CHINA’S CORPORATE CREDIT BOND MARKET: DEVELOPMENT AND POLICY ISSUES*

JOSEPH CHERIAN† JINGYUAN MO‡ CAMRI, NUS Business School NYU Stern School of Business

MARTI G. SUBRAHMANYAM§

NYU Stern School of Business

Abstract The rise of China as the second-largest economy in the world has spurred a

rapidly growing literature on the Chinese bond market, which experienced a ten-fold growth in market capitalization over the past two decades. In this paper, we provide a comprehensive overview of the corporate credit bond market in the two most active trading venues in China: the interbank market and the exchange market. First, we discuss the differences between the two markets, and present a taxonomy of corporate credit bonds traded in the two markets. Second, we discuss the different phases of liberalization, in terms of the improvement in exposure to foreign investment and the simplification of the issuance requirements of domestic firms, accompanied by the emergence of illegal trading activities. Third, we provide descriptive statistics of bond characteristics and trading activity for each of the four categories of credit bonds in China. We also introduce a set of liquidity proxies to measure liquidity levels in the corporate credit bond market. We find that the levels of liquidity, measured by each of these proxies, vary across different types of credit bonds. Fourth, we analyze the impact of policy announcements on the levels of liquidity during the various phases of foreign liberalization in the interbank market and domestic liberalization in the exchange market.

Keywords: corporate credit; bond markets; yield; liquidity; policy intervention; China JEL classification: G01; G12; G15; G18.

* We thank our data vendor, the China Foreign Exchange Trade System, an arm of the People’s Bank of China, for providing this comprehensive dataset that has made this research possible. We are grateful to the NYU Salomon Center, the Center for Global Economics and Business at NYU Stern, and the Center for Asset Management Research and Investments at The National University of Singapore for their research support. Subrahmanyam acknowledges the generous financial support of the Center for Global Economy and Business, the NYU Salomon Center, the NYU Shanghai Center for Business Education and Research, the Volkswagen Foundation, and the Anneliese Maier Award of the Alexander von Humboldt Foundation. Mo acknowledges funding from the Provost’s Global Research Initiatives program at NYU Shanghai. Mo is also thankful for funding from the Provost’s Global Research Initiatives program at NYU Shanghai. We are also grateful to Ting Li and Yingjie Xu for providing us with valuable insights on the Chinese bond market. All errors in the paper remain our own. † 15 Kent Ridge Dr., Singapore 119245; +65 6516 5991; bizjc@nus.edu.sg ‡ 44 West 4th Street, New York, NY 10012, USA; +1 (212) 998-0365; jmo@stern.nyu.edu § 44 West 4th Street, New York, NY 10012, USA; +1 (212) 998-0348; msubrahm@stern.nyu.edu

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1 INTRODUCTION

Unlike the development of bond markets in many developed countries, bond markets in

China were established only in 1950, one year after the People’s Republic of China was

established, as the Chinese government aimed to stimulate the economy and stabilize

consumer goods prices. Therefore, the history of Chinese bond markets is not as long as that

of other large economies. The first bond issued was a government bond, termed “People’s

Victory Public Debt.” However, its issuance was terminated in 1953 as the financial and

economic situation gradually improved. In the following five years, the Ministry of Finance

subsequently issued several government bonds, called “National Economic Construction

Public Debt,” which were aimed at developing the national economy in general. The issuance

of government bonds, however, stopped in 1958 and did not resume until 1981. Thus, from

a practical perspective, the Chinese bond market has a history of less than 40 years.

In recent years, the Chinese bond market has undergone rapid development and

expansion. The market capitalization of bond markets in China increased from less than U.S.

$600 billion in the early 2000s to approximately U.S. $12 trillion by the end of 2017. Although

the market capitalization of all bonds remains lower to that of the United States and Japan,

the market capitalization of the corporate credit bond market in China has already exceeded

that of the United States and became the largest in the world a few years ago. Gail Fosler

Associates (2018) even estimates that the size of China’s fixed income market could

potentially become quadruple that of the equity market by 2025. In a harbinger of the

growing importance of Chinese bond markets, it was announced in January 2019 that they

will soon be included in the Bloomberg Barclays Global Aggregate Index, an important

market barometer. It is, thus, worthwhile and necessary to examine the Chinese corporate

credit bond market in detail, which could potentially grow to become globally important in

the next five years.

Despite the rapid expansion of the Chinese bond markets, they have faced various

problems during their nascent, relatively short stage of development. We discuss four of

these major problems in this paper: (1) financing challenges faced by firms with credit ratings

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lower than “AA”; (2) policy uncertainty regarding the nature of the implicit government

guarantee in the credit bond markets; (3) lack of uniformity in regulations of different

regulatory bodies; and (4) severe segmentation across the two major trading venues. The

biggest problem of the four is the severe segmentation of the two trading venues: the

exchange market and the interbank market. Between the two trading venues, the exchange

market was opened to foreign investment much earlier, even though it accounts for only a

small percentage of both market capitalization and trading volume. On the other hand, the

mainstream interbank market has experienced liberalization at a glacial pace. Despite the

announcement of a series of liberalization policies since 2002, such as the simplification of

approval procedures and the gradual increase of investment quota limits, the percentage of

foreign investors’ holdings in the interbank bond market was a mere 1.1% in June 2017. A

year after the initiation of the Bond Connect trading platform in July 2017, this figure increased

to 1.9%, but remains quite small. We analyze this development in detail in Section 5.

Over the past two decades, the Chinese financial markets have received increasing

attention from investors globally, as China attempts to integrate its financial system with the

rest of the world. As the domestic Chinese economy grew rapidly in recent years, even while

it was gradually opening up to foreign investors, the ability of the government to control and

stabilize financial markets using targeted measures was bound to diminish. It is, therefore,

important to study how government policies affected the credit bond market in the past,

from which valuable lessons can be learned, as well as to forecast the impact of future policy

announcements on the Chinese credit bond market, the broader Chinese bond market, or

even the overall Chinese economy.

In this paper, we provide the first comprehensive, detailed analysis of the corporate

credit bond market across the two major trading venues in China, for the period between

January 2006 and December 2017. We also use a unique dataset obtained from the China

Foreign Exchange Trade System (CFETS). Our dataset contains a list of bond characteristic

variables and trading activity variables, which we later use to construct several widely used

liquidity measures to quantify the evolution of the levels of liquidity over different phases of

bond market liberalization. In addition, bond markets in China are well-known to be

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segmented across its two active-trading venues. Due to the differences along various

dimensions between the two markets, the same type of credit bond may exhibit significantly

different prices in the two markets, after controlling for the variables that are well known to

affect yields to maturity. The discrepancy in prices of the same category of credit bonds

traded across the two markets, therefore, leads to gaps in their respective yield spread. In a

companion paper, Mo and Subrahmanyam (2019) attribute this yield spread differential to

cross-market clientele effects and demonstrate that it changes across different phases of

liberalization, and under different economic conditions. In addition to investigating the

cross-market variation of clientele effects, Mo and Subrahmanyam (2019) also demonstrate

that the yield spread differential between different categories of credit bonds within the same

market, which they attribute to within-market clientele effects, shifts across different phases of

liberalization and under different economic conditions.

The remainder of the paper is organized as follows. Section 2 discusses the academic

literature related to liquidity and the Chinese corporate bond market. Section 3 discusses the

two trading venues and the taxonomy of credit bonds. Section 4 addresses the liberalization

policies, illegal trading activities, and liquidity. Section 5 explains, in detail, the composition

of our dataset, introduces the set of widely-utilized liquidity proxies, and then reports the

descriptive statistics of the variables. Section 6 presents our discussion of the levels of yield

and the levels of liquidity for each category of credit bond. Section 7 concludes.

2 LITERATURE REVIEW

There is a vast literature on bond markets in the U.S. and, to a lesser extent, in Europe. It was

not until the last decade, however, that researchers shifted their attention to the rapidly

growing Chinese bond markets, although most of the research remained preliminary and

most often performed by Chinese scholars and published in Chinese journals. The reason for

this lack of attention is two-fold: (1) the Chinese government did not stimulate the

development of its bond markets until the end of the 2008 global financial crisis; and (2) the

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low quality of the quote and transaction level data available for research, until recently.

