.2 408.7 

Complexity Complexity feature 0 or 1 Complexity features 2 or more 155.5 296.7 11.7 17.3 33.0 67.6 130.6 238.7 21.9 35.9 72.4 144.8 279.6 522.4 

Source: DERA analysis 

Table 24. Transaction costs: Effective spread and trade size 

Distribution of effective spread (in bps) 

5th 10th 25th 75th 90th 95th 

Mean percentile percentile percentile Median percentile percentile percentile 

Overall 

All trades 115.3 4.4 8.7 21.3 52.0 133.9 306.3 437.8 

Interdealer trades 76.6 4.6 8.7 19.4 42.0 85.4 161.0 238.6 

Customer trades 143.1 4.3 8.7 23.3 65.9 197.2 387.8 492.9 

Customer trades by trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

189.5 

80.2 79.8 68.2 

7.2 2.7 2.5 2.4 

14.1 38.2 117.3 283.2 

5.6 14.4 35.8 88.0 5.4 14.8 37.0 84.7 5.1 13.6 32.5 73.2 

453.5 199.9 169.4 143.2 

544.1 307.9 267.3 217.3 

Source: DERA analysis 
Table 25. Transaction costs: Effective spread and bond characteristics 

Mean 

5th 

percentile 

Distribution of effective spread (in bps) 

10th 25th 75th 

percentile percentile Median percentile 

90th 

percenti

Overall 

Effective spread 115.3 4.4 8.7 21.3 52.0 133.9 306.3 

By bond characteristics 

Issue size 

Less than $100M 

Between $100M and $500M 

Greater than $500M 

197.9 155.5 

86.5 

15.7 

8.0 3.4 

27.2 57.9 125.5 264.3 15.4 36.3 82.7 193.4 

6.7 16.1 37.5 91.6 

463.2 383.7 233.2 

Credit quality Investment grade High yield 85.2 170.3 3.5 9.3 6.8 16.3 38.4 96.5 17.9 39.9 87.9 215.1 229.8 424.6 

Age 

Less than 2 years 

Between 2 years and 5 years 

Greater than 5 years 

108.6 118.6 120.2 

4.2 4.4 4.8 

8.3 20.3 48.5 124.2 8.7 21.5 52.9 134.8 9.2 22.6 57.3 146.3 

304.7 305.4 309.6 

Maturity 

Less than 2 years 

Between 2 years and 5 years 

Between 5 years and 20 years 

Greater than 20 years 

168.0 

74.7 

104.6 170.1 

1.9 1.9 4.1 

10.8 

2.4 4.7 61.1 409.3 4.0 10.0 23.3 63.0 8.2 19.6 46.2 116.3 

20.6 47.0 100.2 224.0 

473.9 177.5 276.4 442.3 

Complexity Complexity feature 0 or 1 Complexity features 2 or more 111.2 203.6 4.3 7.8 8.5 20.8 50.4 128.2 16.7 45.4 114.8 263.8 296.3 497.1 

Source: DERA analysis 
Table 26. Transaction costs: price improvement 

Panel A. Price improvement to quoted price and trade size 

Distribution of price improvement (in bps) 

5th 10th 25th 75th 90th 95th 

Mean percentile percentile percentile Median percentile percentile percentile 

Overall 

All trades -43.8 -331.6 -207.7 -50.1 -5.1 6.6 36.7 69.9 

Interdealer trades -7.9 -97.1 -54.1 -16.3 0.0 4.0 27.9 55.9 

Customer trades -69.7 -401.0 -299.7 -114.0 -17.0 9.0 43.0 79.2 

Customer trades by trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

-117.7 

-14.7 

13.8 10.8 

-438.8 -210.8 -113.8 

-97.9 

-376.0 -209.0 -48.6 -3.5 -111.3 -25.4 0.0 21.5 

-63.5 -16.4 5.6 37.7 -56.3 -17.2 4.3 30.3 

16.1 57.3 90.7 73.3 

39.6 94.2 

147.1 120.8 

Panel B. Scaled price improvement to quoted price and trade size 

Distribution of price improvement (the ratio of price improvement to quoted spread) 

5th 10th 25th 75th 90th 95th 

Mean percentile percentile percentile Median percentile percentile percentile 

Overall 

All trades 

Interdealer trades 

Customer trades 

-167.7 -11.26 -5.94 -1.63 -0.16 0.20 0.67 0.84 

-373.7 -4.20 -2.13 -0.59 0.00 0.12 0.60 0.80 

-19.2 -15.48 -8.70 -2.94 -0.48 0.28 0.71 0.86 

Customer trades by trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

-31.7 -20.69 -12.13 -4.99 -1.33 -0.10 0.44 0.71 

-2.1 -7.77 -3.86 -0.96 0.00 0.53 0.82 0.91 -1.7 -3.97 -2.03 -0.54 0.18 0.63 0.86 0.93 -1.1 -4.31 -2.22 -0.63 0.15 0.62 0.85 0.93 

Source: DERA analysis 
	 	 	 	 	

 

 

V. Single‐name Credit Default Swaps (CDS)

A. Introduction 

Single-name CDS are financial contracts that enable market participants to transfer the 

credit risk of an issuer by buying and selling credit protection.199 The single-name CDS market 

experienced expansive growth in the mid-2000s and a decline in activity after the financial crisis. 

In this section, we study the evolution of transaction activity and liquidity in single-name CDS. 

The examination of liquidity in Treasury and corporate bond markets would be 

incomplete without an analysis of the single-name CDS market for two primary reasons. First, 

the market for single-name CDS is an important credit risk market and provides an alternative 

risk transfer mechanism. Because single-name CDS allow market participants to gain exposure 

to the credit risk of underlying bonds, CDS prices contain information about the market’s 

expectation of reference entity credit risks. As we discuss below, single-name CDS may even 

lead corporate bonds in price discovery.200 

Second, transaction activity in the single-name CDS market may have an impact on the 

efficiency and liquidity of the corporate bond market. Since the payoffs on single-name CDS are 

dependent upon the value of underlying securities, activity in the CDS market can be correlated 

with activity in underlying securities markets. In addition, market participants may be able to 

partially or fully replicate exposure to Treasury securities with positions in corporate bonds and 

single-name CDS, and single-name CDS prices reflect the spread between corporate and 

Treasury bonds with comparable interest rate risk exposures.201 Thus, single-name CDS 

contracts can be used as part of arbitrage strategies to eliminate perceived inefficiencies in the 

199 See Basulto and Lee (2016).
 200 See Longstaff et al. (2005), Blanco et al. (2005), Ashcraft and Santos (2009), Das et al. (2014). 201 See, e.g., Duffie (1999), Bai et al. (2013), Kim et al. (2017). 

207 
 

 

bond market. Informational efficiency, pricing, and liquidity in the single-name CDS market 

can, therefore, affect bond markets.202 

From the outset, the direction of liquidity spillovers between corporate bond and singlename CDS markets is unclear. On the one hand, single-name CDS can be used to hedge the 

credit risk of reference securities, allowing market participants to buy credit protection rather 

than selling these securities to manage their credit risk exposures to a given issuer. Further, if 

dealers can hedge bond exposure with actively traded single-name CDS, they may be able to 

hold larger bond inventories, enhancing market making in corporate bonds.203 As a result, there 

may be complementarities between activity in single-name CDS and bond markets. On the other 

hand, the existence of two markets to trade credit risk exposures may fragment liquidity across 

credit risk instruments, forcing large institutions and other sophisticated traders to choose a 

market in which to transact. To the extent that transacting in single-name CDS may involve 

lower costs and higher liquidity than transacting in the bond market, market participants may 

prefer to trade on credit risk in the single-name CDS market over the bond market. In that 

context, there may be substitution between trading in single-name CDS and corporate bonds. 

Below we examine existing research regarding spillovers between corporate bond and 

single-name CDS markets and report findings on changes in the volume and structure of singlename CDS market activity over time. We recognize that our empirical analysis of activity in 

single-name CDS markets faces three primary limitations. First, available data limit the extent to 

which we can quantitatively characterize the market, and we face constraints concerning the time 

period and metrics for which data are available. Specifically, a part of our analysis is based on 

202 See Rel. No. 34-75611, Registration Process for Security-Based Swap Dealers and Major Security-Based Swap
 

Participants (Aug. 5, 2015) 80 FR at 49003 (Registration Adopting Release). See also Massa and Zhang (2012), Das
 

et al. (2014), Oehmke and Zawadowski (2017), Boehmer et al. (2015). 

203 See, e.g., CFA Institute (2016). 

208 
 

 

 

 

data obtained from the Depository Trust & Clearing Corporation (DTCC) Derivatives Repository 

Limited Trade Information Warehouse (TIW), including data regarding the activity of market 

participants in the single-name CDS market during the period from 2008 to 2016. The data 

available to us from TIW do not encompass those CDS transactions that both: (1) do not involve 

U.S. counterparties;204 and (2) are based on non-U.S. reference entities. Notwithstanding this 

limitation, we believe the TIW data provides sufficient information to permit us to identify the 

types of market participants active in the single-name CDS market and the general pattern of 

dealing within that market.205 To supplement TIW data analysis, we use subscription data from 

the ICE Data Services CMA database. This database includes tick-level quote information for 

2008 through the third quarter of 2016 but does not include dealer names or identifiers, and so 

we are unable to measure the number of dealers issuing quotes or making markets. To further 

supplement this analysis and extend data coverage to an earlier time period, we also use 

semiannual statistics on gross market values and notionals published by the Bank for 

International Settlements (BIS) dating back to 2004. 

204 Following publication of the Warehouse Trust Guidance on CDS data access, TIW surveyed market participants, 

asking for the physical address associated with each of their accounts (i.e., where the account is organized as a legal 

entity). This physical address is designated the registered office location by TIW. When an account reports a 

registered office location, we have assumed that the registered office location reflects the place of domicile for the 

fund or account. When an account does not report a registered office location, we have assumed that the settlement 

country reported by the investment adviser or parent entity to the fund or account is the place of domicile. Thus, for 

purposes of this analysis, we have classified accounts as “U.S. counterparties” when they have reported a registered 

office location in the United States. We note, however, that this classification is not necessarily identical in all cases 

to the definition of “U.S. person” under Exchange Act Rule 3a71-3(a)(4). 

205 The challenges we face in estimating measures of current market activity stem, in part, from the absence of 

comprehensive reporting requirements for security-based swap market participants. The Commission has recently 

adopted rules regarding trade reporting, data elements, and public reporting for security-based swaps (Regulation 

SBSR) that are designed, when fully implemented, to provide the Commission with additional measures of market 

activity that will allow us to better understand and monitor activity in the security-based swap market. See Rel. No. 

34-74244, Regulation SBSR—Reporting and Dissemination of Security-Based Swap Information (Feb. 11, 2015) 80 

FR at 14699-14700 (Regulation SBSR Adopting Release); See also Rel No. 34-78321, Regulation SBSR— 

Reporting and Dissemination of Security-Based Swap Information (July 14, 2016) 81 FR 53546. 

209 
Second, it is not clear whether single-name CDS activity before the crisis appropriately 

reflects market liquidity under normal conditions, and the selection of a benchmark period may 

significantly affect the analysis. From the buyers’ standpoint, single-name CDS provide short 

exposure to the credit risk inherent in corporate bonds, and investor demand for single-name 

CDS may increase as the risk of credit events on existing reference obligations rises. At the 

same time, sellers may use CDS contracts to obtain long exposures to corporate bonds, 

particularly when bonds are illiquid or hard to obtain. The analysis below considers both 

liquidity demand and liquidity supply explanations of observed changes in single-name CDS 

market activity and recognizes the sensitivity of potential interpretations of observed time series 

patterns to the selection of the benchmark period. 

Third, the analysis of changes in single-name CDS activity in connection with regulatory 

events faces a number of empirical challenges. As discussed below, relevant regulatory changes 

in the CDS market were proposed, enacted, and adopted over a prolonged period of time. In 

addition, the reforms of interest impacted large groups of single-name CDS market participants 

at the same time (e.g., all single-name CDS dealers and major participants, or all market 

participants). Therefore, we do not have cleanly identified and otherwise comparable 

“treatment” and “control” groups of market participants. 

Lastly, regulatory reforms of interest coincided with a portfolio of other policy 

interventions, such as large scale infusions of liquidity and low interest rates, which are likely to 

have directly impacted single-name CDS activity before and after regulatory reforms. It is 

difficult to disentangle those effects from the impacts of Dodd-Frank, including the Volcker 

Rule, and Basel III on observed changes in CDS liquidity. While we are unable to make causal 

inferences with respect to the transaction activity in CDS, we present a multifaceted analysis of 

210 
 

 

 

the current state and recent changes in transaction activity and participation in the single-name 

CDS market. 

The analysis below is informed by our understanding of both single-name and index CDS 

markets. We have considered existing research on index CDS liquidity in the aftermath of the 

Dodd-Frank reforms;206 however, this section focuses on single-name CDS. A single-name CDS 

contract covers default events for a single reference entity or reference security. In contrast, 

index CDS contracts and related products make payouts that are contingent on the default of 

index components, and allow participants in those instruments to gain exposure to the credit risk 

of the basket of reference entities that comprise the index. A default event for a reference entity 

that is an index component will result in payoffs on both single-name CDS written on the 

reference entity and index CDS written on indices that contain the reference entity. Because of 

this relationship between the payoffs of single-name CDS and index CDS products, prices of 

these products depend upon one another, creating trading opportunities across these markets. 

However, while Treasuries, corporate bonds and single-name CDS contracts present clear 

arbitrage opportunities, index CDS are less direct instruments for cross-market arbitrage of 

mispricings in corporate bonds and Treasuries and may be less likely to directly impact liquidity 

in these markets. Further, single-name CDS contracts are security-based swaps subject to 

Commission oversight, while broad-based index CDS contracts are swaps subject to CFTC 

oversight. 

206 Loon and Zhong (2016) find decreases in transaction costs and increases in liquidity in index CDS following the 

implementation of some of the Dodd-Frank reforms. Specifically, the paper found (1) liquidity improvements 

following public dissemination of OTC derivative trades; (2) reductions in trading costs, price impact, and price 

dispersion for cleared trades, trades executed on exchange-like venues, end-user trades, and bespoke trades. We 

have also considered evidence on the impacts of the Dodd-Frank Act on other over-the-counter derivative markets. 

211 
 

B. Limitations on Analysis of Causal Impacts 

An important feature of the Commission’s regulation of the single-name CDS market is 

that it constitutes one part of a broader set of OTC derivative market reforms. Title VII of the 

Dodd-Frank Act provides a statutory framework for the OTC derivatives market and divided 

authority to regulate that market between the CFTC (which regulates swaps, including broadbased index CDS) and the Commission (which regulates security-based swaps, including singlename CDS and narrow-based index CDS).207 The Dodd-Frank Act directed the Commission to 

establish a regulatory regime governing activities of security-based swap dealers and major 

security-based swap participants, as well as other market participants. As can be seen in 

Appendix B, between 2010 and 2016 the Commission has proposed and adopted a variety of 

rules impacting single-name CDS markets (e.g., dealer definitions, product definitions, dealer 

registration, cross-border activity, swaps data repository registration, business conduct standards) 

and proposed but not yet adopted others (e.g., capital and margin and swap execution facilities). 

Appendix B presents a summary of the Commission’s proposed and adopted rules under Title 

VII. 

Single-name CDS and index CDS markets are characterized by extensive cross-market 

participation. Out of approximately 4,241 DTCC-TIW accounts that participated in the market 

for single-name CDS in 2015, approximately 2,871 accounts, or 68%, also participated in the 

market for index CDS. Of the accounts that participated in both markets, data regarding 

transactions in 2015 suggest that, conditional on an account transacting in notional volume of 

index CDS in the top tercile of accounts, the probability of the same account landing in the top 

tercile in terms of single-name CDS notional volume is approximately 59%. By contrast, the 

207 See 15 U.S.C. § 8302. 

212 
 

 

probability of the same account landing in the bottom tercile of single-name CDS notional 

volume is only 10%. In addition, the Commission previously estimated that the majority of 

entities expected to trigger requirements for registration with the Commission as security-based 

swap dealers or major security-based swap participants are currently registered with the CFTC as 

swap dealers or major swap participants.208 

Therefore, many single-name CDS market participants that would become subject to the 

Commission’s Title VII regime are also affected by the CFTC’s regulatory reforms and oversight 

over the index CDS market. While the Commission has not yet made any mandatory clearing 

determinations for single-name CDS and not all of the substantive Title VII rules governing the 

single-name CDS market have been adopted (e.g., capital and margin requirements for dealers 

and major participants), and compliance with most Title VII single-name CDS rules is not yet 

required (e.g., dealer registration, business conduct standards), many of these regulatory 

requirements are in force in the index CDS market.209 Market participants active in both singlename and index CDS markets may have adjusted some of their business practices in response to 

the CFTC’s index CDS regime. 

As a result of a protracted event timeline and the potential that market participants 

broadly anticipated regulatory reforms, prevailing econometric methods used to establish causal 

inference are not suited to the study of single-name CDS markets. Further, because the period of 

interest includes changes in the regulation of closely-related markets, we lack clean “treatment” 

and “control” groups of otherwise comparable dealers and non-dealer market participants in the 

single-name CDS market. 

208 See Rel. No. 34-77617, Business Conduct Standards for Security-Based Swap Dealers and Major Security-Based
 

Swap Participants (Apr. 14, 2016) (Business Conduct Standards Adopting Release). 209 See, e.g., Loon and Zhong (2016); Business Conduct Standards Adopting Release. 

213 
 

Moreover, macroeconomic effects may confound our ability to infer the effects of 

regulatory reform on trading activity in this market. Buyers of single-name CDS receive short 

exposure to the credit risk of underlying reference securities, and the post Dodd-Frank period is 

characterized by economic recovery—potentially reducing demand for protection against 

corporate default; and low interest rates—providing market participants incentives to shift into 

riskier asset classes and reducing the profitability of traditional dealer business models. These 

circumstances confound the tests of the causal impacts of the Dodd-Frank Act or its component 

reforms on the size, trading activity, or liquidity in single-name CDS markets. Nevertheless, as 

discussed in the following section, we recognize the close relationship between the single-name 

CDS markets and corporate bond markets and the potential for regulatory activities in the singlename CDS market to impact access to capital and market liquidity. Hence, we describe changes 

in the single-name CDS market over time and consider these statistics alongside evidence in 

prior sections. 

C. Spillovers between Bonds and Single-Name CDS 

Existing research on single-name CDS examines a wide range of issues, including CDS 

pricing, the relationship between CDS and the bond and equity markets, and the impact of CDS 

contracts on corporate finance.210 Our analysis below is focused on the interaction between the 

CDS and the bond markets, particularly the potential impact of the CDS market on bond 

liquidity. The introduction of single-name CDS created an alternative market for investors to 

trade credit risk. While this market may enable participants to hedge the risk of underlying 

bonds, it may also fragment liquidity. 

210 For a comprehensive literature review on single-name CDS, see Augustin et al. (2014), Culp et al. (2016). 

214 
 

 

 

 

Single-name CDS may lead corporate bonds in price discovery. In other words, new 

information about a reference entity’s credit risk may be reflected in single-name CDS written on 

the reference entity’s bonds before it is reflected by the price of the bonds themselves. There are 

a number of different theories that support this conclusion. For example, trading frictions may 

be relatively lower in the single-name CDS market, or differences in the composition of the two 

markets may mean that the single-name CDS market has more informed traders than the 

corporate bond market. 

Academic research has also found empirical evidence consistent with the hypothesis that 

the single-name CDS market leads the corporate bond market in price discovery. Blanco et al. 

(2005) analyze a sample of 33 reference entities and find that for 27 of them the CDS market 

contributes, on average, 80% of the price discovery. Further, in four of the remaining six cases, 

changes in CDS prices Granger-cause211 changes in credit spreads. They attribute the price 

discovery role of the CDS market to microstructure factors and suggest that participants in cash 

and derivative markets trade for different reasons. Bilateral CDS transactions involve 

counterparty risk, as transactions may settle over a period of years and therefore are concentrated 

among institutions with relatively high credit ratings. By contrast, a transaction in cash bonds 

involves relatively little counterparty risk as transactions typically settle within 3 days during the 

Report period, accommodating the participation of less sophisticated retail investors. 

211 Granger causality tests show whether past values of one variable help predict another variable (after controlling 

for past values of that other variable). As Wooldridge (2013) notes, this notion of causality should be interpreted 

with caution. Specifically, this definition of causality does not refer to contemporaneous relationships between two 

variables, and does not allow us to determine exogeneity or endogeneity of contemporaneous relationships between 

variables. Granger causality, therefore, does not apply in purely cross-sectional contexts. Instead, it allows us to 

conclude whether past values of an independent variable, after controlling for past values of the dependent variable, 

help predict future values of a dependent variable. For more, see Wooldridge (2013), Ch.18, p. 590. 

215 
Importantly, the paper finds that the CDS market leads in the short-term, but in the long-term the 

CDS and bond markets price risk equally quickly, on average. 

Price discovery in single-name CDS may reduce informational efficiency in the corporate 

bond market. Using an extensive sample of CDS and bond trades between 2002 and 2008, Das 

et al. (2014) find that the sensitivity of bond returns to lagged information is higher after the 

introduction of CDS, indicating that bond market efficiency relative to other securities 

deteriorates after the introduction of CDS on a given underlier. The paper argues that the 

introduction of CDS leads to a decrease in bond market efficiency and no reduction in pricing 

errors or improvements in bond liquidity. Similar to Blanco et al. (2005), they argue that the 

effect may be driven by differentially informed participants being active in the two markets, with 

the CDS market being dominated by more informed institutional traders. Das et al. (2014) also 

address a shift by large institutional traders from participation in the bond market to transacting 

in CDS. They find bond transaction costs for institutional investors increased after the 

introduction of CDS, which they explain by a migration of institutional investors to the CDS 

market. 

Biswas et al. (2015) offer a transaction cost explanation for the migration of price 

discovery from cash to the derivatives market. The paper compares single-name CDS and bond 

transaction costs for trades with different trade sizes. Although for institutional-size trades up to 

$500K, bonds are three times as expensive to trade as the corresponding CDS, at large trade sizes 

bonds are no more expensive to trade than the CDS contracts written on them. By showing that 

CDS are less expensive to trade for a range of trade sizes, the paper provides a potential 

explanation for the shift in price discovery from cash to derivatives (Blanco et al. (2005), Norden 

and Wagner (2008), Norden and Weber (2009), Forte and Pena (2009), and Das et al. (2014)). 

216 
However, the source of this transaction cost differential between an opaque derivatives market 

and a more transparent bond market remains unclear. 

Evidence on the relationship between CDS market liquidity and underlying bond 

liquidity is mixed. Massa and Zhang (2012) argue that CDS improve bond liquidity by reducing 

investors’ need to liquidate the bonds should the issuer’s credit deteriorate and fire sales occur. 

