JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 55, No. 1, Feb. 2020, pp. 1–45

COPYRIGHT 2019, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195

doi:10.1017/S0022109019000231

A Survey of the Microstructure of Fixed-Income

Markets

Hendrik Bessembinder , Chester Spatt, and

Kumar Venkataraman*

Abstract

In this article, we survey the literature that studies fixed-income trading rules and outcomes, including Treasury securities, corporate and municipal bonds, and structured credit

products. We compare and contrast the microstructure and regulation of fixed-income markets with equity markets. We highlight the nature of over-the-counter trading in the face

of search costs and the associated slow evolution of electronically facilitated intermediation. We discuss the databases available to study fixed-income microstructure, as well as

measures and determinants of trading costs, and the important roles dealer networks and

limited transparency play. We also highlight unresolved issues.

I. Introduction

Financial markets create and transact a broad set of fixed-income instruments, characterized by contracts that specify the amount and timing of promised

payments. With a few exceptions to be noted, fixed-income instruments trade in

dealer-oriented over-the-counter (OTC) search markets that differ significantly

from the more widely-studied markets for equity instruments.

Treasury securities issued by central governments are in many ways the

simplest fixed-income instruments, characterized by low default risk, high trading volumes, standardized contracts, and generally liquid markets. Bonds issued

by corporations as well as municipalities constitute one of the largest segments

of the fixed-income markets. In addition, fixed-income markets trade structured

products, which are created by repackaging existing loans. Examples include

mortgage-backed securities (MBS), either with (agency MBS) or without (privatelabel MBS) the implicit backing of semi-government agencies; asset-backed securities (ABS), which are secured by assets such as auto loans or credit card debt;

*Bessembinder (corresponding author), hb@asu.edu, W. P. Carey School of Business, Arizona

State University; Spatt, cspatt@cmu.edu, Tepper School of Business, Carnegie Mellon University;

and Venkataraman, kumar@mail.cox.smu.edu, Edwin L. Cox School of Business, Southern Methodist

University. We thank Jack Bao, Jennifer Conrad (the editor), Darrell Duffie, Michael Fleming, Terry

Hendershott, Edie Hotchkiss, David Krein, Amar Kuchinad, Ananth Madhavan, Artem Neklyudov,

Paul Schultz, and Alex Sedgwick for their valuable comments.

1
2 Journal of Financial and Quantitative Analysis

and packages of bank loans. Fixed-income markets also include collateralized

debt obligations (CDOs), which are created from structured products by dividing

cash-flow promises into various “tranches.”1

In general, fixed-income trading has been the focus of less research attention

as compared to equity market trading, despite the fact that fixed-income markets

are substantially larger and account for more capital raising as compared to equity

markets (discussed further in Section III.A). Recent years have seen an increase

in research, both theoretical and empirical, focused on fixed-income trading. This

article reviews and summarizes the key issues and many of the important studies,

while laying out directions for potential future research.

II. Fixed-Income versus Equity Markets

Fixed-income and equity markets share important common features. Each

facilitates price discovery and the completion of transactions in financial assets.

Investors seeking to buy and to sell do not necessarily arrive simultaneously in

these markets, so intermediaries emerge to supply liquidity. When these intermediaries trade on a principal basis, they absorb order imbalances into inventory and

therefore are subject to price risk on the positions entered to facilitate transactions.

Because the value of both fixed-income and equity instruments depends on marketwide variables, such as central bank policy and macroeconomic growth, as well

as issuer-specific outcomes, and because some participants may have better access

to such information than others, risks attributable to asymmetric information arise

in both markets. Microstructure theory broadly applied therefore implies that the

costs of demanding immediate trade execution in both equity and fixed-income

markets should depend on return volatility, customer arrival rates, and the likelihood of information asymmetries adverse to the intermediaries who effectively

supply liquidity.

A. Contrasts between Fixed-Income and Equity Microstructures

Despite these similarities in the underlying economic issues, fixed-income

microstructure differs significantly from that of the equity markets. Although

most equity trading has migrated in recent years to electronic limit order markets, with the exception of U.S. Treasury instruments and to-be-announced

MBS (TBA MBS, discussed further later), relatively little fixed-income trading occurs on electronic platforms.2 For example, a recent industry report

estimated that just 19% of U.S. investment-grade corporate bond trading

was electronically facilitated, mostly through requests for quotations (RFQs)

transmitted to established dealers https://www.greenwich.com/fixed-income-fxcmds/corporate-bond-electronic-trading-continues-growth-trend (July 28, 2016).

Hendershott and Madhavan (2015) describe trading on the basis of indicative

RFQs on MarketAxess. Clients select the number of dealers to be queried for

1See the Financial Industry Regulatory Authority’s (FINRA) 2017 TRACE Fact Book for detailed

definitions of debt instruments (http://www.finra.org/sites/default/files/2017-trace-fact-book.pdf).

2Abudy and Wohl (2018) examine Tel Aviv Stock Exchange data in 2014, where corporate bonds

trade in a consolidated limit order book market structure, reporting that the market is liquid with

narrow spreads and smaller price dispersion.
Bessembinder, Spatt, and Venkataraman 3

a quote of a specified size in a given bond and specify the time by which quotes

should be submitted. At that time, all quotes are revealed to the client. If satisfied, the client can contact the dealer submitting the best quote for execution.

Hendershott and Madhavan report that RFQ usage is greatest for recently issued,

investment-grade, large-issue-size bonds.3

Quotations in equity markets are firm, in that the submission by another market participant of an opposite-direction order with a sufficiently aggressive limit

price generates an immediate trade execution. In contrast, fixed-income quotes

are typically indicative rather than firm commitments. Fixed-income trading is

often facilitated by the electronic dissemination of quotations to at least some

participants, even though trades cannot be automatically completed on the same

platforms. The lack of electronically executable quotations has largely kept highfrequency trading (HFT) firms from establishing themselves as major players in

the fixed-income markets. As a consequence, most liquidity provision in fixedincome markets continues to rely on dealer firms, with little direct access by public orders.4 The situation is analogous to the design of the National Association of

Securities Dealers Automated Quotations (NASDAQ) equity markets, before the

establishment of order-handling rules 2 decades ago.

Nevertheless, the electronic facilitation of bond trading is increasing. In

Treasury markets, most interdealer trading is now electronic and automated,

and a large share of customer-to-dealer trading relies on electronic communication. Platforms that allow for direct customer-to-customer (or “all-to-all”)

trading have been introduced to corporate bond markets, though their share

of trading remains low. Furthermore, some liquidity providers now respond to

corporate bond RFQs for trades below certain size thresholds using algorithms

rather than human traders (https://www.greenwich.com/press-release/robotshave-entered-corporate-bond-market).

The equity markets are highly transparent, as prices and quantities for completed trades (“post-trade transparency”) as well as the best available bid and

ask quotes (“pre-trade transparency”) are widely disseminated in real time or

with very short delays.5In contrast, as discussed further in Section VI, fixedincome markets are relatively opaque, as quotations are distributed to only some

market participants. The various market venues that trade equities are integrated

by both regulation and competition. Regulation NMS (National Market System)

in particular requires that no equity trade of limited size be executed at a price

worse than the best electronically accessible quotation on any exchange. Fixedincome markets are not subject to a similar regulation. Although, as discussed

further in Section VI, brokerage firms are subject to a duty of best execution,

3MarketAxess reportedly holds an 85% market share in electronically facilitated corporate bond

trading (see Leising, M., and M. Smith. “Electronic Bond Trading Gains Ground.” Bloomberg. Available at https://www.bloomberg.com/news/articles/2018-02-15/electronic-bond-trading-gains-groundas-market-finally-matures (Feb. 15, 2018)).

4Biais and Green (2019) show that municipal and corporate bonds traded actively on the New

York Stock Exchange (NYSE) before World War II (WWII). The study estimates that average trading

costs for retail bond investors on the NYSE before WWII were lower than they are over-the-counter

in recent years.

5

“Dark pools,” which do not generally disseminate quotations, are an exception.
4 Journal of Financial and Quantitative Analysis

the enforcement of such a duty is potentially hindered by the lack of pre-trade

transparency in fixed-income markets. In addition, each equity exchange disseminates electronic quotations that facilitate competition, resulting in what O’Hara

and Ye (2011) refer to (page 459) as “a single virtual market with multiple points

of entry.” In contrast, competition across fixed-income dealers may be mitigated

by limited pre-trade transparency and high search costs.

Bonds, like common stocks, can be sold short by borrowing the security.

Short selling facilitates market making by allowing dealers to accommodate customer buy orders, even if the bond is not already held in inventory. Short selling

also facilitates speculative trading motivated by the possibility of a price decline.

A large literature studies short selling in the stock markets, generally concluding that short selling enhances liquidity (Beber and Pagano (2013)) and, because

short sellers are relatively well informed (Diamond and Verrecchia (1987)), they

improve price discovery. In contrast, the literature on short selling in bond markets

is sparse.

B. Dealer Capital Commitment

With the exception of Treasury securities and TBA MBS, trading in fixedincome instruments is dominated by OTC trading between customers and dealer

firms. Traditionally, dealers committed their own capital, completing most trades

on a principal basis by absorbing customer orders into inventory. In recent years,

however, dealers have reduced their degree of capital commitment in corporate bond markets, as documented by Bessembinder, Jacobsen, Maxwell, and

Venkataraman (2018) and Bao, O’Hara, and Zhou (2018). This reduction reflects

in part an increased reliance on “prearranged” pairs of trades, where the dealer

quickly resells the bond to a counterparty, who was in fact located before the

completion of the initial trade (Schultz (2017), Goldstein and Hotchkiss (2020),

and Choi and Huh (2017)). Furthermore, Hollifield, Neklyudov, and Spatt (2017)

document shorter “intermediation chains,” that is, that fewer dealers are involved

before a bond is transferred to another customer.

Most major bond dealers are affiliated with banks, though nonbank dealers have increased their participation in recent years. For example, Bessembinder

et al. (2018) report that nonbank dealers accounted for 12.5% of customer–dealer

trading volume in corporate bonds during 2014–2016, as compared to just 2.4%

before the 2007–2009 financial crisis. Decreased capital commitment by corporate bond dealers in recent years has been linked to post-financial crisis regulations, including higher capital requirements and the Volcker Rule limitations on

trading by banks, as discussed further in Section VI.

C. Trading Outcomes

Several empirical patterns differ notably across equity and fixed-income markets. Customer trade execution costs tend to be substantially larger in fixedincome markets as compared to equities.6 For example, Harris and Piwowar

(2006) document that effective bid–ask spreads in municipal bonds averaged approximately 2% for trades of $20,000. For the cost of a similar-sized trade in a

6Treasuries, where interdealer trades occur at very narrow spreads, and TBA MBS comprise

potential exceptions. Data on customer trades in Treasury securities are not publicly disseminated.
Bessembinder, Spatt, and Venkataraman 5

$40 stock to be as large, the effective bid–ask spread would be 80 cents, whereas

by comparison, Chordia, Roll, and Subrahmanyam (2008) report that the actual

median effective bid–ask spread in U.S. equity markets at a similar point in time is

just 3.3 cents. This outcome is surprising, as fixed-income markets tend to be less

volatile than equities and fixed-income securities are likely to be less sensitive to

new information regarding issuer fundamentals. That is, standard microstructure

arguments imply a lower cost of immediacy in fixed-income markets, because the

intermediaries who supply liquidity are likely subject to both less inventory risk

and asymmetric information costs.

It is also noteworthy that although trade execution costs in equity markets

tend to be greater for large trades than for small ones, presumably because of

adverse selection and inventory costs, the opposite pattern is observed in fixedincome markets. For example, Edwards, Harris, and Piwowar (2007) report estimated trading costs for corporate bonds that decrease monotonically from 75

basis points (bps) for trades of $5,000 to less than 10 bps for trades of $1 million

or larger. Similarly, Bessembinder, Maxwell, and Venkataraman (2013) report estimates of trading costs for structured credit products (ABS, MBS, etc.) that range

from 83 bps for trades less than $100,000 to just 5 bps for trades larger than

$1 million.

Trade sizes and frequencies also differ dramatically across equity and bond

markets. In recent years, the median trade size in equity markets is 100 shares

(O’Hara and Ye (2014)), or approximately $3,000 to $5,000 for typically priced

stocks. By comparison, a “round-lot” trade in corporate bond markets involves

$1 million. Bessembinder et al. (2018) report an average trade size of $1.2 million

for their sample of corporate bond trades during 2014–2016, and Bessembinder,

Kahle, Maxwell, and Xu (2009) report that round-lot transactions accounted for

nearly 90% of corporate bond dollar trading volume.

Most fixed-income products trade infrequently. For example, Bessembinder

et al. (2013) report that the majority of MBS and ABS (e.g., credit cards and auto

loans) in their 21-month sample never traded at all. Edwards et al. (2007) report

that individual corporate bond issues did not trade on 52% of the days in their sample and that the average number of daily trades in an issue, conditional on trading,

is just 2.4. One reason that individual corporate bonds trade less frequently than

equities is that an issuer often has multiple bond issues outstanding. Although equity shares issued at different points in time by a given firm are fully substitutable,

each bond issue is a distinct contract with differing promised payments, maturity

dates, and priority in case of default, and is therefore traded separately.

III. Descriptive Overview of Fixed-Income Markets

A. Bond Market Size and Ownership

According to Securities Industry and Financial Markets Association

(SIFMA) statistics summarized on Table 1, as of 2017 the largest capital markets relative to the gross domestic product (GDP) are those of the United

States (360%) and Japan (388%), followed by the European Union (245%)

and China (167%) (https://www.sifma.org/resources/research/us-capital-marketsdeck-2018/). Fixed-income markets account for 56% of U.S. capital markets,
6 Journal of Financial and Quantitative Analysis

TABLE 1

Size, Issuance, and Ownership in Bond Markets

Table 1 reports the size of the Treasury, money market, agency debt, mortgage-backed securities, corporate bonds,

municipal bonds, and U.S. equity markets as of the end of 2017. Issuance refers to the extent of capital raising in each

market segment in 2017. Ownership refers to the percentage of outstanding securities held by each category of investors.