Despite this handicap due to the non-availability of reliable data, a few recent studies provide

a good qualitative description of the Chinese bond markets. At a general level, Amstad and

He (2018) present a comprehensive introduction to Chinese bond markets, and Hu, Pan and

Wang (2018) provide an empirical overview of the Chinese capital market. In addition, Fu,

Wan and Zhang (2010) are among the first to examine the effect of policy announcements of

corporate bond issuance, and show that they negatively impact abnormal returns on the

firms’ equity. However, their data include only 31 firms in China and cover two years from

2007 to 2009 and are thus, limited in scope. Fang, Shi and Zhang (2013) study the impact of

ownership type and information quality of corporate bonds at their issuance. They find that

investors pay higher prices for corporate bonds issued by firms in cases in which their

ownership signals an implicit guarantee (low default risk) and that voluntarily disclose their

internal control assurance reports (high information quality). Xin and Gao (2014) investigate

how political relations assist enterprises to obtain corporate bond financing. They

demonstrate that private firms prefer to issue corporate bonds compared to state-owned

enterprises, and that the influence of political ties on corporate bond issuance is weakened

by the “marketization” of state-owned companies and is strengthened for similar private

firms. Among the various topics in the Chinese corporate credit bond market that have been

studied, those related to liquidity have received much attention in recent years, especially as

more detailed end-of-day level quote and transaction data gradually became available.

The study of liquidity in financial markets can be traced back to as early as the 1980s.

Amihud and Mendelson (1986) are the first to analyze the impact of illiquidity (measured by

the bid-ask spread) on asset-pricing by analyzing a model of investors with different trading

horizons. Their study shows that higher returns are required on stocks with higher bid-ask

spreads and, thus, higher liquidity (lower bid-ask spreads) corresponds to higher stock prices.

Testing their earlier model in greater detail, Amihud and Mendelson (1991) show empirically

that there exists a negative relationship between illiquidity and asset pricing, and argue for

the economic importance of liquidity in an efficient market. Amihud (2002) also suggests that

the illiquidity premium may partly explain the expected stock return, and that stock returns

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and contemporaneous unexpected illiquidity are negatively correlated. This work was

expanded and elaborated upon by numerous researchers in a variety of stock markets around

the world (see Amihud, Mendelson, and Pedersen (2013) for a survey of the literature).

Since the inception of the Trade Reporting and Compliance Engine (TRACE) database

disseminated by the Financial Industry Regulatory Authority (FINRA) for the U.S. corporate

bond market in 2002, researchers began to focus more on empirically analyzing liquidity

effects in the corporate bond market. A growing number of studies emerged as researchers

are now able to analyze bond price information after post-trade transparency, due to the

establishment of the TRACE database. Edwards, Harris and Piwowar (2007) are among the

first to document that bid-ask spreads reduced significantly after transaction prices became

available. They further provide evidence that bonds that are more recently issued, have

higher ratings and have less time-to-maturity, have more liquidity, compared to bonds that

are otherwise. Mahanti et al. (2008) propose a new liquidity measure known as “latent

liquidity,” defined as the weighted average of bond turnovers of investors, and find that the

new measure is suitable for bond markets with sparse transactions. Bao, Pan and Wang (2011)

also present a liquidity measure, the gamma estimator, and demonstrate that a large

variation of yield spreads can be explained by movements in bond prices, a transitory

component that the gamma estimator is able to extract. In the OTC market environment,

Jankowitsch, Nashikkar and Subrahmanyam (2011) model price dispersion effects in the

market, and show that transaction prices deviate from the market-wide valuation in the

presence of inventory risk and search costs. Duffie, Garleanu and Pedersen (2007) also

demonstrate that illiquidity discounts are higher when it is difficult to find counterparties,

sellers have less bargaining power, and there is a lack of qualified owners.

As investors became more aware of the importance of liquidity in the U.S. and

European bond markets, it captured the attention of not only academic researchers but also

industry practitioners and policy makers globally, including those involved with the Chinese

bond markets. However, most of the existing studies remain qualitative, given the low data

quality and the low frequency of bond transactions in the secondary markets, which inhibit

the application of rigorous econometric methodologies. Zhu and Xu (2004) use bid-ask

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spread of bilateral quotes to measure Treasury bond liquidity and find no significant

difference among Treasury market liquidity over time. Wang and Wen (2010) provide

evidence of the liquidity linkage between stocks and government bonds in China. They show

that macroeconomic shocks are transmitted from one market to another. Ba and Yao (2013)

discuss the measurement of the turnover rate and liquidity ratio of the interbank bond

market and the exchange bond market. Ma (2015) investigates factors influencing bid-ask

spreads of credit bonds, and determines that market-markets provide insufficient liquidity

and play a limited role in market stabilization.

3 TRADING VENUES AND CREDIT BONDS

In this section, we provide a detailed description of the two active trading venues for bonds

in China, the interbank market and the exchange market, and the four categories of corporate

credit bonds traded across the two markets. We also discuss the several different phases of

international and domestic liberalization between 2006 and 2017 and the two crackdowns of

agent-holding transactions in the interbank market in 2013 and 2016. We then present the

liquidity proxies that we use to measure liquidity in the largely illiquid Chinese corporate

credit bond market.

3.1 The Interbank Market and the Exchange Market

It is well-known that bond markets in China are largely segmented. Bonds with

similar characteristics can be traded at very different prices across the two trading venues,

the interbank market and the exchange market. These two trading venues differ from each

other in a number of aspects. We investigate the following aspects: regulatory structure;

market participants; trading instruments; trading mechanism; trading rules; and collateral

policy.

First, the interbank market is regulated by the People’s Bank of China (PBOC) and

uses the China Foreign Exchange Trade System (CFETS) as its trading platform, while the

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exchange market is regulated by the China Securities Regulatory Commission (CSRC) and

uses the Shanghai and Shenzhen Stock Exchanges as trading platforms. While the exchange

market uses the China Securities Depository and Clearing Corporation (CSDC) for bond

registration, depository and clearing, the interbank market uses the China Central

Depository and Clearing (CCDC) for the same functions.

Second, market participants in the interbank market are mostly institutional investors,

such as banks, securities companies, insurance firms, mutual funds and large financial

institutions, while investors in the exchange market are mostly household investors.

Institutional investors, in particular, can invest in both markets, while household investors

can only invest in the exchange market. In terms of trading volume, institutional

participation renders the secondary market in the interbank market much larger than that in

the exchange market, the latter accounting for only approximately 5% of the aggregated

trading volume. As a result, the interbank market has a much higher level of demand for

liquidity than the exchange market, leading to a lower liquidity premium. Controlling for all

other variables, we anticipate, therefore, to observe a potentially lower volume-weighted

yield spread in the interbank market than in the exchange market.

Third, the two markets also differ in terms of the particular instruments traded. Both

enterprise bonds and corporate bonds are traded in the exchange market, while only

enterprise bonds are traded in the interbank market5.

Fourth, investors in the exchange market have no information about the

counterparties. In an over-the-counter setting, the interbank market is a telephone market in

which traders call several counterparties when they plan to sell or buy bonds, as the large

trading volume induces them to search for the best price. Therefore, in the interbank market

both sides negotiate the yield/price at which they are willing to sell/buy the bond.

Consequently, the bargaining power from the sell side and the buy side in a bilateral

transaction differs across different types of bonds.

5 There exists, however, an approximate counterpart to corporate bonds in the interbank market, called debt-financing instruments of non-financial corporates, with different financing purposes and goals.

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Fifth, transactions in the interbank market are carried out based on inquiries, which

involve market-makers who provide bilateral quotes. In the exchange market, however,

bond transactions are matched through a centralized computer system, just like stocks;

transactions are settled in clean prices only.

Finally, the interbank market has extremely strict restrictions on instruments that can

be pledged as collateral. During normal times, both government bonds and corporate credit

bonds with a AAA rating can be pledged as collateral, while during crises, only interest rate

securities are accepted as collateral.6 In the exchange market, however, most high quality

credit bonds can be pledged as collateral at a certain discount rate (“haircut”) at all times.7

Of course, the haircut would vary over time and across bonds.