Consistent with this argument, the paper finds that the presence of CDS contracts increase 

investors’ willingness to pay for underlying bonds, reducing the bond yield spread by 22 bps for 

investment grade firms, and increases the liquidity in investment grade bonds for a 

comprehensive sample of U.S. corporate bonds over the 2001 to 2009 period. This explanation 

is consistent with investors requiring less compensation for bond illiquidity when they are able to 

buy credit protection in the CDS market. It is worth noting that this finding is based entirely on 

the illiquidity measure of Bao et al. (2011), which is essentially an implied bid-ask spread 

measure. The paper also finds that the presence of CDS contracts reduces the drop in bond 

liquidity and the rise in yield spreads during exogenous fire sales (bond rating downgrades and 

insurance company sales after hurricane Katrina). 

Similarly, Nashikkar et al. (2011) investigates the interaction between CDS market 

liquidity and the price of credit risk. They find that bonds with more liquid CDS tend to have 

lower yields than comparable bonds with less liquid CDS contracts. They also validate a 

measure of a bond’s latent liquidity, which reflects the accessibility of a bond to market 

participants through various funds and is defined as the weighted average turnover of funds 

holding the bond. The paper finds that CDS market liquidity has explanatory power for a bond’s 

latent liquidity. While these findings suggest that there may be positive liquidity spillovers from 

the CDS market to the corporate bond market, this paper does not definitively address 

217 
endogeneity issues, limiting our inference. In other words, the evidence presented by the authors 

cannot rule out the possibility that liquidity in the corporate bond market determines CDS market 

liquidity, or that liquidity in both markets are co-determined by unobservable factors. 

Since the single-name CDS market serves as a market in which investors can trade credit 

risk, fixed income traders may reduce their participation in bond markets, and therefore, liquidity 

in single-name CDS may have a negative spillover on corporate bond market liquidity. As 

referenced earlier, Das et al. (2014) test the impact of CDS trading on bond liquidity, and find 

some liquidity measures (trade size and turnover) in certain bond underliers deteriorated after the 

introduction of CDS on these underliers. At the same time, the paper finds that other measures, 

such as the number of daily trades and trading volume rose, while Amihud and Roll measures of 

illiquidity remained unaffected. Overall, the paper finds mixed results regarding the effect of 

CDS introduction on bond liquidity. 

Oehmke and Zawadowski (2015) develop a liquidity-based model of CDS and bond 

markets. In their model, the presence of CDS reduces demand for bonds (substitution effect) but 

also allows long-term investors to become levered basis traders and buy more illiquid bonds 

(allocation effect). The introduction of a CDS on a given underlier involves a tradeoff: it 

crowds out existing demand for the bond but also improves bond allocation by allowing longterm investors to absorb more bond supply by providing them a means of sharing credit risk with 

short-term investors. In Oehmke and Zawadowski (2017), CDS markets emerge as “alternative 

trading venues” conducive to standardization and liquidity. They argue that the CDS market 

may be particularly attractive to participants when the underlying bond market is fragmented and 

involves higher trading frictions. To test this hypothesis, the paper relies on aggregated CDS 

position and trading data from DTCC, and reports three main results. First, CDS positions and 

218 
trading volume are larger when the reference entity bonds are fragmented into many separate 

issues and have heterogeneous contractual terms. Second, bond fragmentation is associated with 

higher trading costs and lower turnover in the underlying bonds. Third, CDS-bond basis trades 

help to absorb a liquidity shock to bonds when selling pressure exists. Thus, they reach similar 

conclusions to Massa and Zhang (2012) that the introduction of CDS may enhance bond 

liquidity. 

Sambalaibat (2015) builds a dynamic search model to explore the liquidity spillover 

between bond and CDS markets. The analysis shows that, in the short run, because the 

aggregate capital invested in the credit markets remain fixed, speculative CDS purchases attract 

liquidity away from the bond market. However, in the long run, speculative CDS purchases 

attract investors who are willing to take the opposite side of these trades by either selling credit 

protection or buying underlying bonds. Because of search frictions in both the CDS market and 

the bond market, these investors are willing to trade in both markets, causing liquidity in the 

CDS market to spill over into the bond market. In that model, CDS exist not necessarily because 

of their inherent liquidity, but because CDS contracts allow investors to “short” bonds cheaply. 

We note that most existing empirical studies take CDS liquidity as a given. Loon and 

Zhong (2016) examine changes in CDS liquidity and activity around the initiation of central 

clearing. They find that CDS liquidity and trading activity on cleared reference entities improves 

after central clearing. Qiu and Yu (2012) investigate the determinants of single-name CDS 

liquidity. Using the number of quote providers as a measure of CDS liquidity, they find that 

large firms and firms near the investment grade/speculative grade cutoff tend to be the most 

liquid. They also show that CDS liquidity is positively related to the amount of information flow 

219 
from the CDS market to the stock market. Their findings underscore the important role of 

information heterogeneity in the determination of CDS market liquidity. 

In sum, a body of finance research has explored the emergence of single-name CDS and 

the impact of activity in this market on price discovery and liquidity in underlying bonds. As 

discussed above, existing studies do not reach a consensus on the direction and drivers of the 

effects. 

D. Exposures, Participants and Liquidity 

As discussed throughout the Report, liquidity is multifaceted and can be measured using 

a number of empirical approaches. Liquid markets “are generally viewed as those which 

accommodate trading with the least effect on price” (O’Hara (1997)) and where market 

participants have “the ability to trade large sizes quickly, at low cost, when [they] want to trade” 

(Harris (2003)). The analysis below relies on three different sets of measures. First, we examine 

the volume of activity in single-name CDS markets using BIS data for different groups of market 

participants, by credit rating category and tenor. Second, we use DTCC-TIW data to quantify 

changes in the role of various hand-classified groups of market participants, the frequency of 

trades, the fraction of trading days with no transactions and the evolution of trade sizes, among 

others. Third, we use ICE Data Services CMA quote data to document the number of quoted 

underliers, quotes per underlier, and quoted spreads in the cross section and over time. 

1. Volume of Activity 

First, we consider the evolution of global notional amounts outstanding and gross market 

value of single-name CDS using semiannual OTC statistics reported by the Bank of International 

220 
 

 

 

 

 

 

Settlements.212 Notional amount outstanding represents the nominal value of all the deals 

concluded and not yet settled on the reporting date (Tissot (2015)), while gross market value 

represents the costs of replacing all outstanding contracts at market prices prevailing on the 

reporting date and reflects maximum losses in the event of default on the reporting date.213 

While the gross notional value remains fixed through the life of a contract, market value 

fluctuates with changes in the default risk of the underlier: if default becomes more likely, the 

market value of the CDS increases, and vice versa.214 Further, trade compression results in 

fluctuation in outstanding notional, but not market value.215 Figure 36 plots the evolution in the 

total amounts of outstanding notional and market values between 2004 and mid 2016 based on 

the BIS semiannual derivative statistics. We observe that the total amount of outstanding 

notional in single-name CDS has peaked during and in the immediate aftermath of the financial 

crisis. Importantly, where notional amounts fell in 2008, gross market values almost tripled, 

consistent with a dramatic increase in the default risk of reference names. The evidence in 

Figure 36 is consistent with single-name CDS serving as a means of sharing credit risk: the 

demand for CDS may peak when aggregate credit risk as well as the credit risk of various groups 

of individual issuers is high. 

Figure 37 presents the dynamics of CDS market activity, as captured by global net 

notional amounts outstanding as a function of the industry and credit risk quality of the 

212 BIS statistics, available at http://stats.bis.org/statx/toc/DER.html 213 When positions are offset by entering a new contract, notional amounts may be inflated, particularly after the 

emergence of central clearing (under which an alpha transaction is replaced with a beta and an offsetting gamma 

transaction) in the latter parts of the sample period. However, portfolio compression allows early termination of 

redundant contracts, which reduces notional amounts. The gross market value is calculated as the sum of the 

absolute value of gross positive market values and gross negative market values. It provides a more meaningful 

measure of amounts at risk than notional amounts. See BIS Statistical Release (Nov. 2015), available at 

http://www.bis.org/publ/otc_hy1511.pdf. 214 See, e.g., Stulz (2010).
 215 Portfolio compression reduces the number and notional amounts of outstanding contracts without changing
 

underlying economic exposure. 

221 
 

underlying. Single-name CDS activity in nonfinancials and in the A/BBB class appears to have 

experienced a large increase in global net notional in the first half of 2011, declining 

substantially afterwards. An increase in outstanding notional amounts seems to coincide with the 

timing of the proposal of rules implementing the Volcker Rule. 

However, there are three other considerations that could also explain the observed 

changes in activity. First, single-name CDS are security-based swaps, and a number of rules 

implementing Title VII of the Dodd-Frank Act were proposed in 2011. These included rules 

defining security-based swaps subject to Title VII of the Dodd-Frank Act (June 1, 2011), 

business conduct standards for security-based swap dealers and major security-based swap 

participants (June 29, 2011), and rules governing the registration of security-based swap dealers 

and major security-based swap participants (October 12, 2011). Second, Qiu and Liu (2012) 

finds that CDS liquidity provision is concentrated around the investment grade / speculative 

grade cutoff, reflecting investors’ hedging demand or dealers’ risk of supplying liquidity, or 

both. Changes in the level of credit risk of non-financial and low investment grade issuers, a 

potential increase in risk appetite among market participants reaching for yield during low 

interest rate cycles, and a decrease in the demand for single-name CDS on certain underliers 

during market booms could all have contributed to the observed effects. Lastly, changes in gross 

notional amounts post-crisis documented in Figure 36 are driven, at least in part, by compression 

activity. Overall, it is difficult to attribute the findings to just one of the multiple drivers 

referenced above. 

222 
 

   

Figure 36. Volume of activity in single-name CDS 

Notional outstanding Gross market values 

20 2,000

15 1,500

10 1,000

5 500

0 0

35

$ Trillion

3,500
 

Dec‐04Dec‐05Dec‐06Dec‐07Dec‐08Dec‐09Dec‐10Dec‐11Dec‐12Dec‐13Dec‐14Dec‐15

$ Million

30
 3,000
 

25
 2,500
 

Dec‐04Dec‐05Dec‐06Dec‐07Dec‐08Dec‐09Dec‐10Dec‐11Dec‐12Dec‐13Dec‐14Dec‐15

Source: DERA analysis. BIS data for “outstanding – notional amounts”, and outstanding – gross market values.”

 Figure 37. Net notional amounts in single-name CDS by industry and credit 

Underlier industry Credit rating 

12 12
 

10

$ Trillion

6

6

4

4

2

2

0

‐

AAA/AA A/BBB

$ Trillion

10
 

8 8
 

Sovereign Financials Non‐financials BB and below Unrated

Source: DERA analysis. BIS data for “outstanding – notional amounts, single-name CDS, all counterparties (net).”

 Next, we consider the role of various groups of market participants in the single-name 

CDS market. The Bank for International Settlements reports global notionals for reporting 

dealers (net), hedge funds, banks and securities firms, nonfinancial customers, insurance and 

financial guaranty, central counterparties (CCP), special purpose vehicles, companies and 

223 
 

         

     

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

entities (SPV, SPC, and SPE, respectively), and other financial institutions. In Figures 38 and 39 

we present notionals and gross market values of single-name CDS activity by reporting dealers 

(net), hedge funds, banks, insurance firms, and nonfinancial customers and other participants. 

We observe a decrease in the activity of banks and securities dealers, and an increase in the role 

of hedge funds and central counterparties in both global notionals and gross market values of 

single-name CDS. The relative importance of dealers has remained relatively stable over time, 

and dealers represented approximately half of all single-name CDS notional outstanding and 

market value as of mid-2016. 

Figure 38. Outstanding notional by participant type 

0%

20%

40%

60%

80%

100%

Dec 05

Jun 06

Dec 06

Jun 07

Dec 07

Jun 08

Dec 08

Jun 09

Dec 09

Jun 10

Dec 10

Jun 11

Dec 11

Jun 12

Dec 12

Jun 13

Dec 13

Jun 14

Dec 14

Jun 15

Dec 15

Jun 16

Reporting dealers (net) Banks & securities firms

Hedge funds Insurance & financial guaranty

Non financial customers Central counterparties

Source: DERA analysis. BIS data, available at http://stats.bis.org/statx/toc/DER.html. 

224 
         

     

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

‐

Figure 39. Gross market value by participant type 

0%

20%

40%

60%

80%

100%

Dec 05

Jun 06

Dec 06

Jun 07

Dec 07

Jun 08

Dec 08

Jun 09

Dec 09

Jun 10

Dec 10

Jun 11

Dec 11

Jun 12

Dec 12

Jun 13

Dec 13

Jun 14

Dec 14

Jun 15

Dec 15

Jun 16

Reporting dealers (net) Banks & securities firms

Hedge funds Insurance & financial guaranty

Non financial customers Central counterparties

Source: DERA analysis. BIS data, available at http://stats.bis.org/statx/toc/DER.html. 

Market participation of dealers, banks and securities firms, hedge funds, and others in 

single-name CDS varies notably across credit quality tiers and over time. Figure 40 reports 

single-name CDS notional by credit rating for all market participants, dealers, banks and 

securities firms, and hedge funds. First, the structure of market activity has changed over time. 

Approximately 75% of notional was concentrated in unrated and high yield underliers prior to 

the recent crisis, compared to about 25% in 2004. Meanwhile, approximately half of all notional 

was concentrated in medium–low investment grade categories of A and BBB in 2011 and 

onwards, a large increase from a nadir of 17% in 2006. Second, market participants vary widely 

in their activity across credit quality categories. Pre-crisis, a greater fraction of dealer exposure 

was concentrated in the CDS of unrated issuers (e.g., about 49% at the end of 2006). In recent 

years, dealers and banks and securities firms are primarily exposed to CDS on investment grade 

underliers. In addition, hedge funds accumulated larger exposures to CDS on AAA and AA 

underliers prior to the financial crisis (2006-2007), and shifted into high yield names (BB and 

225 
below) in 2010. These observations are generally consistent with the interpretation that market 

participants with various levels of risk tolerance and informational advantages are transacting on 

credit risk of different underliers over time, and endogenously demand liquidity in single-name 

CDS on different groups of underliers at various points in time. We also recognize that time 

series patterns of compression activity may influence gross notionals. 

Single-name CDS are credit risk transfer instruments, the value of which depends on the 

expectations of market participants concerning the probability of a credit event and the recovery 

rate during the life of the CDS contract. As a result, the prices and volumes of activity in singlename CDS with varying tenors may reflect market expectations concerning the term structure of 

credit risk of a given underlier, as well as liquidity demand in certain maturities. Figure 41 

reports single-name CDS notional outstanding by tenor for reporting dealers, banks and 

securities firms, and hedge funds. We observe a large decline in exposure in contracts with 

tenors of greater than 5 years and a corresponding increase in dealer and bank exposure in tenors 

of less than 1 year. 

226 
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

‐‐‐‐‐‐‐‐‐‐‐‐‐‐

‐‐‐‐‐‐‐‐‐‐‐‐‐‐

0%

20%10%

0%

Dec 04 Dec 04

Jun 05 Jun 05 AAA/AA A/BBB BB and below Unrated Hedge funds Reporting dealers (net) 100% 100% 80% 80% 60% 60% 40% 40% 20% 20% 0%

Dec 05

Jun 06

Dec 06

Jun 07

Dec 07

Jun 08

Dec 08

Jun 09

Dec 09

Jun 10

Dec 10

Jun 11

Dec 11

Jun 12

Dec 12

Dec 05

Jun 06

Dec 06

Jun 07

Dec 07

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Dec 08

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Dec 11

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Dec 12

Jun 13 Jun 13

Dec 13

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Dec 14

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Dec 13

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Dec 14

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Jun 16 Jun 16

Dec 04 Dec 04

Jun 05 Jun 05

Dec 05

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Dec 10

Jun 11

D11

Dec 05

Jun 06

Dec 06

Jun 07

Dec 07

AAA/AA A/BBB BB and b

Jun 08

Dec 08

Jun 09

Dec 09

Jun 10

Dec 10

Jun 11

D11

Figure 40. Single-name CDS notional outstanding by rating Banks & securities 

All counterparties (net) 100% 90%

80% 100%

70% 90%60% 80%50% 70%40% 60% 30% 50% 20% 40% 10%

30% 0%

AAA/AA A/BBB BB and b

AAA/AA A/BBB BB and below Unrated

Source: DERA analysis. BIS data, available at http://stats.bis.org/statx/toc/DER.html 
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

 ‐ 

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

 ‐ 

‐‐‐‐‐‐‐‐‐‐‐‐‐‐

 ‐ 

‐‐‐‐‐‐‐‐‐‐‐‐‐‐

 ‐ 

0%

0%

<=1 year 1 5 years 

<=1 year 1 5 years > 5 years

216 Source: DERA analysis. BIS data, available at http://stats.bis.org/statx/toc/DER.html 

<=1 year 1 5 years > 5 years Hedge funds 

Reporting dealers (net) 100%

100% 80%

80%

60%

60%

40%

40%

20%

20%

0%

Dec 04 Dec 04

Jun 05 Jun 05

Dec 05 Dec 05

Jun 06 Jun 06

Figure 41. Single-name CDS notional outstanding by tenor216 Banks & securities 

All counterparties (net) 100%

100% 80%

80% 60%60% 40%40% 20%20% 0%

Dec 06

Jun 07

Dec 07

Jun 08

Dec 08

Jun 09

Dec 09

Jun 10

Dec 10

Jun 11

Dec 11

Jun 12

Dec 12

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Dec 13

Jun 14

Dec 06

Jun 07

Dec 07

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Dec 08

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Dec 10

Jun 11

Dec 11

Jun 12

Dec 12

Jun 13

Dec 13

Jun 14

Dec 14 Dec 14

Jun 15 Jun 15

Dec 15 Dec 15

Jun 16 Jun 16

Dec 06

Dec 04 Dec 04 Jun 05 Jun 05 Dec 05 Dec 05

Jun 06 Jun 06 <=1 year 1 5 years

Dec 06

Jun 07 Jun 07

Dec 07 Dec 07

Jun 08 Jun 08

Dec 08 Dec 08

Jun 09 Jun 09

Dec 09 Dec 09

Jun 10 Jun 10

Dec 10

Jun 11

Dec 10

Jun 11

D11
 

 

 

2. Market Participants 

The analysis above relies on BIS participant classifications and provides aggregated 

statistics on participant activity in single-name CDS. We now turn to the analysis of transactionlevel data from DTCC-TIW. DTCC provides clearing, settlement, and other services for OTC 

derivatives. TIW was established in November 2006 as a centralized global repository for trade 

reporting and post-trade processing of OTC credit derivatives contracts. According to the 

DTCC, TIW data contain approximately 98% of all credit derivative transactions in the global 

marketplace. Our sample period spans from November 2006 to December 2016.217 

In addition to dealers, thousands of other participants appear as counterparties to singlename CDS in our sample, and include, but are not limited to, investment companies, pension 

funds, private (hedge) funds, sovereign entities, and industrial companies. We use machine 

matching and hand classification of TIW accounts to categorize market participants.218 We 

observe that most non-dealer users of security-based swaps do not engage directly in the trading 

of swaps, but trade through banks, investment advisers, or other types of firms acting as dealers 

or agents. Based on an analysis of the counterparties to trades reported to the TIW, our sample 

includes 1,957 entities that engaged directly in trading during the sample period. As shown in 

217 We recognize certain time series limitations of TIW transaction data. While TIW coverage dates back to 

November 2006, the combined trade notional of those transactions is a fraction of the global notional reported in 

publicly reported semiannual BIS notional statistics. While we believe backfilled data for 2006 through 2007 may 

be indicative of pre-crisis activity in single-name CDS, we are cautious in our interpretation of reported statistics for 

the 2006-2008 period. We, therefore, consider evidence from TIW activity statistics in conjunction with BIS data. 218 These 2,038 entities, which are presented in more detail in Table 27, below, include all DTCC-defined “firms” 

shown in TIW as transaction counterparties that report at least one transaction to TIW as of December 2016. The 

staff in the Division of Economic and Risk Analysis classified these firms, which are shown as transaction 

counterparties, by machine matching names to known third-party databases and by manual classification. See, e.g., 

Rel. No. 34-72472, Application of “Security-Based Swap Dealer” and “Major Security-Based Swap Participant” 

Definitions to Cross-Border Security-Based Swap Activities (Jun. 25, 2014) 79 FR 47277, n.139 (Cross-Border 

Adopting Release); id. at n.1304. Manual classification was based in part on searches of the EDGAR and 

Bloomberg databases, the SEC’s Investment Adviser Public Disclosure database, and a firm’s public website or the 

public website of the account represented by a firm. The staff also referred to ISDA protocol adherence letters 

available on the ISDA website. 

229 
 

 

 

 

Table 27, below, close to three-quarters of these entities (DTCC-defined “firms” shown in TIW, 

which we refer to here as “transacting agents”) were identified as investment advisers, of which 

approximately 40 percent (about 31 percent of all transacting agents) were registered as 

investment advisers under the Investment Advisers Act of 1940.219 Although investment 

advisers comprise the vast majority of transacting agents, the transactions they executed account 

for only 12.3 percent of all single-name CDS trading activity reported to the TIW, measured by 

number of transaction-sides (each transaction has two transaction sides, i.e., two transaction 

counterparties). The vast majority of transactions (83.8 percent) measured by number of 

transaction-sides were executed by International Swaps and Derivatives Association (ISDA)­

recognized dealers.220 Panel B reports changes in market participants over time. Similar to the 

results above, we find that banks excluding G16 dealers have reduced their participation in 

single-name CDS markets as measured by transaction share in the latter 3 years in the sample 

period. 

3. Transaction Activity 

We now turn to the evolution in the levels and structure of transaction activity of various 

market participants. Using TIW data, we consider the volume of price-forming transactions by 

dealers and nondealers, using Markit Red6 codes to identify unique underlier firms. We note that 

TIW serves as the gold record repository for CDS transactions. Because it includes both 

219 See 15 U.S.C. 80b1–80b21. Transacting agents participate directly in the security-based swap market, without 

relying on an intermediary, on behalf of principals. For example, a university endowment may hold a position in a 

security-based swap that is established by an investment adviser that transacts on the endowment’s behalf. In this 

case, the university endowment is a principal that uses the investment adviser as its transacting agent. 220 For the purpose of this analysis, the ISDA-recognized dealers are those identified by ISDA as belonging to the 

G14 or G16 dealer group during the period: JP Morgan Chase NA (and Bear Stearns), Morgan Stanley, Bank of 

America NA (and Merrill Lynch), Goldman Sachs, Deutsche Bank AG, Barclays Capital, Citigroup, UBS, Credit 

Suisse AG, RBS Group, BNP Paribas, HSBC Bank, Lehman Brothers, Société Générale, Credit Agricole, Wells 

Fargo and Nomura. See, e.g., 2010 ISDA Operations Benchmarking Survey, available at: 

http://www.isda.org/c_and_a/pdf/ISDA-Operations-Survey-2010.pdf. 