FHLB stands for Federal Home Loan Bank. Data sources are reported in the table footnotes.

Outstanding Issuance Ownership

Type of Bond Issuer ($B, 2017)a($B, 2017)a(as of 2016)

Treasury U.S. Treasury 14,450 2,200 Foreign (42%), Federal

Reserve (17%), mutual

funds (12%), pension funds

(10%), banks (5%),

insurance (2%)b

Money U.S. Treasury, financial 950 —

market institutions, corporations

Agency debt Freddie Mac, Fannie 1,950 750

Mae, FHLBs

Mortgage- Financial institutions 9,300 2,000 Foreign (14%), Federal

backed Reserve (20%), mutual

securities funds (11%),

insurance/pension (9%),

banks (25%)c

Asset-backed Financial institutions 1,450 600

securities

Corporate Corporations 9,000 1,650 Foreign (29%), insurance

companies (27%), mutual

funds (16%), pension funds

(10%), households (6%),

banks (7%)d

Municipal State and local 3,850 450 Households (51%), banks

governments and (30%), mutual funds

agencies (17%)e

U.S. fixed 40,950 7,650

income

U.S. stock Corporations 32,100 224

market

U.S. capital 73,050 7,874

markets

U.S. GDP 19,400

a

Securities Industry and Financial Markets Association (SIFMA) 2018.

b

Board of Governors of the Federal Reserve System, Financial Accounts of the United States.

c

Gao, Schultz, and Song (2017).

d

Federal Reserve Flow of Funds.

e

Municipal Securities Rulemaking Board (MSRB), Trends in Municipal Bond Ownership, 2017.

based on market capitalization. Figure 1 displays the relative size of various U.S.

fixed-income markets from 2003 to 2017, and for comparison the size of U.S.

public equity markets, demonstrating that the fixed-income market in aggregate is

substantially larger than the equity market. Fixed-income markets accounted for

97% of all U.S. security issuance during 2017. The volume of new issuance reflects in part that many fixed-income securities have short terms and are reissued

(i.e., refunded) at maturity. Treasury bonds accounted for 29% of all issuance,

whereas mortgage-related and corporate issuances accounted for 26% and 22%,

respectively.

Foreign investors held 42% of outstanding U.S. Treasury bonds as of 2016

and were also significant holders of corporate bonds (29% of total) and agency

MBS (14% of total).7 The U.S. Federal Reserve (Fed) has been a large owner of

7Foreign ownership includes the holdings of foreign subsidiaries of U.S. corporations.
Bessembinder, Spatt, and Venkataraman 7

FIGURE 1

Size of Equity and Fixed-Income Markets (2003–2017)

Figure 1 reports the size of the Treasury, mortgage-related, corporate bond, municipal bonds, U.S. equity, and U.S.

fixed-income markets as of year-end between 2003 and 2017. Source: Author calculations based on data provided by

Securities Industry and Financial Markets Association (SIFMA).

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Year

Corporate U.S. Fixed Income Market U.S. Equity Market

Municipal Treasury Mortgage Related

$billions

Treasury bonds and agency MBS. The Fed held significant quantities of bonds

even before the 2007–2009 financial crisis, but its holdings of Treasury and MBS

securities spiked as a result of asset purchase programs during the crisis. Direct

holdings by households account for less than 10% of all Treasury and corporate

bond holdings. In contrast, households account for more than 50% of municipal

bond holdings, reflecting their federal tax-exempt status. Mutual funds account

for 12%, 17%, and 16%, respectively, of holdings of Treasury, municipal, and

corporate bonds (each as of 2016).

B. Bond Trading Participation and Logistics

The trading activity in the secondary market for fixed-income securities is

dominated by institutions, such as pension funds, mutual funds, hedge funds,

insurance companies, and sovereign wealth funds. Quotation data are available

to such institutions. Investment-grade bonds are quoted in terms of spread over

a Treasury security of similar maturity. Lower rated bonds, the value of which

depends more on firm-specific information (Schultz (2001)), are quoted in dollars. Dealers broadcast indicative bids and offers on lists of actively traded bonds

(called “runs”) to potential institutional clients. In the past, dealers broadcast runs

once a day, but in recent years, runs are updated many times daily using automated pricing models. Data aggregators (e.g., Bloomberg or Algomi ALFA)
8 Journal of Financial and Quantitative Analysis

provide buy-side institutions with information from multiple sources, including

recent transactions, proprietary dealer runs, quotations from electronic bond platforms, and electronic RFQ platforms (e.g., MarketAxess). Institutions can contact

dealers, via instant messaging or phone, to obtain information on a bond that is

not quoted, to obtain “color” on market conditions, or to negotiate the terms of

a transaction. In some cases, institutional customers submit bid or offer wanted

in competition (BWIC or OWIC, respectively) lists to dealers, requesting quotations on a specified set of bonds. Newer all-to-all trading venues (e.g., Trumid or

MarketAxess’s Open Trading) offer institutions the ability to trade directly with

each other, use hidden order types, and allow for midpoint pricing. Retail investors

purchase and sell bonds through retail brokers, and generally do not have direct

access to dealer quotations.

Dealer firms trade with one another in the interdealer market, either directly

or on interdealer broker (IDB) platforms, some of which offer fully electronic

trading as well as over-the-phone trading via voice-assisted brokers. For corporate

and structured bonds, transactions among dealers are most often completed on a

bilateral basis using traditional (voice) methods. For U.S. Treasuries, about 70%

of interdealer trading occurs on electronic IDB platforms (e.g., BrokerTec), where

nondealer participants, such as hedge funds and principal trading firms (HFTs),

also participate. Wu (2018) reports that 60% of the interdealer trades in municipal

bonds occur on electronic alternative trading systems.8

U.S. Securities and Exchange Commission (SEC)-registered broker-dealers

and trading platforms are required to report their transactions in Treasury, corporate, and structured bonds to FINRA, and in municipal bonds to the Municipal

Securities Rulemaking Board (MSRB). With the exception of Treasury bonds and

some categories of structured bonds, data on completed transactions are made

available to the public. Mandatory reporting of trade prices for most credit instruments was phased in, beginning in 2002, according to the timeline summarized on

Table 2. Before 2002, there was no comprehensive reporting of bond transaction

data to regulators or the public.9

C. Data Sources for Fixed-Income Microstructure Studies

Table 3 summarizes sources of historical fixed-income data. Secondary market transaction data, including both dealer-to-customer trades and interdealer

trades, are publicly available from MSRB through the Electronic Municipal Market Access (EMMA) platform for municipal bonds and from FINRA through

the Trade Reporting and Compliance Engine (TRACE) platform for corporate

bonds and structured products. For each trade, the data include the bond CUSIP

(Committee on Uniform Securities Identification Procedures) number, trade time,

price, transacted quantity (to a specified maximum reportable size), indication

of whether the trade was interdealer or dealer-customer, as well as a buy–sell

8For Treasury statistics, see http://libertystreeteconomics.newyorkfed.org/2018/09/unlocking-thetreasury-market-through-trace.html. For municipal bond statistics, see Wu (2018).

9A partial exception is the U.S. Treasury markets. In June 1991, GovPX was launched under SEC

guidance to provide institutional market participants with real-time interdealer book and transactions

data in Treasury securities. Because reporting was voluntary, GovPX’s coverage of the interdealer

Treasury market varied between 66% in the early 1990s to less than 42% in 2001.
Bessembinder, Spatt, and Venkataraman 9

TABLE 2

Regulatory Data Collection and Post-Trade Reporting

in U.S. Fixed-Income Markets (1997–2017)

Table 2 describes the timeline for regulatory data collection and post-trade reporting between 1997 and 2017 for U.S. municipal bonds, corporate bonds, agency debentures, asset-backed securities, mortgage-backed securities, and Treasury

security markets. Data sources are the Municipal Securities Rulemaking Board (MSRB) 2017 Fact Book for the municipal

market and the Financial Industry Regulatory Authority (FINRA) TRACE 2017 Fact Book for other fixed-income markets.

Transactions that exceed a size threshold are reported with quantity field = Threshold+. For example, the threshold for

Investment Grade corporates (i.e., rated Baa (by Moody’s) or BBB (by S&P and Fitch) or above) is $5 million and for High

Yield corporates (i.e., those with lower credit ratings than investment grade) is $1 million. Transactions that exceed the

thresholds are reported as $5 million+ and $1 million+, respectively. Source: 2017 FINRA Fact Book; FINRA Rulings.

Municipal Municipal Primary Market

Corporate–Phase II

Corporate–Phase I

Corporate–Phase IIIb

Corporate–Phase IIIa

MBS–TBA

ABS/MBS

MBS–Specified Pools

Collateralized Mortgage

Obligations

Asset Backed Securities

Treasury Securities

Rule 144 Securities

7/1/19977/1/19987/1/1999

7/1/20007/1/20017/1/20027/1/20037/1/20047/1/20057/1/20067/1/2007

7/1/20087/1/20097/1/20107/1/20117/1/20127/1/20137/1/20147/1/20157/1/20167/1/2017

Transparency Timeline

Date Milestone Notes

July 1997 Municipal Dealers report all municipal bond transactions to MSRB after close of

business each day. Publicly disseminated after 2 weeks.

July 2002 Corporate–Phase I Dealers report all corporate bond transactions to FINRA within 75 minutes.

Public dissemination is immediate for large 500 IG and 50 HY bonds.

Transactions in other bonds are not disseminated.

Mar. 2003 Corporate–Phase II Public dissemination is expanded to smaller IG bonds in Mar. 2003 and

further expansion to approximately 4,650 corporate bonds in Apr. 2003.

Reporting lag is reduced to 45 minutes in Oct. 2003.

Oct. 2004 Corporate–Phase IIIa Additional corporate bonds are included for public dissemination.

Reporting lag is reduced to 30 minutes.

Jan. 2005 Municipal Dealers report all municipal bond transactions to MSRB within 15 minutes.

Public dissemination is immediate.

Feb. 2005 Corporate–Phase IIIb Additional corporate bonds are included for public dissemination.

Approximately 99% of transactions in registered corporate are covered.

Reporting lag is reduced to 15 minutes in July 2005. All registered

corporate bonds are disseminated by Jan. 2006.

Mar. 2010 Primary market Dealers report all U.S. agency debenture transactions, as well as primary

market transactions in TRACE-eligible securities.

May 2011 ABS/MBS Dealers report all transactions in asset-backed securities (ABS) and

mortgage-backed securities (MBS) to FINRA. These trades are not

publicly disseminated.

Nov. 2012 MBS–TBA To-be-announced (TBA) transactions are included for public

dissemination.

July 2013 MBS–Specified pools MBS transactions are included for public dissemination.

June 2014 Rule 144 securities Transactions in U.S. Securities and Exchange (SEC) Rule 144A Corporate

securities are included for public dissemination.

June 2015 Asset-backed

securities

Transactions in ABS are included for public

dissemination.

Mar. 2017 Collateralized

mortgage obligations

Transactions in collateralized mortgage obligations are included for public

dissemination.

July 2017 Treasury securities Dealers report all Treasury security transactions to TRACE. Transactions

are not reported to the public.
10 Journal of Financial and Quantitative Analysis

TABLE 3

Data Sources, Trading Platforms, and Trading Activity

Table 3 reports transaction data sources, data coverage period, daily trading volume (in $ billion), average transaction

size (in $ million), percentage of trading volume that is dealer to customer, trade execution costs based on dealer-tocustomer trades (in basis points), and the dominant trading platforms for Treasury securities, agency mortgage-backed

securities that specify pools (MBS) and to-be-announced (TBA), commercial mortgage-backed securities (CMBS), assetbacked securities (ABS), registered corporate bonds (Reg), Rule 144A corporate bonds (144A), and municipal bonds.

The footnotes report the source (academic study) for the statistics, the sample period, and the data source. ‘‘Retail’’

refers to trading cost estimates based on trades of less than $100,000, and ‘‘Institutional’’ refers to trade cost estimates

on trades that exceed $1 million. Data sources are Trade Reporting and Compliance Engine (TRACE) for Agency MBS,

CMBS, ABS, and corporate bonds; Municipal Securities Rule Making Board’s EMMA database for municipal bonds, and

interdealer broker (IDB) platforms GovPX and BrokerTec for Treasury bonds. NAIC stands for National Association of

Insurance Commissioners. The market structure of the trading platforms are identified using the following superscripts in

the table: x refers to the electronic limit order book IDB platform, y refers to the hybrid voice and electronic IDB platform,

and z refers to request-for-quote (RFQ) platforms.