3.2 A Taxonomy of Corporate Credit Bonds in China

In this paper, we study all five types of corporate credit bonds in China: (1) enterprise

bonds and (2) corporate bonds in the exchange market; and (3) enterprise bonds, (4) medium-

term notes, and (5) commercial paper in the interbank market. It should be noted that both

corporate bonds and enterprise bonds would be categorized as corporate bonds in the U.S.

and European bond markets, while they are named “corporate credit bonds” in China, or

simply “credit bonds” in some literature, aiming at emphasizing the default risk of the bonds.

In China, however, they have different names mainly due to their respective issuing bodies,

financing purposes, and trading venues.

Although all types of bonds are issued by entities with corporate credit, corporate

bonds are mostly issued by limited liability, joint-stock companies (listed and unlisted), and

enterprise bonds are mostly issued by state-owned enterprises and state-holding companies,

both at the national and provincial level. Several million firms in China can issue credit bonds,

while only 200,000 of these can issue enterprise bonds. They also differ in their financing

purposes: enterprise bonds are issued to finance projects approved by the central

6 Interest rate securities refer to low risk instruments, including financial bonds, government bonds, local government bonds, central bills, and policy bonds. 7 Credit bonds are classified into eight collateral buckets, each with a different discount rate (“haircut”) depending on the bond rating, and whether it is publicly or privately issued.

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government or local governments, such as infrastructure projects and government projects,

while the issuance of corporate bonds enjoys more flexibility, in terms of debt payment, long-

term investment, and projects that benefit future development of the corporations. Moreover,

enterprise bonds are traded in both the interbank market and the exchange market, while

corporate bonds are traded only on the exchange. Consequently, issuers can choose to issue

enterprise bonds in both markets, and since 2005, approximately 90% of enterprise bonds are

listed on both markets, the year that the exchange market started to admit enterprise bond

issuance. However, enterprise bonds issued by the same entity cannot be traded across the

two markets; they are assigned different trading codes across the two markets and can be

regarded as two distinct bonds, even if their terms are identical. This gives us a perfect setup

to identify the discrepancy in the levels of liquidity across the two segmented venues. In

terms of maturity, corporate bonds and enterprise bonds are issued with maturities spanning

from one to twenty years, and are therefore considered as short-term, medium-term, or long-

term bonds. However, in this paper, we focus on maturities of less than or equal to 10 years,

due to the negligible amount of bonds trading with maturities beyond 10 years.

The counterpart to exchange market corporate bonds in the interbank market,

commonly referred to as “debt-financing instruments of non-financial corporates”

(DFINFCs), is further classified into two types of instruments: commercial paper and

medium-term notes. Commercial paper is issued by non-financial firms and functions as a

short-term financing tool, similar to commercial paper in the U.S., with maturities of less than

one year and are usually associated with almost zero credit risk. The issuance is highly

regulated and requires the approval of the PBOC. On the other hand, medium-term notes

often have maturities between one and ten years. They, therefore, function as a perfect

complement to commercial paper, albeit with a longer maturity, and together form the

corporate counterpart in the interbank market. In addition, the first issuance of commercial

paper could be traced back to as early as 1989, despite the halt in their issuance since 1997,

due to a sequence of scandals, while medium-term notes were not issued until approximately

10 years ago. Due to the short-term feature of the maturity of commercial paper, we do not

include them in the liquidity analyses in Section 5.

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Although financial bonds are not covered in this investigation, it would be beneficial

to understand their functional differences, as compared to the corporate credit financing

channels covered in this paper. Financial bonds are mostly issued by large banks, which

usually have three major sources of funding: bank deposits; inter-bank borrowings; and

issuance of financial bonds. First, investors/households tend to withdraw their savings

during financial downturns, which exposes banks to the funding instability of their deposits.

Second, inter-bank borrowings are used for short-term funding purposes, while banks

usually need to finance medium-term and long-term projects, causing mismatch in the

maturities of funding sources and uses. The issuance of financial bonds can successfully

resolve these problems and bridge their asset-liability mismatch. Moreover, banks can issue

financial bonds with flexible maturities, tailored to the maturities of the projects to be

financed. In addition, most financial bonds can only be traded, but not redeemed prior to

maturity, which ensures funding stability for the banks. In general, financial bonds have less

credit risk than other non-financial bonds and are considered as relatively safe investments.

Thus, the yields of financial bonds are usually lower than those of enterprise bonds, but

higher than those of government bonds, controlling for other variables.

[Insert Figure 1 Here]

4 POLICY ISSUES AND LIQUIDITY

In this section, we provide a detailed description of the several different phases of

international and domestic liberalization between 2006 and 2017 and the two crackdowns of

agent-holding transactions in the interbank market in 2013 and 2016. Then, we present the

liquidity proxies that we use to measure liquidity in the largely illiquid Chinese corporate

credit bond market.

4.1 Liberalization Policies of the Interbank Market

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Foreign investment in the Chinese bond market is often restricted by two limitations:

(1) the difficulty in obtaining an investment quota from the State Administration of Foreign

Exchange (SAFE); and (2) the low trading volume and high illiquidity. Over the past decade,

China had gradually opened its bond markets to international institutional investors.

However, the past decade witnessed a series of major and minor announcements regarding

how the regulators planned to reform the bond markets, including granting access to more

qualified foreign investors, simplifying approval procedures, and increasing foreign

investment quota limits. It is, thus, critical to identify which announcements had the greatest

impact on the changes in the fundamental characteristics of the bond market. Figure 1

presents the timeline of a few major policy changes that will be discussed and studied in

detail in this paper.

The first phase of liberalization occurred between 2002 and 2010, which was primarily

characterized by the publication of the Regulation on Domestic Investment by Qualified Foreign

Institutional Investor (QFII) in 2006, which details the qualifications necessary for being a QFII

candidate. This regulation was preceded by another temporary document publicized in 2002.

It was, however, not until 2005 that the Pan Asia Fund and Asia Debt China Fund became

the first two foreign institutions approved to invest in the Chinese interbank bond market.

Although more foreign institutional investors were granted access to the interbank

market, large-scale liberalization did not arrive until August 2010, when the PBOC launched

a pilot scheme that granted foreign central banks, monetary authorities, RMB-settlement

banks, and cross-border RMB-settlement participating banks in Hong Kong and Macau,

access to the interbank bond market. This also opened the second phase of liberalization

which lasted from 2011 to 2015, during which more qualified foreign institutions were

allowed to invest in the interbank market, accompanied by reduced quota limits and

simplified approval procedures. A major announcement made during this period was the

approval of 18 RMB-qualified foreign institutional investors (RQFII) to enter the interbank

market in May 2012, which was regarded as a milestone towards interbank liberalization.

One month later, in April 2012, the qualified foreign investment quota was increased from

U.S. $30 billion to U.S. $80 billion. Therefore, it is reasonable to expect significant changes in

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the levels of liquidity, and the extent to which liquidity is priced in the bond liquidity

premium and as a consequence, yield spreads.

The third phase lasted from 2015 to the present, starting with a notice from the PBOC

specifying even further-reduced approval requirements and quota limits. The announcement

in May 2016 allowed almost all types of foreign institutional investors to invest in the

interbank market, if they are “mid-term” or “long-term” in their investment orientation. A

further notice released in July 2015 allowed large foreign institutional investors to invest in

the interbank market without approval requirements and quota limits. We conjecture that

this decision may have left a much stronger impact on the relevant parameters of interest

studied in this paper. We would expect that an almost tripling in the quota limits of the

largest foreign institutional investors should have had a more dominant effect than

introducing smaller-sized foreign investors. We test this conjecture, along with the quota

limit increase during phase 2, by applying a structural break test to detect endogenously from

the data the dates around which structural changes actually occurred.

Despite the above sequence of regulatory changes, only 1.1% of bonds in the interbank

market were held by foreign investors by June 2017. In July 2017, however, Bond Connect, a

trading platform that allows offshore investors and mainland China investors to invest in

each other’s bond markets, was established in Hong Kong. The northbound link of this

channel, allowing offshore investors to invest in the domestic China interbank bond market,

was opened on July 3, 2017. One year later, we observe an increase of foreign investors’

holdings to 1.9%, a vast increase of 70%, compared to the original percentage. The southbound

link, which allows mainland China investors to invest in overseas bond markets, however,

is yet to be explored and may be opened at a later stage.