230 
 

 

 

 

 

 

transactions that do and do not affect participants’ market risk positions, we apply a filter to 

include only “price-forming” transactions. Price-forming transactions include new trades 

between two counterparties, terminations of existing trades, and assignments to a third 

counterparty. These transactions provide information about the price of credit risk and so allow 

us to focus on single-name CDS market activity that results in the transfer of credit risk among 

participants by excluding trades that do not reflect changes in participants’ exposure to credit 

risk. Specifically, for the purposes of this analysis and similar to Biswas et al. (2015) and Porter 

(2015), we exclude:221 

(1) transactions caused by the reorganization of a participant; 

(2) terminations and trades due to third-party compression and netting services; 

(3) records reflecting clearing of pre-existing bilateral trades; 

(4) trades between two affiliated entities; 

(5) duplicate reporting of trades conducted by prime brokers on behalf of their clients; 

(6) assignments that do not affect the pricing terms, and 

(7) post-Big Bang222 trades with standardized coupons, but no upfront payments. 

Figure 42 Panel A reports transaction activity as measured by combined trade sizes of all 

price-forming transactions by year and participant group, and accounts are identified by their 

firm identifier and matched against the ISDA 16 dealer group. “Other” represents transactions 

among firm accounts that are not identified as dealers, therefore reflecting customer – customer 

221 For 2015, the filter considers 31% (or 771,718 transactions) of transactions in the TIW to be price-forming ones. 

This is almost 19% (or $3.90 trillion) of the total trade size for 2015. 

222 The “CDS Big Bang” refers to global and North American changes to CDS contracts and quoting conventions 

that occurred on April 8, 2009. Global contract changes included changes to the effective dates for all CDS 

contracts, determination committees of credit and succession events and auction terms, and hardwiring of the 

auction mechanism for CDS following credit events. Additional changes to North American CDS included a move 

to trading with a fixed coupon. See Markit, The CDS Big Bang (Spring 2009), at 

https://www.markit.com/assets/en/docs/markit-magazine/issue-4/60-cds-big-bang.pdf. 

231 
 

 

 

       

transaction activity in single-name CDS. Figure 42 Panel B repeats the analysis, tabulating 

transaction activity by month. We observe a large spike in interdealer activity (measured based 

on price-forming transactions) immediately prior to the financial crisis, and a dramatic decline in 

interdealer single-name CDS transaction volume after 2008, while at the same time, dealercustomer activity remained stable.223 This may suggest that dealers are less able to find liquidity 

to offload positions in the interdealer market. Alternatively, efficiencies in the search for 

counterparties may have shortened dealer intermediation chains, with fewer interdealer trades 

necessary to connect natural customer buys to customer sells. We note that dealers continue to 

play a central role in intermediating customer transactions in single-name CDS, and direct 

transaction activity among customers remains a negligible part of the market. 

Figure 42. Trade volume 

Panel A. Annual 

$8
 

$7
 

$6
 

$5
 

$4
 

$3
 

$2
 

$1
 

$‐

Dealer to Customer Dealer to Dealer Other

Trillions

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
 

223 We note that the decline in interdealer activity in 2016 was largely explained by an accompanying increase in the 

volume of cleared trades, which is classified as “other” in Figure 42. 

232 
 

       

Panel B. Monthly 

$‐

$100

$200

$300

$400

$500

$600

$700

$800

$900

Billions

Nov‐06

May‐07

Nov‐07

May‐08

Nov‐08

May‐09

Nov‐09

May‐10

Nov‐10

May‐11

Nov‐11

May‐12

Nov‐12

May‐13

Nov‐13

May‐14

Nov‐14

May‐15

Nov‐15

May‐16

Nov‐16

Dealer to Customer Dealer to Dealer Other

Source: DERA analysis. 

The fraction of zero trading days (i.e., the number of days with no trading as a fraction of 

all available trading days) is another measure of trading activity. While it does not differentiate 

between liquidity supply and liquidity demand explanations, the fraction of zero trading days 

may capture the relative illiquidity of a particular single-name CDS in the sense that a more 

(less) illiquid single-name CDS trades less (more) often and thus has a higher (lower) fraction of 

zero trading days. In Figure 43, we show the number of days with at least one transaction in a 

given underlier per year and per month, reporting the evolution of the median, mean, top and 

bottom quartiles of the measure over time. 

233 
Figure 43. Nonzero trading days 

Panel A. Annual 

9
 

8
 

7
 

6
 

5
 

4
 

3
 

2
 

1
 

0
 

2006 2008 2010 2012 2014 2016
 

75th %‐ile
 Median 25th %‐ile Mean

Panel B. Monthly 

14
 

12
 

10
 

8
 

6
 

4
 

2
 

0
 

Nov‐06 Mar‐08 Jul‐09 Dec‐10 Apr‐12 Sep‐13 Jan‐15 Jun‐16

75th %‐ile Median 25th %‐ile Mean

234 
Panel C. Annual, by credit 

Investment grade High yield 

140120100

80604020

0

70605040302010

0

2006 2008 2010 2012 2014 2016 2006 2008 2010 2012 

75th %‐ile Median 25th %‐ile Mean 75th %‐ile Median 25t

Panel D. Monthly, by credit 

Investment grade High yield 

14 14

12 1210 10

8 86 64 42 20 0

Nov‐06

Jun‐07

Jan‐08

Aug‐08

Mar‐09

Oct‐09

May‐10

Dec‐10

Jul‐11

Feb‐12

Sep‐12

Apr‐13

Nov‐13

Jun‐14

Jan‐15

Aug‐15

Mar‐16

Oct‐16

Nov‐06
 

Jun‐07
 

Jan‐08
 

Aug‐08
 

Mar‐09
 

Oct‐09
 

May‐10
 

Dec‐10
 

Jul‐11
 

Feb‐12
 

Sep‐12
 

Apr‐13
 

N13

75th %‐ile Median 25th %‐ile Mean 75th %‐ile Median 25th %

Source: DERA analysis. 
 

In Figure 44, we estimate the fraction of days per month that a given underlier does not 

trade, and report the distribution of that measure. We note two key patterns in the data. First, 

single-name CDS are generally inactively traded instruments. For instance, the median singlename CDS had between 8 and 32 nonzero trading days per year during our sample period and 

did not have a single trade between 80- 90% of the time in monthly data. Second, the more 

actively traded quartile of single-name CDS experienced a large increase in transaction activity 

through 2012 and remained flat afterwards. Breaking down by underlying bond, we observe a 

slight decline in the measure of trading activity for the more liquid names in the investment 

grade category and some increase in activity in the right tail of the high yield distribution in the 

last few years in our sample. At the same time, the least actively traded quartile is illiquid and 

remains so in the post-reform period across credit categories. 

Figure 44. Fraction of zero trading days per month 

1.1

1
 

0.9
 

0.8
 

0.7
 

0.6
 

0.5
 

0.4
 

Nov‐06

May‐07

Nov‐07

May‐08

Nov‐08

May‐09

Nov‐09

May‐10

Nov‐10

May‐11

Nov‐11

May‐12

Nov‐12

May‐13

Nov‐13

May‐14

Nov‐14

May‐15

Nov‐15

May‐16

Nov‐16

75th %‐ile Median 25th %‐ile Mean

Source: DERA analysis. 

Figure 45 reports the number of accounts transacting in a given underlier for underliers 

with different levels of trading activity. Similar to our prior findings, the median underlier is 

236 
 

25

20

15

10

5

0

traded by between 0 and 6 accounts, whereas the top quartile of the measure ranges between 10 

and 18 during the post-crisis period. Importantly, we do not observe notable changes in the 

number of market participants transacting in various underliers in TIW data after the passage of 

the Dodd-Frank Act. 

Figure 45. Accounts trading per underlier 

35
 

30
 

Nov‐06

Apr‐07

Sep‐07Feb‐08

Jul‐08

Dec‐08

May‐09

Oct‐09

Mar‐10

Aug‐10

Jan‐11

Jun‐11

Nov‐11

Apr‐12

Sep‐12Feb‐13

Jul‐13

Dec‐13

May‐14

Oct‐14

Mar‐15

Aug‐15

Jan‐16

Jun‐16

Nov‐16

75th %‐ile Median 25th %‐ile Mean

Source: DERA analysis. 

In Figure 46, we plot the average number of price-forming trades on a given day for 

traded underliers. While the overall magnitude of the figure remains relatively low and driven 

by the more actively traded underliers, we observe a spike in this measure of trading activity 

during the financial crisis, after the passage of the Dodd-Frank Act (end of 2010) and some 

increase in this measure in the last 3 years of our sample period (2013 through 2015). 

237 
 

Figure 46. Number of trades per day 

Panel A. Annual 

9

8

7

6

5

4

3

2

1

0

2006 2008

75th %‐ile

2010 2012

Median 25th %‐ile

Source: DERA analysis. 

2014

Mean

2016

Panel B. Monthly 

14

12

10

8

6

4

2

0

Nov‐06 Mar‐08 Jul‐09 Dec‐10 Apr‐12 Sep‐13 Jan‐15 Jun‐16

75th %‐ile Median 25th %‐ile Mean

Source: DERA analysis. 

238 
 

Finally, in Figure 47 we analyze average transaction sizes for traded underliers, 

aggregated to annual and monthly frequencies and broken down into dealer-to-dealer and dealerto-customer trades. We observe that trade sizes have declined over time, with median interdealer 

trade size beginning to decline after 2010, but median dealer-to-customer trade sizes beginning 

to decline after the onset of the financial crisis in 2008. A decrease in transaction size may point 

to a decrease in liquidity of larger trades, as participants become less able to transact large blocks 

without significant price impact and choose to split block trades into multiple trades. A less 

active single-name CDS market with a large number of counterparties, few trades, and less 

demand for liquidity in short credit quality products is also consistent with economic booms. 

Crucially, the timing of the declines in dealer-customer trade sizes may suggest that changing 

risk tolerances around the financial crisis served as a contributing factor. 

239 
Figure 47. Trade size, price forming transactions 

Panel A. Annual 

Millions$10

$8

$6

$4

$2

$‐

2006 2008 2010 2012 2014 2016

75th %‐ile Median 25th %‐ile Mean

Panel B. Monthly 

$12

$10

$8

$6

$4

$2

$‐

Nov‐06 Mar‐08 Jul‐09 Dec‐10 Apr‐12 Sep‐13 Jan‐15 Jun‐16

75th %‐ile Median 25th %‐ile Mean

Millions

240 
Millions Millions

Panel C. Annual, by type 

Dealer to dealer 

Dealer to customer $12 $9

$8 $10

$5

$6 $4

$3 $4$2

$2 $1

$‐ $‐ 2006 2008 2010 2012 

2006 2008 2010 2012 2014 2016

75th %‐ile Median 25th %‐ile Mean 75th %‐ile Median 25th 

Panel D. Monthly, by type 

Dealer to dealer Dealer to custom

$12 $12

Millions

Millions $7

$8 $6

$8 $8$6 $6$4

$4

$2

$2

$‐

$‐ Nov‐06 Mar‐08 Jul‐09 Dec‐10 Apr‐12 Sep‐13 Jan‐15 Jun‐16

Nov‐06 Mar‐08 Jul‐09 Dec‐10 Apr‐12 

75th %‐ile Median 25th %‐ile Mean 75th %‐ile Median 2

Source: DERA analysis. 

$10 $10
 

 

4. Quotes and Quoted Spreads 

Finally, we complement the analysis of trade-based measures with intraday quote data 

from ICE Data Services CMA (CMA) for January 1, 2008, through September 29, 2016. 

Dealers play a central role in liquidity provision and the number of quotes may be correlated 

with trading volume and dealer willingness to provide liquidity.224 We note that quote-based 

metrics could also reflect liquidity demand. 

We consider the overall volume of quotation activity in single-name CDS both at the 

market and underlier level. Figure 48 and Table 28 present the number of quoted underliers and 

quotes per underlier on a given trading day through the time series. We note a gradual decline in 

the measure in 2013 onwards. As shown in Table 28, over the course of the period, the median 

number of quoted underliers decreased from 976 in 2008 to 888 in 2015. 

Figure 48. Daily quoted underliers 

1100

1000
 

900
 

800
 

700
 

600
 

2008 2009 2010 2011 2012 2013 2014 2015 2016

Mean 25th %‐ile Median 75th %‐ile

Source: DERA analysis. 

Figure 49 and Panels B and C of Table 28 report the evolution in daily quotes for each 

underlier by credit quality and by tenor. Not surprisingly, single-name CDS on high yield 

underliers have more quote activity – higher default rates among riskier names may give rise to 

224 Qiu and Yu (2012) also use the number of quote provider as a measure for the proxy of the CDS market. 

242 
greater credit risk hedging and more active re-pricing. Interestingly, investment grade quote 

activity has been steadily decreasing since 2010, whereas daily quotes per underlier on high yield 

names peak in 2012. Since the Dodd-Frank and Basel reforms discussed above could have 

impacted single-name CDS in both investment grade and high yield underliers, these data could 

point to demand driven explanations of changes in CDS quote activity. For instance, the current 

environment of low interest rates and default rates might have reduced the demand for CDS 

instruments. In addition, although the single-name CDS activities seemed to have declined since 

2008 in terms of the metrics on quotes, the 2016 figure is not substantially lower than that in 

2008, the peak of the crisis. 

Next, we group quotes by tenor and report the median number of quotes per underlier per 

day for a tenor of less than 5 years, 5 years, and longer than 5 years. We note that quotes on 

CDS with standard 5-year tenors have been on the decline since 2010; however, quotes on 

shorter term tenors have more than doubled between 2010 and 2014. Since differences in prices 

across tenors of single-name CDS contracts reflect market expectations about the term structure 

of default risk of underliers, changes in quote activity across various tenors may reflect 

fundamental macroeconomic shifts in credit risk. At the same time, a shift towards voluntary 

clearing and accompanying standardization of single-name CDS contracts predicts greater 

concentration of activity in standardized tenors. 

Relative quoted spreads also may be indicative of liquidity in single-name CDS. In 

Figure 50, we plot the end-of-day relative quoted spreads by credit and tenor. For the purposes 

of this analysis, we define relative quoted spread as the end of day quoted bid-ask spread scaled 

by the quote midpoint, and determine credit quality based on the size of the standardized coupon 

of a given instrument. Our findings are threefold. First, median quoted spreads for investment 

243 
grade as well as liquid high yield underliers rose between 2011 and 2014 and have remained flat 

since. Second, quoted spreads for the most illiquid high yield underliers have increased 

substantially since 2013. Third, the widening of quoted spreads is heavily concentrated in CDS 

with short tenors, with the most illiquid single-name CDS with tenors under 5 years experiencing 

an almost threefold increase in the spread since 2013. 

The source of these observed changes is unclear. As discussed earlier and shown in 

Appendix A of this report, a number of significant regulatory changes have, indeed, occurred in 

2013: the beginning of the Basel III phase-in, the adoption of the final Basel III rule by the FRB, 

the OCC, and the FDIC, and the adoption of the final Volcker rules. However, our evidence 

indicates the widening of the quoted spreads in 2013 was concentrated primarily in the most 

illiquid high yield single names and in short tenors, which may point to greater uncertainty about 

short-term credit risk of a subset of the underlying single names. 

While existing studies use indicative quoted spreads to capture CDS transaction costs and 

to capture liquidity (e.g., Acharya and Johnson (2007)), we recognize that the midpoint may not 

accurately reflect the true price and that effective spreads may be a more precise measure of 

transaction costs. Biswas et al. (2015) find that indicative quoted spreads are, on average, twice 

as large as effective spreads and that quoted spreads explain cross-sectional variation in effective 

spreads primarily for trades between dealers and customers on North American investment grade 

bonds. The paper estimates individual round-trip transaction costs (effective half-spreads) of 14 

bps notional for dealer-customer trades in a sample of 851 single-name CDS during 2009-2014, 

and 12 bps for interdealer trades in the same sample, with wider effective spreads for more 

actively traded contracts and contracts with higher valuation uncertainty. 

244 
 

       

Figure 49. Daily quotes per underlier
 

Panel A. All Quotes 

80

70

60

50

40

30

20

10

0

2008 2009 2010 2011 2012 2013 2014 2015 2016

Mean 25th %‐ile Median 75th %‐ile

Panel B. By credit quality and tenor (at the median) 

70

60

50

40

30

20

10

0

60

50

40

30

20

10

0

2008 2010 2012 2014 2016 2008 2010 2012 2014 2016

IG HY < 5 years 5 years > 5 years

Source: DERA analysis. 

245 
Figure 50. Quoted spreads scaled by midpoint 

All 

Investment grade H

30%25%20%15%10%

5%0%

30%25%20%15%10%

5%0%

45%40%35%30%25%20%15%10%

5%0%

2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 2008 2010 

25th %‐ile Median 75th %‐ile 25th %‐ile Median 75th %‐ile 25th %‐ile 

< 5 years 5 years 

120% 18% 25%

16%

100%

14% 20%

80% 12%

10% 15%

60%

8%

10%

40% 6%

4%

20% 5%

2%

0% 0% 0%

2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 2008 2010 

25th %‐ile Median 75th %‐ile 25th %‐ile Median 75th %‐ile 25th %‐ile 

Source: DERA analysis. 
E. Summary

 Our examination of various metrics on CDS market liquidity has yielded mixed results. 

While outstanding notionals have decreased relative to pre-crisis years, gross market values of 

CDS outstanding remain generally comparable with pre-crisis levels, consistent with underlying 

economic exposure in CDS remaining comparable with pre-crisis levels. Dealers continue to 

play a critical role in the single-name CDS market, and the decrease in positions of banks and 

securities firms after the crisis has been accompanied by continued hedge fund participation and 

a rise in clearing activity. While interdealer trade activity has declined after 2010, dealercustomer activity has remained stable. This is consistent with alternative explanations that are 

not mutually exclusive: (1) a reduced ability by dealers to find CDS liquidity on the interdealer 

market, and (2) greater efficiency in dealer intermediation chains matching CDS buyers to CDS 

sellers. We have also shown that the number of participants transacting in a given underlier has 

remained relatively flat, the fraction of zero trading days has decreased, and the frequency of 

trading for active underliers has remained flat or elevated after the financial crisis and reforms 

that followed. These observations suggest that liquidity in this market has remained stable or 

improved. At the same time, however, we have also shown that trade sizes have decreased 

somewhat, quoting activity has declined, and quoted spreads for the least liquid high yield 

underliers have risen, supportive of a conclusion that liquidity in at least some portions of this 

market has deteriorated. 

In conclusion, we have considered a large body of rigorous academic research and have 

presented a multifaceted data analysis on the evolution of trading activity and liquidity in singlename CDS. The evidence on aggregate trends in market activity and liquidity is mixed, and, in 

light of the methodological challenges discussed above, it is difficult to disentangle causal 

247 
impacts. Crucially, changes in both liquidity supply and liquidity demand may be driving 

observed trends, and other factors, such as macroeconomic conditions, resolution of uncertainty 

concerning systematic and idiosyncratic credit risk, and policy interventions coinciding with and 

unrelated to Dodd Frank, including Volcker, and Basel III, may have impacted CDS activity in 

various underliers. As discussed above, it is unclear that the crisis period, and the corresponding 

rise in default risk and uncertainty, is an appropriate benchmark period for trading activity in 

short credit risk instruments, and the trend analysis is sensitive to the choice of the benchmark 

period. 

248 
 

 

 

 

 

 

 

 

 

 

F. Tables on Single-name CDS 

Table 27. The number of transacting agents by counterparty type and the fraction of total 

trading activity, from November 2006 through December 2016, represented by each 

counterparty type.225 

Panel A. 2006-2016 

Transacting Agents I II III 

Investment Advisers 1,571 77.1% 12.3%

 - SEC registered 629 30.9% 8.2% 

Banks 256 12.6% 3.5% 

Pension Funds 29 1.4% 0.1% 

Insurance Companies 42 2.1% 0.2% 

ISDA-Recognized Dealers226 17 0.8% 83.8% 

Other 123 6.0% 0.2% 

Total 2,038 100.0% 100% 

I – number of participants, II – percent of participants, III – transaction share 

Panel B. By time period 

2006-2009 2010-2012 2013-2016 

Transacting Agents I II III I II III I II III 

Investment Advisers 

- SEC registered 

Banks (excluding G16) 

Pension Funds 

Insurance Companies 

ISDA - Recognized 

Dealer 

Other

899 

363

178 

20 

20 

17 

46 

76.1% 

 30.7% 

15.1% 

1.7% 

1.7% 

1.4% 

3.9% 

11.1% 

6.6% 

4.3%

0.1%

0.4%

83.9%

0.2%

1067 

447

 219 

22 

29 

16 

83 

74.3% 

 31.1% 

15.2% 

1.5% 

2.0% 

1.1% 

5.8% 

13.0% 

9.2% 

4.8%

0.1%

0.1%

81.8%

0.1%

1055 

457 

 168 

19 

28 

17 

63 

78.2% 

33.9% 

12.4% 

1.4% 

2.1% 

1.3% 

4.7% 

13.2% 

9.6% 

1.0% 

0.2% 

0.0% 

85.4% 

0.1% 

Total 1,181 100.0% 100.0% 1,437 100.0% 100.0% 1,350 100.0% 100.0% 

I – number of participants, II – percent of participants, III – transaction share 

Source: DERA analysis. 

225 The figures in this table exclude clearing counterparties.
 226 For the purpose of this analysis, the ISDA-recognized dealers are those identified by ISDA as belonging to the G14 or G16 

dealer group during the period: JP Morgan Chase NA (and Bear Stearns), Morgan Stanley, Bank of America NA (and Merrill 

Lynch), Goldman Sachs, Deutsche Bank AG, Barclays Capital, Citigroup, UBS, Credit Suisse AG, RBS Group, BNP Paribas, 

HSBC Bank, Lehman Brothers, Société Générale, Credit Agricole, Wells Fargo and Nomura. See, e.g., 2010 ISDA Operations 

Survey, available at: http://www.isda.org/c_and_a/pdf/ISDA-Operations-Survey-2010.pdf.
 