Daily

Trading Customer/ Customer Electronic

Data Coverage Volume Average Trade Total Trading, Trading Costs, Trading

Type of Bond Source Begins ($B, 2017)a Size ($M) % Basis Points Platforms

Treasury GovPX 1991 505 2-year: 28b 50 2-year: 1b BrokerTecx

5-year: 12 5-year: 1 eSpeedx

10-year: 10 10-year: 2

BrokerTec 2001

Agency MBS TRACE 2011 209 TBA: 33c TBA: 52c TBA: 2d BrokerTecx

MBS: 6 SP: 80 MBS: 80 Tradewebx,y

CMBS TRACE 2011 3 Reg: 18e 84d Reg: 53e Tradeweby

144A: 47 144A: 72

ABS TRACE 2011 1 Reg: 8e 80e Reg: 48e Tradeweby

144A: 19 144A: 0.44

Corporate TRACE 2002 31 1.2f 70f Retail: 124f Market

NAIC 1995 Institution: 36 Axessz

Municipal MSRB 1997 11 0.41g 70g Retail: 75g Municenterx

Institution: 20

a

Securities Industry and Financial Markets Association (SIFMA) Fact Book 2018, p. 34 (available at https://www.sifma.org/resources/research/

sifma-fact-book-2018/).

b

Adrian, Fleming, and Vogt (2017). Sample period: 2017. BrokerTec IDB transactions data.

c

Gao, Schultz, and Song (2017). Sample period: May 2011–Apr. 2013. TRACE data.

d

Bessembinder, Maxwell, and Venkataraman (2013), Table 7. Sample period: May 2011–Jan. 2013. TRACE data.

e

Hollifield, Neklyudov, and Spatt (2017), Table 2. Sample period: May 2011–Feb. 2012. TRACE data.

f

Bessembinder, Jacobsen, Maxwell, and Venkataraman (2018), Table 3. Sample period: Apr. 2014–Oct. 2016. TRACE data.

g

Municipal Securities Rulemaking Board (MSRB) 2017 Fact Book (available at http://www.msrb.org/∼/media/Files/Resources/MSRB-Fact-Book2017.ashx?la=en); Wu (2018). Sample period: 2017. MSRB data.

indicator for dealer–customer trades.10 An enhanced version of the corporate bond

TRACE data is now available from FINRA for academic research and includes

information on masked dealer IDs and actual transaction size. The enhanced

TRACE data also include trades reported to FINRA but not disseminated to the

public (e.g., trades in nonregistered “144A” securities that were completed before July 2014). Beginning July 2017, dealers are required to report both dealer–

customer and interdealer trades in Treasury bonds to TRACE for regulatory analysis, but the data are not yet available to the public. In addition, data on insurance company bond transactions are available beginning 1995 from the National

Association of Insurance Commissioners (NAIC). Data on interdealer trades for

10FINRA rules on the requisite timing of trade reports for bond categories are available at

http://www.finra.org/industry/trace-reporting-timeframes. For large transactions, the trade size that

is disseminated to the public is capped to allow dealers the opportunity to unwind inventory positions. For example, trade size is provided for investment grade and high-yield corporate bonds if the

par value transacted does not exceed $5 million and $1 million, respectively; otherwise an indicator

variable (“5MM+” and “1MM+,” respectively) denotes a trade of more than the capped size.
Bessembinder, Spatt, and Venkataraman 11

Treasury securities completed on IDB platforms are available beginning 1991

from GovPX, and after 2001, from eSpeed and BrokerTec.

Information on bond characteristics (e.g., issue size, offering date, maturity date, credit rating, and issuer industry) is available for non-Treasury securities from Mergent Fixed Income Securities Database (FISD) or Bloomberg, and

for Treasury securities from the U.S. Treasury (https://www.treasurydirect.gov/).

Data on investor flows into or out of bond mutual funds are available from the

Investment Company Institute. Data on bond mutual fund holdings are available

from the Center for Research in Security Prices (CRSP) Mutual Fund database,

Morningstar database, and Thomas Lipper eMaxx database, and for individual

Treasury securities from the U.S. Treasury. Securities lending data for bonds

are available from Markit Securities Finance database, and previously from Data

Explorers.

IV. Trading Activity in Bond Markets

A. Treasury Bonds

According to a 2017 Treasury report, the market for U.S. Treasury

bonds is among the largest and most liquid markets in the world (https://www

.treasury.gov/press-center/press-releases/Documents/A-Financial-System-CapitalMarkets-FINAL-FINAL.pdf). Between Aug. 1, 2017 and July 31, 2018 (the

first year dealers were required to report Treasury transactions to FINRA), daily

Treasury trading volume averaged $574 billion (http://libertystreeteconomics

.newyorkfed.org /2018/09/unlocking-the-treasury-market-through-trace.html).11

Transactions between dealers and customers account for about half ($269 billion)

of Treasury activity. Intermediation in the dealer-to-customer market is provided

by 23 primary government bond dealers, as designated by the Federal Reserve

Bank of New York. Trades among dealers have historically been intermediated

by IDBs. In recent years, approximately 70% of interdealer activity occurs

on electronic IDB platforms, and the remainder on voice and manual screen

platforms. According to the 2015 Joint Staff Report on the U.S. Treasury

Market (https://www.treasury.gov/press-center/press-releases/Documents/Joint

Staff Report Treasury 10-15-2015.pdf), nonbank principal trading firms that

implement HFT strategies account for a larger share of trading activity (62%)

than primary bond dealers (33%) on electronic IDB platforms.12 Treasury IDB

trading activity is concentrated in on-the-run securities, that is, the most recently

issued notes or bonds of a particular maturity. In terms of 2017 daily trading

volume, the 5-year note is the most active on-the-run security ($48 billion par),

followed by the 10-year ($40 billion par) and 2-year ($18 billion par) notes.

Trading activity in off-the-run issues is weighted toward shorter maturity bonds

and is more likely to be intermediated on voice and manual IDB platforms.

Graphs A and B of Figure 2 displays average daily trading volume and average

trade size for on-the-run Treasury securities between 1991 and 2017.

11We thank Michael Fleming and BrokerTec for providing 2017 IDB Treasury trading data.

12Principal trading firms account for 60% to 70% of the trading volume in Treasury bonds and

approximately 50% of the trading volume in Treasury futures in recent years. The firms participate as

short-term liquidity providers and rarely carry overnight inventory positions.
12 Journal of Financial and Quantitative Analysis

FIGURE 2

Trading Activity, Bid–Ask Spreads, and Depth in the U.S. Treasury Bond Market (1991–2017)

Figure 2 plots trading volume (Graph A), average trade size (Graph B), average bid–ask spread (Graph C), and average

order book depth at the inside spread (bid and offer side) (Graph D) in the U.S. Treasury market. The data sources are

the interdealer broker (IDB) trading platform GovPX and BrokerTec, estimated by day from June 17, 1991 to June 30,

2017. Plotted lines are exponentially weighted moving averages. This figure was reproduced with permission from Adrian,

Fleming, and Vogt (2017).

Billions of Dollars Hundreds of Trades

Basis Points Billions

Graph A. Trading Volume

Graph B. Trade Size

Graph C. Bid–Ask Spread

Graph D. Depth

1991 1996 2001 2006 2011 2016

1991 1996 2001 2006 2011 2016

1991 1996 2001 2006 2011 2016

1991 1996

2-year 5-year 10-year

2001 2006 2011 2016

80

40

50

40

30

20

10

0

4

3

2

1

0

3

2

1

0

0
Bessembinder, Spatt, and Venkataraman 13

Macroeconomic announcements are an important source of uncertainty for

the valuation of Treasury instruments. Fleming and Remolona (1999) show that

trading activity and bid–ask spreads adjust to scheduled macroeconomic announcements in stages (see Balduzzi, Elton, and Green (2001), Green (2004),

among others, for related evidence). Before an announcement, the sensitivity

of prices to order flow is lower than normal, suggesting an absence of information leakage. The announcement induces a sharp and nearly instantaneous

price change accompanied by a reduction in trading volume and an increase in

bid–ask spreads. These results are consistent with French and Roll’s (1986) assertion (page 9) that public information “affects prices before anyone can trade on it”

as well as models of inventory risk (e.g., Ho and Stoll (1983)), where dealers set

wider bid–ask spreads when volatility is high. In the seconds after announcements, trading volume increases, as do price volatility and the price impact of

trades. These patterns support theoretical predictions (e.g., Kim and Verrecchia

(1991), (1994)) regarding informed traders’ ability to quickly interpret the valuation implications of macro announcements. In the 5–15 minutes after announcement, the market is characterized by high liquidity and smaller price effects of

trades, consistent with the interpretation that uninformed traders participate in

portfolio rebalancing trades in the wake of these announcements.

Brandt and Kavajecz (2004) document substantial variation in yields on

Treasury instruments, even in the absence of macroeconomic or other news releases. They find that order-flow imbalances account for 26% of yield variation

on days with no news, implying that the trading process itself contains information for price discovery in government bonds. This finding parallels the French

and Roll (1986) result that the trading process provides substantial information

for equity pricing, even absent fundamental news.

B. Structured Bonds

The most actively traded structured securities are TBAs, which are forward

contracts that allow the seller to deliver any agency MBS that meets agreed-upon

criteria based on coupon, maturity, and issuer, on the settlement date.13 The buyer

and seller negotiate the trade price and MBS face value to be transacted. The

TBA price reflects the cheapest-to-deliver MBS that fulfills the terms of the TBA

trade, although traders do not make or take delivery in most cases. The TBA

market is used by banks and other intermediaries to hedge mortgages that they

originate and by investors that buy and sell already-issued MBS. Gao, Schultz, and

Song (2017) report that the average dealer-to-customer transaction size of outright

TBA trades during their May 2011 to Apr. 2013 sample period is $32.6 million,

and about 37% of trades between dealers and customers exceeds $10 million.

Individual structured MBS bonds trade in the “specified pool” (SP) market, where

buyers and sellers agree to exchange a particular bond. The average customer

transaction size of SP trades during the same period is $6.5 million, and 10.7% of

these transactions are for $10 million or more.

13MBS are pass-through securities in which interest and principal payments on the underlying

mortgages are passed on to bond investors. A basic pool of assets may have more than 100 securities

or tranches, each with a unique payoff structure, with many of these issues being very small.
14 Journal of Financial and Quantitative Analysis

Bessembinder et al. (2013) study trading in structured products from May

2011 to Jan. 2013. Notably, they report that most outstanding structured bonds

do not trade at all during their sample. They report that agency MBS are more

likely to trade than structured bonds issued by private entities, such as commercial mortgage-backed securities (CMBS) or ABS, which are subject to credit risk.

Trading activity in both the MBS and ABS markets is negatively related to bond

age, credit risk, and interest rate risk and positively related to issue size.14 The

study also finds that dealer-to-customer trades represent almost 80% of trading

volume for individual structured bonds, compared to 52% for the TBA market.

Gao et al. (2017) attribute the high percentage of TBA interdealer trades to hedging activities of dealers, who also hold positions in the SPs that are eligible for

TBA delivery.

C. Corporate Bonds

Trading activity in corporate debt is fragmented because an issuer often has

multiple bond issues outstanding. Because each involves a unique set of promised

payments, bond issues are not fungible, and each trades separately. As of Dec.

2017, firms in the Standard & Poor’s (S&P) 500 index had 11,990 outstanding

bond CUSIPs, whereas firms in the Russell 1000 index had 13,083 outstanding

bond CUSIPs. Mizrach (2016) finds that trades for 33,945 distinct bond CUSIPs

are reported in the TRACE database during the first 9 months of 2015. More

than 90% of TRACE-reported bonds are rated investment grade, and the most

common credit rating is BBB. A majority of corporate securities are privately

placed structured bonds that do not trade actively.

Bessembinder et al. (2018) report that aggregate corporate bond trading activity, measured by either number of trades or transaction volume, surges over

their 2006–2016 sample period. This increase in trading activity is accompanied

by even more rapid growth in corporate bond issuance. Annual trading activity

relative to the amount of corporate bonds outstanding (i.e., turnover) generally

trends downward, from 94% in 2006 to 62% in 2015. The share of overall trading volume increases for investment-grade bond issues and large-issue-size bonds

during that period, as does the share of transactions that are likely to be facilitated

by electronic venues.

The distribution of the number of days that a corporate bond issue trades in

a year is highly skewed. It is reported that 9.3% of TRACE bonds never trade

during 2016, 39% trade on 5 or fewer days, and only 1.5% trade every day.15

Among bonds with issue size greater than $1 billion, the average number of days

in a year with any trading is 179 for investment-grade bonds and 150 for highyield bonds. As a consequence, estimates (e.g., of transactions costs) obtained by

weighting observations across observed trades largely represent outcomes in the

more active segments of the corporate bond market.

14See also Friewald, Jankowitsch, and Subrahmanyam (2014), Atanasov and Merrick (2012), and

Hollifield et al. (2017), each of which studies structured product trading.

15See the Citi report, “Developments in Credit Market Liquidity,” Jan. 11, 2018, presented at the

SEC’s Fixed-Income Market Structure Advisory Committee (FIMSAC) meeting (https://www.sec

.gov/spotlight/fixed-income-advisory-committee/citi-developments-in-credit-market-liquidity-fimsa011118.pdf).
Bessembinder, Spatt, and Venkataraman 15

Institutional-sized trades (greater than $1 million) account for most of

the dollar volume of trades, in particular, 88% of customer trading volume

between 2014 and 2016 (Bessembinder et al. (2018)). Retail-size trades (less

than $100,000) account for between 60% and 70% of reported customer transactions, but only 2% of customer trading volume during the same period. Average

trade size has decreased over the last decade, from $2 million in 2006–2007 to

$1.21 million in 2014–2016.16 The decrease in average trade size could reflect

higher participation by retail investors, increased use of electronic venues that

typically execute smaller trades, and/or difficulty in locating counterparties for

large trades that are desired.

Informed traders are active in corporate bonds, as evidenced by the finding in Wei and Zhou (2016) that abnormal bond trading activity before corporate

earnings announcements predicts both the direction of earnings surprises and the

post-announcement bond returns. Kedia and Zhou (2014) present evidence of an

increase in trading activity and abnormal returns in the bonds of target companies before acquisition announcements. Das, Kalimipalli, and Nayak (2014) report that large institutional traders shift from corporate bonds to credit default

swap (CDS) trading after the introduction of the latter, resulting in reduced bond

price efficiency.

Informed trading may occur simultaneously in equity, debt, and derivative

instruments of a firm. Back and Crotty (2015) examine the price impact of order

flows in the stock and bond markets. When investors’ private information involves

asset mean value, Back and Crotty’s model predicts that the cross-market price impacts are positive, and when investor’s information involves asset risk, the crossmarket price impacts are negative. They show empirically that cross-market price

impacts are positive, implying that private information primarily involves mean

asset values, and that both cross-market and own-market price impact estimates

are larger for firms with high-yield debt than for firms with investment-grade debt.

Asquith, Au, Covert, and Pathak (2013) study corporate bond lending by

one major depository institution, reporting that the cost of borrowing bonds is

between 10 bps and 20 bps (comparable to the cost of borrowing stocks) and

declines during their 2004–2007 sample period. In contrast to results obtained for

equity short selling, Asquith et al. report that bond short selling does not forecast

price changes over the following month.