Despite being more open to foreign investments, the exchange market accounts for a

very small percentage of the trading volume and is usually ignored in academic research in

China. However, a sequence of regulatory changes in the interbank bond market also pushed

forward the development of the exchange market. For example, in the case of corporate

bonds, in terms of either issuance amount or trading volume, the interbank market accounts

for more than 90% of the entire bond market. However, The Corporate Bond Issuance and

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Trading Regulations, File No. 113, issued in January 2015, largely increased the issuance of

corporate bonds in the exchange market. Not only did File No. 113 lower restrictions on

issuers, offering methods and offering period of corporate bonds, it also simplified the

issuance approval procedure. More importantly, the change of regulations allowed many

unlisted companies to issue corporate bonds in the exchange market, which largely lowered

their financing cost.

The benefits of increased foreign participation in the Chinese bond markets are multi-

fold. First, it creates a more efficient allocation of limited capital through a more diversified

group of investment objectives. Second, it promotes the internationalization of RMB through

increasing bond holdings in the domestic currency. Third, it ameliorates the negative impact

of uncertainties in foreign capital outflows and inflows by injecting the domestic market with

projects of medium-term and long-term investment horizons. Fourth, it promotes a global

environment in which investors are more willing to hold RMB-denominated assets. However,

it should not be overlooked that some of the institutional features may also impede foreign

participation. Overall, the existence of multiple regulators and the competition among these

regulators have exacerbated the segmented market structure of bond markets in China. The

fact that large institutional investors account for most of the outstanding value of each type

of bond also impedes liquidity due to asymmetric demand and supply schedules across the

various bond market participants.

4.2 Crackdowns on Agent-holding Transactions

While the government’s aim is to develop the Chinese corporate credit bond market,

some financial institutions, inevitably, make use of the legal loopholes inherent in the

guidelines to meet their own objectives. One typical example is that banks and security firms

all possess the incentive to boost trading volume, one of the most important grading criteria

in the institutions’ regulatory ratings in the assessment of the qualification to underwrite

sovereign bonds as primary dealers. This led to the emergence of agent-holding activities since

2010. 8 Agent-hold transactions are strictly not illegal when used as a normal means of

8 See Appendix B for a detailed description of agent-holding transactions.

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conducting bank business; however, they would be prohibited if investors use them to cover

up bad bond investments or to satisfy private uses, not within the qualified categories of

investment.

The interbank bond market underwent two major crackdowns of agent-holding

practices in the past decade, in 2013 and in 2016. The 2013 crackdown began in early 2013,

which led to a drastic drop in trading volume of almost 90%, as compared to that prior to the

crackdown announcement. The situation stabilized thereafter, before agent-holding

transactions re-emerged in 2015, which led to a second crackdown in 2016, leading to a 70%

decline in trading once again. We will examine the impact of agent-holding crackdowns on

liquidity levels in Section 6.

4.3 Measuring Liquidity

Our study employs a wide range of liquidity measures that have been proven to be

appropriate and useful for the U.S. and European bond markets. First of all, we include bond

characteristic variables and trading activity variables that have been used as liquidity proxies in

the literature. The bond characteristic variables that we use include age, time to maturity,

coupon rate, duration, convexity, and rating dummies. The trading activity variables that we

use include the trading interval, the zero-return measure, the zero-trade measure, and daily

trading volume. In addition, Friewald, Jankowitsch and Subrahmanyam (2012) are the first

to apply a range of commonly utilized liquidity measures to examine the yield spreads of

corporate bonds. Schestag, Schuster and Uhrig-Homburg (2016) also find that most low-

frequency liquidity proxies measure transaction costs equally well, and high-frequency

measures are also strongly correlated. We focus on the low-frequency measures due to the

constraints of data availability and quality. Our liquidity measures include the Amihud

measure in Amihud and Mendelson (1986), the high-low spread estimator in Corwin and

Schultz (2012), the price dispersion measure in Jankowitsch, Nashikkar and Subrahmanyam

(2011), the comparative high-low (CHL) measure in Abdi and Ronaldo (2011), and the inter-

quartile bid-ask spread estimator in Han and Zhou (2007).

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The Amihud ratio measures the price impact of trades and is an illiquidity measure,

measured on a daily basis in this paper. Note that only daily total trading volume is available,

rather than per-trade trading volume. The former is used to compute the Amihud measure,

given by |𝑟𝑟𝑡𝑡|/𝑉𝑉𝑡𝑡 . A low Amihud measure would indicate high liquidity. To compute the

Amihud measure on a weekly basis, for each end-of-day transaction, we divide the absolute

value of the daily return, 𝑟𝑟𝑗𝑗 , measured in basis points, by the daily trading volume 𝑉𝑉𝑗𝑗 ,

measured in dollar terms, and take the average of all observations over a week t with 𝑁𝑁𝑖𝑖,𝑡𝑡

observed trading days:

(Amihud)𝑖𝑖,𝑡𝑡 =1𝑁𝑁𝑖𝑖,𝑡𝑡

��𝑟𝑟𝑗𝑗�𝑉𝑉𝑗𝑗

𝑁𝑁𝑖𝑖,𝑡𝑡

𝑗𝑗=1

The interquartile estimator is another measure that we use; a low interquartile-range

estimator indicates high liquidity. The interquartile-range estimator, for a given bond, I, on

day t, measured on a daily basis, is calculated as half of the spread between the highest

trading price 𝐻𝐻𝑖𝑖,𝑡𝑡 and the lowest price, 𝐿𝐿𝑖𝑖,𝑡𝑡, divided by the weighted average of price on that

day:

(IQR)𝑖𝑖,𝑡𝑡 =𝐻𝐻𝑖𝑖,𝑡𝑡 − 𝐿𝐿𝑖𝑖,𝑡𝑡

2𝑊𝑊𝑊𝑊𝑖𝑖,𝑡𝑡

The price dispersion measure introduced in Jankowitsch, Nashikkar and

Subrahmanyam (2011) measures how transaction prices deviate from the market-wide

valuation and can be considered as the transaction cost of a trade. Since the market-wide

valuation is not available in our data, we instead use the weighted average of daily

transaction prices within a week, 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑡𝑡 . A low price dispersion measure indicates that

transaction prices are close to the market-wide valuation and transaction costs are low, which

corresponds to high liquidity. The weekly price dispersion measure, for bond I, is defined as

the root-mean of the average of squared deviations of end-of-day j transaction prices 𝑃𝑃𝑖𝑖,𝑗𝑗 from

𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑖𝑖,𝑡𝑡, weighted by the daily trading volume 𝑉𝑉𝑗𝑗:

(price dispersion)𝑖𝑖,𝑡𝑡 = �1

∑ 𝑉𝑉𝑗𝑗𝑗𝑗� �𝑃𝑃𝑖𝑖,𝑗𝑗 − 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑖𝑖,𝑡𝑡�

2𝑉𝑉𝑗𝑗𝑗𝑗

16

The high-low spread estimator is introduced in Corwin and Schultz (2012). A high

HL-spread estimator indicates high liquidity and low discrepancy between the highest and

the lowest transaction prices across the two-day period. Assuming that the highest trading

price 𝐻𝐻𝑡𝑡 and the lowest trading price 𝐿𝐿𝑡𝑡 during a day t are from the buy side and the sell side,

respectively, the ratio of the two prices is assumed to reflect the information asymmetry of

the two sides. For a given bond i, the high low spread measure is defined as:

(HL spread)𝑖𝑖,𝑡𝑡 =2(exp(𝛼𝛼) − 1)

exp(𝛼𝛼) + 1

where

𝛼𝛼 =�2𝛽𝛽 − �𝛽𝛽

3 − 2√2−�

𝛾𝛾3 − 2√2

,𝛽𝛽 = �log �𝐻𝐻𝑡𝑡𝐿𝐿𝑡𝑡��2

+ �log �𝐻𝐻𝑡𝑡+1𝐿𝐿𝑡𝑡+1

��2

, 𝛾𝛾 = �log�max{𝐻𝐻𝑡𝑡 ,𝐻𝐻𝑡𝑡+1}max{𝐿𝐿𝑡𝑡 , 𝐿𝐿𝑡𝑡+1}

��2

The last liquidity measure that we use is the CHL estimator, introduced in Abdi and