249 
 

 

 

 

 

 

 

 

 

 

 

Table 28. Daily quotes 

Panel A. Quoted underliers 

Year Mean 25th %-ile 50th %-ile 75th %-ile 

2008 931 921 976 1032 

2009 926 939 975 995 

2010 935 939 968 999 

2011 923 915 965 999 

2012 916 934 961 977 

2013 927 945 970 989 

2014 900 904 938 962 

2015 847 834 888 929 

2016 773 774 793 813 

Panel B. Quotes per underlier 

Year Mean 25th %-ile 50th %-ile 75th %-ile 

2008 25 17 25 33 

2009 42 28 39 54 

2010 58 43 57 72 

2011 54 37 52 70 

2012 47 33 45 58 

2013 46 32 44 59 

2014 38 23 35 50 

2015 22 13 20 29 

2016 17 11 16 21 

Panel C. Quotes per underlier: by credit quality 

Year 

Investment grade High yield 

Mean 25th %-ile 50th %-ile 75th %-ile Mean 25th %-ile 50th %-ile 75th %-ile 

2008 26 18 26 34 29 17 28 40 

2009 43 29 40 54 51 31 46 66 

2010 60 45 58 73 62 41 58 79 

2011 53 38 51 67 65 38 59 86 

2012 42 31 41 52 69 46 65 88 

2013 41 29 40 52 68 45 63 87 

2014 37 22 35 50 44 26 40 57 

2015 21 13 19 28 23 13 20 31 

2016 17 11 16 21 18 10 16 23 

250 
 

 

 

 

 

 

 

Panel D. Quotes per underlier: by tenor 

Year 

< 5 years 5 years > 5 years 

25th %-ile 50th %-ile 75th %-ile 25th %-ile 50th %-ile 75th %-ile 25th %-ile 50th %-ile 75th %-ile 

2008 2 4 6 16 24 32 3 5 8 

2009 4 8 14 26 35 47 4 7 13 

2010 5 10 19 39 50 62 5 10 18 

2011 6 12 22 34 47 59 5 10 17 

2012 10 16 25 28 37 46 5 8 12 

2013 12 20 32 23 30 38 5 9 14 

2014 12 20 30 16 22 29 5 9 14 

2015 6 11 17 11 16 21 3 5 8 

2016 5 9 13 10 14 18 2 4 5 

Source: DERA analysis. 

Table 29. Relative quoted spreads (quoted spread scaled by the midpoint) 

Panel A. Quoted spreads 

Year Mean 25th %-ile 50th %-ile 75th %-ile 

2008 13% 10% 12% 15% 

2009 13% 10% 13% 16% 

2010 11% 9% 10% 12% 

2011 11% 8% 10% 12% 

2012 15% 11% 13% 17% 

2013 19% 14% 18% 22% 

2014 22% 15% 19% 25% 

2015 24% 14% 17% 25% 

2016 27% 13% 17% 26% 

Panel B. Quoted spreads: by credit 

Year 

Investment grade High yield 

25th %-ile 50th %-ile 75th %-ile 25th %-ile 50th %-ile 75th %-ile 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

11% 13% 16% 

11% 13% 16% 

9% 10% 12% 

9% 10% 12% 

10% 12% 15% 

15% 18% 22% 

17% 20% 24% 

15% 17% 20% 

14% 17% 23% 

6%

8%

8%

7%

11% 

13% 

12% 

11% 

12% 

 8%

 11% 

 10% 

 10% 

16% 

17% 

17% 

18% 

17% 

 10% 

16% 

13% 

15% 

23% 

23% 

32% 

41% 

41% 

251 
 

Panel C. Quoted spreads: by tenor 

Year 

< 5 years 5 years > 5 years 

25th %-ile 50th %-ile 75th %-ile 25th %-ile 50th %-ile 75th %-ile 25th %-ile 50th %-ile 75th %-ile 

2008 11% 13% 16% 10% 12% 15% 8% 9% 11% 

2009 18% 23% 28% 10% 12% 15% 11% 13% 16% 

2010 19% 24% 31% 8% 10% 11% 10% 11% 13% 

2011 22% 27% 36% 8% 9% 11% 9% 10% 12% 

2012 31% 38% 48% 10% 12% 14% 12% 14% 16% 

2013 45% 52% 61% 12% 13% 16% 15% 16% 19% 

2014 42% 49% 63% 12% 14% 16% 15% 16% 18% 

2015 40% 48% 71% 13% 14% 16% 16% 18% 20% 

2016 38% 49% 109% 13% 15% 17% 14% 15% 17% 

Source: DERA analysis. 

252 
		

 

 

 

VI. Funds

A. Introduction 

While corporate and Treasury bonds are directly owned by several types of investors— 

including households, pension plans, insurance companies, and other institutions—investors can 

also gain exposure to bonds indirectly via investment companies.227 Investment companies are 

therefore an alternative channel through which investors finance the real investment activity of 

firms in the economy. Open-end funds (OEFs), closed-end funds (CEFs), and, more recently, 

ETFs, all actively participate in corporate and Treasury bond markets.228 In this section, we 

explore trends in bond ownership and important limitations to the interpretation of those trends. 

B. Evidence on Bond Liquidity from Bond Ownership 

Liquidity in underlying bond markets may affect the portfolio decisions of the investors 

who participate in those markets. For example, households may respond to changes in bond 

market liquidity by adjusting the relative quantity of bonds they hold directly and bonds they 

hold through funds. In particular, a decrease in bond market liquidity, which would increase the 

cost of transacting in bond markets directly, could lead investors to increase the quantity of 

bonds held through funds. Conversely, an increase in bond market liquidity, which would 

reduce the cost of transacting in bond markets directly, could lead investors to decrease the 

quantity of bonds held through funds.229 If the Dodd-Frank Act or the Volcker Rule affected 

227 Accredited investors can also gain exposure to corporate debt and treasuries via private funds, but for simplicity 

we focus our analysis on investment companies registered under the Investment Company Act of 1940 because they 

hold significantly more assets, are accessible to a broader base of investors, and data on their holdings is more 

readily available.

228 We refer to OEFs, CEFs, and ETFs, collectively, as “funds”. 229 There are structural and operational differences between mutual funds, CEFs, and ETFs that result in differences 

in how investors gain access to each fund type. However, all of these fund types provide alternative means of 

achieving exposure to bond markets. We therefore assume that while the magnitude of any effects might differ 

[Footnote continued on next page] 

253 
 

 

bond market liquidity, and to the extent that bond funds may involve cost efficiencies or liquidity 

transformation, we might expect to observe a change in the relative quantities of bonds held 

directly and bonds held through funds. 

In this section, we examine data from January 1977 through June concerning aggregate 

trends in two bond market measures: (1) the share of fund assets invested in debt securities such 

as Treasury and corporate bonds, and (2) the share of outstanding debt that is owned by funds in 

aggregate. Although we find some significant changes in the participation of investment 

companies in the bond markets, for reasons discussed in Part B.VI.C below, these trends are 

likely influenced by a range of factors that confound our ability to reach conclusions regarding 

the effect of changes in bond market liquidity that may have occurred during the period relevant 

to the Dodd-Frank Act and the Volcker Rule. 

Figure 51 shows that the combined holdings of OEFs, ETFs, and CEFs grew from $50 

billion in the late 1970s to roughly $15.7 trillion in 2016.230 We note that holdings of corporate 

and foreign bonds increased from $692 billion in 2009 to $2.2 trillion in 2016. Figure 52 shows 

the share of total fund assets invested in different types of debt. From 2009 through 2016, the 

percentage of fund assets held in corporate bonds increased from 11.3% to 13.9%. 

across these fund types, any effects associated with a change in underlying bond market liquidity would be in the 

same direction. For example, a decrease in the liquidity of the underlying bond market would make substituting to 

any of these fund types relatively more attractive. 

230 All statistics and figures in this section are derived from Federal Reserve Flow of Funds data. See 

http://www.federalreserve.gov/releases/z1/current/default.htm 

254 
Figure 51. 1940 Act fund assets by asset type. Other assets include equities. 

Source: DERA analysis. 

Figure 52. 1940 Act Fund allocations to debt securities. "other_debt" includes agency 

bonds, syndicated loans, repo, and other forms of lending. 

Source: DERA analysis. 

To the extent that bond market participants change their relative quantities of bonds held 

directly and through funds as a function of liquidity in the underlying bond markets, the above 

255 
 

 

 

could be interpreted as evidence of a decrease in bond market liquidity from 2009 to 2016; 

however there are several important issues with that interpretation. First, it is not clear when the 

regulatory action that is the subject of the Report might have started to affect market liquidity. 

While some of the increase in both holdings of bonds by funds and the percentage of fund 

holdings invested in bonds since 2009 may be attributable to regulatory effects, these increases 

may simply be the continuation of a longer-term trend. Second, the dollar value of fund holdings 

and the percentage of fund holdings in bonds are also affected by the relative market values of 

bonds and other securities. That means some or all of the changes in fund allocation to bonds 

from 2009 to 2016 might instead be attributable to changes in the values of bonds and other 

securities. We note similar fluctuations in fund allocations vis-à-vis underlying valuations over 

the entire sample period (1977 to 2016). Finally, any change in the relative quantities of bonds 

held directly and through funds may be due to factors other than the liquidity of the underlying 

bond market, such as changes in demand composition (i.e., the relative demand for bonds by 

different types of investors who are more or less likely to achieve bond ownership through 

funds), changes in investor appetite for diversification, evolving investor expectations about 

relative bond and bond fund costs and performance, and potential changes to the costs of 

launching an ETF and their availability to investors, among others.231 

Figures 53-55 provide a more detailed examination of fund debt holdings. In particular, 

they show investment in debt by fund type. Overall, the patterns within fund types are consistent 

with holdings across fund types. In particular, the percentage of assets held in corporate and 

231 For example, investors in defined contribution retirement plans may be more likely to gain exposure to various 

asset classes by investing in mutual funds rather than directly in the underlying securities markets. 

256 
 

foreign bonds increases from 2009 to 2016 for OEFs, ETFs, and CEFs.232 However, the same 

caveats that applied across all fund holdings apply within fund types, so these increases cannot 

be directly attributed to changes in bond market liquidity or regulatory actions. 

Figure 53. OEF allocations to debt securities. "other_debt" includes agency bonds, 

syndicated loans, repo, and other forms of lending. 

Source: DERA analysis. 

232 Ownership by CEFs from 2012 to 2016 remained relatively stable. 

257 
Figure 54. CEF allocations to debt securities. "other_debt" includes agency bonds, 

syndicated loans, repo, and other forms of lending. 

Source: DERA analysis. 

Figure 55. ETF allocations to debt securities. "other_debt" includes agency bonds, 

syndicated loans, repo, and other forms of lending. 

Source: DERA analysis. 

258 
Figures 56-57 show ownership of corporate bonds by owner and fund type. From 2009 

to 2016 the percentage of corporate and foreign bonds held by households decreased from 22.0% 

to 8.6%, while the percentage of corporate and foreign bonds held by funds increased from 8.5% 

to 18.3%. These results would seem to be consistent with households allocating away from 

holding underlying bonds directly towards holding them through funds, potentially as a result of 

a decrease in underlying bond market liquidity. As was the case earlier, however, that 

interpretation is subject to several meaningful caveats. The largest changes to household and 

fund allocations occurred during the financial crisis of 2008-2009, a historically anomalous time 

period. Because we cannot isolate when any regulatory actions would have taken effect in the 

bond market, this shift in ownership may instead be the continuation of a trend that began with 

the financial crisis. Importantly, viewed over the entire sample period, household ownership 

appears to have returned to a level consistent with much of the previous two decades. In 

addition, over the same period, the percentage of equities held by households increased from 

36% to 40%, so some of the decreased household share of outstanding corporate and foreign 

bonds may also reflect a reallocation of exposure towards equities rather than towards indirect 

bond holdings through funds. 

259 
Figure 56. Share of corporate/foreign debt by owner type 

Source: DERA analysis. 

Figure 57. Share of corporate/foreign debt by fund type 

Source: DERA analysis. 

260 
 

 

 

C. Limitations in Interpreting the Data 

The above analysis could be interpreted as consistent with a decrease in bond market 

liquidity from 2009 to 2016, however, these results cannot necessarily be attributed to changes in 

bond market liquidity or any regulatory actions during that period. In this section, we describe 

additional considerations that limit our ability to interpret the results from 2009 to 2016. 

First, in equilibrium, fund behavior and bond market liquidity are likely to be jointly 

determined by multiple factors including macroeconomic conditions, regulatory constraints, and 

investor preferences. The above discussion takes fund behavior as a given and considers 

potential changes in investor behavior as a result of contemplated changes in bond market 

liquidity. It is the case, however, that changes in the economic environment may drive changes 

in fund behavior, which in turn, could affect bond market liquidity. For example, it could be the 

case that funds trade more actively than the individual investors who make up their client base. 

If a change in the economy causes these investors to shift some of their assets from direct bond 

ownership towards funds, the increased trading presence of funds could induce greater liquidity 

in the bond market. Thus, decreases in direct bond investment and increasing investment in 

bonds through funds could imply an increase in bond market liquidity rather than a decrease.233 

The more general point, however, is that when funds trade assets, their buying or selling 

activity can affect the underlying market for these assets. For example, Coval and Stafford 

(2007) provide evidence supporting the hypothesis that when mutual funds collectively sell large 

quantities of a stock, the stock’s price can be pushed away from its fundamental value for a 

233 This conclusion is sensitive to the assumption that funds trade more actively than individuals. Alternatively, if 

funds tend to trade less than households—for example, because they aggregate demand across all fund investors 

before trading—an increase in allocation to bond funds could lead to a decrease in underlying bond market liquidity. 

261 
 

period of months, followed by a reversion back towards fundamental value.234 Several more 

recent studies look for a similar effect in corporate bond markets and find mixed evidence. Cai 

et al. (2016) find that institutional investors, including mutual funds, exhibit herding behavior 

when they sell corporate bonds and that this herding pushes prices temporarily away from their 

fundamental value. A primary reason funds choose to sell bonds is to accommodate 

redemptions. Choi and Shin (2016) find some evidence that bond funds experiencing outflows 

during periods of market stress such as the Taper Tantrum exert pressure on bond prices, but not 

under normal circumstances. Hoseinzade (2016) finds no evidence supporting the hypothesis 

that fund outflows lead to price pressure in underlying corporate bond markets. Both 

Hoseinzade (2016) and Choi and Shin (2016) point out that bond fund managers do not 

necessarily meet redemptions by selling corporate bonds, but may use more liquid assets to meet 

redemptions instead. This could explain the lack of a clear outflow effect on bond prices. 

Chernenko and Sunderam (2016) find evidence supporting this explanation. The authors show 

that mutual funds hold substantially more cash to engage in “liquidity transformation” when they 

hold less liquid assets, during periods of lower overall market liquidity, and when they face more 

volatile fund flows. Finally, both Bhattacharya and O’Hara (2016) and Israeli, Lee, and 

Sridharan (2016) show that if price discovery migrates from underlying stocks or bonds to ETFs 

that invest in those assets, some measures of liquidity in the underlying market can deteriorate. 

To the extent that the above studies provide evidence that fund activity affects the 

underlying bond market, they should be viewed as a potential confounding factor in any analysis 

of bond market liquidity with respect to the regulatory reforms that are the subject of the Report. 

234 Temporary price deviations from an asset’s “efficient” or fundamental value are generally considered to be 

indicators of illiquidity and cause deteriorations in some measures of market liquidity. See Hasbrouck (2007) for a 

more detailed discussion. 

262 
 

For example, if a given regulatory action took effect at the same time funds experienced outflows 

for an unrelated reason—for example, if it coincided with an aggregate reduction in investor 

demand for bonds—fire-sale effects associated with those outflows could show up in measures 

of bond market liquidity despite being unrelated to the regulatory action.235 On balance, the 

evidence on price effects suggests that, if they exist at all, they are more prominent during 

periods of market stress, so the risk of a confounding effect may be highest for regulatory events 

that occur during such periods. 

D. Summary 

Changes in bond market liquidity could lead to changes in investor allocation between 

direct investment in bonds and investment in bonds through funds. That observation suggests 

that an analysis of changes in fund holdings could provide indirect evidence on bond market 

liquidity. To the extent that the increased ownership of corporate bonds by funds demonstrated 

above is due to a reallocation by investors from the underlying bond market, funds could also be 

viewed as a substitute for any deterioration in the liquidity of the underlying market. If funds are 

able to provide liquidity transformation—accommodating subscriptions and redemptions in a 

more liquid manner than the underlying assets they invest in—this type of shift could partially 

offset increased illiquidity in the underlying bond market. However, it is important to note that 

we cannot isolate the cause of the increased share of fund ownership in bonds. It is possible that 

the shift is due to other factors, like a reallocation of investors from other asset classes into 

corporate bonds to, for example, gain exposure to higher yields during a low interest rate 

environment on less risky debt securities. To the extent that increased fund ownership of bonds 

235 If, on the other hand, price movements due to increased fund trading activity were exclusively associated with the 

incorporation of new fundamental information, typical measures of liquidity would not decrease. 

263 
is driven by factors such as the latter, funds cannot be interpreted as providing a substitute for 

any deterioration in underlying bond markets. Furthermore, the trading activity of funds that 

participate in the bond market may affect bond market liquidity in ways that confound our ability 

to identify the impacts of regulatory action on bond market liquidity. 

264 
 

References 

1. Acharya, Viral, Engle, Robert, Figlewski, Stephen, Lynch, Anthony, and Marti 

Subrahmanyam, 2009, “Chapter 11: Centralized Clearing for Credit Derivatives.” Edited by 

Acharya Viral and Matthew Richardson in Restoring Financial Stability: How to Repair a 

Failed System, Wiley and NYU Stern. 

2.	 Acharya, Viral, and Johnson, Timothy, 2007, “Insider Trading in Credit Derivatives.” 

Journal of Financial Economics 84, pp. 110-141. 

3.	 Adrian, Tobias, Fleming, Michael, Shachar, Or, and Erik Vogt, 2017a, “Market Liquidity 

after the Financial Crisis.” Federal Reserve Bank of New York Staff Report No. 796. 

Available at 

https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr796.pdf. 

4.	 Adrian, Tobias, Boyarchenko, Nina, and Or Sachar, 2017b, “Dealer Balance Sheets and 

Bond Liquidity Provision.” Journal of Monetary Economics, forthcoming. 

5.	 Adrian, Tobias, Fleming, Michael, Stackman, Daniel, and Erik Vogt, 2015 “Has Liquidity 

Risk in the Treasury and Equity Markets Increased?” Federal Reserve Bank of New York 

Liberty Street Economics. Available at 

http://libertystreeteconomics.newyorkfed.org/2015/10/has-liquidity-risk-in-the-treasury-andequity-markets-increased.html. 

6.	 Amihud, Yakov, Mendelson, Haim, and Lasse Heje Pedersen, 2013, “Market Liquidity: 

Asset Pricing, Risk, and Crises.” Cambridge University Press. 

7.	 Anderson, Mike, and Rene Stulz, 2017, “Is Post-Crisis Bond Liquidity Lower?” Working 

Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2943020 

8.	 Arikan, Asli, and Rene Stulz, 2016, “Corporate Acquisitions, Diversification, and the Firm’s 

Life Cycle,” Journal of Finance 71(1), 139–194 

9.	 Ashcraft, Adam, and Santos, João, 2009, “Has the CDS Market Lowered the Cost of 

Corporate Debt?” Journal of Monetary Economics 56(4), pp. 514-523 

10. Asquith, Paul, Covert, Thomas, and Parag Phatak, 2014, “The Effects of Mandatory 

Transparency in Financial Market Design: Evidence from the Corporate Bond Market." 

NBER Working Paper 19417. 

11. Augustin, Patrick, Subrahmanyam, Marti, Tang, Dragon Yongjun, and Wang, Sarah Qian, 

2014, “Credit Default Swaps–A Survey.” Foundations and Trends in Finance, 9(1-2), 1-196. 

12. Autorité des Marchés Financiers, 2015, “Study of Liquidity in French Bond Markets,” 

http://www.amf-france.org/en_US/Reglementation/Dossiers-thematiques/Societes-cotees-etoperations-financieres/Marches-obligatoires/Etude-marche-obligataire. 

13. Bai, Jennie and Pierre Collin-Dufresne, 2013, “The CDS-Bond Basis” Working Paper. 

Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2024531 

14. Bai, Jushan, and Pierre Perron, 1998, “Estimating and Testing Linear Models with Multiple 

Structural Changes.” Econometrica , 66(1), pp. 47-78. 

265 
 

 

 

 

 

 

 

15. Bai, Jushan, and Pierre Perron, 2003, “Computation and Analysis of Multiple Structural 

Change Models.” Journal of Applied Econometrics , 18(1), pp. 1-22. 

16. Bank for International Settlements, 2014, “Market-Making and Proprietary Trading: Industry 

Trends, Drivers and Policy Implications.” Committee on the Global Financial System Paper 

No 52. Available at http://www.bis.org/publ/cgfs52.pdf 

17. Bank for International Settlements, 2016, “Fixed Income Market Liquidity.” Committee on 

the Global Financial System Paper No 55. Available at http://www.bis.org/publ/cgfs55.pdf 

18. Bao, Jack, O’Hara, Maureen, and Xing (Alex) Zhou, 2016, “The Volcker Rule and MarketMaking in Times of Stress.” Working Paper 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2836714 

19. Bao, Jack, Pan, Jun, and Jiang Wang, 2011, “The Illiquidity of Corporate Bonds.” Journal of 

Finance, 66(3), pp. 911-946. 

20. Basulto, Vincent, and Lee, Richard (Richards Kibbe & Orbe LLP), 2016, “Single-Name 

Credit Default Swaps: A Primer.” Available at 

http://www.lexology.com/library/detail.aspx?g=dc5a3a58-7b12-41a5-9447-6592bd235e9c. 

21. Bhattacharya, Ayan and Maureen O’Hara, 2016, “Can ETFs Increase Market Fragility? 

Effect of Information Linkages in ETF Markets.” Working Paper 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2740699 

22. Bauguess, Scott, Gullapalli, Rachita and Vladimir Ivanov, 2015, “Capital Raising in the U.S.: 

An Analysis of the Market for Unregistered Securities Offerings, 2009–2014.” Available at 

http://www.sec.gov/dera/staff-papers/white-papers/unregistered-offering10-2015.pdf 

23. Bech, Morten, Illes, Anamaria, Lewrick, Ulf, and Andreas Schrimpf, 2016, “Hanging Up the 

Phone – Electronic Trading in Fixed Income Markets and Its Implications.” BIS Quarterly 

Review, March 2016, http://www.bis.org/publ/qtrpdf/r_qt1603h.htm 

24. Bessembinder, Hendrik, and William Maxwell, 2008. “Transparency and the Corporate 

Bond Market.” Journal of Economic Perspectives, 22(2), pp. 217–234. 

25. Bessembinder, Hendrik, Jacobsen, Stacey, Maxwell, William, and Kumar Venkataraman, 

2016. “Capital Commitment and Illiquidity in Corporate Bonds.” Working Paper, available 

at https://ssrn.com/abstract=2752610 

26. BIS Quarterly Review, September 2015, http://www.bis.org/publ/qtrpdf/r_qt1509.pdf 

27. Biswas, Gopa, Nikolova, Stanislava, and Stahel, Christof, 2015, “The Transaction Costs of 

Trading Corporate Credit.” Working paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2532805. 

28. BlackRock Viewpoint, September 2014, “Corporate Bond Market Structure: The Time For 

Reform Is Now.” Blackrock Credit Research. 

29. BlackRock Viewpoint, February 2016, “Addressing Market Liquidity: A Broader Perspective 

on Today’s Bond Markets.” Blackrock Credit Research. 