Two recent studies conclude that short selling predicts bond returns in scenarios where private information is more likely. Hendershott, Kozhan, and Raman

(2019), who study data during 2006–2011, report that short selling predicts bond

returns in high-yield corporate bonds. They also show that short positions on average increase following periods of buying order imbalances, which is consistent

with short sellers “leaning against the wind” in a de facto market-making role.

Using bond lending data over 2006 to 2015, Anderson, Henderson, and Pearson

(2018) also find that proxies for short-sale constraints predict negative 3-month

abnormal returns. The study concludes that information is incorporated more

slowly in bonds than in equities because of lower trading frequency and liquidity.

16By comparison, the sizes of equity trades also declined dramatically over the years, in part

because of Regulation NMS, and in an earlier era, because of decimalization.
16 Journal of Financial and Quantitative Analysis

On balance, the shorting of fixed-income instruments comprises a promising area

for future research.

The literature finds mixed evidence regarding the relative information efficiency of stock versus corporate bond markets. Kwan (1996) and Downing,

Underwood, and Xing (2009) report that stock prices lead corporate bond prices

in incorporating firm-specific information, whereas Hotchkiss and Ronen (2002)

and Ronen and Zhou (2013) report that corporate bond markets are as informationally efficient as stock markets. Back and Crotty (2015) find that the equity

market leads the bond market for the portion of returns that are not explained by

order flows.

D. Municipal Bonds

Trading activity in municipal bonds is highly fragmented, as more than

50,000 issuers, including state and local governments, towns, cities, and counties,

have issued over 1.5 million distinct bonds, with an outstanding principal value

of $3.9 trillion at the end of 2017.17 Municipal bonds are issued in series, with

larger issuances consisting of several smaller bonds of differing maturity. Municipal bond issuers typically disclose financial information at the time of bond issue,

but they are not required to make periodic financial disclosures (see Downing

and Zhang (2004)). Investor ownership tends to be geographically concentrated,

as many jurisdictions exempt only own-state issues from state and local income

taxes. Furthermore, limited disclosure may result in bonds being disproportionately held by more knowledgeable local investors.

Most municipal bonds have had a credit rating of AA or better, reflecting the

issuers’ tax authority and, before the financial crisis, the widespread use of credit

enhancement via insurance.18 The use of municipal bond insurance comprises an

interesting puzzle, as risk sharing is often viewed as a central purpose of financial

markets, but it is unclear to what extent insurers have the capacity to bear capital market risks that have substantial systematic components. Indeed, the limited

capacity to bear correlated risk was at the root of the failure of mortgage insurers

during the financial crisis.

Chalmers et al. (2019) study MSRB data. For their most recent (2011–2012)

subperiod, they report that transactions of less than $25,000 account for more

than half of the trades, and those less than $100,000 account for nearly 99%

of trades, reflecting that individual investors hold the majority of outstanding

municipal bonds. Trades greater than $1 million account for 1.5% of transactions but represent 45% of volume traded. Wu (2018) reports that interdealer

trades account for 28% to 37% of all par value traded between 2005 and 2017.

The volume of customer-buy transactions significantly exceeds the volume of

customer-sell transactions, indicating that investors frequently hold municipal

17Studies of municipal bond trading include Downing and Zhang (2004), Edwards et al. (2007),

Green, Hollifield, and Schurhoff (  ̈ 2007a), (2007b), Green, Li, and Schurhoff (  ̈ 2010), Schultz (2012),

Chalmers, Liu, and Wang (2019), and Wu (2018).

18One potential policy innovation is to introduce a forward market for municipal bonds, similar to

the TBA market for agency MBS. For municipals, the basis risk is due to tax status and default risk,

whereas for MBS, the basis risk is due to prepayment and default risk.
Bessembinder, Spatt, and Venkataraman 17

bonds until maturity. However, the difference in buy versus sell volume has

declined in recent years.

According to 2018 MSRB statistics, electronic platforms account for

59% of all interdealer trading in municipal bonds (https://emma.msrb.org/

MarketActivity/ViewStatistics.aspx). The states with the highest secondary market trading volume in the municipal bond market are California (14.6%), Texas

(13.8%), New York (9.3%), and Illinois (9.1%). Less than 1% of outstanding municipal bonds trade on a typical day, and less than 15% of the outstanding bonds

trade in a typical month. Trading activity is highest during the month of the bond

issuance and drops off sharply by the second month.

V. Trading Costs in Fixed-Income Markets

The estimation of trading costs in bond markets is challenging for several

reasons. With the exception of Treasury securities, dealers do not post firm bid and

ask quotes, and other indications of trading interest, such as indicative quotes and

limit orders, are not widely disseminated and may not be available to researchers.

This implies that simple measures often used in equity markets, such as quoted

spreads (the difference between the bid and ask quote) or effective spreads (the

difference between the trade price and the quote midpoint), cannot be readily

implemented in the majority of bond markets.

Researchers have estimated bond market liquidity using methods that rely

on transaction price data alone. For example, Bao, Pan, and Wang (2011) estimate illiquidity in bond markets using the return reversal measure of Roll (1984),

whereas Chen, Lesmond, and Wei (2007) focus on the percentage of trading days

with zero returns, taken to be indicative of a lack of trading due to high costs.

Schestag, Schuster, and Uhrig-Homburg (2016) study several bond liquidity measures including i) trade price reversal measures such as Roll’s (1984) estimator,

Hasbrouck’s (2009) Gibbs sampler, and Pastor and Stambaugh’s (2003) measure; ii) price impact measures such as Hasbrouck’s (2009) lambda and Amihud’s

(2002) measure; and iii) Corwin and Schultz’s (2012) high-to-low price ratio

measure, which exploits that daily high (low) prices are most often buy (sell) orders. Jankowitsch, Nashikkar, and Subrahmanyam (2011) and Feldhutter (2012),

among others, use the dispersion in bond transaction prices to indirectly measure

customer search costs attributable to limited pre-trade transparency.

A significant challenge in using transaction prices to estimate trading costs

arises from the infrequent trading activity in the majority of corporate bonds, implying that there is potential for substantial information arrival between adjacent

transactions, which introduce noise in the empirical estimates. In the absence of

quotation data, an additional challenge arises in the selection of a benchmark price

for measuring trading costs.19

The most frequently implemented approaches to estimating trading costs in

bond markets are the dealer round-trip (DRT) cost method and indicator-variable

regression models. These methods take advantage of the fact that bond databases

19For example, Hendershott and Madhavan (2015) use the last interdealer trade in the bond as

the benchmark price. However, this benchmark contains noise, as interdealer trades cannot readily be

classified as buyer or seller initiated.
18 Journal of Financial and Quantitative Analysis

such as TRACE identify interdealer versus dealer–customer trades, and for customer trades, whether the customer was the buyer or the seller in the transaction.

However, these measures rely on completed trades and thus do not account for

trades that were desired but not completed because of a lack of liquidity. Furthermore, transaction-based estimates are inherently biased toward bonds and periods

with frequent trading, which tends to occur when liquidity is plentiful.

A. DRT Cost

The DRT cost measures the difference between the average dealer sale price

and the average dealer purchase price in dealer-to-customer transactions for a

given bond during a specified window. The interval is most frequently 1 day

(e.g., Green et al. (2007b)), though some studies examine shorter (15 minutes

in Feldhutter (2012)) or longer (5 days in Goldstein, Hotchkiss, and Sirri (2007))

windows.20 Earlier studies calculate the average sale and purchase price across all

dealers (e.g., Hong and Warga (2000), Chakravarty and Sarkar (2003)), whereas

studies using enhanced TRACE data with masked dealer identifiers calculate the

average prices on an individual dealer basis (e.g., Goldstein et al. (2007)).21 The

advantages of the DRT measure are that i) it is simple to interpret as an average realized bid–ask spread and ii) it does not rely on any model assumptions. A

limitation is that the measure does not capture transactions that are not offset by

opposite-direction trades within the observation window, which limits the amount

of usable data and makes the method difficult to implement for bonds that trade

infrequently.

B. Indicator-Variable Regression Model of Trading Costs

The indicator-variable regression model was first used to estimate trading

costs in equity markets (see Huang and Stoll (1997), Madhavan, Richardson, and

Roomans (1997)) but has since been adapted to bond markets.22 The model presumes that the transaction price for a customer buy (sell) is the intrinsic value

of the bond at the time of transaction, plus (minus) half the bid–ask spread. The

spread reflects dealers’ inventory, adverse selection, and order-processing costs,

and, potentially, monopoly power over less sophisticated customers.

Specifically, trading cost estimates can be obtained by regressions of 1Pst ,

the percentage change in the trade price for a given bond between an observed

trade at time s to the next observed trade at time t, on 1Qst = Qt − Qs, where Qs

20In fact, Hollifield et al. (2017) discard intermediation chains that take more than 15 days to

complete, controlling for valuation changes between the execution times by using several marketwide

control variables, and Goldstein and Hotchkiss (2020) examine sales within 60 days of the dealer’s

buy order.

21Studies that calculate DRT costs using NAIC insurance data include Chakravarty and Sarkar

(2003) and Hong and Warga (2000), MSRB municipal bonds data include Green et al. (2007b),

TRACE corporate bonds data include Goldstein et al. (2007) and Feldhutter (2012), and TRACE

structured bonds data include Hollifield et al. (2017).

22For applications in the bond market, see Schultz (2001), Bessembinder, Maxwell, and Venkataraman (2006), Harris and Piwowar (2006), and Edwards et al. (2007). Some differences in approaches

exist across the studies. Schultz uses bid quotes as of end of prior month from FISD with adjustments for change in interest rates. Harris and Piwowar present a time-series trading cost model that

incorporates interdealer transactions.
Bessembinder, Spatt, and Venkataraman 19

and Qt are indicator variables equal to 1 for customer buys and −1 for customer

sells at times s and t. The resulting slope coefficient estimates the effective oneway trade execution cost and can be interpreted as half the difference between the

price at which dealers will sell a bond to a customer and the price at which they

will purchase the bond from a customer. The analysis is typically implemented

using trades between dealers and customers. To account for information flow between adjacent transactions, the model can incorporate additional measures of

observable public information, such as changes in Treasury interest rates, stock

returns, market volatility, and so on, that affect the intrinsic bond value. A key

advantage is that the method can include all bond transactions and, using pooled

estimation, can be implemented for subsets of bonds with modest trading activity. A limitation is that the impact of public information on intrinsic bond values

could differ across bonds, which might introduce measurement errors in pooled

estimation.

C. Estimates of Trading Costs in Bond Markets

The secondary market for Treasury bonds is extremely liquid, with an estimated interdealer bid–ask spread on the BrokerTec IDB during 2017 of about

1 bp.23 The average quoted depth at the best price on the BrokerTec limit order

book averaged $645 million for the 2-year note, $125 million for the 5-year note,

and $109 million for the 10-year note. The market is even deeper when considering the hidden portion of iceberg orders and the “work-up” feature that allows participants to negotiate to expand the trade size after agreeing on a price.24 Spreads

are higher for longer duration bonds and smaller for larger issue size and recently

issued bonds. Graphs C and D of Figure 2 display average daily bid–ask spreads

and depth in the IDB market for on-the-run Treasury securities between 1991 and

2017. Bid–ask spreads exhibit a spike during the peak of the financial crisis but

are relatively stable over the last decade.

In the structured bond market, Bessembinder et al. (2013) report an average customer round-trip trade execution cost between 2011 and 2013 of 0.48%.

Trading cost estimates decline with trade size, averaging 1.65% for small (less

than $100,000), 0.48% for medium (between $100,000 and $1 million), and only

0.10% for large (greater than $1 million) trades. Trade execution costs vary substantially across types of structured bonds, with higher cost estimates observed for

products of smaller issue size. The lowest trading costs are estimated for TBA securities (0.02%), whereas the highest cost estimates are for agency collateralized

mortgage obligations (CMOs, 1.5%). Average customer trading cost estimates are

0.80% for agency MBS, 0.24% for CMBS, and 0.48% for ABS.

23We thank Michael Fleming for providing BrokerTec statistics for 2017. Additional description

of BrokerTec is provided by Fleming, Mizrach, and Nguyen (2018).

24The importance of a workup mechanism in Treasuries in which additional size can be transacted immediately after a transaction at the specified price is highlighted by Boni and Leach (2004).

Fleming and Nguyen (2020), Fleming, Schaumburg, and Yang (2015), and Schaumburg and Yang

(2016). Duffie and Zhu (2017) show that such a workup or dark pool mechanism can limit the

allocative inefficiency that may result from larger transactions, but because of adverse selection about

the order imbalances of other investors would only be used by investors with relatively large position

imbalances.
20 Journal of Financial and Quantitative Analysis

For agency CMOs, Hollifield et al. (2017) find that 144A instruments

have smaller spreads than registered instruments, which may reflect the greater

bargaining power of the relatively more sophisticated investors in 144A bonds

as compared to investors in registered bonds. Friewald, Jankowitsch, and

Subrahmanyam (2012) report that trading costs for structured bonds are lower if

they are traded in institutional size, are issued by a federal authority, or have low

credit risk. Vickery and Wright (2013) and Gao et al. (2017) find that mortgage

bonds that are eligible for delivery in the TBA market are cheaper to trade than

TBA-ineligible mortgage bonds, reflecting that the TBA market allows dealers to

effectively hedge TBA-eligible MBS positions.

Schultz (2001) relies on the NAIC database to report the first large-sample

estimates of corporate bond trading costs, documenting that average trading costs

are large as compared to equities, and that costs for large institutional trades exceed those that are small. Bessembinder et al. (2018) provide what may be the

broadest study of corporate bond trading, studying TRACE data from 2006 to

2016.25 For the recent 2014–2016 period, they estimate average round-trip trading

costs of 0.84%.26 Trading cost estimates decrease with trade size, averaging 1.25%

for trades less than $100,000, 0.58% for trades between $100,000 and $1 million,

0.40% for trades between $1 million and $10 million, and 0.32% for block trades

that exceed $10 million. Graph A of Figure 3 plots estimated average one-way

trading costs over 2006–2016. Although trading costs temporarily increase during the financial crisis, average cost estimates at the end of the sample period are

nearly equal to those at the beginning. Edwards, Harris, and Piwowar (2007) report that corporate bond trading costs are lower for corporate bonds with larger

issue size, with better credit quality, for younger (more recently issued) bonds and

those closer to maturity, for simpler bonds without options, and for bonds issued

by private issuers, Rule 144a issues, and foreign issuers.