Ranaldo (2017). They propose a new method to estimate the bid-ask spread for less liquid

stocks when quote data are not available. This fits our setting perfectly, because trading

frequency of bonds in China is quite low and quote data, though available, is largely

incomplete. The daily CHL measure for bond i is given by:

(CHL)𝑖𝑖,𝑡𝑡 = 2�max ��log𝑃𝑃𝑡𝑡 − log �𝐻𝐻𝑡𝑡 + 𝐿𝐿𝑡𝑡

2 �� �log𝑃𝑃𝑡𝑡 − log �

𝐻𝐻𝑡𝑡+1 + 𝐿𝐿𝑡𝑡+12 ��

, 0�

and the weekly CHL measure is simply

(CHL)𝑡𝑡 =1𝑁𝑁𝑖𝑖,𝑡𝑡

� (CHL)𝑖𝑖,𝑡𝑡𝑁𝑁𝑖𝑖,𝑡𝑡

𝑡𝑡=1

where 𝑁𝑁𝑖𝑖,𝑡𝑡 is the number of days that bond i is traded in week t.

5 DATA DESCRIPTION

In this section, we present the unique dataset we have assembled for this paper. Our dataset

covers virtually all end-of-day transactions from all types of bonds in both the interbank

17

market and the exchange market, including government bonds, local-government bonds,

enterprise bonds, corporate bonds, financial bonds, etc. We focus on all corporate credit

bonds that are actively traded in the two venues: enterprise bonds; corporate bonds; and

medium-term notes.9 Recall that the source of the dataset is CFETS, an arm of the PBOC,

which functions in a manner similar to the FINRA-developed platform, TRACE, which has

been instrumental in facilitating the mandatory reporting of secondary market transactions

in the U.S.10

5.1 Data Regulation and Composition

Our dataset contains two subsets and covers an 18-year period, from January 2000 to

December 2017. The beginning date of the transaction data for each type of bond may vary,

depending on the date that transactions associated with that type of bond were first required

to be reported to CFETS. The first subset contains fundamental bond characteristic variables,

which include the trading date, issue date, time-to-maturity (remaining), duration, convexity,

coupon rate, bond ratings, rating agencies, type of interest rate (floating or fixed), trading

market, and special clauses. Many of these characteristics are frequently used as simple

liquidity proxies in the literature. The second subset contains detailed information about all

end-of-day bond transactions, including trading code, daily trading volume, highest and

lowest traded prices (clean and dirty), opening and closing prices (clean and dirty), and yield-

to-maturity based on the closing dirty price, which are widely used in much of the literature

on liquidity research.

Due to data regulations of the PBOC, CFETS is only allowed to provide end-of-day

transaction data. In other words, whether a bond is traded once or more than once during a

day, it appears only once as a bond-day observation in our data set. As a result, we only

know what bonds are traded during a day, but cannot discern how many times each bond

was traded on that day. This restriction certainly poses some serious challenges to our

investigation, such as constructing certain liquidity measures, as detailed trading

9 Sovereign bonds will be examined in a companion paper. 10 To the best of our knowledge, we are the first to receive formal authorization to use this CFETS dataset for academic research.

18

information during a day of each traded bond is not available. However, this is the best data

available for the Chinese bond market, and we have to conduct our research in the presence

of this constraint. We, therefore, employ a complex filtering procedure to ensure the

soundness of our final dataset, which is described in Appendix A.

5.2 Completing Missing Data Points of Yield-to-maturity

One of the key variables employed in our study is the yield-to-maturity of each bond.

However, in the early years of our dataset between 2000 and 2009, quite a few data points of

yield-to-maturity are missing for all types of bonds. We explicitly compute each missing

yield-to-maturity by following the instructions in the official document issued by the PBOC.

As in other bond markets, on each trading day, the dirty price is related to the clean price

through the following equation:

dirty price = clean price + 𝑊𝑊𝐴𝐴

where 𝑊𝑊𝐴𝐴 is the accrued interest and is computed as � 𝑡𝑡365/𝑓𝑓

× 𝐶𝐶� . In this expression, 𝑡𝑡 is the

number of days between the last coupon payment date and the trading date; 𝑓𝑓 is the

frequency of coupon payments per year; and 𝑐𝑐 is the amount of the next coupon payment.

The dirty price is related to the yield-to-maturity through the following equation:

𝑃𝑃𝑉𝑉 =𝐹𝐹𝑉𝑉

(1 + 𝑦𝑦/𝑓𝑓 )𝑁𝑁−1+𝑑𝑑

(365/𝑓𝑓)+ �

𝐶𝐶𝑖𝑖/𝑓𝑓

(1 + 𝑦𝑦/𝑓𝑓)𝑘𝑘+𝑑𝑑

(365/𝑓𝑓)

𝑁𝑁−1

𝑘𝑘=0

where PV is the dirty price; y is the annualized yield-to-maturity; 𝐶𝐶𝑖𝑖 is the coupon payment

per period 𝑖𝑖; f is the frequency of coupon payments per year; d is the number of days between

the trading date and the next coupon payment date; N is the total number of coupon

payments; and FV is the notional face value of the bond. The yield-to-maturity y does not

have a closed form solution, except if the trading date is with the final coupon payment

period. In other words, if only one cash flow remains (i.e., 𝑁𝑁 = 1), the yield can be found

explicitly as:

19

𝑦𝑦 =365𝑑𝑑

× �𝐶𝐶𝑁𝑁 + 𝐹𝐹𝑉𝑉𝑃𝑃𝑉𝑉

− 1�

Moreover, for enterprise bonds and corporate bonds in the two markets, the volume-

weighted average yield in each week is calculated as follows:

𝑦𝑦𝑖𝑖𝑡𝑡 =∑ 𝑦𝑦𝑖𝑖𝑡𝑡𝑘𝑘 × 𝑄𝑄𝑖𝑖𝑡𝑡𝑘𝑘𝑘𝑘∑ 𝑄𝑄𝑖𝑖𝑡𝑡𝑘𝑘𝑘𝑘

where 𝑦𝑦𝑖𝑖𝑡𝑡 is the volume-weighted average yield of bond k in week t; 𝑦𝑦𝑖𝑖𝑡𝑡𝑘𝑘 is the yield

corresponding to the day k of bond k in week t; and 𝑄𝑄𝑖𝑖𝑡𝑡𝑘𝑘 is the daily volume of day k in week

t.

5.3 Rating Agencies and Bond Rating Quantification

Similar to bond ratings in the U.S., bonds with higher default risk are given lower

ratings, in principle. Bonds in China are usually rated by six rating agencies: China Chengxin

Credit Rating Group, Shenzhen United Ratings, Shanghai Brilliance Credit Rating, Dagong

Global Credit Rating, Pengyuan Credit Rating, and China Orient Golden Credit Rating. Their

credit ratings are similar to those of Standard and Poor’s (S&P) and range as follows: AAA,

AAA-, AA+, AA, AA-, A+, A, A-, BBB+, BBB, BBB-, BB+, BB-, B+, B, B-, CCC, CC, and C.11 In

the Chinese bond markets, bonds with ratings below ‘BBB’ are considered to be speculative-

grade bonds, while bonds that have ratings equal to or above ‘BBB’ are considered as

investment-grade bonds. To control for credit risk differentials, we adopt the following two

approaches. The first approach is similar to that of Friewald, Jankowitsch and

Subrahmanyam (2012). We quantify the ratings by creating a measure called “Rating

Number”, being multiples of 1, i.e., AAA = 1, AAA- = 2, AA+ = 3, etc. This, however, is valid

if and only if we assume that: (1) all rating agencies have the same rating criteria (i.e., if a

bond is given a rating x by rating agency 1, it receives the same rating x from all other rating

agencies); and (2) the credit differential between two adjacent ratings is equal.

11 Although the nomenclature of the ratings appears similar to those of the major U.S. (international) credit rating agencies – S&P, Moody’s, and Fitch – the credit risk implied by the ratings is quite different. In general, the Chinese ratings imply greater credit risk than their U.S. counterparts.