30. Blanco, Robert, Brennan, Simon, and Marsh, Ian, 2005, “An Empirical Analysis of the 

Dynamic Relation Between Investment‐Grade Bonds and Credit Default Swaps.” Journal of 

Finance 60(5), pp. 2255-2281. 

266 
 

 

31. Boehmer, Ekkehart, Chava, Sudheer, and Tookes, Heather, 2015, “Related Securities and 

Equity Market Quality: The Cases of CDS.” Journal of Financial and Quantitative Analysis 

50(3), pp 509-541. 

32. Brunnermeier, Markus K., and Lasse Heje Pedersen, 2009, “Market Liquidity and Funding 

Liquidity.” Review of Financial Studies, 22(6), 2201–2238. 

33. Cai, Fang, Song Han, Dan Li, and Yi Li, 2016, “Institutional Herding and Its Price Impact: 

Evidence from the Corporate Bond Market” Working Paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2693717. 

34. CFA Institute, September 2016, “Secondary Corporate Bond Market Liquidity Survey 

Report.” 

35. Chakravarty, Sugato, and Asani Sarkar, 2003, “Trading Costs in Three U.S. Bond Markets, 

Journal of Fixed Income, 13, 39-48. 

36. Chaplinsky, Susan, Hanley, Kathleen, and Katie S. Moon, 2016, “The JOBS Act and the 

Cost of Going Public.” Journal of Accounting Research, forthcoming. 

37. Chen, Liang, Dolado, Juan, and Jesus Gonzalo, 2014, “Deleting Big Structural Breaks in 

Large Factor Models.” Journal of Econometrics, 180, pp. 30-48. 

38. Chernenko, Sergey and Adi Sunderam, 2016, “Liquidity Transformation in Asset 

Management: Evidence from the Cash Holdings of Mutual Funds.” Working Paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2807702. 

39. Choi, Jaewon, and Yesol Huh, 2017, “Customer Liquidity Provision in Corporate Bond 

Markets.” Working Paper, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2848344 

40. Choi, Jaewon, and Or Shachar, 2016, “Did Liquidity Providers Become Liquidity Seekers?” 

New York Fed Staff Report No. 650, 

https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr650.pdf 

41. Choi, Jaewon and Seungun Shin, 2016, “Is There Flow-Driven Price Impact in Corporate 

Bond Markets?” Working Paper. Available at 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2731844. 

42. Coval, Joshua and Eric Stafford, 2007, “Asset Fire Sales (and Purchases) in Equity Markets.” 

Journal of Financial Economics 86(2), pp. 479-512 

43. Culp, Christopher, van der Merwe, Andria, and Starkle Bettina, 2016, “Single-name Credit 

Default Swaps: A Review of the Empirical Academic Literature.” ISDA publication. 

Available at: http://www2.isda.org/news/isda-publishes-new-academic-paper-on-singlename-cds-market. 

44. Dambra, Michael, Field, Laura, and Matthew Gustafson, 2015, “The JOBS Act and IPO 

Volume: Evidence that Disclosure Costs Affect the IPO Decision.” Journal of Financial 

Economics 116, pp.121-143. 

45. Das, Sanjiv, Kalimipalli, Madhu, and Subhankar Nayak, 2014, “Did CDS trading improve 

the market for corporate bonds?” Journal of Financial Economics 111, pp. 495-525. 

46. Dick-Nielsen, Jens, and Marco Rossi, 2016, “The Cost of Immediacy for Corporate Bonds.” 

Working Paper, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2139403 

267 
 

 

 

47. Di Maggio, Marco, Kermani, Amir, and Zhaogang Song, 2017, “The Value of Trading 

Relationships in Turbulent Times.” Journal of Financial Economics 124(2), pp. 266-284. 

48. Doidge, Craig, Karolyi, Andrew, and Rene Stulz, 2017, “The U.S. Listing Gap.” Journal of 

Financial Economics 123(3), pp. 464-487. 

49. Duffie, Darrell, 1999, “Credit Swap Valuation”. Financial Analysts Journal, 55(10), pp. 73­

87. 

50. Edwards, Amy, Harris, Lawrence, and Michael Piwowar, 2007, “Corporate Bond Market 

Transaction Costs and Transparency.” Journal of Finance, 62(3), pp. 1421-1451 

51. Fang, Lily Hua, 2005, “Investment Bank Reputation and the Price and Quality of 

Underwriting Services.” Journal of Finance 60, pp. 2729-2761 

52. Federal Financial Institutions Examination Council, 2017, “National Information Center: A 

repository of financial data and institution characteristics collected by the Federal Reserve 

System.” Federal Reserve System. https://www.ffiec.gov/nicpubweb/nicweb/NicHome.aspx. 

53. Feldhütter, Peter, 2012, “The Same Bond at Different Prices: Identifying Search Frictions 

and Selling Pressures. Review of Financial Studies, 25, pp. 1155-1206. 

54. Fender, Ingo, and Ulf Lewrick, 2015, “Shifting Tides – Market Liquidity and Market-Making 

in Fixed Income Instruments.” BIS Quarterly Review, March 2015, 

http://www.bis.org/publ/qtrpdf/r_qt1503i.pdf 

55. Financial Conduct Authority, 2016, “Liquidity in the UK Corporate Bond Market: Evidence 

from Trade Data.” Occasional Paper No. 14. https://www.fca.org.uk/publication/occasionalpapers/occasional-paper-14.pdf 

56. Fleming, Michael J., 2000, “Financial Market Implications of the Federal Debt Paydown.” 

Brookings Papers on Economic Activity, 2000 (2), pp. 221–251. 

57. Forte, Santiago, and Pena, Juan Ignacio, 2009, “Credit Spreads: An Empirical Analysis on 

the Informational Content of Stocks, Bonds, and CDS”. Journal of Banking and Finance 33, 

pp. 2013-2025. 

58. French, K., M. Baily, J. Campbell, J. Cochrane, D. Diamond, D. Duffie, A. Kashyap, F. 

Mishkin, R. Rajan, D. Scharfstein, R. Shiller, H. S. Shin, M. Slaughter, J. Stein, and R. 

Stulz, 2010, “The Squam Lake Report: Fixing the Financial System”. Princeton University 

Press. 

59. Gao, Ziaohui, Ritter, Jay, and Zhongyan Zhu, 2013, “Where Have All the IPOs Gone?” 

Journal of Financial and Quantitative Analysis 48(6), 1663–1692. 

60. Goldstein, Michael, and Edith Hotchkiss, 2017, “Providing Liquidity in an Illiquid Market: 

Dealer Behavior in U.S. Corporate Bonds,” available at 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2977635 

61. Goldstein, Michael, Hotchkiss, Edith, and Erik Sirri, 2007, “Transparency and Liquidity: A 

Controlled Experiment on Corporate Bonds.” Review of Financial Studies 20(2), pp. 235­

273 

62. Government Accountability Office, 2012, “Factors that May Affect Trends in Regulation A 

Offerings. GAO-12-839 (July), available at http://www.gao.gov/assets/600/592113.pdf. 

268 
 

 

 

 

63. Green, Joseph, and John Coyle, 2016, Crowdfunding and the not-so-safe SAFE, 102 Virginia 

Law Review Online 168, http://ssrn.com/abstract_id=2830213. 

64. Greenwich Associates, 2015, “Corporate Bond Trading Market Structure Update: Overview 

and Insights.” 

65. Greenwich Associates, 2015, “U.S. Treasury Trading: The Intersection of Liquidity Makers 

and Takers”. 

66. Han, Song, Huang, Alan Guoming, Kalimipalli, Madhu, and Ke Wang, 2016, “Information 

and Liquidity of the Over-The-Counter Securities: Evidence from Public Registration of 

Private Debt.” Working Paper, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2846229 

67. Harris, Lawrence, 2015, “Transaction Costs, Trade Throughs, and Riskless Principal Trading 

in Corporate Bond Markets.” Working Paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2661801 

68. Harris, Lawrence, 2003, “Trading and Exchanges: Market Microstructure for Practitioners.” 

Oxford University Press. 

69. Harris, Lawrence, and Michael Piwowar, 2006, Secondary Trading Costs in the Municipal 

Bond Market, Journal of Finance, 61, 1361-1397. 

70. Hasbrouck, Joel, 2007, “Empirical Market Microstructure: The institutions, economics, and 

econometrics of securities trading.” Oxford University Press. 

71. Helwege, Jean, and Nellie Liang, 2004, “Initial Public Offerings in Hot and Cold Markets,” 

Journal of Financial and Quantitative Analysis 39(3), 541–569. 

72. Hendershott, Terrence, and Ananth Madhavan, 2015, “Click or Call? Auction versus Search 

in the Over-the-Counter Market.” Journal of Finance 70(1), pp. 419-447 

73. Hong, Gwangheon, and Arthur Warga, 2000, An Empirical Study of Bond Market 

Transactions, Financial Analysts Journal, 56, 32-46. 

74. Hoseinzade, Saeid 2016, “Do Mutual Funds Destabilize the Corporate Bond Market?” 

Working Paper, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2638547. 

75. Hu, Grace Xing, Pan, Jun, and Jiang Wang, 2013, “Noise as Information for Illiquidity.” 

Journal of Finance, 68(6), pp. 2341–2382. 

76. IOSCO Consultation Report, “Examination of Liquidity of the Secondary Corporate Bond 

Markets”, CR01/2016, August 2016, 

https://www.iosco.org/library/pubdocs/pdf/IOSCOPD537.pdf 

77. Israeli, Doron, Lee, Charles M. C. and Suhas A. Sridharan, 2017, “Is There a Dark Side to 

Exchange Traded Funds? An Information Perspective.” Review of Accounting Studies, 

forthcoming. 

78. Ivanov, Vladimir, and Anzhela Knyazeva, 2017, “U.S. Securities-based Crowdfunding 

Under Title III of the JOBS Act.” Available at https://www.sec.gov/dera/staff-papers/whitepapers/28feb17_ivanov-knyazeva_crowdfunding-under-titleiii-jobs-act.html 

269 
 

 

 

79. Joint Economic Committee, U.S. Congress, 2001, “Federal Debt: Market Structure and 

Economic Uses for U.S. Treasury Debt Securities.” Joint Economic Committee Study, 

August 2001. 

80. Joint Staff Report, 2015, “The U.S. Treasury Market on October 15, 2014,” U.S. Department 

of the Treasury, Board of Governors of the Federal Reserve System, Federal Reserve Bank of 

New York, U.S. Securities and Exchange Commission, U.S. Commodity Futures Trading 

Commission. Available at https://www.treasury.gov/press-center/pressreleases/Documents/Joint_Staff_Report_Treasury_10-15-2015.pdf 

81. Kim, Gi H., Li, Haitao, and Weina Zhang, 2017, “The CDS-Bond Basis Arbitrage and the 

Cross Section of Corporate Bond Returns.” Journal of Futures Markets, forthcoming. 

82. Korajczyk, Robert A, and Ronnie Sadka, 2008, “Pricing the Commonality across Alternative 

Measures of Liquidity,” Journal of Financial Economics 87, 45–72. 

83. Krishnamurthy, Arvind, and Annette Vissing-Jorgensen, 2012, “The Aggregate Demand for 

Treasury Debt.” Journal of Political Economy, 120(2), pp. 233–267. 

84. Kyle, Albert, 1985, “Continuous Auctions and Insider Trading.” Econometrica, 53(6), pp. 

1315–1335. 

85. Li, Jiacui, and Wenhao Li, 2016, “Agency Trading and Principal Trading.” Working Paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2760457 

86. Longstaff, Francis, Mithal, Sanjay, and Neis, Eric, 2005, “Corporate Yield Spreads: Default 

Risk or Liquidity? New Evidence from the Credit Default Swap Market.” Journal of Finance 

60(5), pp. 2213-2253. 

87. Longstaff, Francis, 2004, “The Flight-to-Liquidity Premium in U.S. Treasury Bond Prices.” 

Journal of Business, 77(3), pp. 511–526. 

88. Loon, Yee Cheng, and Zhong, Zhaodong, 2016, “Does Dodd-Frank Affect OTC Transaction 

Costs and Liquidity? Evidence from Real-time CDS Reports.” Journal of Financial 

Economics 119(3), pp. 645-672. 

89. Lowry, Michelle, and G. William Schwert, 2002, “IPO Market Cycles: Bubbles or 

Sequential Learning?” Journal of Finance 57(3), 1171–1200. 

90. Lowry, Michelle, Roni Michaely, and Ekaterina Volkova, 2017, “Initial Public Offerings: A 

Synthesis of the Literature and Directions for Future research.” Working paper. 

91. Markit Magazine, Spring 2009, “The CDS Big Bang”. Available at 

https://www.markit.com/assets/en/docs/markit-magazine/issue-4/60-cds-big-bang.pdf. 

92. Massa, Massimo, and Lei Zhang, 2012, “Credit Default Swaps, and the Liquidity Provision 

in the Bond Market.” Working Paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2164675 

93. Mizrach, Bruce, 2015, “ Analysis of Corporate Bond Liquidity.” FINRA Office of the Chief 

Economist Research Note, 

http://www.finra.org/sites/default/files/OCE_researchnote_liquidity_2015_12.pdf 

270 
 

94. Nashikkar, Amrut, Subrahmanyam, Marti, and Mahanti, Sriketan, 2011, “Liquidity and 

Arbitrage in the Market for Credit Risk.” Journal of Financial and Quantitative Analysis 46, 

pp. 627-656. 

95. Norden, Lars, and Wagner, Wolf, 2008, “Credit Derivatives and Loan Pricing”. Journal of 

Banking and Finance 32, pp. 2560-2569. 

96. Norden, Lars, and Weber, Martin, 2009, “The Co-movement of Credit Default Swap, Bond 

and Stock Markets: An Empirical Analysis”. European Financial Management 15, pp. 529­

562. 

97. Oehmke, Martin, and Adam Zawadowski, 2015, “Synthetic or Real? The Equilibrium Effects 

of Credit Default Swaps on Bond Markets.” Review of Financial Studies 28(12), pp. 3303– 

37. 

98. Oehmke, Martin, and Adam Zawadowski, 2017, “The Anatomy of the CDS Market.” Review 

of Financial Studies 30(1), pp. 80-119. 

99. O’Hara, Maureen, 1997, “Market Microstructure Theory.” 

100.	 Porter, Burt, 2015, “Single-Name Corporate Credit Default Swaps: Background Data 

Analysis on Voluntary Clearing Activity.” SEC DERA White Paper, available at 

https://www.sec.gov/dera/staff-papers/white-papers/voluntary-clearing-activity.pdf 

101.	 PriceWaterhouseCoopers, 2015, “Global Financial Markets Liquidity Study.” Available 

at http://www.pwc.se/sv/pdf-reports/global-financial-markets-liquidity-study.pdf 

102.	 Proskauer, 2017, “IPO Study”, available at 

http://www.proskauer.com/files/uploads/Proskauer-2017-IPO-Study.pdf. 

103.	 Qiu, Jiaping, and Yu, Fan, 2012, “Endogenous Liquidity in Credit Derivatives.” Journal 

of Financial Economics 103(3), pp. 611-631. 

104.	 Rel. No. 33-9415, Eliminating the Prohibition Against General Solicitation and General 

Advertising in Rule 506 and Rule 144A Offerings (July 10, 2013) 

105.	 Rel. No. 33-9470, Crowdfunding (Oct. 23, 2013) 78 FR 66427 (Crowdfunding 

Proposing Release) 

106.	 Rel. No. BHCA-1, Prohibitions and Restrictions on Proprietary Trading and Certain 

Interests In, and Relationships with, Hedge Funds and Private Equity Funds (Dec. 10, 2013) 

79 FR at 5639 (Volcker Rule Adopting Release). 

107.	 Rel. No. 33-9497, Proposed Rule Amendments for Small and Additional Issues 

Exemptions Under Section 3(b) of the Securities Act, (December 18, 2013), 79 FR 3925. 

108.	 Rel. No. 34-72472, Application of “Security-Based Swap Dealer” and “Major SecurityBased Swap Participant” Definitions to Cross-Border Security-Based Swap Activities (Jun. 

25, 2014) 79 FR 47277, n.139 (Cross-Border Adopting Release); 

109.	 Rel. No. 33-9638, Asset-Backed Securities Disclosure and Registration (Sept. 4, 2014) 

79 FR at 57192 (Regulation AB2 Adopting Release) 

110.	 Rel. No. 34-73407, Credit Risk Retention (Oct. 22, 2014), 79 FR 77602 (Credit Risk 

Retention Adopting Release). 

271 
 

111.	 Rel. No. 34-74244, Regulation SBSR—Reporting and Dissemination of Security-Based 

Swap Information (Feb. 11, 2015) 80 FR at 14699-14700 (Regulation SBSR Adopting 

Release) 

112.	 Rel. No. 33-9741, Amendments to Regulation A (Mar. 25, 2015) 80 FR 21805 

(Regulation A+ Adopting Release) 

113.	 Rel. No. 34-75611, Registration Process for Security-Based Swap Dealers and Major 

Security-Based Swap Participants (Aug. 5, 2015) 80 FR at 49003 (Registration Adopting 

Release). 

114.	 Rel. No. 33-9974, Crowdfunding (Oct. 30, 2015) 80 FR 71387 (Crowdfunding 

Adopting Release) 

115.	 Rel. No. 34-77617, Business Conduct Standards for Security-Based Swap Dealers and 

Major Security-Based Swap Participants (Apr. 14, 2016) 81 FR 29960 

116.	 Rel No. 34-78321, Regulation SBSR—Reporting and Dissemination of Security-Based 

Swap Information (July 14, 2016) 81 FR 53546. 

117.	 Rel. No. 34-79116, Self-Regulatory Organizations; Financial Industry Regulatory 

Authority, Inc.; Notice of Filing of Amendment No. 1 and Order Granting Accelerated 

Approval of a Proposed Rule Change, as Modified by Amendment No. 1, Relating to the 

Reporting of Transactions in U.S. Treasury Securities to TRACE (Oct. 18, 2016) 81 FR 

73167 (Oct. 24, 2016). 

118.	 Rel. No. 33-10238 Exemptions to Facilitate Intrastate and Regional Securities Offerings 

(Oct. 26, 2016) 81 FR 83494. 

119.	 Rel. No. 33-10332, Inflation Adjustments and Other Technical Amendments Under 

Titles I and III of the JOBS Act (Mar. 31, 2017), available at 

https://www.sec.gov/rules/final/2017/33-10332.pdf 

120.	 Roll, Richard, 1984, “A Simple Implicit Measure of the Effective Bid-Ask Spread in an 

Efficient Market.” Journal of Finance, 39(4), pp. 1127–1139. 

121.	 Sambalaibat, Batchimeg, 2015, “A Theory of Liquidity Spillover Between Bond and 

CDS Markets.” Working paper, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2404512. 

122.	 Sarr, Abdourahmane, and Lybek, Tonny, 2002, “Measuring Liquidity in Financial 

Markets.” International Monetary Fund Working Paper WP/02/232. 

123.	 Schestag, Raphael, Schuster, Philipp, and Marliese Uhrig-Homburg, 2016, “Measuring 

Liquidity in Bond Markets.” Review of Financial Studies 29(5), pp. 1170-1219. 

124.	 Schultz, Paul, 2001, “Corporate Bond Trading Costs: A Peek Behind the Curtain.” 

Journal of Finance, 56(2), pp. 677-698. 

125.	 Small Business Administration, Office of Advocacy, United States Small Business 

Profile (2016), https://www.sba.gov/sites/default/files/advocacy/United_States.pdf. 

126. Small Business Administration, Small Business Quarterly Bulletins, at 

https://www.sba.gov/advocacy/small-business-quarterly-bulletins. 

272 
127.	 Stock, James, and Mark Watson, 2011, “Dynamic Factor Models.” Edited by Clements, 

Michael, and David Hendry in Oxford Handbook of Forecasting. Oxford: Oxford University 

Press. 

128.	 Stulz, Rene, 2010, “Credit Default Swaps and the Credit Crisis.” Journal of Economic 

Perspectives 24(1), pp. 73-92. 

129.	 Tissot, Bruno, 2015, “Derivatives Statistics: the BIS Contribution.” Proceedings of the 

60th International Statistical Institute World Statistics Congress. 

130.	 Trebbi, Francesco, and Kairong Xiao, 2016, “Regulation and Market Liquidity”. 

Working Paper, available at http://faculty.arts.ubc.ca/ftrebbi/research/tx.pdf. 

131.	 Warren, Manning, 2015, “The False Promise of Publicly Offered Private Placements.” 

SMU Law review, 68, pp. 899-911. 

132.	 Wooldridge, Jeffrey, 2013, “Introductory Econometrics: A Modern Approach”, Cengage 

Learning. 

133.	 Wroldsen, Jack, 2017, Crowdfunding investment contracts, 11 Virginia Law & Business 

Review (forthcoming), http://ssrn.com/abstract_id=2844771. 

273 
Appendices 

Appendix A. Regulatory Reform Timeline 

 February 13, 2008: Economic Stimulus Act of 2008 signed into law. 

 March 16, 2008: Bear Stearns sold to JP Morgan. 

 September 15, 2008: Lehman Bros files for bankruptcy. 

 October 3, 2008: Emergency Economic Stabilization Act of 2008 (including TARP) 

signed into law. 

 January 21, 2010: President Obama announces support for Volcker Rule-type restrictions 

on proprietary trading by banking entities. 

	 April 7, 2010: Proposed amendments to Regulation AB issued by Commission. 

	 May 18-20, 2010: Amendment to include a version of the Volcker Rule in Dodd-Frank is 

introduced in the Senate, but is not approved. 

	 June 29, 2010: Dodd-Frank Act conference report filed; contains Volcker Rule. 

	 June 30, 2010: Dodd-Frank Act conference report passes House. 

	 July 15, 2010: Dodd-Frank Act conference report passes Senate. 

	 July 21, 2010: Dodd-Frank Act signed into law (including Volcker). 

	 December 2010: Basel III capital framework finalized by BCBS. 

	 January 18, 2011: FSOC issues a study and recommendations on implementing the 

Volcker Rule. 

 March 2011: Treasury Department panel discussion on IPO problem; gives rise to IPO 

Task Force. 

	 March 30, 2011: Proposed interagency rule on credit risk retention issued by the SEC. 

	 July 26, 2011: Reproposal of amendments to Regulation AB in light of Dodd-Frank 

requirements. 

	 October 5, 2011: Proposed draft implementing Volcker rules published in American 

Banker. 

	 October 11, 2011: Implementing rules for Volcker first proposed. 

274 
	 October 20, 2011: Rebuilding the IPO On-Ramp proposal released by Treasury IPO Task 

Force. 

	 November 2011: Series of bills advancing IPO Task Force proposals passed House. 

	 March 8, 2012: JOBS Act (rolling up several of the House bills) passed House. 

	 March 22, 2012: Senate passes JOBS Act with amendments to crowdfunding. 

	 March 27, 2012: House passes Senate’s version of JOBS Act. 