Bessembinder et al. (2018) report that the majority of corporate bond transactions, more than 90% each year between 2006 and 2016, are facilitated by dealers

as “principal” trades, where the dealer commits capital to take bonds into inventory. Several studies (see Harris (2015), Choi and Huh (2017), and Schultz (2017))

report that the incidence of agency trading, where a dealer facilitates trades between customers without taking ownership or where the dealer briefly owns the

bond before completing a previously arranged transfer to another customer, has

increased in recent years. Choi and Huh (2017) report that trading costs for transactions where dealers continue to commit capital have increased in recent years.

Harris and Piwowar (2006) estimate trading costs for municipal bonds during the earlier Nov. 1999–Oct. 2000 period. They report higher trading cost

25The SAS programs used by Bessembinder et al. (2018) to read and clean the enhanced corporate

bond TRACE data, estimate trading costs using indicator regression model, and calculate dealer capital

commitments are available at https://people.smu.edu/kumar/tracecorporatebonds/.

26See Edwards et al. (2007) for cross-sectional analysis of trading costs. Time-series analysis of

trading costs is presented in Bessembinder et al. (2018), Bao et al. (2018), Dick-Neilsen and Rossi

(2019), Schultz (2017), Trebbi and Xiao (2019), and Anderson and Stulz (2017), among others. For

a sample of actively traded corporate bonds, Schestag et al. (2016) find that trading cost measures

using intraday data are correlated with each other, and that low-frequency measures using daily data

are representative of trading costs based on intraday data.
Bessembinder, Spatt, and Venkataraman 21

FIGURE 3

Trading Costs and Dealer Capital Commitment in the U.S. Corporate Bond Market

Figure 3 shows estimated trade execution costs paid by customers in customer-to-dealer principal trades in the corporate

bond market from Jan. 2006 to Oct. 2016. In Graph A, transaction costs (one-way) are estimated following the indicator

regression model implemented by Bessembinder, Maxwell, and Venkataraman (2006) using enhanced Trade Reporting

and Compliance Engine (TRACE) data. Overnight dealer capital shows the 6-month moving average aggregate overnight

capital scaled by volume for the top 70% dealers examined by Bessembinder, Jacobsen, Maxwell, and Venkataraman

(2018). Graph B shows trade execution costs in customer-to-dealer principal trades for four trade size categories: small

trades, which are less than or equal to $100,000; medium trades, which are greater than $100,000 and less than or equal

to $1 million; large trades, which are greater than $1 million and less than or equal to $10 million; and institutional trades,

which are greater than $10 million.

0.25

0.35

0.45

0.55

One-Way Trading Costs (%)

Overnight Dealer Capital/Volume (%)

0.65

0.75

0.85

11

13

15

17

19

21

23

Jan.-06

May-06

Sep.-06

Jan.-07

May-07

Sep.-07

Jan.-08

May-08

Sep.-08

Jan.-09

May-09

Sep.-09

Jan.-10

May-10

Sep.-10

Jan.-11

May-11

Sep.-11

Jan.-12

May-12

Sep.-12

Jan.-13

May-13

Sep.-13

Jan.-14

May-14

Sep.-14

Jan.-15

May-15

Sep.-15

Jan.-16

May-16

Sep.-16

Dealer Capital Trading Cost

Graph A. Customer Trading Costs and Dealer Capital Corporate Bond Market (2006–2016)

Graph B. Monthly Corporate Bond Trading Costs (%) on Customer Transaction by Trade Size

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Jan.-06

July-06

Jan.-07

July-07

Jan.-08

July-08

Jan.-09

July-09

Jan.-10

July-10

Jan.-11

July-11

Jan.-12

July-12

Jan.-13

July-13

Jan.-14

July-14

Jan.-15

July-15

Jan.-16

July-16

Retail Size (≤ $100K) Size > $100K & ≤ $1M

Size > $1M & ≤ $10M Institutional Size > $10M
22 Journal of Financial and Quantitative Analysis

estimates for municipal bonds of lower credit quality, smaller issue size, and with

complex features. Trading cost estimates are also lower for younger bonds and

those near maturity. Wu (2018) studies DRT trading costs for municipal bonds

using MSRB transactions data between 2005 and 2018, reporting average cost estimates of 0.90% for retail-size trades (par value less than $10,000) versus 0.15%

for institutional-size trades (par value exceeds $1 million) in Apr. 2018. Figure 4

displays annual estimates of round-trip municipal bond trading costs compiled by

Wu. Estimated trading costs decline, particularly for small trades, between 2009

and 2018, whereas costs increase for all trade size categories during the financial crisis.27 Although the trading cost differential between retail and institutional

trades has narrowed in recent years, institutional-size trades continue to obtain

lower cost executions as compared to retail-size trades in municipal bond markets.

These estimates of trade execution costs focus on the direct cost of trades

that were successfully completed. As such, they do not account for the potential

delays in completing transactions or costs associated with trades that were desired

but not completed. Unfortunately, proprietary data sets from buy-side bond institutions that include information on execution delays and fill rates are not available

to researchers.28 To obtain additional perspective, researchers have studied dealers’ capital commitment (i.e., the extent to which dealers use their own capital

to absorb imbalances in customer order flow). Capital commitment is particularly

important in markets (e.g., corporate bonds) where customers arrive infrequently

and search costs are large.

It is possible to estimate dealers’ capital commitment using the enhanced

TRACE database that includes masked individual dealer identities. Bessembinder

et al. (2018) and Bao et al. (2018) both report that although average transaction

costs have not increased in recent years, dealers’ capital commitment has declined

markedly (see Graph A of Figure 3). The decreases in capital commitment are attributable to bank-affiliated dealers, whereas nonbank dealers increase their capital commitment, albeit from low initial levels. Each study concludes that postcrisis banking regulation, including the Volcker Rule (which restricts proprietary

trading by banks), has likely contributed to the decline in bank-affiliated dealer

capital commitment, and has accelerated the increase in capital commitment by

nonbank dealers because of their equilibrium response. Goldstein and Hotchkiss

(2020) show that dealers are particularly hesitant to commit capital to accommodate larger trades in riskier bonds. Duffie (2018) and Adrian, Fleming, Shachar,

and Vogt (2017) observe that Basel 2.5 and Basel III banking regulations have

increased the required capital (i.e., equity) requirements for inventory holdings of

credit products with higher default risk. This has led to an increase in dealers’ inventory funding costs, particularly for corporate bonds and credit derivatives, and

likely has contributed to the decline in dealer capital committed to market making.

A striking pattern observed in most bond markets is that smaller transactions, which are more likely to originate from retail investors, are more expensive

27However, the reasons for this decline are the source of some debate (see https://www.bondbuyer

.com/news/bdas-problems-with-msrb-spreads-study).

28In contrast, data on institutional investors’ actual and desired equity market transactions are

compiled by Abel Noser (a consulting firm that works with institutional investors to monitor execution

costs) (see, e.g., Puckett and Yan (2011), Anand, Irvine, Puckett, and Venkataraman (2012)).
Bessembinder, Spatt, and Venkataraman 23

FIGURE 4

Trading Costs in the U.S. Municipal Bond Market (2005–2018)

Graph A of Figure 4 plots the dealer round-trip effective spread trading costs measured as a percentage of the

midpoint between average customer buy and sell prices (green line) and as a percentage of customer sell price

(blue line) for all customer-to-dealer trades, and for five transaction size groups for municipal bonds between 2005

to 2018 using the Municipal Securities Rulemaking Board (MSRB) database, as reported in Wu (2018). The light blue

line reports the effective spread estimates from Sirri (2014). Graph B shows the effective spread by five trade-size

groups during the relevant period: less than $10,000 par value or less, $10,001–$25,000 par value, $25,001–$100,000

par value, $100,001–$1,000,000 par value, and more than $1,000,000 par value trades. This figure was reproduced with

permission from Wu (2018).

70

250

200

150

100

50

0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 April

2018

2005 2006 2007

Par Value $10,000 or less

Par Value Over $1,000,000 

Par Value $25,001–$100,000

Par Value $10,001–$25,000

Par Value $100,001–$1,000,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 April

2018

80

90

100

110

120

130

140

In Basis Points 150

160

170

180

190

Sirri Same-Day Customer-Customer Differential (2014)

MSRB Same-Day Effective Spread as a Percent of Customer Sell Price (2018)

MSRB Same-Day Effective Spread as a Percent of Customer Buy and Sell Midpoint Price (2018)

Graph A. Municipal Bond Effective Spread for Customer-Buy and -Sell Trades (2005–2018)

Graph B. Municipal Bond Effective Spread for Customer-Buy and -Sell Trades by Trade Size (2005–2018)
24 Journal of Financial and Quantitative Analysis

to complete than larger transactions, which more likely originate from institutional investors. Graph B of Figure 3 displays annual estimates of customer trade

execution costs in U.S. corporate bond markets, for small (less than $100,000),

medium ($100,000 to $1 million), large ($1 million to $10 million), and institutional (greater than $10 million) trades. This pattern of higher percentage execution costs for small trades is the opposite of that typically observed in equity

markets. It is difficult to attribute the trade size cost differential in bond markets

to higher adverse selection, as institutional investors have more resources than individual investors to analyze credit risk, or to higher inventory risk, as large trades

involve more dealer capital than small trades.

The explanation for differential effects of trade size on transaction costs

across bond and equity markets likely lies in market structure. Equity markets

are more transparent and governed by order-handling rules that require execution at the best available price, whether posted by the trading public or a dealer.

The opaque and decentralized nature of the bond market involves high search

costs that can place less sophisticated investors at a significant disadvantage.

Duffie, Garleanu, and Pedersen ( ˆ 2005) develop the seminal search and matching model of an OTC market, deriving the bid–ask spreads charged by dealers

and predicting that bilaterally negotiated prices depend on the relative bargaining

power of traders, whereas Green et al. (2007b) predict that fragmentation and the

lack of transparency in the bond market create opportunities for dealers to exploit

monopoly power over retail investors.

Although the market power hypothesis may have considerable explanatory

power (especially for municipal bonds, where retail participation is especially

high), it is not necessarily a complete explanation. For example, smaller trades

have larger spreads than larger trades in securitized trading (e.g., Hollifield et al.

(2017)), even though structured products can be purchased only by accredited investors. Furthermore, as discussed later, a similar size effect arises in the studies

of Bessembinder, Maxwell, and Venkataraman (2006) and O’Hara, Wang, and

Zhou (2018), even though their samples are based entirely on insurance company

transactions.

D. Trading Costs and Equilibrium Asset Pricing

Trade execution costs matter not only because they measure the cost of completing trades, but also because they can affect the cost of capital for issuing entities. Amihud and Mendelson (1986) propose that expected asset returns should

increase with asset illiquidity, as investors require compensation for the costs of

buying and selling an asset. Amihud and Mendelson (1991) compare yields on

short-term U.S. Treasury notes and Treasury bills with the same maturity. Although each instrument has identical cash flows and interest rate risk, the market

for bills is substantially more liquid than the market for notes. The study finds

that yields on notes are higher than those on bills, and the differential is a decreasing function of time to maturity, which supports the theoretical prediction

that bond market liquidity affects the pricing of credit instruments. Similarly,

Krishnamurthy (2002) attributes the yield differential between on-the-run and

off-the-run Treasury bonds to liquidity.
Bessembinder, Spatt, and Venkataraman 25

In corporate bonds, illiquidity has been offered as an explanation for the puzzle that credit spreads (i.e., yields relative to Treasury securities) are higher than

those predicted by structural models of corporate default risk. Chen et al. (2007)

present evidence that bond illiquidity significantly explains variation in yield

spreads across corporate bonds. Chen, Huang, Sun, Yao, and Yu (2018) find that

the relation between trading costs and illiquidity in corporate bonds is concave,

and they attribute the nonlinearity to clientele effects where investors with longer

horizons hold less liquid bonds, as also predicted by Amihud and Mendelson

(1986).

Friewald et al. (2012) and Dick-Nielsen, Feldhutter, and Lando (2012) find

that the impact of liquidity on bond pricing is significantly larger both during

crisis periods and for non-investment-grade bonds. Bao, Pan, and Wang (2011)

report that changes in marketwide illiquidity explains a substantial portion of the

time-series variation in yield spreads of high-rated (AAA to A) bonds. They conclude that illiquidity risk is more important in explaining the increase in corporate

yield spreads during the 2008 financial crisis than is credit risk. Friewald and

Nagler (2020) find that time-varying measures of inventory, search, and bargaining frictions in the corporate bond market explain the systematic component of

yield spread changes. Lin, Wang, and Wu (2011) report that the sensitivity of

corporate bond returns to fluctuations in an aggregate corporate bond liquidity

factor helps explain corporate bond yields, implying that systematic liquidity risk

is relevant for bond valuation. However, Bongaerts, de Jong, and Driessen (2017)

argue that although exposure to the equity market price and liquidity risk affect

corporate bond yields, the effect of exposure to a corporate bond liquidity factor

is economically negligible.

VI. Relevance of Dealer Networks

A. Dealer Networks and Client Costs

Researchers in recent years have increasingly focused on the fact that dealer

markets, including those that dominate fixed-income trading, exhibit a rich network structure. The dealer network in many bond markets exhibits a core–

periphery structure. The central or core dealers are highly interconnected, participate in intermediation chains that can involve multiple dealers, and account for a

high proportion of trading activity. For example, 10–12 dealers account for 70%

of trading volume in corporate bonds between 2006 and 2016 (see Bessembinder

et al. (2018)). Peripheral dealers are sparsely connected and execute relatively few

transactions.