20

[Insert Table 1 here]

Table 1 displays the distribution of ratings for the five types of credit bonds in the two

markets. We combine medium-term notes and commercial paper in the interbank market to

form the corporate counterpart. The numbers of bonds assigned to each credit rating class in

each market are listed in the diagonal entries of each panel of the table. The numbers in the

off-diagonal entries represent the number of bonds that shifted from one credit rating to the

other, which can be either an upgrade or a downgrade during their respective trading

periods. For instance, the credit ratings of 559 corporate bonds in the interbank market

shifted from the rating “AAA” to “AA”, or vice versa. The average credit ratings are also

listed. Enterprise bonds in the exchange market seem to have higher average credit rating

than those in the interbank market, while for corporate bonds the scenario is reversed.

However, the average credit rating of each category of credit bond lies between 2 and 2.4,

indicating that the average credit rating of each credit bond is between “AAA-” and “AA+”.

[Insert Figure 2 here]

Figure 2 graphically displays the distribution of ratings for the five types of credit

bonds in the two markets, after the filtering procedure in Appendix A. We observe that

almost all traded bonds traded have a rating above or equal to “AA-“. The total number of

bonds traded in the interbank market is approximately six times larger than that in the

exchange market, which again shows that the former is the dominant market of the two. We

also observe a vast decrease in the number of bonds traded in the exchange market, with

corporate bonds declining approximately 20% and enterprise bonds declining nearly 65%,

due to early redemption before maturity in the exchange market. These bond transaction

records are discarded through the filtering procedure. Figure 3 shows the number of unique

bonds traded each year in the Chinese credit bond market from 2006 to 2016. In the interbank

market, the numbers of all three bonds traded grew steadily until 2012. While the number of

enterprise bonds and medium-term notes traded continue to grow even after 2012, the

growth of commercial paper traded decreased abruptly. This has to do with the maturity of

commercial paper, ranging from 1 week to 1 year, with an average maturity of 30 days. Thus,

21

most of the commercial paper traded in a given year is issued in the same year. Since the

government began to crack down on illegal agent-holding practices in the interbank market in

early 2013, the number of issuances decreased, leading to a corresponding decrease in the

amount of commercial paper that could be traded in 2013, while not affecting the numbers

of medium-term notes and enterprise bonds available for trade. In the exchange market, the

number of corporate bonds traded increased dramatically by 150% from 2015 to 2016, while

the growth was steady, but lower, before. This was due to the permission granted to unlisted

companies to issue corporate bonds in the exchange market since January 2015. Enterprise

bonds have always been the less popular of the two, and even experienced a decrease in the

number of bonds traded after corporate bonds attracted attention in 2015.

[Insert Figure 3 here]

5.4 The Nelson-Siegel Three-factor Estimation

The dependent variable in our regression analysis is the yield spread of a particular

type of bond, which is defined as the difference between the yield-to-maturity of that bond

and the zero rate corresponding to the same time to maturity on the same trading day.

However, in most cases, the two sets of maturities do not exactly match. For instance, a bond

that is traded on one trading day may have a time to maturity of 3.22 years, while there is no

government bond traded on the same day with exactly the same time to maturity. Without

knowing the corresponding zero rate, it would be impossible to calculate the yield spread for

that bond on that day. In order to better capture the non-linear relationship between bond

yields and time to maturities, and to obtain zero rates of all possible maturities on a given

trading day, we fit our daily data with a Nelson-Siegel three-factor model as in Nelson and

Siegel (1987) as modified by Diebold and Li (2006). The three-factor estimation for any given

maturity 𝜏𝜏 is given by:

𝑦𝑦𝑡𝑡(𝜏𝜏) = 𝛽𝛽0𝑡𝑡𝑋𝑋0𝑡𝑡 + 𝛽𝛽1𝑡𝑡𝑋𝑋1𝑡𝑡 + 𝛽𝛽2𝑡𝑡𝑋𝑋2𝑡𝑡

where

22

𝑋𝑋0𝑡𝑡 = 1, 𝑋𝑋1𝑡𝑡 =1 − exp(−𝜆𝜆𝑡𝑡𝜏𝜏)

𝜆𝜆𝑡𝑡𝜏𝜏, 𝑋𝑋2𝑡𝑡 =

1 − exp(−𝜆𝜆𝑡𝑡𝜏𝜏)𝜆𝜆𝑡𝑡𝜏𝜏

− exp(−𝜆𝜆𝑡𝑡𝜏𝜏)

The three factors in the Nelson-Siegel model, the level factor 𝛽𝛽0𝑡𝑡, the slope factor 𝛽𝛽1𝑡𝑡,

and the curvature factor 𝛽𝛽2𝑡𝑡, are interpreted as the latent dynamic factors. The 𝜆𝜆𝑡𝑡 parameter

is the time-dependent decay factor that determines the level of contribution of 𝛽𝛽0𝑡𝑡,,𝛽𝛽1𝑡𝑡, and

𝛽𝛽2𝑡𝑡 . It should be noted that while 𝜆𝜆𝑡𝑡 is often taken to be time-invariant in numerous past

papers, we allow this decaying factor to be time-varying on a daily basis, similar to what was

originally implemented in Nelson and Siegel (1987).

For each type of bond with a total number of N trading days, we generate a N × 1000

grid which contains a term structure of zero rates for each trading day, and for all maturities

𝜏𝜏 ranging from 0.01 year to 10 years, with an increment of 0.01 year. In this way, we can

match a zero rate to the yield of every traded bond on each trading day until maturity.

6 DESCRIPTIVE STATISTICS

This section presents the descriptive statistics, and the discussion of yields and the levels of

liquidity for each category of the credit bonds.

6.1 Summary Statistics of Variables

Table 2 provides an overview of the substantial cross-sectional summary statistics for

the variables, including bond characteristic variables, trading activity variables, and liquidity

proxies. For time-dependent variables, the statistics are computed as the weekly averages of

each individual bond. In our dataset, 1769 enterprise bonds and 2,581 corporate bonds were

traded in the exchange market. However, transaction records with “PR” in their names need

to be discarded, as “PR” indicates that early redemption prior to maturity was initiated. This

leaves 758 enterprise bonds, a dramatic decrease, and 2,579 corporate bonds in our dataset.

In the interbank market, 3,238 enterprise bonds, 4,784 medium-term notes, and 12,569 issues

of commercial paper were traded. Further processing of the dataset by deleting bonds with

23

a trading span less than 30 days, and number of trade times less than 10, leaves us with 501

enterprise bonds and 1,713 corporate bonds in the exchange market, and 2,801 enterprise

bonds, 4,030 medium-term notes, and 9608 commercial paper in the interbank market. This

shows that the number of bonds traded in the interbank market is seven times more than that

in the exchange market. Moreover, the total trading volumes of enterprise bonds and

corporate bonds in the exchange market are 957 billion and 2745 billion RMB, respectively,

while the total trading volumes of enterprise bonds, medium-term notes, and commercial

paper in the interbank market are 44,661 billion, 74,180 billion, and 63,213 billion RMB,

respectively. The total trading volume of credit bonds in the interbank credit bond market

accounts for more than 98% of the trading volume of bonds across the two markets. In short,

due to the participants in the interbank market, the interbank market dominates the exchange

market in terms of both the total number of bonds traded and the aggregate trading volume.

In the exchange market, the yield spread between the 5th and 95th percentiles ranges

from 77 to 444 basis points (bps) for enterprise bonds, with a mean of 221 bps, and from 64

to 496 bps for corporate bonds, with a mean of 243 bps. In the interbank market, however,

the corresponding gap is smaller, and ranges from 138 to 398 bps for enterprise bonds, with

a mean of 258 bps, from 95 to 331 bps for medium-term notes, with a mean of 331 bps, and

from 21 to 354 bps for commercial paper, with a mean of 156 bps. We observe a larger

variation in the interbank market than in the exchange market, which could be explained by

individual trading activity that leads to larger credit risk spreads in the exchange market.