	 April 5, 2012: JOBS Act signed into law; Titles I, V, and VI take effect. 

	 June 2012: FRB, FDIC, and OCC propose Basel III rules (Three NPRs: (1) Regulatory 

Capital Rules: Regulatory Capital, Implementation of Basel III, Minimum Regulatory 

Capital Ratios, Capital Adequacy, and Transition Provisions; (2) Regulatory Capital 

Rules: Standardized Approach for Risk-Weighted Assets; Market Discipline and 

Disclosure Requirements; and (3) Regulatory Capital Rules: Advanced Approaches Riskbased Capital Rule; Market Risk Capital Rule). [FR published Aug 30, 2012.] 

	 August 29, 2012: SEC proposed amendments to Regulation D to implement Title II of 

JOBS Act, lifting ban on general solicitation. 

	 January 1, 2013: BCBS phase-in begins: Minimum common equity capital ratio (initially 

3.5%), minimum common equity plus capital conservation buffer (initially 3.5%), 

minimum Tier 1 capital (initially 4.5%). 

	 July 2, 2013: Basel III final rule adopted by FRB, and OCC and FDIC shortly thereafter 

(July 9). [OCC and Fed rule published in FR Oct 11, 2013]. FDIC adopted as interim 

final rule; went final in April 2014. 

	 July 10, 2013: SEC adopted amendments to Regulation D to implement Title II of JOBS 

Act, lifting ban on general solicitation; SEC adopted amendments to disqualify certain 

“bad actors” from Rule 506 of Regulation D to implement requirements of the DoddFrank Act; SEC proposes additional conditions for Regulation D offerings. 

	 July 2013: FRB, FDIC, and OCC propose leverage ratio rule. 

	 August 28, 2013: Credit risk retention rules re-proposed. 

	 September 23, 2013: Regulation D amendments take effect. 

	 October 23, 2013: SEC proposes rules implementing Title III of the JOBS Act 

(Crowdfunding). 

	 October 2013: FRB, FDIC, and OCC publish proposed Liquidity Coverage Ratio rule 

(diverges from Basel III). 

275 
	 December 10, 2013: Final Volcker rules adopted. 

	 December 18, 2013: SEC proposes rules implementing Title IV of the JOBS Act 

(Regulation A+). 

	 January 1, 2014: BCBS phase-in continues: Minimum common equity capital ratio 

increases to 4%, minimum common equity plus capital conservation buffer increases to 

4%, and minimum Tier 1 capital increases to 5.5%; phase-in of deductions from CET1 set 

at 20%, minimum total capital set at 8%, and minimum total capital plus conservation 

buffer set at 8%. 

	 January 1, 2014: Effective date for most of OCC and Fed final rule on regulatory capital; 

compliance date for all banks that are not S&Ls. 

	 February 25, 2014: Reopening of comment period for revised approach to Regulation AB 

amendments. 

	 April 1, 2014: Effective date (but not compliance date) for Volcker Rule. 

	 April 8, 2014: FRB, FDIC, and OCC adopt final leverage ratio rule; issued proposed rule 

to modify the calculation for supplementary leverage ratio per recent BCBS changes 

thereto. 

	 July 21, 2014: Original end of Volcker Rule conformance period; extended to July 21, 

2015. 

	 August 27, 2014: SEC adopts revisions to Regulation AB. 

	 September 3, 2014: FRB, FDIC, and OCC adopt final rule modifying definition of 

Supplementary Leverage Ratio to conform to Basel III changes. Adopt final Liquidity 

Coverage Ratio rule for large banks and a modified LCR rule for smaller banks 

[published Oct. 2014]. 

	 September 4, 2014: SEC issues the Regulation AB revisions. 

	 October 22, 2014: Final interagency credit risk retention rules adopted by SEC. 

	 January 1, 2015: BCBS phase-in continues: Minimum common equity capital ratio 

increases to 4.5%, minimum common equity plus capital conservation buffer increases to 

4.5%, phase-in of deductions from CET1 increases to 40%, and minimum tier 1 capital 

increases to 6%. Liquidity coverage ratio minimum implemented at 60%. 

	 January 1, 2015: effective date for most of remaining OCC and Fed final rule 

implementing regulatory capital and LCR final rule (although compliance with LCR 

phased in thru 1/1/17); compliance date with regulatory capital rule for remaining banks. 

276 
	 March 25, 2015: SEC adopts final rules implementing Title IV of the JOBS Act 

(Regulation A+). 

	 June 19, 2015: Regulation A+ final rules take effect. 

	 July 21, 2015: End of general Volcker Rule conformance period—deadline for 

conforming to the Volcker Rule’s prohibition on proprietary trading and on ownership of 

covered funds not owned before December 31, 2013. 

	 October 30, 2015: SEC adopts final rules implementing Title III of the JOBS Act 

(Crowdfunding). 

	 January 1, 2016: BCBS phase-in continues: New capital conservation buffer at 0.625%. 

Minimum common equity plus capital conservation buffer increases to 5.125%. 

Deduction from CET1 phase-in increases to 60%. Minimum total capital plus 

conservation buffer increases to 8.625%. LCR increases to 70%. 

	 May 3, 2016: FRB, OCC, and FDIC propose net stable funding ratio rule. 

	 May 16, 2016: Crowdfunding final rules take effect. 

	 July 21, 2017: Deadline for conforming to the Volcker Rule’s prohibition on ownership of 

covered funds owned by banks on or before December 31, 2013. 

277 
Appendix B. Regulatory Reform Timeline: Security-Based Swap Markets 

Date Type Rule 

Oct. 14, 2010 

Nov. 3, 2010 

Nov. 19, 2010 

Nov. 19, 2010 

Dec. 7, 2010 

Dec. 15, 2010 

Dec. 15, 2010 

Proposed 

Proposed 

Proposed 

Proposed 

Proposed 

Proposed 

Proposed 

Ownership Limitations and Governance Requirements for SecurityBased Swap Clearing Agencies, Security-Based Swap Execution 

Facilities, and National Securities Exchanges with Respect to 

Security-Based Swaps under Regulation MC 

Prohibition Against Fraud, Manipulation, and Deception in 

Connection with Security-Based Swaps 

Regulation SBSR - Reporting and Dissemination of Security-Based 

Swap Information 

Security-Based Swap Data Repository Registration, Duties, and Core 

Principles 

Further Definition of "Swap Dealer," "Security-Based Swap Dealer," 

"Major Swap Participant," "Major Security-Based Swap Participant" 

and "Eligible Contract Participant"(Proposed Interpretations) 

End-User Exception to Mandatory Clearing of Security-Based 

Swaps 

Process for Submissions for Review of Security-Based Swaps for 

Mandatory Clearing and Notice Filing Requirements for Clearing 

Agencies; Technical Amendments to Rule 19b-4 and Form 19b-4 

Applicable to All Self-Regulatory Organizations 

Jan. 14, 2011 Proposed Trade Acknowledgment and Verification of Security-Based Swap 

Transactions 

Feb. 2, 2011 Proposed Registration and Regulation of Security-Based Swap Execution 

Facilities (Proposed Interpretation) 

Mar. 17, 2011 Proposed Beneficial Ownership Reporting Requirements and Security-Based 

Swaps 

Further Definition of “Swap,” “Security-Based Swap,” and 

Apr. 29, 2011 Proposed “Security-Based Swap Agreement”; Mixed Swaps; Security-Based 

Swap Agreement Recordkeeping (Proposed Interpretations) 

Further Definition of "Swap," "Security-Based Swap," and 

Jun. 1, 2011 Proposed "Security-Based Swap Agreement"; Mixed Swaps; Security-Based 

Swap Agreement Recordkeeping (Joint Proposed Rules; Proposed 

Interpretation) 

Jun. 8, 2011 Adopted Beneficial Ownership Reporting Requirements and Security-Based 

Swaps (Confirmation) 

Jun. 9, 2011 Proposed Exemptions for Security-Based Swaps Issued by Certain Clearing 

Agencies 

Jun. 29, 2011 Proposed Business Conduct Standards for Security-Based Swap Dealers and 

Major Security-Based Swap Participants 

Oct. 12, 2011 Proposed Registration of Security-Based Swap Dealers and Major SecurityBased Swap Participants 

Mar. 30, 2012 Adopted Exemptions for Security-Based Swaps Issued by Certain Clearing 

Agencies 

278 
Apr. 27, 2012 

Jun. 28, 2012 

Jul. 18, 2012 

Oct. 18, 2012 

Adopted 

Adopted 

Adopted 

Proposed 

Further Definition of “Swap Dealer,” “Security-Based Swap 

Dealer,” “Major Swap Participant,” “Major Security-Based Swap 

Participant” and “Eligible Contract Participant” (Joint Final Rule; 

Joint Interim Final Rule; Interpretation) 

Process for Submissions for Review of Security-Based Swaps for 

Mandatory Clearing and Notice Filing Requirements for Clearing 

Agencies; Technical Amendments to Rule 19b-4 and Form 19b-4 

Applicable to All Self-Regulatory Organizations 

Further Definition of “Swap,” “Security-Based Swap,” and 

“Security-Based Swap Agreement”; Mixed Swaps; Security-Based 

Swap Agreement Recordkeeping 

Capital, Margin, and Segregation Requirements for Security-Based 

Swap Dealers and Major Security-Based Swap Participants and 

Capital Requirements for Broker-Dealers 

May 1, 2013 Proposed 

Cross-Border Security-Based Swap Activities; Re-Proposal of 

Regulation SBSR and Certain Rules and Forms Relating to the 

Registration of Security-Based Swap Dealers and Major SecurityBased Swap Participants 

Apr. 17, 2014 

Jun. 25, 2014 

Sep. 8, 2014 

Proposed 

Adopted 

Proposed 

Recordkeeping and Reporting Requirements for Security-Based 

Swap Dealers, Major Security-Based Swap Participants, and BrokerDealers; Capital Rule for Certain Security-Based Swap Dealers 

Application of "Security-Based Swap Dealer" and "Major SecurityBased Swap Participant" Definitions to Cross-Border Security-Based 

Swap Activities 

Treatment of Certain Communications Involving Security-Based 

Swaps That May Be Purchased Only By Eligible Contract 

Participant 

Feb. 11, 2015 Proposed Regulation SBSR—Reporting and Dissemination of Security-Based 

Swap Information 

Feb. 11, 2015 Adopted Regulation SBSR-Reporting and Dissemination of Security-Based 

Swap Information 

Feb. 11, 2015 Adopted Security-Based Swap Data Repository Registration, Duties, and Core 

Principles 

Application of Certain Title VII Requirements to Security-Based 

Swap Transactions Connected with a Non-U.S. Person's Dealing 

Apr. 29, 2015 Proposed Activity That Are Arranged, Negotiated, or Executed By Personnel 

Located in a U.S. Branch or Office or in a U.S. Branch or Office of 

an Agent 

Applications by Security-Based Swap Dealers or Major SecurityAug. 5, 2015 Proposed Based Swap Participants for Statutorily Disqualified Associated 

Persons to Effect or Be Involved in Effecting Security-Based Swaps 

Aug. 5, 2015 Adopted Registration Process for Security-Based Swap Dealers and Major 

Security-Based Swap Participants 

Sep. 4, 2015 Proposed Access to Data Obtained by Security-Based Swap Data Repositories 

and Exemption from Indemnification Requirement 

279 
Establishing the Form and Manner with which Security-Based Swap 

Dec. 11, 2015 Proposed Data Repositories Must Make Security-Based Swap Data Available 

to the Commission 

Security-Based Swap Transactions Connected with a Non-U.S. 

Person's Dealing Activity That Are Arranged, Negotiated, or 

Feb. 10, 2016 Adopted Executed By Personnel Located in a U.S. Branch or Office or in a 

U.S. Branch or Office of an Agent; Security-Based Swap Dealer De 

Minimis Exception 

Business Conduct Standards for Security-Based Swap Dealers and Apr. 14, 2016 Adopted Major Security-Based Swap Participants 

Trade Acknowledgment and Verification of Security-Based Swap Jun. 8, 2016 Adopted Transactions 

Regulation SBSR—Reporting and Dissemination of Security-Based Jul. 14, 2016 Adopted Swap Information 

Aug. 29, 2016 Adopted Access to Data Obtained by Security-Based Swap Data Repositories 

280 
	 	 		

 

 

 

Appendix C. Appendix to Access to Capital Analysis 

This appendix describes the procedures used to collect the Regulation D sample and the 

data on the other offerings. One of the original purposes of Form D, first adopted in 1982, was 

to collect and analyze data on issuers using Regulation D.236 However, until 2008, issuers filed 

Form D on paper, making the extraction of information for large-scale statistical analysis 

cumbersome. In February 2008, the SEC adopted amendments to Form D that required issuers 

to submit their Form D filings electronically, in a structured data format. 237 As a result of those 

requirements, which were phased in from September 2008 through March 2009, Form D filings 

are now machine-readable. Using basic text parsing tools, DERA staff was able to extract the 

reported elements and place them in a database enabling the large-scale statistical analysis 

reported here. 

Regulation D Sample

1.	 We collected all Form D filings (new filings and amendments) on EDGAR starting in 

January 2009 through December 2014. We extracted all fields from each filing and 

applied the following treatments to arrive at our final sample. 

2.	 Subsequent amendments to a new filing are treated as incremental fundraising and 

recorded in the calendar year in which the amendment is filed. For offerings initiated 

prior to 2009 and continuing into future years, an issuer would have filed only Form D 

amendments in an electronic (machine readable) form required for this analysis. If these 

236Rel. No. 33-6389 Revision of Certain Exemptions From Registration for Transactions Involving Limited Offers 

and Sales (Mar. 8, 1982); 47 FR 11251 (1982) (adopting Form D as a replacement for Forms 4(6), 146, 240 and 

242). 

237 Rel. No. 33-8891 Electronic Filing and Revision of Form D (Feb. 27, 2008) 70 FR 10592 (2008). 

281 
amendments reference a post-2008 sale date, the first filed amendment is treated as an 

original Form D filing, as we do not have access to Form D data prior to 2009. 

3.	 The incremental amount sold between two successive filings of the same issuer is 

determined by taking the difference between the “total amounts sold” reported in each 

such filing. We estimate the incremental amount of capital raised and reported in 

amended filings for which there is no original filing in electronic form. This occurs only 

in 2009. The estimated incremental capital raised in these instances is based on a 

“haircut” of the total amount sold reported in the latest filed amendment. This percentage 

is the average incremental amount sold in all amendments for which there is an original 

filing in electronic form, calculated separately for funds and non-funds. This resulted in 

haircut percentages of 11% and 27%, respectively. This treatment is unnecessary for 

offerings starting in 2010. 

4.	 We separate amendments filed into two categories. The first category is comprised of 

amendments that are filed in the same year when the offering was initiated, while the 

second category consists of amendments filed in the year subsequent to the year of initial 

offering. “Same year” is defined as 12 months from the initial date of filing. A number 

of pooled investment funds appear to report, in their annual amendments, net asset values 

for the total amount sold under the offering. Net asset values could reflect fund 

performance as well as new investment into, and redemptions from, the fund. Since it is 

not possible to distinguish between the two impacts, we present the second category of 

amendments (filed for offerings initiated prior to 2014) separate from the total amount 

raised in initial offerings. However, it is plausible that amendments filed within the same 

year when offerings are initiated, would reflect updates to capital raising efforts of the 

282 
	 	

 

issuers. Therefore, we use the incremental amounts in the first set of amendments in 

calculating the total amount raised in initial offerings. 

5.	 Foreign issuers are determined based on the information on Issuer State that they provide. 

6.	 When an issuer checks the box to claim more than one offering exemption (Rule 504, 

505, or 506), for the purpose of this analysis, we assume that any issuer that checks the 

box for Rule 506 is in fact relying on Rule 506. 

Other Offerings

1.	 Data on IPOs, equity offerings by seasoned issuers (EOSIs), convertible debt offerings, 

public debt offerings, and private offerings are taken from Securities Data Corporation’s 

New Issues database (Thomson Financial). Data on non-ABS Rule 144A offerings are 

taken from Securities Data Corporation’s New Issues database and Mergent database. 

2.	 Data on ABS offerings are taken from the Asset-Backed Alert and Commercial Mortgage 

Alert publications. We use non-U.S. collateral backed deals to proxy for deals done by 

foreign issuers. 

3.	 Public debt offerings by government, state, municipal, and quasi-governmental issuers 

(e.g., Fannie Mae, Freddie Mac) are excluded from the public debt sample. 

283 
	 	 	 	 	 	 	 	

   

   

Total Capital Raised In Registered and Unregistered Offerings

Figure C.1. Capital raising through registered offerings (in $ billions), 2005-2016 

1,600

0

200

400

600

800

1,000

1,200

1,400

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Public capital raised

Source: DERA analysis 

Figure C.2. Capital raising through unregistered offerings (in $ billions), 2009-2016 

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

2009 2010 2011 2012 2013 2014 2015 2016

Private capital raised

Source: DERA analysis 

284 
	 	 	 	

 

 

 

Appendix D. Appendix to Market Liquidity Analysis: Corporate Bonds 

Data Filters and Estimation

1. TRACE Data 

As discussed in Part B.IV.C, we collect corporate bond transaction data from the 

regulatory version of TRACE and merge it with bond characteristics from the Mergent Fixed 

Income Securities Database (FISD). The FISD database includes issuance date, issue size, 

original maturity date, and instrument complexity features (i.e., variable rate, non-standard 

interest payment frequencies, pay-in-kind, redeemable, convertible, unconventional day count 

basis for accrued interests, and sinking fund). Our sample period spans January 2, 2003, through 

September 19, 2016. 

We construct our corporate bond transaction data sample using the trade reporting filters 

and the discretionary filters below: 

• Trade reporting filters 

(1) Remove all corporate bond trade reports that are missing the security identifier, CUSIP. 

(2) Remove corporate bond trade reports that were subsequently corrected or canceled. 

(3) Remove duplicate interdealer trade reports.238 

• Discretionary filters 

(1) Remove all primary market corporate bond trades (“P1” trades). 

(2) Remove all trades for Rule 144A corporate securities. 

238 The regulatory version of TRACE includes two trade reports for each interdealer trade reported by respective 

dealers. 

285 
 

 

(3) Remove corporate bond trade reports that are missing any of the following information: 

executing dealer identifier, contra-party identifier, buy or sell indicator, dealer capacity 

code, trade execution date, trade execution time, price, and par volume. 

(4) Remove corporate bond trade reports with zero or negative price. 

(5) Remove corporate bond trade reports with zero, negative, or non-integer par volume. 

(6) Remove corporate bond trades executed on holidays and weekends. 

(7) Remove all corporate bonds that are not covered by the FISD database and corporate 

bonds with missing issue size239 in the FISD database. 

(8) Remove corporate bond trade reports for which the trade execution date is prior to the 

issuance date of the bond. 

(9) Remove corporate bond trade reports for which the trade execution date falls after the 

original maturity date of the bond. 

(10) Remove corporate bond trade reports with par volume greater than one-half of its issue 

size. 

Table D.1 reports the sample composition for each sub-period after applying filters listed 

under the trade reporting filters and discretionary filters. From Table D.1, we note that the 

sample reduction from applying the discretionary filters is larger for the “Post-crisis,” the 

“Regulatory,” and the “Post-regulatory” sub-period than earlier sub-periods. This larger 

reduction in our sample for the later sub-periods is primarily driven by our filters based on the 

FISD database: the portion of TRACE-eligible bond issues covered by the FISD database 

239 Issue size is one of the data items with the fewer number of missing observations in the FISD database. We use 

issue size to gauge the availability of data on various bond characteristics and instrument complexity features (e.g., 

issuance date, issue size, original maturity date, variable rate, non-standard interest payment frequencies, pay-inkind, redeemable, convertible, unconventional day count basis for accrued interests, and sinking fund) in the FISD 

database. 

286 
appears to have dropped for the “Post-crisis,” the “Regulatory,” and the “Post-regulatory” subperiod compared to earlier sub-periods. The sample composition change due to the changes in 

the FISD database coverage could result in a bias in our liquidity analysis. For example, if the 

reduction in the FISD database coverage is primarily on the bonds that trade infrequently, then 

eliminating TRACE-eligible bond issues that are not covered in the FISD database from our 

liquidity analysis could leave us with a sample of bonds that are more liquid and bias the results 

towards finding better liquidity in the corporate bond market than there actually is. 

To the extent that the changes in the FISD database coverage of bonds could influence 

our findings on corporate bond market liquidity, we examine trading activities of TRACEeligible bond issues that are not covered by the FISD database (hence excluded from our 

liquidity study) for each sub-period. Specifically, in Table D.2 Panel A, for TRACE-eligible 

bonds that are not covered by the FISD database, we report average daily statistics for the 

portion of bonds with trades, the number of trades, and par volume across sub-periods. From 

Table D.2 Panel A, we note that the average daily portion of bonds with trades is substantially 

larger during the “Post-crisis,” the “Regulatory,” and the “Post-regulatory” sub-period than 

earlier sub-periods. During the “TRACE Phase-in,” the “Pre-crisis,” and “Crisis” sub-period, the 

average daily portion of bonds with trade is at most 0.3% whereas the average daily portion of 

bonds with trades is at least 2.2% and as large as 8.4% during the “Post-crisis” and the 

“Regulatory” subperiod, respectively. Furthermore, we observe substantially larger average 

daily trade count and par volume for TRACE-eligible bonds with no coverage in the FISD 

database during the “Post-crisis,” the “Regulatory,” and the “Post-regulatory” sub-period than 

earlier sub-periods. Based on our comparative analysis on trading activity statistics for TRACEeligible bonds with no coverage in the FISD database across sub-periods, the exclusion of 

287 
TRACE-eligible bonds that are not covered by the FISD database from our liquidity analysis is 

less likely to bias our results towards finding better liquidity in the corporate bond market for the 

later sub-periods, the “Post-crisis,” the “Regulatory,” and the “Post-regulatory” sub-period. 

Finally, Table D.2 Panel B reports the portion (in percentage) of final sample 

observations with missing bond characteristics (i.e., issue size, credit quality, age (time since 

issuance), original maturity, and complexity features) after applying the data filters discussed 

above by each sub-period. From Panel B of Table D.2, we note that the portion of our final 

sample observations with missing bond characteristics is small for each sub-period; hence, it 

minimally influences distributional statistics reported throughout the sections in Part B.IV.C. 

2. Transaction Cost Estimation 

We closely follow the empirical design of the corporate bond transaction cost estimation 

method (EHP regression model) presented in Edwards, Harris, and Piwowar (2007). The EHP 

regression-based transaction cost estimation method controls for bond pricing factors: corporate 

bond market returns, duration risk, and credit risk, in estimating round-trip transaction costs for 

customer trades. Identification of the EHP regression model requires at least eight observations 

(eight trades for each corporate bond (CUSIP)). In addition, the EHP cost regression model 

requires at least one of each customer buy and sell trade for identification. 