An interesting way to cast the dealer network is that it arises in response to

heterogeneity in dealer valuations and/or costs.29 This has been a central theme

in much of the theoretical network literature that has emerged to study fixedincome trading, reflecting a significant extension of the original Duffie, Garleanu, ˆ

and Pedersen (2005) model in which dealers are homogenous and the interdealer

29Neklyudov (2019) offers a baseline search and matching model with heterogeneous search technology that leads to a centrality discount (lower spreads for central dealers than for peripheral dealers).
26 Journal of Financial and Quantitative Analysis

market is frictionless.30 This article provides the foundation for the extensive

theoretical literature on interdealer trading networks and intermediation chains

that followed. Hugonnier, Lester, and Weill (2015) develop solution techniques to

analyze a framework with general heterogeneity and examine model implications

with regard to average time to trade, the distribution of the length of intermediation chains, the volume of trade in dealer–customer and interdealer trades, and

the relation between intermediation chain length and the bid–ask spread, and to

calibrate their model to market characteristics. In their framework, an agent with

a median preference for holding bonds emerges in a central position and endogenously intermediates the order flow.

Given the widespread empirical importance of a core–periphery interdealer

structure for a broad set of markets, a fundamental feature of network formation

demonstrated by Wang (2018) is that a core–periphery structure arises as an endogenous response even when all agents are ex ante identical. An important facet

of Wang’s framework is that it provides a game theoretic foundation for the core–

periphery structure in which agents who form the core do not have special ex

ante advantages. Wang analyzes the equilibrium number of “core agents” (e.g.,

dealers) as reflecting a trade-off between the benefits of concentration, including

lower inventory risk due to the ability to quickly offset trades, and enhanced price

competition that may arise from the presence of a larger number of dealers.

Dealers can unwind unwanted inventory by trading with their own clients,

or via the dealer network. Schultz (2017) shows that interdealer trades are more

important than trades with customers for managing inventory risk in corporate

bonds. Li and Schurhoff (  ̈ 2019) find that central dealers in municipal bond markets

are more likely than peripheral dealers to trade on a principal basis and more likely

to unwind the position with a customer than with another dealer. They also report

that central dealers are associated with shorter intermediation chains and more

informative trades, and Hollifield et al. (2017) report corresponding evidence for

structured bonds. On balance, the results suggest that dealers with wider trading

networks are better able to manage inventory risk and to extract information from

customer order flow.

The empirical regularity that bid–ask spreads tend to be larger for small

trades may also be a manifestation of the importance of dealer networks. In particular, Hugonnier, Lester, and Weill (2015) present a competitive model with a

search friction, whereas in Bernhardt, Dvocracek, Hughson, and Werner’s (2005)

model, brokers who have more valuable relationships with dealers use more frequent and larger orders. Babus and Kondor (2018) argue that dealers with few

trading partners (peripheral dealers) must execute smaller traders and face greater

adverse selection and wider spreads (while aggregating less information) than

dealers with more trading partners (central dealers). Complementing this asymmetric information analysis, Colliard, Foucault, and Hoffman (2018) analyze the

effect of inventory on OTC dealer networks. Core dealers share the inventory

costs in an efficient manner, providing liquidity to peripheral dealers who have

30Duffie, Garleanu, and Pedersen ( ˆ 2007) extend the model to focus on how search affects asset

pricing in the presence of risk aversion and risk limits.
Bessembinder, Spatt, and Venkataraman 27

heterogeneous access to them. This work analyzes the connection between inventory management and the structure of the interdealer trading network.

There is evidence indicating that bid–ask spreads paid by customers vary depending on a dealer’s position in the network. Schultz (2001) shows that small

bond dealers charge customers more than large dealers. Di Maggio, Kermani, and

Song (2017) and Li and Schurhoff (  ̈ 2019) report evidence of a “centrality premium,” by which central dealers charge customers significantly higher spreads

than peripheral dealers for corporate and municipal bonds, respectively.31 In contrast, Hollifield et al. (2017) report a negative relation between a dealer’s centrality and bid–ask spreads in structured product markets. The theoretical model

in Hollifield et al. suggests that the contrasting results across markets can be explained by differences in customer sophistication. Customers in structured bonds

tend to be sophisticated institutional investors with more information and better

alternatives as compared to retail investors, who are more important in corporate

and municipal bonds. Issa and Jarnecic (2019) report that relationship strength

is associated with higher client trade execution costs, except during periods of

heightened information asymmetry when customer order flow is more valuable.

In the interdealer market, Di Maggio et al. find that trading costs are significantly

lower among dealers with strong trading relationships.

Trading relationships are relevant to trading costs paid not only across retail

and institutional investors, but also for large versus small institutions (see Schultz

(2001), Bessembinder et al. (2006), O’Hara et al. (2018), Hendershott, Li, Livdan,

and Schurhoff (  ̈ 2017)). Schultz (2001) studies NAIC insurance company transactions in corporate bonds from 1995 to 1997, reporting that large institutions (based

on the size of bond holdings) obtain lower trading costs than small institutions.

Focusing on 2001–2011, O’Hara et al. (2018) report that trading cost differences

across large and small insurance companies are i) larger for small-size trades (but

not different for block trades), ii) larger when a dealer maintains a dominant market presence in the bond, and iii) smaller after introduction of TRACE reporting.

Their evidence supports the reasoning that dealers possess sufficient market power

to price discriminate among clients.

Hendershott et al. (2017) find that insurance companies typically form few,

long-lasting relationships with dealers and that less active insurance companies

benefit from a concentrated trading relationship with dealers.32 Their model implies that customers trade off the benefits of existing relationships, including those

that accrue outside of trading per se, against potential speed and execution improvements that could be obtained from searching across dealers. Empirically,

they report a nonmonotonic relation between trading costs and network size in

the corporate bond market.

31Using a broader sample and additional control variables, Goldstein and Hotchkiss (2020) obtain

different results from Di Maggio et al. (2017) in applying network measures to corporate bonds.

32According to the 2017 Greenwich Associates North American Fixed Income survey, the typical

institutional investor executes approximately 55% (85%) of their trading volume in investment-grade

corporate bonds with their top 5 (10) dealers. According to the 2018 Citi report “Developments in

Credit Market Liquidity,” for 1 active (unnamed) corporate bond dealer, the top 25 (101) institutional

clients account for 50% of total U.S. investment-grade volume.
28 Journal of Financial and Quantitative Analysis

B. Dealer Networks and Activity around New Bond Issuance

The impact of trading relationships is pronounced during the new bond issuance process. Investment banks negotiate terms with issuers, and their trading

arms serve as dealers in the secondary market. Underwriting banks are compensated by purchasing the bond from the issuer at the offering price and placing it

with customers at a higher reoffering price. Unlike Treasuries, where the primary

allocation is determined by an auction that is open to the public, the allocation

of corporate and municipal bonds to customers is at the discretion of the underwriting syndicate, similar to the standard practice for equity market initial public

offerings (IPOs).33 Customers with established underwriter relationships that help

the syndicate by participating in a “cold” issue are likely to obtain better allocations in subsequent offerings. Allocations are valuable because of underpricing.34

In addition, some institutions “flip” the bond on the first trading day by selling

it to smaller dealers or institutions who did not obtain allocations in the primary

offering.

Unlike equity offerings where the entire issue is sold at the reoffering price,

Green et al. (2007a) find that a substantial portion of municipal bonds are sold

to customers at a premium to the stated reoffering price. In particular, large purchases take place at or near the reoffering price, whereas small purchases involve

substantial markups over the reoffering price. Schultz (2012) finds that the dispersion in purchase prices declines sharply with the introduction of trade reporting

for municipal bonds in Jan. 2005. Markups over the reoffering price also tend

to increase with the length of the sequence of interdealer trades that precede the

eventual purchase by a bond investor.35

Corporate bonds trading activity tends to be greatest in recently issued bonds.

According to Mizrach (2016), the share of overall market volume attributable to

newly issued corporate bonds declines significantly 3 months after issuance, and

the decline in trading activity in the quarter after bond issuance is substantially

greater in 2015 than in 2007. The decline likely reflects both that long-horizon

investors, such as insurance companies and pension funds, which implement buyand-hold strategies, have acquired the bonds and that institutional investors are receiving higher allocations in recent years. The relation between new issuance and

trading activity is particularly striking for Treasury bonds. Barclay, Hendershott,

and Kotz (2006) report that upon the issuance of a new Treasury security, trading volume in the previously issued security of the same maturity drops by more

than 90%.

33In the equity IPO context, Spatt and Srivastava (1991) demonstrate the potential optimality of

a fixed-price mechanism with pre-play communication and participation restrictions for allocating a

new issue.

34The magnitude of underpricing in corporate bonds averages approximately 50 bps and is more

pronounced for riskier bonds (Datta, Iskandar-Datta, and Patel (1997), Cai, Helwege, and Warga

(2007)) and mega bonds (Helwege and Wang (2017)). In Treasury bonds, underpricing is approximately 1 bp when the auction price is compared with the benchmark price on the auction day

(Goldreich (2007)) and approximately 9 bps when compared with the benchmark price 5 days before the auction (Lou, Yan, and Zhang (2013)).

35Hollifield et al. (2017) note in the context of securitization trading that secondary market bid–ask

spreads increase with chain length.
Bessembinder, Spatt, and Venkataraman 29

VII. Regulation of Fixed-Income Trading

Regulation plays a central role in the organization of fixed-income markets

because of the classic liquidity externality associated with trading, issues of standardization and coordination in the design of markets, the potential importance of

adverse selection, and the degree of delegated decision making to broker-dealers

and investment funds. The 2008 financial crisis highlighted the importance of

liquidity and systemic risk and led to a larger role for government and central

banks in the fixed-income markets and their design.

A. Transparency

The regulation of fixed-income markets is influenced to a degree by the underlying market structure. A prominent example concerns market transparency,

that is, the extent to which relevant market information is readily observable to

all participants. For example, although a limit order market structure involves

the continuous compilation of information regarding unexecuted orders at various prices, it naturally accommodates pre-trade transparency, but this is not an

intrinsic feature of an OTC market. Similarly, because a limit order market necessarily gathers a record of all transactions that occur on the market, dissemination

does not involve further information aggregation. In contrast, transaction price

data in an OTC market are dispersed; therefore, dissemination requires additional

information aggregation. Indeed, most fixed-income markets have moved toward

post-trade transparency only since 2002, as a result of regulation.

The timing of the adoption of post-trade transparency varies across markets

(see Table 3). U.S. regulators first moved to require post-trade transparency in

markets they viewed as relatively more liquid, and they initially allowed dealers

a delay between the time of a trade and its public report. This was done to avoid

potential adverse liquidity consequences that could arise if a dealer were not able

to hedge or sell a portion of his position before disclosure of the trade.

Despite the overall movement in recent years toward greater transparency in

fixed-income markets, the SEC’s FIMSAC recently proposed that the dissemination of prices for very large transactions (at least $10 million for investment grade

and $5 million for high-yield bonds) be delayed for 48 hours.36 Back, Liu, and

Teguia (2019) provide a model showing that the disclosure of transaction prices

conveys information possessed by the dealer about the asset quality and reduces

the dealer’s rents when she disposes of the inventory in a second transaction.

Much of the empirical literature on fixed-income trading has emerged in

the aftermath of transaction prices becoming available through TRACE, which

reflects mandatory reporting of OTC secondary market transactions in eligible

fixed-income securities by all FINRA members. These prices were collected by

FINRA even before they began to be disseminated to the public. For example,

FINRA began to collect transaction prices for securitized products in May 2011

36FIMSAC had diverse views regarding the desirability of a price reporting delay, with academic

members generally opposed and many industry members in favor, as evidenced in the transcript

available at https://www.sec.gov/spotlight/fixed-income-advisory-committee/fimsa-040918transcript

.txt. Some committee members wrote a dissenting letter, available at https://www.sec.gov/comments/

265-30/265-30.htm.
30 Journal of Financial and Quantitative Analysis

with an eye toward public post-trade reporting, which was implemented for various securitized products at later dates, as summarized in Table 2. As a consequence, some authors were able to study transactions prices from still-opaque

markets, reflecting FINRA’s interest in understanding the characteristics of these

markets before introducing transparent trading and pricing (Atanasov and Merrick

(2012), Bessembinder et al. (2013), Friewald, Jankowitsch, and Subrahmanyam

(2014), and Hollifield et al. (2017)).

The first 3 studies of transparency in the corporate bond market use different, but complementary, methods. The introduction of transparency in corporate

bonds in July 2002 required price dissemination only for a set of liquid bonds,

rather than a randomized control design. Goldstein et al. (2007) study the third set

of bonds (all BBB rated) phased into transparency in Apr. 2003. These bonds were

selected as a stratified random sample, along with a control sample of nondisseminated bonds. Edwards et al. (2007) compare execution costs for transactions disseminated to the public to those for trades not publicly disseminated between Jan.

2003 and Jan. 2005. Bessembinder et al. (2006) use a difference-in-difference

design, studying changes in trade execution costs for insurance company bond

trades in the NAIC database after July 2002 for bonds where transaction reporting

was initially mandated as compared to bonds where it was not. Despite the differing samples and research designs, all 3 studies conclude that public transaction

reporting through TRACE is associated with significant reductions in customer

trade execution costs. The results in Bessembinder et al. (2006) also indicate the

possibility of a “liquidity externality” by which trade reporting for some bonds led

to reduced execution costs, even for bonds without transaction reporting (which

would suggest that the bonds were competing as trading substitutes). The introduction of post-trade transparency has also been shown to be associated with reduced trading costs in CDS markets (Loon and Zhong (2016)), the TBA MBS

market (Schultz and Song (2019)), and the Rule 144A corporate bond market

(Jacobsen and Venkataraman (2018)).

In Europe, fixed-income securities traded without either pre- or post-trade

transparency until the implementation of the Markets in Financial Instruments

Directive (MiFID II) in Jan. 2018. MiFID II requires broker-dealers and trading venues (e.g., exchanges or electronic platforms) to report transactions in

fixed-income securities, including sovereign bonds and corporate bonds, to a

national regulator. The timing of public disclosure of transactions depends on

security characteristics, transaction size, and the venue where the transaction is

conducted.37 Although the framework is complex, the guiding principles are that

securities that are categorized by regulators as liquid and transactions that are

deemed by regulators as nonblock (based on security and asset class specific

size thresholds) are subject to real-time dissemination of completed transactions.