Commercial paper appears to have the shortest mean trading interval of 0.038 year,

meaning that, on average, each individual issue of commercial paper was traded once every

14 days. Medium-term notes, on the other hand, have the longest mean trading interval, 0.087

year, which means, on average, that each medium-term note was traded once every 32 days.

Moreover, corporate bonds in the exchange market have the largest mean 0.585 for the zero-

return measure, which means that we observe no price variation during a trading day 58.5%

of the time. Enterprise bonds in the interbank market, however, have the shortest mean zero

return, 0.299, which means that on 30% of the days, we observe no price variation during the

24

trading day. The mean of zero trade measure ranges from 0.466 to 0.598 in the two markets,

indicating that, on average, each bond was traded once every two days.

The mean of the Amihud measure in the exchange market is much higher than that in

the interbank market, mainly because of the vast difference in trading volume between the

two markets. The mean values of the remaining four illiquidity measures across the two

markets have approximately the same magnitude. However, in both markets, the mean value

of each illiquidity measure is uniformly higher for the enterprise bonds than that of its

corporate counterpart. This shows that, within each market, corporate bonds have higher

liquidity than enterprise bonds. In addition, for both enterprise bonds and corporate bonds,

the standard deviations of the illiquidity measures in the exchange market are larger than

those in the interbank market. This reveals that the distribution of illiquidity measures is

more spread-out in the exchange market than in the interbank market. One possible

explanation for this finding is that participants in the interbank market are highly

concentrated in commercial banks, leading to a lack of heterogeneity, while the exchange

market is populated with household investors with a large cross-sectional variation in

investment behavior and, certainly, vast differences in demand for liquidity.

6.2 Data-based Yields to Maturity

We now compute the data-based weekly volume-weighted yields to maturity for each

credit bond in the two markets. We also compare the difference in the yields to maturity

between credit bonds in the interbank market and their counterparts in the exchange market,

by pooling the daily observations in the two markets into 13 fixed maturity buckets.12 Table

4 presents the cross-sectional descriptive statistics of the mean, standard deviation, minimum,

maximum, median value, 25th and 75th percentile for the data-based yields to maturity of

each bucket. Since the maturities of traded bonds in our dataset account for less than 1% of

bonds with maturities longer than 10 years, we present the descriptive statistics of bonds

with maturities less than or equal to 10 years. For bonds with multiple trades in one week,

12 The 13 maturity buckets have mean maturities (in years) of 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, and 10. Note that commercial paper in the interbank market is divided into 10 equally-spaced maturity buckets, given its maturity of less than or equal to one year.

25

we estimate the yields from individual daily transactions by computing a volume-weighted

average for that week. For each bucket, we calculate the cross-sectional descriptive statistics

in each week, and report the time series average of each statistic.

[Insert Table 4 Here]

In the interbank market, for medium-term notes and commercial paper, the average

yields increase in maturity monotonically from 4.77% to 5.2%, and from 4.42% to 4.84%,

respectively, though the increments are small. However, for enterprise bonds, yields peak at

medium-term maturities (up to 6.16%) and then gradually decrease to 5.6%. In the exchange

market, yields again peak at medium-term maturities for both enterprise bonds and

corporate bonds. A cross-market comparison of enterprise bonds indicates a lower average

yield for maturities less than five years, and a higher average yield at maturities of six years

or more. A cross-market comparison of corporate bonds indicates that there exists a virtually

uniform yield difference, with corporate bonds in the exchange market having a consistently

higher average yield than those of their counterparts, except for the last two maturity buckets.

Figure 4 presents a graphical representation of the volume-weighted yields to

maturity of the five credit bonds by credit ratings. From the sub-figures, one can see that the

yield curves of bonds with credit ratings below “AA” exhibit irregular shapes, partly due to

the lack of observations, while the yield curves of bonds with the top-three credit ratings are

slightly increasing. In the exchange market, the term structures exhibit a hump-shaped

feature, while in the interbank market, the term structure of enterprise bonds increases

steadily as maturity increases, while the term structure of its corporate counterpart does not

exhibit much variation over maturity. The last sub-figure displays the overall term structure

of the four credit bonds (excluding commercial paper). Except for medium-term notes, the

other three credit bonds all exhibit hump-shaped yield curves, with yields peaking at

maturities between three to seven years.

[Insert Figure 4 Here]

26

6.3 Trading Activities

We now examine trading activities at a more detailed level. Figure 5 plots the monthly

trading volume of the five corporate bonds. In the exchange market, we observe a sharp

increase in monthly trading volume after 2015, which corresponds to the policy of allowing

unlisted firms to issue corporate bonds. In contrast, we observe a steady decrease in the

trading volume of enterprise bonds since 2015, which has experienced a steady increase since

2008. This shows that corporate bonds have attracted investors’ attention and have diverted

their attention away from enterprise bonds. In the interbank market, the three patterns follow

similar trends. Interestingly, we observe a drastic plunge in trading volume at the beginning

of 2013 of all three credit bonds, due to the agent-holding crackdown, which prohibited most

spurious trading activity (indicated by the black bar). The interbank market regained attention

with a gradual increase in trading volume in the following two years, before it experienced

another dramatic decline in trading volume, due to the second round of regulations

involving agent-holding activities, which occurred in the second half of 2015. This was due to

the large capital inflow into the interbank market after the Chinese stock market crash. In

addition, we observe a dramatic rebound in commercial paper trading volume in the

aftermath of the stock market crash, while that of enterprise bonds and medium-term notes

only increased slightly. This further demonstrates investors’ high demand for short-term

liquidity during financial downturns. The yellow bars indicate the watershed of the three

phases of liberalization. For all three credit bonds in the interbank market, we observe a slight

decline in trading volume after the two policy announcements, followed by a dramatic

increase, before most trading activities were impeded by the two crackdowns of agent-holding

activities. Since each watershed date is followed by the beginning of a crackdown event, it is

difficult to disentangle the relative effects of each policy announcement on the level of

monthly trading volume. However, it is patent that the impact of crackdown announcement

dominates that of foreign liberalization. One explanation for this is that foreign institutional

holdings of Chinese bonds remained less than 1% before 2017, while almost all agent-holding

transactions occurred among domestic institutions. Figure 6 plots the mean trading volume

per bond per day, which, as expected, exhibits similar patterns to those in Figure 5.

27

[Insert Figures 5 and 6 Here]

Figure 7 plots the trading activity variables of corporate bonds and enterprise bonds

in the exchange market. These alternative measures of illiquidity include zero-trade, zero-

return, and trading interval. The green bar indicates the announcement date on which unlisted

firms were allowed to issue corporate bonds. For both corporate bonds and enterprise bonds,

we observe that, in general, all three illiquidity measures increase over time. As mentioned

in Chen et al. (2018), household investors mostly speculate in the exchange market, while

sophisticated institutional investors trade in the interbank market. This leads to much more

speculative trading activities in the exchange market, while trading volume remains

negligible compared to the interbank market. Consequently, the exchange market has

immediacy, but is much less deep, as compared to the interbank market. Since all three

variables measure illiquidity, this indicates a decline in speculative trading activities over the

past decade. For enterprise bonds, for which we have data before the 2008 financial crisis, we

observe a gradual decline in trading intensity prior to 2009 for all three variables, which

reveals an increase in speculative trading activities before the crisis, given that the exchange

market was expanding in the early stage. However, this trend reversed post-2009, once retail

investors began to adopt safer investment strategies, i.e., they invested in bonds for longer

holding periods.

[Insert Figures 7 and 8 Here]

Figure 8 plots the trading activity variables of medium-term notes and enterprise

bonds in the interbank market. The three trading activity variables exhibit similar patterns

for the two categories of credit bond. Before early 2013, we observe a steady decrease in the

three measures in the case of enterprise bonds, implying increasing trading activities.