For EHP transaction cost estimation, in addition to all filters listed under the trade 

reporting error and the discretionary filters in Table D.1, we apply the following price-based 

filters that include criteria to remove trade reports that appear to be incorrectly reported: 

• Price-based filters 

(1) Remove corporate bonds trades that executed at special price using special price flag in the 

regulatory version of TRACE. 

288 
(2) Remove corporate bond trades executed at prices at least ten percent above or below the 

previous trade price in the same day. 

(3) Remove corporate bond trades executed at prices at least ten percent above or below the daily 

median trade price. 

(4) Remove corporate bond trades executed at prices at least ten percent above or below the 9­

day centered median trade price. 

Table D.1 reports the effects of applying the price-based filters and applying EHP 

transaction cost regression on our sample for each sub-period. Furthermore, in Table D.4 Panel 

A through Panel F, we report EHP transaction cost estimation results that include the following 

statistics: average transaction costs, median transaction costs, and the confidence interval for 

average transaction cost estimates for each sub-period. 

3. FOCUS Data 

FOCUS consists of Part I and Part II. We use quarterly data obtained from Part II of 

FOCUS reports to analyze changes in dealer inventory, dealer risk-taking, and dealer 

profitability over time. Our sample period spans from the first quarter of 2003 to the third 

quarter of 2016 (55 quarters). We use the following filters to construct our FOCUS data set: 

(1) Remove observations with unknown designated examining authorities; 

(2) Remove SEC identification exclusions; 

(3) Remove missing dealer identifications (i.e., missing “ID8”); 

(4) Remove legacy exclusions; 

(5) Remove additional dealer firm-quarter observations submitted by multiple designated 

examining authorities; and 

(6) remove duplicate observations. 

289 
Finally, we exclude dealer firms using the Alternative Net Capital (ANC) computation 

for at least one quarter during the sample period because such filers do not report data items that 

are comparable to the data items reported by other dealer firms for the duration of our sample. 

The dealer firms using the ANC computation also all filed FOCUS Form Part II for some 

quarters in our sample period prior to using the ANC computation. To mitigate the effects on 

data trend from discontinuing filing FOCUS Form Part II, we identify those unique dealer firms 

(i.e., unique variable “ID8”) using the ANC computation (8 dealer firms: Goldman, Sachs & Co., 

Merrill Lynch, Pierce, Fenner & Smith Inc., Citigroup Global Markets Inc., Lehman Brothers 

Inc., Morgan Stanley & Co. LLC, J.P. Morgan Securities LLC (formerly Bear, Stearns & Co. 

Inc), J.P. Morgan Securities Inc., and Barclays Capital Inc.) and remove them for the duration of 

the sample period. Applying the above filters results in a total of 999 dealer firms and 29,027 

observations (dealer firm-quarter pairs) for our final sample. 

Part II of FOCUS reports includes the following sections: Statement of Financial 

Condition, Computation of Net Capital, and Statement of Income. For corporate obligations 

inventory, we use Line Item 400 (securities and spot commodities owned, at market value: 

corporate obligations) in the Statement of Financial Condition section. To normalize corporate 

obligations inventory, we divide corporate obligations inventory by total capital and allowable 

subordinated liabilities (Line Item 3530 in the Computation of Net Capital section). For haircuts, 

we use Line Item 3710 (haircuts on securities: corporate obligations) in the Computation of Net 

Capital section. To normalize haircuts, we divide haircuts by total securities and spot 

commodities owned at market value (Line Item 850 in the Statement of Financial Condition 

section). For gains or losses from trading in debt securities, we use Line Item 3944 (gains or 

290 
losses on firm securities trading accounts from trading in debt securities) in the Statement of 

Income section. 

291 
Sample Composition

Table D.1. Data filters and sample composition by sub-period 

TRACE eligible 

bond issues 

with trades 

(count) 

Number of 

trades 

(count) 

Par volume 

(in $billion) 

Initial 34,909 24,862,674 27,832 

Trade reporting filters 34,374 18,221,833 14,975 

"TRACE Phase-in" period Sub-period 1 Discretionary filters 27,993 16,987,387 12,258 

Price-based filters 24,063 16,866,073 12,109 

After applying EHP regression 23,065 16,843,209 11,997 

Initial 29,048 11,036,180 12,391 

Trade reporting filters 28,438 7,898,921 7,298 

"Pre-crisis" period Sub-period 2 Discretionary filters 23,735 7,424,664 5,958 

Price-based filters 20,294 7,393,163 5,897 

After applying EHP regression 19,765 7,377,770 5,851 

Initial 30,126 17,279,585 13,662 

Trade reporting filters 29,484 12,290,780 7,832 

"Crisis" period Sub-period 3 Discretionary filters 25,203 11,901,803 6,713 

Price-based filters 21,342 11,632,492 6,499 

After applying EHP regression 20,569 11,608,541 6,470 

Initial 54,348 48,749,042 39,896 

Trade reporting filters 53,759 34,592,369 21,865 

"Post-crisis" period Sub-period 4 Discretionary filters 32,062 30,606,643 14,192 

Price-based filters 26,934 30,442,782 14,093 

After applying EHP regression 25,217 30,373,438 14,074 

Initial 43,973 32,031,283 29,892 

Trade reporting filters 43,360 22,707,533 16,494 

"Regulatory" period Sub-period 5 Discretionary filters 26,564 18,653,183 8,813 

Price-based filters 22,125 18,611,287 8,782 

After applying EHP regression 20,190 18,546,003 8,772 

Initial 49,956 37,084,671 37,205 

Trade reporting filters 49,325 26,551,653 22,221 

"Post-regulatory" period Sub-period 6 Discretionary filters 27,364 21,207,304 11,066 

Price-based filters 21,695 21,113,617 11,005 

After applying EHP regression 20,146 21,061,943 10,994 

Source: DERA analysis 
Table D.2. Panel A. Trading activity for TRACE-eligible bonds with no coverage in the FISD database 

"TRACE Phase-in" period Sub-period 1 

"Pre-crisis" period Sub-period 2 

"Crisis" period Sub-period 3 

"Post-crisis" period Sub-period 4 

"Regulatory" period Sub-period 5 

"Post-regulatory" period Sub-period 6 

Number of TRACE eligible 

TRACE eligible bond issues Number of 

bond issues with trades trades Par volume 

(CUSIPs) (%) (counts) (in $billion) 

21,022 0.3 176 0.1 

28,740 0.1 108 0.1 

8,099 0.3 81 0.1 

16,513 2.2 1,449 0.9 

7,212 8.4 3,435 2.6 

11,349 7.4 4,202 3.7 
Table D.2. Panel B. Observations in the final sample with missing bond characteristics 

Obervations with missing bond characteristics (%) 

After dropping After applying After applying 

Rule 144A issues and the trade reporting and the discretionary filters the discretionary, an

issues with no FISD coverage TRACE eligible TRACE eligible 

TRACE eligible bond issues bond issues 

bond issues with trades Trades Par volume with trades T

Issue size 0.0 0.0 0.0 0.0 0.0 

Credit quality 6.1 0.2 0.0 0.1 0.1 

"TRACE Phase-in" period Age 0.5 0.0 0.0 0.0 0.0 

Maturity 0.5 0.0 0.0 0.0 0.0 

Complexity 0.0 0.0 0.0 0.0 0.0 

Issue size 0.0 0.0 0.0 0.0 0.0 

Credit quality 5.3 0.0 0.0 0.0 0.0 

"Pre-crisis" period Age 2.3 0.0 0.0 0.0 0.0 

Maturity 2.3 0.0 0.0 0.1 0.0 

Complexity 0.0 0.0 0.0 0.0 0.0 

Issue size 0.0 0.0 0.0 0.0 0.0 

Credit quality 0.0 0.0 0.0 0.0 0.0 

"Crisis" period Age 0.1 0.0 0.0 0.0 0.0 

Maturity 0.1 0.0 0.0 0.0 0.0 

Complexity 0.0 0.0 0.0 0.0 0.0 

Issue size 0.0 0.0 0.0 0.0 0.0 

Credit quality 0.0 0.0 0.0 0.0 0.0 

"Post-crisis" period Age 0.1 0.0 0.0 0.0 0.0 

Maturity 0.1 0.0 0.0 0.1 0.0 

Complexity 0.0 0.0 0.0 0.0 0.0 

Issue size 0.0 0.0 0.0 0.0 0.0 

Credit quality 0.0 0.0 0.0 0.0 0.0 

"Regulatory" period Age 0.0 0.0 0.0 0.0 0.0 

Maturity 0.0 0.0 0.0 0.0 0.0 

Complexity 0.0 0.0 0.0 0.0 0.0 

Issue size 0.0 0.0 0.0 0.0 0.0 

Credit quality 0.0 0.0 0.0 0.0 0.0 

"Post-regulatory" period Age 0.0 0.0 0.0 0.0 0.0 

Maturity 0.1 0.1 0.1 0.3 0.1 

Complexity 0.0 0.0 0.0 0.0 0.0 

Source: DERA analysis 
	 	

 

 

Transaction Costs

Figure D.1. Cross-sectional EHP average transaction costs 

Panel A. By issue size ($20,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel B. By credit quality ($20,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

295 
 

 

Panel C. By age ($20,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Panel D. By maturity ($20,000 trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

296 
 

 

Figure D.2. Cross-sectional EHP average transaction costs. By complexity ($1,000,000 

trade size) 

Note: Sub-period 1 – “TRACE Phase-in”; Sub-period 2 – “Pre-Crisis”; Sub-period 3 – “Crisis”; Sub-period 4 – 

“Post-crisis”; Sub-period 5 – “Regulatory”; Sub-period 6 – “Post-regulatory” 

Source: DERA analysis 

297 
Table D.3. Zero commission vs. non-zero commission customer agency trades 

Dealer capacity 

Agency 

zero non-zero 

commission commission 

Customer trade count 1,297 324 

Portion (%) of total customer trades 8.0 2.0 

Trade size (%) (%) 

"TRACE Phase-in" period Sub-period 1 <$100,000 83.8 88.8 

$100,000 - $1,000,000 11.1 9.2 

$1,000,000 - $5,000,000 3.5 1.7 

>$5,000,000 1.6 0.2 

Customer trade count 1,301 313 

Portion (%) of total customer trades 10.0 2.4 

Trade size (%) (%) 

"Pre-crisis" period Sub-period 2 <$100,000 87.3 88.3 

$100,000 - $1,000,000 9.8 9.4 

$1,000,000 - $5,000,000 1.8 2.0 

>$5,000,000 1.0 0.2 

Customer trade count 1,969 526 

Portion (%) of total customer trades 12.1 3.2 

Trade size (%) (%) 

"Crisis" period Sub-period 3 <$100,000 93.1 89.7 

$100,000 - $1,000,000 5.5 8.8 

$1,000,000 - $5,000,000 0.8 1.4 

>$5,000,000 0.6 0.1 

Customer trade count 2,697 708 

Portion (%) of total customer trades 12.1 3.2 

Trade size (%) (%) 

"Post-crisis" period Sub-period 4 <$100,000 93.2 89.7 

$100,000 - $1,000,000 5.7 8.5 

$1,000,000 - $5,000,000 0.9 1.6 

>$5,000,000 0.2 0.2 

Customer trade count 3,412 708 

Portion (%) of total customer trades 15.3 3.2 

Trade size (%) (%) 

"Regulatory" period Sub-period 5 <$100,000 90.7 88.9 

$100,000 - $1,000,000 7.3 9.3 

$1,000,000 - $5,000,000 1.5 1.6 

>$5,000,000 0.5 0.2 

Customer trade count 3,725 742 

Portion (%) of total customer trades 16.6 3.3 

Trade size (%) (%)
 

"Post-regulatory" period Sub-period 6 <$100,000
 89.2 86.6 

$100,000 - $1,000,000 8.2 10.9 

$1,000,000 - $5,000,000 2.0 2.2 

>$5,000,000 0.6 0.3 

Source: DERA analysis 

298 
 

 

 

Table D.4. Panel A. EHP transaction costs. “TRACE Phase-in” sub-period 

Effective half-spread Effective half-spread Weighted lower Weighted upper 

Trade size Weighted average cost Median cost confidence limit of confidence limit of 

(in thousand) (bps) (bps) weighted average weighted average 

5 82.8 67.8 82.1 83.5 

10 76.5 61.5 76.1 77.0 

20 66.2 52.0 65.8 66.5 

50 48.3 36.5 47.9 48.6 

100 35.4 25.3 35.1 35.8 

200 25.2 17.5 24.8 25.6 

500 14.9 10.3 14.4 15.4 

1000 9.2 6.2 8.7 9.8 

2000 5.6 3.5 5.0 6.2 

5000 3.2 1.4 2.2 4.2 

10000 5.3 2.7 2.9 7.6 

Table D.4. Panel B. EHP transaction costs. “Pre-crisis” sub-period 

Effective half-spread Effective half-spread Weighted lower Weighted upper 

Trade size Weighted average cost Median cost confidence limit of confidence limit of 

(in thousand) (bps) (bps) weighted average weighted average 

5 58.3 44.0 57.5 59.0 

10 52.5 39.0 52.1 53.0 

20 43.2 31.2 42.8 43.5 

50 30.2 21.2 29.9 30.5 

100 22.8 16.0 22.4 23.1 

200 16.7 11.7 16.3 17.0 

500 10.2 7.0 9.7 10.6 

1000 6.4 4.2 5.9 6.9 

2000 4.1 2.6 3.6 4.7 

5000 2.9 1.7 2.1 3.8 

10000 4.0 2.2 2.1 5.9 

299 
 

 

 

Table D.4. Panel C. EHP transaction costs. “Crisis” sub-period 

Effective half-spread Effective half-spread Weighted lower Weighted upper 

Trade size Weighted average cost Median cost confidence limit of confidence limit of 

(in thousand) (bps) (bps) weighted average weighted average 

5 77.8 69.4 76.8 78.7 

10 70.4 61.5 69.8 71.0 

20 62.4 55.1 62.0 62.9 

50 51.2 44.7 50.7 51.6 

100 42.6 36.1 42.0 43.1 

200 34.8 28.9 34.1 35.5 

500 25.7 20.8 24.8 26.5 

1000 19.9 16.2 19.0 20.9 

2000 15.5 12.8 14.5 16.6 

5000 12.1 9.7 10.2 13.9 

10000 11.3 7.6 7.4 15.2 

Table D.4. Panel D. EHP transaction costs. “Post-crisis” sub-period 

Effective half-spread Effective half-spread Weighted lower Weighted upper 

Trade size Weighted average cost Median cost confidence limit of confidence limit of 

(in thousand) (bps) (bps) weighted average weighted average 

5 67.8 60.9 67.6 68.1 

10 65.5 58.2 65.4 65.7 

20 60.4 52.6 60.3 60.6 

50 47.3 39.9 47.2 47.4 

100 36.2 29.5 36.1 36.4 

200 27.0 21.7 26.9 27.2 

500 17.9 14.0 17.7 18.1 

1000 12.6 9.7 12.3 12.8 

2000 8.4 6.4 8.1 8.7 

5000 5.8 4.5 5.3 6.2 

10000 6.3 4.5 5.2 7.5 

300 
 

 

Table D.4. Panel E. EHP transaction costs. “Regulatory” sub-period 

Effective half-spread Effective half-spread Weighted lower Weighted upper 

Trade size Weighted average cost Median cost confidence limit of confidence limit of 

(in thousand) (bps) (bps) weighted average weighted average 

5 43.5 33.7 43.2 43.7 

10 43.0 30.9 42.8 43.1 

20 39.8 27.1 39.7 39.9 

50 30.5 19.7 30.4 30.6 

100 22.8 14.8 22.7 22.9 

200 16.7 11.3 16.6 16.8 

500 11.0 7.9 10.8 11.1 

1000 7.7 5.8 7.6 7.9 

2000 5.3 4.3 5.2 5.5 

5000 4.4 3.5 4.1 4.8 

10000 5.9 4.0 5.1 6.8 

Table D.4. Panel F. EHP transaction Costs. “Post-regulatory” sub-period 

Effective half-spread Effective half-spread Weighted lower Weighted upper 

Trade size Weighted average cost Median cost confidence limit of confidence limit of 

(in thousand) (bps) (bps) weighted average weighted average 

5 31.0 22.1 30.9 31.1 

10 30.5 20.3 30.4 30.6 

20 27.7 17.7 27.6 27.7 

50 21.0 13.3 20.9 21.0 

100 15.4 9.6 15.4 15.5 

200 11.0 6.9 10.9 11.0 

500 6.8 4.4 6.7 6.8 

1000 4.5 3.1 4.4 4.6 

2000 3.0 2.2 2.9 3.1 

5000 2.8 1.9 2.7 3.0 

10000 4.3 2.2 3.9 4.7 

Source: DERA analysis 

301 
	 	

 

 

 

 

 

Dealer Activity

Figure D.3. Corporate obligations inventory, excluding ANC filers 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and 

spot commodities owned, at market value: corporate obligations. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phasein”; 1Q 2006 – 2Q 2007 (non-shaded) – “Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 

(non-shaded) – “Post-crisis”; 3Q 2012 – 2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – 

“Post-regulatory” 

Source: DERA analysis 

302 
 

 

 

Figure D.4. Share of aggregate (non-ANC sample) corporate obligations inventory. Top 25 

constant dealer sample 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and 

spot commodities owned, at market value: corporate obligations – for the Top 25 Constant Dealer Sample relative to 

the Non-ANC Sample. 1Q 2003 – 4Q 2005 (shaded) – “TRACE Phase-in”; 1Q 2006 – 2Q 2007 (non-shaded) – 

“Pre-crisis”; 3Q 2007 – 1Q 2009 (shaded) – “Crisis”; 2Q 2009 – 2Q 2012 (non-shaded) – “Post-crisis”; 3Q 2012 – 

2Q 2014 (shaded) – “Regulatory”; 3Q 2014 – 3Q 2016 (non-shaded) – “Post-regulatory” 

Source: DERA analysis 

303 
Table D.5. Panel A. Corporate obligations inventory, excluding ANC filers 

Distributions, by sub-periods ($million) 

Number of Number of 1st 10th 25th 75th 90th 

dealer firms observations Mean percentile percentile percentile Median percentile percen

"TRACE Phase-in" period 715 7,071 188.6 0.0 0.0 0.0 0.0 0.0 14.8 

"Pre-crisis" period 621 3,393 285.9 0.0 0.0 0.0 0.0 0.0 20.2 

"Crisis" period 600 3,826 242.8 0.0 0.0 0.0 0.0 0.0 11.3 

"Post-crisis" period 620 6,689 143.8 0.0 0.0 0.0 0.0 0.0 28.9 

"Regulatory" period 533 3,829 117.6 0.0 0.0 0.0 0.0 0.0 50.1 

"Post-regulatory" period 524 4,219 91.4 0.0 0.0 0.0 0.0 0.0 42.1 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and spot commodities owcorporate obligations. The number of observations corresponds to the number of dealer firm-quarter pairs in each sub-period. “TRACE 2003 – 4Q 2005; “Pre-crisis” period – 1Q 2006 – 2Q 2007; “Crisis” period – 3Q 2007 – 1Q 2009; “Post-crisis” period – 2Q 2009 – 2Q period – 3Q 2012 – 2Q 2014; “Post-regulatory” period – 3Q 2014 – 3Q 2016 
Table D.5. Panel B. Corporate obligations inventory by affiliation. Top 25 constant dealer sample 

Distributions, by sub-periods ($million) 

Number of Number of 10th 25th 75th 

Affiliation dealer firms observations Mean percentile percentile Median percentile 

Top 25 "TRACE Phase-in" period Non-BHC BHC 18 30 216 360 117.8 2,708 0.0 0.0 0.7 0.0 13.0 43.4 83.5 445.4 

Non-Top 25 667 6,495 51.3 0.0 0.0 0.0 0.0 

Top 25 "Pre-crisis" period Non-BHC BHC 18 30 108 180 198.7 3,920 0.0 0.0 0.0 0.0 28.0 20.5 72.5 738.0 

Non-Top 25 573 3,105 78.3 0.0 0.0 0.0 0.0 

Top 25 "Crisis" period Non-BHC BHC 18 30 126 210 109.2 3,314 0.0 0.0 0.4 0.7 12.2 43.9 49.5 382.0 

Non-Top 25 552 3,490 62.8 0.0 0.0 0.0 0.0 

Top 25 "Post-crisis" period Non-BHC BHC 18 30 234 390 425.1 1,495 0.0 0.0 10.2 4.9 48.0 93.4 247.5 560.3 Non-Top 25 572 6,065 46.0 0.0 0.0 0.0 0.0 Top 25 "Regulatory" period Non-BHC BHC 18 30 144 240 438.5 1,104 0.0 0.0 1.5 9.5 46.6 180.5 125.3 860.1 

Non-Top 25 485 3,445 35.5 0.0 0.0 0.0 0.0 

Top 25 "Post-regulatory" period Non-BHC BHC 18 30 162 270 344.2 700.4 0.0 0.0 0.0 14.1 21.6 105.5 148.7 759.5 

Non-Top 25 476 3,787 37.2 0.0 0.0 0.0 0.0 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 400 – securities and spot commodities owcorporate obligations. The number of observations corresponds to the number of dealer firm-quarter pairs in each sub-period. “TRACE 2003 – 4Q 2005; “Pre-crisis” period – 1Q 2006 – 2Q 2007; “Crisis” period – 3Q 2007 – 1Q 2009; “Post-crisis” period – 2Q 2009 – 2Q period – 3Q 2012 – 2Q 2014; “Post-regulatory” period – 3Q 2014 – 3Q 2016 Source: DERA analysis 
Table D.6. Panel A. Haircuts, excluding ANC filers 

Distributions, by sub-periods ($million) 

Number of Number of 1st 10th 25th 75th 90th 

dealer firms observations Mean percentile percentile percentile Median percentile percen

"TRACE Phase-in" period 715 7,071 13.6 0.0 0.0 0.0 0.0 0.0 1.6 

"Pre-crisis" period 621 3,393 19.7 0.0 0.0 0.0 0.0 0.0 1.9 

"Crisis" period 600 3,826 18.9 0.0 0.0 0.0 0.0 0.0 1.2 

"Post-crisis" period 620 6,689 12.0 0.0 0.0 0.0 0.0 0.0 3.1 

"Regulatory" period 533 3,829 9.8 0.0 0.0 0.0 0.0 0.0 4.3 

"Post-regulatory" period 524 4,219 8.9 0.0 0.0 0.0 0.0 0.0 4.7 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3710 – haircuts on securities: corporate obobservations corresponds to the number of dealer firm-quarter pairs in each sub-period. “TRACE Phase-in” period – 1Q 2003 – 4Q 201Q 2006 – 2Q 2007; “Crisis” period – 3Q 2007 – 1Q 2009; “Post-crisis” period – 2Q 2009 – 2Q 2012; “Regulatory” period – 3Q 2012 regulatory” period – 3Q 2014 – 3Q 2016 
Table D.6. Panel B. Haircuts by affiliation. Top 25 constant dealer sample 