MiFID II also includes a pre-trade transparency requirement to report individual

bids and offers for small prospective transactions in a limited number (i.e., 200

bonds) of sovereign securities. As these data become available to researchers, it

will be of interest to study whether the broad patterns observed in U.S. markets

37See the transcript of the SEC panel titled “Pre-Trade Transparency under MiFID II,” available at

https://www.sec.gov/spotlight/fixed-income-advisory-committee/fimsac-071618transcript.txt.
Bessembinder, Spatt, and Venkataraman 31

are also observed in European markets, and whether differences in country-level

regulation within the European Union could explain variations in bond market

quality across markets and securities.

B. Fairness in Pricing

Some of the key regulatory issues in fixed-income markets are oriented

toward the fairness of pricing. In particular, regulators restrict price markups,

thereby limiting dealer profits. It is atypical for financial regulators to regulate

pricing directly, and such regulation may effectively limit the market power of

dealers in thinly traded and relatively opaque search markets. Indeed, the enforcement of markup rules has been an important component of the regulatory

agenda in fixed-income markets in recent years (see, e.g., http://www.seclaw.com/

markups-markdowns/). By misstating the origins and history of a position, a

dealer can misrepresent the pricing allowed under regulation and the willingness

of a counterparty to accept a given price.38 In the aftermath of an SEC enforcement action in 2015, both FINRA for corporate bonds and MSRB for municipal

bonds passed rules effective May 14, 2018 requiring markup disclosure on confirmation slips for riskless principal trades.39 The markup disclosure rule points to

some interesting open questions. At a high level, the rule presents an opportunity

to evaluate the importance of “consumer” disclosure requirements. For example,

after the rule goes into effect, what happens to the implicit spread on riskless principal trades and the mix between principal and agency trades? How does this vary

among instruments and dealers?

Traditional approaches to the regulation of equity markets have been adapted

in the fixed-income markets to a limited degree. FINRA adopted a best execution

rule for trading corporate bonds, and MSRB adopted such a rule for municipal

bonds. However, there is no analogy to the equity-market order-handling rules

(which require respecting the potential priority of orders that originate with investors rather than dealers) or to the order protection rule under Regulation NMS,

which bans “trade-throughs” (i.e., the execution of a trade at a price worse than

the best available quote).

The lack of systems for creating consolidated quotations in bond markets implies that many participants, particularly retail investors and smaller institutional

traders, do not see the best available prices as they do in equities, and therefore

they have difficulty identifying trade-throughs and in measuring execution quality. Harris (2015) documents a high proportion of trade-throughs in the corporate

bond market when he compares trade prices to a nonpublic database of indicative

quotations. A related problem is the lack of uniform practices regarding the quotation data produced by dealers and electronic venues. Larger institutions that have

38Enforcement actions by FINRA and the SEC arise due to violations of the markup rules. As a

recent example, a dealer allegedly attempted to misstate the history of a bond acquisition and was

prosecuted by the Department of Justice as a criminal matter (see Larson, E.; B. Van Voris; and

C. Dolmetsch. “Tweaking Chat Trips Up Ex-Jefferies Bond Trader Jesse Litvak.” Bloomberg. Available at https://www.bloomberg.com/news/articles/2017-01-27/tweaking-a-chat-trips-up-ex-jefferiesbond-trader-jesse-litvak (Jan. 27, 2017)).

39Of course, historically, agency trades have required commission disclosure. To the extent that implied commissions on riskless principal trades are large, the required markup disclosure could lead to

resistance from retail clients and tighter implied commissions, especially on retail-sized transactions.
32 Journal of Financial and Quantitative Analysis

the resources to consolidate quotation data benefit from the information advantage. Nor do fixed-income markets have regulations analogous to SEC Rules 605

and 606, which require disclosure of order execution quality and broker orderrouting procedures for equities. Regulatory initiatives that lead to a centralized

quotation system with standardized price data could help investors better understand the quality of their trade executions.

C. Systemic Risk and Market Stability

The regulation of fixed-income markets in recent years has emphasized systemic risk and financial stability issues, as well as the impact of the changing

environment on liquidity. Among other initiatives, regulators now require most

standardized derivatives, including interest rate swaps, to be cleared through

centralized clearinghouses. Benos, Payne, and Vasios (2019) study proprietary

trading data and conclude that centralized clearing has reduced bid–ask spreads

and enhanced liquidity, a result they attribute to enhanced competition between

dealers.

At the heart of the changes in regulatory focus since the financial crisis is the

Volcker Rule, an important part of the Dodd–Frank Act. The Volcker Rule restricts

the trading activity of banks and major financial institutions to avoid subsidizing

them in light of their regulatory advantages (e.g., deposit insurance, access to the

Fed discount window, and implicit “too big to fail” guarantees) with the intent

of mitigating incentives toward excessive risk taking in proprietary trading. The

Volcker restrictions effectively bar proprietary trading desks and internal hedge

funds for the affected institutions, resulting in their decisions to spin off these

entities.

The effects of the Volcker Rule on market making are more complicated, and

arguably more significant, for fixed-income markets. As Duffie (2012) observes,

although the Volcker Rule allows market-making activities by banks, the distinction between a proprietary trade and a trade undertaken to facilitate customer activity is subtle. The empirical evidence discussed earlier supports the reasoning

that the Volcker Rule has had the effect of reducing the willingness of dealers to

commit capital to market making, even though the rule was not intended to affect

market making.

In Aug. 2019, five US regulatory agencies approved a set of modifications to

the Volcker Rule (dubbed by some observers as “Volcker Rule 2.0”), including a

provision that “banking entities that trade within internal risk limits set under the

conditions in this final rule are engaged in permissible market making.”40 Assessing the potential impact of this modification on the microstructure of fixed income

trading also comprises an interesting avenue for future research.

Financial institutions may attribute part of the decline in committing their

capital to fixed-income trading to the greater capital costs associated with higher

bank capital (equity) requirements. In effect, the underlying structure of trading

has changed in recent years; there is a move toward pre-arranged trading that does

not rely on dealer inventory (Schultz (2017), Choi and Huh (2017)), as well as toward shorter intermediation chains. This can be seen as a natural response to the

40See https://www.fdic.gov/news/news/press/2019/pr19073.html and https://www.bloomberg

.com/opinion/articles/2019-08-20/volcker-2-0-is-too-little-too-late-for-wall-street-s-traders.
Bessembinder, Spatt, and Venkataraman 33

changes in the regulatory environment and greater reluctance of dealers to commit capital. However, focusing only on capital supplied by bank-affiliated dealers

potentially overstates the impact on the markets, because it does not capture endogenous responses such as the increased involvement of nonbank dealers, the

growth in electronic intermediation services, or the effective provision of liquidity by customers through prearranged trades.

Harris (2015) observes that most bond brokers do not display limit orders

from their customers in electronic bond trading systems, an important liquidity

source in equity markets. Recent years have witnessed growth in “all-to-all” trading platforms that allow select buy-side institutions to participate in liquidity provision (e.g., Market Axess’s Open Trading platform). Nonetheless, access remains

limited, and the bond dealer remains involved in virtually all transactions between

buyers and sellers. Regulatory initiatives that focus on increasing customer access

can reduce the reliance on dealer capital and significantly expand the pool of

liquidity suppliers.

Market making in Treasuries is presumably less affected by the Volcker Rule,

as Treasury securities are less risky and more liquid as compared to speculative

securities targeted by the Volcker Rule. It would be of interest to assess the extent

to which dealer capital commitment to Treasury market making has changed in

recent years, and to contrast the results to those for corporate bonds and analyze

the impact on spreads.

Data on transactions in U.S. Treasury securities intermediated by SECregistered broker-dealers is currently being collected by regulators through

TRACE under an initiative of the Treasury and SEC, but the data have not yet

been made available to academics for analysis (though they are available to at

least some researchers within regulatory institutions). The trade-offs facing Treasury officials, who are responsible for both financial stability and government

funding costs, are of particular interest. It could be argued that Treasuries (and

perhaps some other sovereigns) should be exempt from the Volcker Rule, as they

are subject to less credit risk, and the Volcker Rule is intended to limit risk taking

and the costs of too-big-to-fail guarantees. Of course, government bonds are subject to interest rate risk, and sovereign credit is not necessarily free of default risk.

Under crisis conditions in particular, the challenges to sovereign credit would be

greatest and the implications of the Volcker Rule would be most germane.

Indeed, in the parallel context of bank capital adequacy, the use of “zero

risk” weights for sovereign credit has been widely criticized in Europe and has

potentially created vulnerability to sovereign credit issues. More broadly, regulatory policies and the use of unconventional monetary policy may have reduced

the willingness to commit dealer capital to market making. In contrast to the

Modigliani–Miller irrelevance propositions central to academic thinking, financial institutions appear to view equity capital as especially costly, so higher equity

capital requirements (under Basel and U.S. regulation) also would be expected to

result in less dealer capital being allocated to market making.

Among the environmental changes that may have affected the commitment

of dealer capital to market making are the enhancement of transparency for structured products, as dealers have argued that greater transparency reduces the profitability of market making, as well as other changes in market conditions. Both
34 Journal of Financial and Quantitative Analysis

low interest rates and increases in interest rate volatility potentially affect the willingness of dealers to commit capital to market making.41

The nature of market making has adjusted in response to the changing environment, as hedge funds and the buy side partially fill the void created by reduced

bank involvement. Dick-Nielsen and Rossi (2019) study bond index exclusion

events and conclude that liquidity is reduced around these events, requiring greater

customer patience, and Bessembinder et al. (2018) document increased market

participation by nonbank market makers. Researchers (e.g., Ellul, Jotikasthira,

and Lundblad (2011), Choi and Shin (2018)) document the existence of “fire

sales” in corporate bond markets, where the need to sell specific bonds results

in temporary downward price pressure. In extreme circumstances, the reduced

availability of capital for market making could result in fire sales of increased

magnitude. Although the goal of the Volcker Rule is to increase financial stability, the potential for increased fire sales implies that stability may be reduced

instead, which could increase the desirability of maintaining standby capital (see

Menkveld (2016)).

Although regulatory and monetary policy each arguably contributes to a perception of a recent decline in fixed-income market liquidity, it would be a mistake

to conclude that liquidity before the financial crisis was necessarily optimal and

therefore provides the right benchmark. In particular, the period before the financial crisis may have been characterized by unrecognized costs that were not

internalized by institutions deemed too big to fail and that emerged only during

the financial crisis.

D. Bond Exchange-Traded Funds and Mutual Funds

Investors can obtain exposure to fixed-income markets by purchasing individual instruments or ownership interests through mutual funds or exchangetraded funds (ETFs). Funds provide an important mechanism for obtaining liquidity, especially in light of the limited liquidity associated with the dispersed

trading of individual instruments. Theory (e.g., Subrahmanyam (1991), Gorton

and Pennacchi (1993)) predicts that portfolio liquidity should be superior to that

of individual instruments, because of reduced adverse selection.

At the same time, there are structural issues associated with fixed-income

portfolio trading. The open-end mutual fund model is priced on a daily basis,

based on closing valuations of the underlying assets. A question that arises is

whether the apparent liquidity of the composite portfolio could be illusionary.

Many open-end funds allow investors to transact at the fund closing price, which

is in turn based on the closing prices of the underlying holdings. This offers an

illusion of costless liquidity (see Spatt (2014)), though it is not actually costless

for a mutual fund to offer such liquidity. In an attempt to mitigate the possibility of “runs” by mutual fund investors, recent regulatory changes allow the use

of “swing pricing,” by which a mutual fund can attempt to internalize the consequences of substantial orders at the close.

41It would be interesting to examine how changes to the monetary policy interest rate environment

(e.g., changing interest rate levels, interest rate volatility, and Fed ownership of longer term debt under

“Quantitative Easing” programs) influence market making, including the number of dealers and dealer

capital measures.
Bessembinder, Spatt, and Venkataraman 35

The liquidity mismatch also arises between individual bonds and ETFs,

though they are all traded instruments. An ETF is traded directly on stock exchanges throughout the trading day, and its price is anchored to those of its components through a redemption and creation mechanism for the underlying fund

in which only a limited set of “authorized participants” can engage in the arbitrage mechanism. The lower liquidity of underlying credit instruments relative to

that of the ETF potentially increases the fragility of liquid ETFs and reduces the

arbitrage capacity of ETF markets. Indeed, when the conflict in the roles of authorized participants as bond dealers and ETF arbitrageurs is large, mispricing can

arise (see Pan and Zeng (2017), Goldstein, Jiang, and Ng (2017)).42

Because many individual bonds are highly illiquid, bond index funds often

rely on sampling methods rather than purchasing all of the bonds that compose

the index to be replicated. Of course, the discrepancy between trading individual

bonds and a fund offering costless liquidity through closing prices is greater when

the underlying component assets held by the mutual fund are themselves highly

illiquid. On Oct. 16, 2016, the SEC adopted reporting requirements for mutual

fund liquidity to quantify for regulators and the public the extent to which assets

could be sold over a specified interval. However, these requirements have been

the subject of active debate, and the final rule adopted on Sept. 10, 2018 only

requires funds to disclose information about the operation and effectiveness of

their liquidity risk management program (https://www.sec.gov/rules/final/2018/

ic-33142.pdf). Managers of index funds are often evaluated based on the performance of their fund relative to the specified index. However, the weighting on

individual bonds in the index changes each month, as new bonds are issued, existing bonds approach maturity, and so on. Ottonello (2019) documents predictable

patterns in bond returns related to the combination of mutual funds rebalance

trading and the illiquidity of the underlying bonds.

Because mutual funds normally hold some cash reserves, they potentially

supply liquidity at times. Wang, Zhang, and Zhang (2018) document that mutual

funds constitute an important source of liquidity supply when other market participants are forced to sell bonds. Anand, Jotikasthira, and Venkataraman (2018)

show that some bond mutual funds exhibit a persistent trading style that supplies

liquidity by absorbing the inventory positions of dealers. To the extent that liquidity management rules in the event of a shock preclude a mutual fund from being

a net buyer, such rules may lead to more dramatic fire sales.