However, both zero-trade and zero-return measures experienced a jump in early 2013, with the

zero-trade measure doubling and zero-return measure tripling for both medium-term notes

and enterprise bonds. This occurred immediately after the first crackdown on agent-holding

activities, which took place in the interbank market, demonstrating a clear sign of a drastic

decline in trading activities in the interbank market. The impact of this policy announcement

28

on the trading interval measure was also significant, as we observe an approximate

quadrupling of this measure within merely half a year of the crackdown announcement in

early 2013, showing that, on average, institutional investors had dramatically increased their

holding period of interbank bonds. This finding confirms the severity of the agent-holding

phenomenon occurring during the early phase of interbank liberalization, as well as the

government’s determination to eradicate such illegal trading activities. The reemergence of

agent-holding transactions during 2015 led to increased trading intensity, as can be seen from

the decrease of the three variables during that year. The second round of the crackdown on

agent-holding activities in early 2016, once again, led to a gradual increase in the three

measures since 2016. Once again, due to the small percentage of foreign institutional holdings

of bonds in China, the two policy announcements of foreign liberalization fail to show any

impact on the three trading activity variables.

6.4 Levels of Liquidity

Figures 9 to 12 plot the levels of liquidity, separately measured by five widely-utilized

liquidity proxies, of the four categories of credit bonds across the two markets. Due to the

lack of intraday data and the infrequent trading of bonds in China, some liquidity measures,

which work efficiently in liquid markets, may not perform as well in the largely illiquid

Chinese bond market. One way to circumvent this problem is to use the “latent liquidity”

methodology of Mahanti et al. (2008). However, this measure cannot be constructed due to

the lack of requisite variables, including the identities of investors in the holdings’ data of

insurance companies, pension funds, hedge funds, etc.

First, we examine the levels of liquidity in the exchange market. For corporate bonds

(see Figure 9), we observe that immediately after unlisted firms were allowed to issue

corporate bonds in the exchange market in January 2015, illiquidity declined, as measured

by all five liquidity proxies, except for the Amihud measure, in which the decline is not as

obvious. This observation demonstrates that lowering the issuance requirements of corporate

bonds, one kind of domestic liberalization, did improve liquidity in the corporate bond

exchange market. The improvement in liquidity is patently obvious in the case of the

29

interquartile range estimator, the high-low spread estimator, and the CHL measure. For enterprise

bonds in Figure 10, we fail to observe any discernable trend in liquidity over years. This can

be explained by the fact that the government has not focused on the development of the

enterprise bond exchange market, leaving it as the smallest credit bond market in terms of

both trading volume and the number of bonds traded annually. From the figure, the first

three proxies of illiquidity remain stable over the years, demonstrating no clear improvement

in liquidity. However, the CHL measure does exhibit an obvious decrease in illiquidity since

2015, possibly due to the liquidity spillover from the exchange corporate bond market in

which liquidity had largely improved since 2015.

[Insert Figures 9 and 10 Here]

Second, we examine the levels of liquidity in the interbank market. For both medium-

term notes in Figure 11 and enterprise bonds in Figure 12, we observe similar patterns for

each of the five liquidity proxies. For the price dispersion measure and the interquartile range

estimator, we observe that liquidity increased steadily over time. For the Amihud ratio, we fail

to observe discernable patterns. For the high-low spread estimator, we observe that liquidity

decreased first prior to 2015 and then increased afterwards, forming a V-shape pattern. The

steadiest liquidity measure of the five is the CHL estimator, created by Abdi and Ronaldo

(2017), which is built to effectively estimate the bid-ask spread for less liquid markets. This

proxy indeed seems to work well in the Chinese corporate credit bond setting. We observe

almost a linear decline in illiquidity over time. Interestingly, the two crackdowns of agent-

holding activities in early 2013 and 2016 did not negatively affect liquidity in the interbank

bond market. Instead, liquidity seemed to have improved immediately following the

announcement of these two crackdowns. This finding, along with the fact that trading

activities drastically decreased after the two announcements of the crackdown, further

substantiates the fact that most agent-holding activities, though not strictly prohibited by law,

do not contribute to improving liquidity in the corporate bond market. Rather, they are

frequently used by financial institutions to potentially enlarge the scale of assets under

control, to increase the leverage of investments, and to disguise the bad condition of a firm

during evaluations by rating agencies. Furthermore, followed by the first policy

30

announcement of foreign liberalization in May 2012, we observe a slight decline in illiquidity

for the first few months in almost all measures for both credit bonds, before the early 2013

crackdown pushed down illiquidity further. For the second policy announcement of foreign

liberalization in July 2015, interestingly, we observe a slight increase in illiquidity in the case

of medium-term notes, before the early 2016 crackdown pushed down illiquidity again. The

change of liquidity in the case of enterprise bonds between the two announcements, however,

is not as obvious.

[Insert Figures 11 and 12 Here]

In light of the above discussion, the ability of each liquidity proxy to measure the level

of liquidity of each category of credit bond across the two markets varies dramatically. We

attribute this inconsistency in measurement to the low trading frequency in the Chinese bond

markets and the lack of intraday transaction details. To overcome this issue, Mo and

Subrahmanyam (2018) create a novel liquidity proxy to measure the aggregate level of

liquidity in the Chinese corporate credit bond market. They construct the first principle

component of the Amihud measure, the price dispersion measure, the interquartile range estimator

and the CHL measure, and verify that structural changes in the level of liquidity, measured

by this novel liquidity proxy, did indeed occur around the policy announcement dates,

concerning foreign liberalization, domestic liberalization, and crackdown on illegal trading

activities. The policy announcements in their investigation include the allowance of unlisted

firms to issue corporate bonds in the exchange market and the two crackdowns of agent-

holding transactions in early 2013 and 2016. Their findings reinforce our above arguments.

Similarly, Chen et al. (2018) use the same novel measure to examine the levels of liquidity

and liquidity effects in the Malaysian sovereign bond market, and find the approach to be

equally effective.

7 CONCLUSION

31

Over the past decade, the study of liquidity in the Chinese bond markets has received closer

attention from both academic researchers and industry practitioners. This is particularly true

in the interbank bond market, which has gone through a sequence of policy changes towards

liberalization to facilitate foreign investment. In this paper, our analyses, based on the most

complete dataset on Chinese bond markets currently available, provide a general description

and analysis of the levels of liquidity of the corporate credit bonds, traded in the two active

venues, the interbank market and the exchange market. The primary challenge in studying

bond markets in China is that they are much less liquid than bond markets in the U.S. and,

of course, the corresponding equity markets. In addition, data limitations make constructing

more accurate liquidity proxies in the Chinese bond market challenging, particularly since

only a very small proportion of corporate credit bonds in China are traded every few days; a

substantial proportion of the trading volume occurs in the interbank market, and not in the

exchange market, and the most reliable dataset consists only of end-of-day transactions,

which could be noisy. To overcome potential biases, we explore a set of liquidity measures

that are widely applicable in more liquid markets. We then examine whether these liquidity

proxies work efficiently in the much less liquid corporate credit bond market in China.

Our analysis explores the time-series aspects of the yields to maturity, the levels of

liquidity, and the intensity of trading activities. We find that the levels of liquidity vary across

different categories of credit bonds and change significantly before and after important

policy announcement dates. We also find that the levels of liquidity increased as the

interbank market became more liberalized over the past decade (for example, by permitting

more foreign investors, simplifying approval procedures, and increasing investment quotas),

and as the exchange market permitted unlisted firms to issue corporate bonds since early

2015. As a consequence, we observe a liquidity spillover from corporate bonds to enterprise

bonds in the exchange market under the CHL measure, which works most efficiently in less

liquid markets.

The analysis in this investigation will be useful for many practical applications in the

Chinese bond markets, especially for regulatory policy, risk management, credit ratings, and

the further study of the pricing of liquidity effects in bond yield spreads. Since the Chinese

32

corporate credit bond markets are still in a nascent stage of development, and more detailed

and robust findings await better data and further research, descriptive studies in this paper

remain at a general level, and should be interpreted with caution when extended to a broader

setting (for example, with regard to financial bonds and government bonds in the two

markets). However, the findings in this paper constitute a first step towards achieving a

broad understanding of how the levels of liquidity of credit bonds in China evolved during

the various policy changes, with respect to liberalization and regulation.

Several tasks remain to be completed in the coming years. Two examples will illustrate the

nature of the tasks ahead. A detailed analysis of government bonds and financial bonds

needs to be conducted since they are larger in size and fundamentally different in their

importance to the overall economy. Similarly, understanding the linkage between China’s

active repo markets, special versus general collateral repo markets, and the underlying bond

market liquidity is another important issue to be explored.

33

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