Distributions, by sub-periods ($million) 

Number of Number of 10th 25th 75th 

Affiliation dealer firms observations Mean percentile percentile Median percentile 

Top 25 "TRACE Phase-in" period Non-BHC BHC 18 30 216 360 7.9 188.8 0.0 0.0 0.1 0.0 1.6 3.5 7.5 48.2 

Non-Top 25 667 6,495 4.1 0.0 0.0 0.0 0.0 

Top 25 "Pre-crisis" period Non-BHC BHC 18 30 108 180 9.7 263.3 0.0 0.0 0.0 0.0 2.7 1.7 7.8 57.4 

Non-Top 25 573 3,105 6.0 0.0 0.0 0.0 0.0 

Top 25 "Crisis" period Non-BHC BHC 18 30 126 210 6.0 254.6 0.0 0.0 0.0 0.2 1.0 5.8 4.8 39.7 

Non-Top 25 552 3,490 5.2 0.0 0.0 0.0 0.0 

Top 25 "Post-crisis" period Non-BHC BHC 18 30 234 390 32.6 121.6 0.0 0.0 0.7 0.5 4.5 9.7 22.9 41.0 

Non-Top 25 572 6,065 4.2 0.0 0.0 0.0 0.0 

Top 25 "Regulatory" period Non-BHC BHC 18 30 144 240 35.1 86.0 0.0 0.0 0.1 0.7 4.1 13.9 17.0 67.2 

Non-Top 25 485 3,445 3.4 0.0 0.0 0.0 0.0 

Top 25 "Post-regulatory" period Non-BHC BHC 18 30 162 270 33.2 71.3 0.0 0.0 0.0 1.3 2.7 15.7 11.0 64.8 

Non-Top 25 476 3,787 3.5 0.0 0.0 0.0 0.0 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3710 – haircuts on securities: corporate obobservations corresponds to the number of dealer firm-quarter pairs in each sub-period. “TRACE Phase-in” period – 1Q 2003 – 4Q 201Q 2006 – 2Q 2007; “Crisis” period – 3Q 2007 – 1Q 2009; “Post-crisis” period – 2Q 2009 – 2Q 2012; “Regulatory” period – 3Q 2012 regulatory” period – 3Q 2014 – 3Q 2016 Source: DERA analysis 
Table D.7. Panel A. Gains or losses from trading in debt securities, excluding ANC filers 

Distributions, by sub-periods ($million) 

Number of Number of 1st 10th 25th 75th 90th 

dealer firms observations Mean percentile percentile percentile Median percentile percen

"TRACE Phase-in" period 715 7,071 2.5 -8.0 0.0 0.0 0.0 0.0 3.3 

"Pre-crisis" period 621 3,393 2.6 -5.0 0.0 0.0 0.0 0.0 3.0 

"Crisis" period 600 3,826 -13.0 -170.5 0.0 0.0 0.0 0.0 3.0 

"Post-crisis" period 620 6,689 2.1 -22.8 0.0 0.0 0.0 0.0 4.5 

"Regulatory" period 533 3,829 0.8 -34.7 0.0 0.0 0.0 0.1 3.7 

"Post-regulatory" period 524 4,219 0.4 -31.3 0.0 0.0 0.0 0.0 3.4 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3944 – gains or losses on firm securities trtrading in debt securities. The number of observations corresponds to the number of dealer firm-quarter pairs in each sub-period. “TRA

1Q 2003 – 4Q 2005; “Pre-crisis” period – 1Q 2006 – 2Q 2007; “Crisis” period – 3Q 2007 – 1Q 2009; “Post-crisis” period – 2Q 2009 – period – 3Q 2012 – 2Q 2014; “Post-regulatory” period – 3Q 2014 – 3Q 2016 
Table D.7. Panel B. Gains or losses from trading in debt securities by affiliation. Top 25 constant dealer 

Distributions, by sub-periods ($million) 

Number of Number of 10th 25th 75th 

Affiliation dealer firms observations Mean percentile percentile Median percentile 

Top 25 "TRACE Phase-in" period Non-BHC BHC 18 30 216 360 2.2 26.8 0.0 -0.5 0.0 0.0 1.6 0.9 10.3 11.5 

Non-Top 25 667 6,495 1.2 0.0 0.0 0.0 0.0 Top 25 "Pre-crisis" period Non-BHC BHC 18 30 108 180 4.5 16.8 0.0 -1.4 0.0 0.0 1.5 0.9 8.8 9.3 

Non-Top 25 573 3,105 1.7 0.0 0.0 0.0 0.0 Top 25 "Crisis" period Non-BHC BHC 18 30 126 210 6.4 -231.0 0.0 -334.7 0.0 0.0 0.7 0.1 5.2 8.4 

Non-Top 25 552 3,490 -0.6 0.0 0.0 0.0 0.0 

Top 25 "Post-crisis" period Non-BHC BHC 18 30 234 390 0.5 6.4 0.0 -7.4 0.0 0.0 2.3 0.7 9.4 16.0 

Non-Top 25 572 6,065 1.9 0.0 0.0 0.0 0.0 

Top 25 "Regulatory" period Non-BHC BHC 18 30 144 240 -2.2 -0.2 -11.9 -18.5 0.0 0.0 0.2 0.5 5.9 13.2 

Non-Top 25 485 3,445 1.0 0.0 0.0 0.0 0.0 Top 25 "Post-regulatory" period Non-BHC BHC 18 30 162 270 -7.9 0.3 -10.1 -8.8 0.0 -0.7 0.4 0.0 5.1 12.3 

Non-Top 25 476 3,787 0.8 0.0 0.0 0.0 0.0 

Note: Data represent quarterly broker-dealers’ responses to FOCUS Form Part II, Line Item 3944 – gains or losses on firm securities trtrading in debt securities. The number of observations corresponds to the number of dealer firm-quarter pairs in each sub-period. “TRA

1Q 2003 – 4Q 2005; “Pre-crisis” period – 1Q 2006 – 2Q 2007; “Crisis” period – 3Q 2007 – 1Q 2009; “Post-crisis” period – 2Q 2009 – period – 3Q 2012 – 2Q 2014; “Post-regulatory” period – 3Q 2014 – 3Q 2016 Source: DERA analysis 
 

 

Table D.8. Panel A. Trade size by issue size 

Average daily trade count & par volume 

Less than $100M Between $100M and $500M Greater than $500M 

Trade Par volume Trade Par volume Trade Par volume 

(count) (in $billion) (count) (in $billion) (count) (in $billion) 

"TRACE Phase-in" period 2,336 0.2 6,176 5.2 5,988 6.9 

Portion (%) 16.1 1.3 42.6 42.3 41.3 56.4 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 93.2 26.9 59.3 1.6 60.5 1.3 

$100,000 - $1,000,000 5.5 17.4 17.5 6.8 17.9 4.8 

$1,000,000 - $5,000,000 1.1 27.9 18.4 42.2 13.7 24.5 

>$5,000,000 0.3 27.8 4.8 49.4 8.0 69.4 

"Pre-crisis" period 2,072 0.1 4,637 5.0 4,709 6.7 

Portion (%) 18.1 1.0 40.6 42.1 41.2 56.9 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 93.6 31.2 54.8 1.2 56.8 1.0 

$100,000 - $1,000,000 5.3 19.0 17.6 5.4 19.5 4.3 

$1,000,000 - $5,000,000 0.9 27.1 21.1 40.2 13.8 20.8 

>$5,000,000 0.2 22.6 6.5 53.2 9.9 73.9 

"Crisis" period 1,612 0.1 4,276 3.7 7,848 7.4 

Portion (%) 14.1 0.9 37.4 32.9 68.7 66.2 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 93.3 31.5 61.1 1.7 63.6 1.7 

$100,000 - $1,000,000 5.7 20.6 16.2 6.1 17.4 5.4 

$1,000,000 - $5,000,000 0.9 26.2 17.4 39.8 11.7 24.2 

>$5,000,000 0.2 21.7 5.4 52.4 7.3 68.8 

"Post-crisis" period 2,421 0.1 6,192 3.8 10,313 9.3 

Portion (%) 12.8 1.0 32.7 28.5 54.5 70.6 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 93.3 35.0 67.3 2.6 61.0 1.7 

$100,000 - $1,000,000 5.8 23.5 16.9 9.0 21.4 7.4 

$1,000,000 - $5,000,000 0.7 23.1 12.3 41.2 11.8 27.5 

>$5,000,000 0.1 18.4 3.5 47.2 5.8 63.4 

"Regulatory" period 1,575 0.1 5,950 3.5 10,688 9.8 

Portion (%) 8.6 0.7 32.7 26.4 58.7 72.9 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 90.8 32.3 64.9 2.7 56.2 1.7 

$100,000 - $1,000,000 8.2 27.3 19.7 10.8 24.9 9.0 

$1,000,000 - $5,000,000 0.9 23.8 12.2 41.8 13.2 30.6 

>$5,000,000 0.2 16.6 3.2 44.7 5.6 58.8 

"Post-regulatory" period 1,043 0.1 5,353 3.5 11,529 11.1 

Portion (%) 5.8 0.6 29.9 23.7 64.3 75.7 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 89.5 24.7 62.1 2.4 53.4 1.5 

$100,000 - $1,000,000 9.0 24.4 21.4 10.8 26.5 9.0 

$1,000,000 - $5,000,000 1.2 26.7 13.0 40.8 14.2 31.3 

>$5,000,000 0.3 24.3 3.5 46.0 6.0 58.2 

310 
 

Table D.8. Panel B. Trade size by credit quality 

Average daily trade count & par volume 

Investment grade High yield 

Trade Par volume Trade Par volume 

(count) (in $billion) (count) (in $billion) 

"TRACE Phase-in" period 

Portion (%) 

10,683 8.4 3,810 3.9 

73.7 68.2 26.3 31.8 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

71.6 2.1 47.9 1.1 

15.0 5.7 17.5 6.0 

7.9 21.3 29.6 55.3 

5.5 71.0 5.0 37.6 

"Pre-crisis" period 

Portion (%) 

7,093 7.4 4,325 4.5 

62.1 62.4 37.9 37.6 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

67.0 1.5 55.2 1.3 

17.0 5.0 14.8 4.9 

8.8 18.3 23.7 46.9 

7.2 75.2 6.3 46.9 

"Crisis" period 

Portion (%) 

10,749 7.3 2,988 3.9 

78.3 64.9 21.8 35.1 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

72.2 2.5 48.3 0.9 

15.5 6.4 16.1 4.4 

7.3 21.5 27.5 43.7 

5.0 69.5 8.1 51.0 

"Post-crisis" period 

Portion (%) 

13,916 9.0 5,010 4.3 

73.5 67.8 26.5 32.2 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

69.4 2.6 60.8 1.7 

18.6 8.7 16.3 6.4 

8.0 25.7 17.8 43.3 

4.0 63.0 5.1 48.6 

"Regulatory" period 

Portion (%) 

12,982 9.5 5,231 3.9 

71.3 70.8 28.7 29.2 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

62.8 2.3 60.3 2.1 

22.6 9.9 19.8 8.9 

10.2 29.3 15.8 43.8 

4.5 58.5 4.1 45.2 

"Post-regulatory" period 

Portion (%) 

12,516 10.2 5,408 4.5 

69.8 69.6 30.2 30.4 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

59.0 2.0 55.9 1.8 

24.9 9.9 21.7 8.7 

11.1 28.7 17.8 44.5 

5.1 59.5 4.6 45.0 

311 
 

 

Table D.8. Panel C. Trade size by age 

Average daily trade count & par volume 

Less than 2 years Between 2 years and 5 years Greater than 5 years 

Trade Par volume Trade Par volume Trade Par volume 

(count) (in $billion) (count) (in $billion) (count) (in $billion) 

"TRACE Phase-in" period 5,912 6.2 5,240 4.5 3,348 1.7 

Portion (%) 40.8 50.0 36.1 36.3 23.1 13.7 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 62.2 1.4 63.5 1.7 73.1 3.3 

$100,000 - $1,000,000 15.9 4.8 16.6 6.2 14.0 8.4 

$1,000,000 - $5,000,000 15.1 29.0 14.5 33.9 10.0 39.1 

>$5,000,000 6.8 64.8 5.4 58.1 2.9 49.2 

"Pre-crisis" period 3,428 5.9 5,157 4.0 2,833 2.0 

Portion (%) 30.0 49.9 45.2 33.5 24.8 16.6 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 49.3 0.7 67.6 2.0 69.8 2.4 

$100,000 - $1,000,000 18.1 3.5 15.7 6.3 14.8 6.5 

$1,000,000 - $5,000,000 20.9 25.7 12.0 32.3 11.0 33.2 

>$5,000,000 11.8 70.1 4.8 59.4 4.4 57.9 

"Crisis" period 4,942 5.8 4,687 3.4 4,108 2.0 

Portion (%) 36.0 51.5 34.1 30.7 29.9 17.8 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 58.6 1.2 67.8 2.3 75.0 3.6 

$100,000 - $1,000,000 16.5 4.5 15.9 6.6 14.0 8.1 

$1,000,000 - $5,000,000 16.1 27.0 11.8 33.2 7.6 30.7 

>$5,000,000 8.9 67.3 4.5 58.0 3.4 57.5 

"Post-crisis" period 6,823 6.6 6,252 4.4 5,850 2.3 

Portion (%) 36.1 49.7 33.0 33.1 30.9 17.2 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 58.1 1.6 66.9 2.2 77.9 4.4 

$100,000 - $1,000,000 21.5 7.2 18.2 8.0 13.8 10.3 

$1,000,000 - $5,000,000 14.2 30.5 10.8 32.2 6.1 32.5 

>$5,000,000 6.3 60.7 4.1 57.6 2.2 52.8 

"Regulatory" period 7,509 7.3 5,735 3.8 4,970 2.3 

Portion (%) 41.2 54.3 31.5 28.7 27.3 17.0 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 54.0 1.6 63.2 2.4 72.8 3.7 

$100,000 - $1,000,000 24.4 8.5 22.1 10.6 17.5 11.6 

$1,000,000 - $5,000,000 15.5 33.6 11.0 34.1 7.2 32.6 

>$5,000,000 6.1 56.3 3.7 52.9 2.6 52.1 

"Post-regulatory" period 6,761 7.5 6,853 5.0 4,309 2.2 

Portion (%) 37.7 51.2 38.2 33.8 24.0 15.0 

Trade size (%) (%) (%) (%) (%) (%) 

<$100,000 49.3 1.3 60.2 2.2 68.6 3.2 

$100,000 - $1,000,000 26.6 8.2 23.5 10.4 20.4 12.2 

$1,000,000 - $5,000,000 16.9 32.4 12.3 35.3 8.2 33.4 

>$5,000,000 7.1 58.1 4.0 52.1 2.8 51.2 

312 
 

Table D.8. Panel D. Trade size by maturity 

Trade Par volume Trade Par volume Trade Par volume Trade Par volume 

(count) (in $billion) (count) (in $billion) (count) (in $billion) (count) (in $billion) 

"TRACE Phase-in" period 46 0.1 1,084 1.5 10,601 8.3 2,768 2.4 

Portion (%) 0.3 0.7 7.5 12.5 73.1 67.5 19.1 19.3 

Trade size (%) (%) (%) (%) (%) (%) (%) (%) 

<$100,000 77.9 8.4 62.1 1.1 64.2 1.9 71.2 1.8 

$100,000 - $1,000,000 9.7 8.3 17.2 3.9 16.8 6.7 11.0 4.1 

$1,000,000 - $5,000,000 5.1 15.4 13.4 20.5 14.2 35.9 11.3 28.3 

>$5,000,000 7.4 68.0 7.3 74.5 4.8 55.4 6.6 65.8 

"Pre-crisis" period 23 0.1 692 1.6 8,344 7.7 2,355 2.5 

Portion (%) 0.2 0.5 6.1 13.4 73.1 64.7 20.6 21.3 

Trade size (%) (%) (%) (%) (%) (%) (%) (%) 

<$100,000 65.9 5.2 54.5 0.6 61.8 1.7 67.7 1.3 

$100,000 - $1,000,000 13.6 8.2 18.7 2.8 17.4 5.9 11.0 3.4 

$1,000,000 - $5,000,000 8.7 17.4 15.0 14.9 14.9 33.4 12.6 25.9 

>$5,000,000 11.7 69.3 11.8 81.6 5.8 59.0 8.8 69.4 

"Crisis" period 35 0.1 735 1.4 10,349 7.6 2,613 2.2 

Portion (%) 0.3 0.6 5.3 12.5 75.3 67.5 19.0 19.3 

Trade size (%) (%) (%) (%) (%) (%) (%) (%) 

<$100,000 62.7 5.7 52.0 0.8 66.1 2.3 72.8 1.8 

$100,000 - $1,000,000 15.5 9.9 18.5 3.3 16.7 6.7 10.3 4.4 

$1,000,000 - $5,000,000 11.8 26.4 17.9 20.9 12.2 32.2 10.5 27.5 

>$5,000,000 10.0 58.0 11.6 75.0 5.0 58.9 6.5 66.3 

"Post-crisis" period 50 0.1 1,191 1.8 13,839 9.3 3,840 2.1 

Portion (%) 0.3 0.4 6.3 13.6 73.1 70.1 20.3 15.9 

Trade size (%) (%) (%) (%) (%) (%) (%) (%) 

<$100,000 65.1 5.8 52.2 1.0 66.5 2.4 74.3 2.8 

$100,000 - $1,000,000 20.5 14.9 24.1 5.6 18.7 8.5 13.3 8.1 

$1,000,000 - $5,000,000 9.0 29.2 16.3 23.9 10.6 32.7 8.7 33.2 

>$5,000,000 5.4 50.1 7.4 69.6 4.2 56.4 3.7 55.9 

"Regulatory" period 47 0.0 1,149 1.5 13,225 9.6 3,788 2.3 

Portion (%) 0.3 0.3 6.3 11.2 72.6 71.5 20.8 17.0 

Trade size (%) (%) (%) (%) (%) (%) (%) (%) 

<$100,000 66.2 6.3 44.1 1.0 61.8 2.3 68.3 2.9 

$100,000 - $1,000,000 20.4 15.6 29.3 8.2 22.2 9.8 18.2 10.0 

$1,000,000 - $5,000,000 9.2 33.7 19.8 32.1 11.8 33.9 9.6 33.7 

>$5,000,000 4.2 44.5 6.9 58.8 4.3 54.0 4.0 53.4 

"Post-regulatory" period 44 0.0 1,275 1.7 13,150 10.2 3,435 2.7 

Portion (%) 0.2 0.3 7.1 11.5 73.4 69.6 19.2 18.4 

Trade size (%) (%) (%) (%) (%) (%) (%) (%) 

<$100,000 61.6 5.5 41.7 0.9 59.2 2.0 60.6 2.0 

$100,000 - $1,000,000 24.5 17.6 30.2 8.2 23.6 9.8 22.7 9.5 

$1,000,000 - $5,000,000 9.2 30.7 21.1 33.2 12.7 34.4 11.3 30.9 

>$5,000,000 4.7 46.2 7.0 57.7 4.6 53.7 5.4 57.6 

Less than 2 yrs Between 2 yrs and 5 yrs Between 5 yrs and 20 yrs Greater than 20 yrs 

Average daily trade count & par volume 

313 
 

Table D.8. Panel E. Trade size by complexity 

Average daily trade count & par volume 

Complexity feature 0 or 1 Complexity features 2 or more 

Trade Par volume Trade Par volume 

(count) (in $billion) (count) (in $billion) 

"TRACE Phase-in" period 

Portion (%) 

12,473 9.3 2,027 3.0 

86 75.4 14 24.6 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

66.1 2.1 61.7 0.9 

16.3 6.9 11.5 2.6 

12.8 34.4 18.0 25.4 

4.8 56.6 8.8 71.1 

"Pre-crisis" period 

Portion (%) 

9,524 8.5 1,895 3.3 

83 72.1 17 27.9 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

63.2 1.7 59.7 0.7 

17.1 6.0 11.6 2.3 

13.6 31.5 18.2 22.9 

6.1 60.7 10.5 74.1 

"Crisis" period 

Portion (%) 

12,172 8.7 1,565 2.5 

89 78.0 11 22.0 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

67.8 2.2 59.3 0.9 

16.0 6.5 12.3 2.9 

11.2 30.5 18.6 26.4 

5.1 60.7 9.9 69.8 

"Post-crisis" period 

Portion (%) 

16,582 11.5 2,344 1.7 

88 86.9 12 13.1 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

66.3 2.3 73.3 2.5 

18.8 8.4 12.1 5.4 

10.6 31.6 10.4 29.9 

4.3 57.7 4.2 62.3 

"Regulatory" period 

Portion (%) 

16,589 12.4 1,624 1.0 

91 92.4 9 7.6 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

61.3 2.1 70.0 3.0 

22.3 9.8 16.3 8.0 

11.9 33.5 10.4 33.9 

4.5 54.6 3.3 55.1 

"Post-regulatory" period 

Portion (%) 

16,846 13.7 1,078 1.0 

94 93.4 6 6.6 

Trade size 

<$100,000 

$100,000 - $1,000,000 

$1,000,000 - $5,000,000 

>$5,000,000 

(%) (%) (%) (%) 

57.9 1.9 61.4 1.8 

24.2 9.7 19.8 7.2 

13.0 33.7 14.1 32.6 

4.9 54.8 4.8 58.3 

Source: DERA analysis 

314 
Table D.9. Amount of pre-trade information: Quote standing for at least 2 seconds 

Number of 

Trades Mean 

5th 

percentile 

10th 

percentile 

Distribution 

25th 

percentile Median 

75th 

percentile 

90th 

percentile 

95th 

percentile 

Issue size (in $million) 

Without quote 

With quote 

85,557 

1,569,160 

408.5 

1,050.4 

1.5 

63.2 

3.0 

250 

20.0 

450 

250 

750 

500 

1,320 

1,000 

2,250 

1,500 

2,750 

Credit quality Without quote With quote 85,554 1,569,148 NM NM HY HY HY HY HY HY HY IG IG IG IG IG IG IG 

Age (in years) 

Without quote 

With quote 

85,544 

1,569,130 

1.8 

3.8 

0.0 

0.2 

0.0 

0.4 

0.1 

1.3 

0.9 

2.6 

2.1 

4.8 

4.4 

8.1 

7.4 

11.4 

Maturity (in years) 

Without quote 

With quote 

85,037 

1,567,913 

10.0 

12.1 

1.5 

4.4 

3.0 

5.0 

4.0 

6.6 

7.0 

10.0 

10.6 

10.2 

21.1 

30.0 

30.0 

30.1 

Complexity Without quote With quote 85,557 1,569,160 NM NM 0 0 0 0 0 0 0 0 1 0 1 0 1 1 

NM – not meaningful HY – high yield IG – investment grade Note: The value 0 for the complexity variable indicates that a corporate bond issue has zero or one complexity feature and the value 1 variable indicates that a corporate bond issue has two or more complexity features. Source: DERA analysis