E. Short-Term Funds

The financial crisis also highlights the importance of the market structure for

short-term financial instruments. Money market mutual funds in particular played

an important role during the financial crisis. These issues arose in the aftermath

of the collapse of Reserve Fund, which did not have policies and procedures in

42Many ETFs require the authorized participants to exchange the ETF for a basket of securities. An

in-kind redemption mechanism allows the ETF to avoid selling securities to raise cash to meet redemptions. Under stressful market conditions, it is important to understand whether in-kind redemptions

work smoothly, whether price discovery occurs at the ETF or the underlying bonds, and the relevance

of publishing intraday net asset values (NAVs) that are possibly incorrect when the underlying bond

market is illiquid.
36 Journal of Financial and Quantitative Analysis

place to adjust its valuation from the $1 per share level considered standard for

money market funds, even in the face of adverse events. The conversion of shares

to cash was suspended, setting off a run on money market mutual funds. This run

was amplified by the custom of pricing funds at $1 rather than marking the NAV

to market.

Schmidt, Timmerman, and Wermers (2016) study the aftermath of the

Lehman Brothers collapse, showing that institutional products are more susceptible to runs. A series of reforms emerged after the Lehman Brothers collapse,

intended to ensure that funds have appropriate policies and procedures for altering their valuations, to limit the riskiness of the assets in the fund, and to require

floating NAVs for institutional funds that invest in nongovernmental securities.

These reforms led to a substantial reduction in the holdings and size of money

market mutual funds, especially institutional “prime portfolios,” effectively transforming the U.S. system of short-term financing. Much of the institutional holdings shifted to governmental money market funds from prime (nongovernmental)

funds, suggesting considerable demand for quasi-fixed pricing.

F. Fed Policy and Fixed-Income Markets

Another structural change in the aftermath of the financial crisis is the extent

of central bank participation in various fixed-income markets. In particular, the

Fed dramatically expanded its balance sheet and acquired a substantial portion of

outstanding MBS, reflecting efforts to circumvent limitations on monetary policy

attributable to the nominal “zero interest rate bound.”43 In addition to providing

direct support to the housing market, this led to a substantial increase in the maturity and duration profile of Fed holdings.44 Of course, the underlying issues are not

specific to the United States, as the consequences of the zero interest rate bound

are relevant around the globe. For example, a recent Wall Street Journal article

reports that the European Central Bank (ECB) became one of the largest buyers

of corporate bonds a few years ago, which has had substantial effects on narrowing interest rate spreads between Treasuries and corporate bonds, and across the

credit spectrum.45

The effects of central bank transactions on the microstructure of fixedincome markets constitute a promising research area. Among the contributions,

Schlepper, Hofer, Riordan, and Schrimpf (2019) study ECB activity, reporting

that greater purchases are associated with lower transactions costs but reduced

book depth. Pasquariello, Roush, and Vega (2019) develop a model implying that

43An alternative way (compared to holding long-term bonds) for a central bank to engage in expansionary policy near the zero interest bound is to support negative interest rates (e.g., by taxing deposits

and/or currency).

44This was viewed as somewhat controversial because of the extent of active credit allocation undertaken by the Fed and questions about whether the macroeconomy/monetary policy required such

activities. The first round of the Fed’s balance sheet expansion, QE, was widely supported as part of

the response to the core of the financial crisis, but subsequent rounds, QE2 and QE3, were more controversial and perhaps less effective, as there was less of a surprise, diminishing returns and increasing

concerns about the difficulty of unwinding these exposures.

45Bird, M. “Investors Prepare for the End of ECB’s Corporate-Bond Buying.” https://www.wsj

.com/articles/investors-prepare-for-the-end-of-ecbs-corporate-bond-buying-1520591407. Wall Street

Journal (Mar. 9, 2018).
Bessembinder, Spatt, and Venkataraman 37

greater central bank activity should improve liquidity despite heightened information asymmetry, and they report supportive evidence for U.S. markets.

VIII. Some Open Questions

Although there have been considerable advances in understanding the microstructure of fixed-income markets over the years, there remains a range of

important and interesting unresolved questions. Among the most fundamental

questions is why trading costs tend to be higher in dealer-oriented fixed-income

markets as compared to equity markets. Are fixed-income markets structured optimally? The large number of individual issues, infrequent trading, and the relative dearth of retail participation are clearly relevant but may reflect endogenous

outcomes. That is, to what extent do infrequent trading and a lack of retail participation result from higher execution costs? Are higher execution costs an inherent

feature of dealer-oriented OTC markets, or are such markets the optimal mechanism for liquidity provision in light of the costs associated with infrequent trading

and search? Would the market function more effectively if it were more consolidated? If so, how can the consolidation of a search-oriented dealer market be

facilitated?

Currently, most customers have limited access to fixed-income markets, as

they may not be able to observe in real time even the indicative quotes that are

available from dealers, and their orders are not displayed as quotations to other

market participants. Would regulations requiring the public display of customer

trading interest (along the lines of the 1997 NASDAQ equity market reforms studied by Barclay, Christie, Harris, Kandel, and Schultz (1999)) as advocated by

Harris (2015) improve the functioning of fixed-income markets? More broadly,

would regulations requiring all quotations and indications of interest, whether

originating with customers or dealers, to be consolidated and publicly displayed

enhance the functioning of fixed-income markets? What would be the effect of

requiring that electronically disseminated quotations be firm and subject to automated execution?

Many of these questions relate to whether the market or portions of it should

be organized more like an exchange rather than an OTC market. An interesting

perspective that bears on this indirectly by explaining the prevalence of intermediation chains is Glode and Opp (2016), who suggest that a chain of moderately

informed intermediaries can mitigate market power compared to a market structure organized around a dominant broker (or exchange) and that a multiround

intermediation chain can reduce adverse selection. Long intermediation chains

can lead to limited adverse selection at each stage of trading to avoid the breakdown of trading, providing a fundamental explanation for intermediation chains

and the emergence of trading networks. Glode and Opp (2020) argue that the

OTC markets where most fixed-income trading occurs encourage the acquisition

of expertise, which is useful when it improves the efficiency of allocations but

problematic when it results in adverse selection. This provides a potential explanation for the coexistence of exchange and OTC trading and the choice of trading

protocol for different types of instruments. Lee and Wang (2018) present a model
38 Journal of Financial and Quantitative Analysis

where investors can choose between OTC and exchange trading, showing that

dealers’ ability to price discriminate can benefit customers in some cases.

Closely related is the need to better understand the differential relation

between trading costs and trade sizes across fixed-income and equity markets.

Do fixed-income markets inherently favor large and sophisticated customers who

are better informed and better able to negotiate as compared to retail customers?

Markup restrictions on bond pricing play an important role in the regulation of

fixed-income markets but lack a direct counterpart in the equity markets at least

since the abandonment of “price continuity” rules along with the specialist system

on the New York Stock Exchange. The underlying question is in which markets

and circumstances do direct restrictions on pricing improve outcomes?

Differences in regulatory treatment and instrument characteristics across equity and bond markets can potentially be exploited to enhance our understanding

of these issues. For example, to what degree do best execution requirements in

the bond market have bite as compared to the equity market, and how might these

requirements be better tailored to the specific circumstances of the fixed-income

markets? These issues are tied to whether there should be a centralized market: Is

it more advantageous to promote competition among dealers or intermediaries to

reduce trading fees or to facilitate competition across orders to achieve the best

price execution?

As noted, fixed-income instruments tend to trade infrequently, in part because of the large number of unique instruments issued. To what extent is there

scope to increase trading frequencies and decrease trading costs by consolidating instruments?46 Helwege and Wang (2017) study very large bond offerings as

compared to multiple simultaneous smaller offerings by the same issuer and conclude that although liquidity is improved for mega issues, yields are not lower,

a result they attribute to price pressure associated with issue size. An alternative

is to issue identical securities at multiple dates. In “tack-on” offerings, the terms

of new issues are selected to match those of an existing issue. However, tack-on

offerings are uncommon.47 Are tack-on issues fully fungible with existing issues?

More broadly, are there inefficient impediments to such issues? In mortgage markets, TBA instruments, which are packages of yet-to-be issued SP instruments,

trade frequently with relatively low customer costs. Is there scope for the trading

of packages of corporate bonds based on a set of prescribed characteristics, along

the lines of “blind auction” transactions in equity markets (see Kavajecz and Keim

(2005))?

Another prominent and unresolved issue concerns the role of post-trade

transparency in fixed-income markets. Though several studies have considered

changes in post-trade transparency, the optimal degree of transparency has not

been established, as illustrated by the ongoing debate about whether the dissemination of information regarding large trades should be delayed. Much of the

evidence regarding post-trade transparency focuses on execution costs for completed trades, which may reflect a degree of order splitting, and do not capture the

potential cost and difficulty in executing large orders. Dealers, and in some cases

46Garriott, Lefebvre, Nolin, Rivadeneyra, and Walton (2018) provide specific proposals for consolidation of sovereign debt issues.

47Goh and Yang (2018) report only 77 tack-on offerings from 2006 to 2016.
Bessembinder, Spatt, and Venkataraman 39

large buy-side investors, are often critical of greater transparency. It would be beneficial to further investigate the economic issues underlying the dealers’ perspective. Is it simply that market making is less profitable for dealers in a transparent

setting, or is liquidity for large orders reduced such that large buy-side customers

are disadvantaged by transparency? Furthermore, in light of the large number of

instruments and cross-sectional variation in both clientele and trading frequencies, the transparency regime that is optimal in one fixed-income market may not

be optimal in another.

As noted, the extent of electronic quotation and trading in fixed-income

markets has been modest, particularly as compared to equity markets. However,

the role of electronically facilitated trading in fixed-income markets is growing, and electronic interdealer trading of Treasury instruments is routine. Still,

future growth in the electronic trading of fixed income instruments is far from

guaranteed.48 U.S. Treasury markets were, on Oct. 15, 2014, subject to a “flash

crash” where the yield on the 10-year Treasury note dropped by 16 bps and

then recovered, within a 12-minute period.49 This incident raises the question of

whether algorithmic trading is associated with an inherent potential for instability. It will be of interest to assess the effects of electronically facilitated trading

on fixed-income microstructure outcomes as its role increases. More broadly, it

is periodically asserted that financial crises tend to originate in credit markets.

Studies of fixed-income microstructure focusing on how regulations affect liquidity provision, execution costs, and trades’ price impacts have the potential to

inform policy makers regarding the effects of policy initiatives on the fragility

of credit markets.

An interesting puzzle related to the municipal bond market is the widespread

use of municipal bond insurance before the financial crisis. Why would the capital market not simply bear the default risk just as it bears (and shares) so many

types of capital market risks? Default risks are presumably positively correlated

across bonds and, as such, have a substantial systematic component. To the extent the insurance is credible, it should result in instruments that are relatively

more substitutable. One hypothetical advantage of such increased fungibility is to

support the use of forward or futures contracts in which different insured instruments can be delivered, thereby potentially resulting in improved liquidity, similar to TBA MBS or the trading of generic MBS instruments as opposed to those

“on special” (because of their prepayment or perhaps default characteristics, as

discussed by Spatt (2004)). Of course, the fungibility of municipal bonds might

still be limited by differences in tax treatment associated with the within-state

tax exemption.

48Notable failures of electronic bond platforms have been observed, including BondBook (which

was backed by several major Wall Street firms (see Parry, J. “BondBook, an Online Bond-Trading Site,

Closes Down Despite High-Profile Backers.” Wall Street Journal. Available at https://www.wsj.com/

articles/SB1004385008243215960 (Oct. 30, 2001)) and Aladdin, which was backed by BlackRock

(see Grind, K., and T. Demos. “BlackRock Shelves Platform for Bonds.” Wall Street Journal. Available at https://www.wsj.com/articles/SB10001424127887323551004578441053526969438 (Apr. 23,

2013)).

49The official U.S. government report on the Treasury market event of Oct. 15, 2014 is available at

https://www.treasury.gov/press-center/press-releases/Documents/Joint Staff Report Treasury 10-15-

2015.pdf.
40 Journal of Financial and Quantitative Analysis

The shorting and lending of fixed-income securities has received relatively

little attention relative to equity markets.50 This also seems to be an important

area of research in light of the limited liquidity in most bond markets and the lack

of transparency about the shorting process (in the equity as well as fixed-income

markets).

As noted, in recent years the magnitude of bond issuances has far outstripped

that of equity issues. Equity issuances, including both IPOs and seasoned equity offerings (SEOs) has been studied extensively, in terms of both issue pricing

and post-issue liquidity. In contrast, the issuance process and microstructure of

fixed-income issues has been little studied. Furthermore, the diversity of fixedincome instruments implies that regularities established for one segment of the

fixed-income markets (e.g., Treasuries) may not carry over to other segments (e.g.,

corporate or municipal bonds).

A substantial portion of the empirical research related to fixed-income microstructure focuses on data generated during or after the financial crisis, which

were periods of unconventional monetary policy. As a consequence, it is desirable to develop a better understanding of the interrelations between monetary policy, post-crisis regulations, and microstructure outcomes. For example, how does

the impact of the zero interest rate bound affect monetary policy, interest rates,

and the liquidity of instruments that may be involved in nontraditional policy?

How do changes in the Fed’s ownership of various instruments under quantitative easing and successor policies influence spreads, the number of dealers, and

dealer capital? What can we learn about optimal market structure from crosscountry comparisons, particularly in light of the variation in monetary policies

across countries?

Monetary policy affects corporate issuance decisions, due in part to its effects

on interest rates. The near-zero interest rates over the last decade are associated

with a record amount of corporate bond issuance, with Triple B–rated corporate

bonds now representing almost 50% of all outstanding investment-grade debt.

Does the combination of increased market size, decreased creditworthiness, and

dealers who may be less inclined to commit capital to market making imply that

a future economic downturn could prompt larger fire sales and greater financial

fragility?

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