Lighting up the dark: A preliminaryanalysis of liquidity in the German

corporate bond market

Yalin Gündüz∗ Giorgio Ottonello† Loriana Pelizzon‡

Michael Schneider§ Marti G. Subrahmanyam¶

October 4, 2017

Abstract

Post-trade transparency has proven crucial in understanding the US corporatebond market structure and liquidity. A similar laboratory is still missing in theEuropean market, of which extremely little is known. In this paper, we address thislacuna by making use of a regulatory dataset of transactions of German financialinstitutions to study liquidity in German corporate bonds. We compare our findingswith the US market, noting significant differences, and also across financial and non-financial bonds. We are the first providing the “before” picture for future studiesassessing the impact of transparency implied by MiFid II.

Keywords: Corporate Bonds, Mifid II, WpHG, Liquidity

∗Deutsche Bundesbank; Wilhelm-Epstein-Straße 14, 60431 Frankfurt am Main, Germany; phone: +4969 9566-8163; email: yalin.gunduz@bundesbank.de

†VGSF (Vienna Graduate School of Finance); Welthandelsplatz 1, 1020 Vienna, Austria; phone:+43(1)31336 4815; email: giorgio.ottonello@vgsf.ac.at

‡Goethe University Frankfurt, SAFE and Ca’ Foscari; SAFE-Goethe University Frankfurt, Theodor-W.-Adorno-Platz 3, 60323 Frankfurt am Main, Germany; phone: +49 69 30047; email: pelizzon@safe.uni-frankfurt.de

§Deutsche Bundesbank and Scuola Normale Superiore Pisa; Deutsche Bundesbank, Wilhelm-Epstein-Straße 14, 60431 Frankfurt am Main, Germany; phone: +49 69 9566-3592; email: michael.schneider@sns.it

¶NYU, Stern School of Business; Kaufman Management Center, 44 West Fourth Street, New York, NY10012; phone: 212-998-0348; email: msubrahm@stern.nyu.edu

This paper represents the authors’ personal opinions and does not necessarily reflect the views of theDeutsche Bundesbank, its staff, or the Eurosystem. We are responsible for all remaining errors.

1 Introduction

One of the most interesting developments in the transparency of financial markets

occurred in 2002 with the initial launch of the Trade Reporting and Compliance Engine

(TRACE) platform by the Financial Industry Regulatory Authority (FINRA) for reporting

details regarding transactions in the over-the-counter (OTC) corporate bond market in the

U.S.6 Apart from substantially improving the transparency in the market, and hence its

liquidity, this development provided an exciting opportunity for financial economists to

study an over-the-counter market for the first time. This line of research lead to a plethora

of studies focusing on various aspects of the bond markets including improvement in the

measurement of liquidity, the examination of the interplay between credit and liquidity,

and the measurement of recovery rates.7

While databases similar to TRACE are available in other countries such as China, India,

Malaysia etc., they are not as accessible and have not yet been validated through intensive

analysis by researchers as has been done for the US corporate bond market, thus far.

Elsewhere, particularly in Europe, research on the corporate bond market has been quite

sparse and certainly not based on reliable, large-scale databases. In the European context,

in the absence of a comprehensive database of corporate bond transactions containing

information about quotes and trades, it is very difficult to assess market liquidity. Large

volumes of dark trading, i.e., transactions not contributing to the price discovery processes

in capital markets, occur in European markets, due to missing pre and/or post trade

publication, leaving both market participants and regulators quite uninformed. Indeed,

this lack of transparency has been one of the key regulatory concerns regarding financial

markets in Europe. A better understanding of the liquidity of the corporate bond market, in

6The TRACE platform was extended to other fixed income markets in the U.S., including the structuredproduct market in May 2011 (see Friewald et al. (2017)), and most recently, the Treasury bond market inJuly 2017.

7A partial list of these studies are reviewed in Jankowitsch et al. (2011), Friewald et al. (2012), andFriewald et al. (2017).

2

general, and of individual bonds, in particular, will also be beneficial both to understanding

the effects of the various asset purchase programs launched by the European Central Bank

(ECB), especially the corporate bond purchases in the ECBs “Corporate Sector Purchase

Programme” (CSPP) since June 2016, and to their execution by the ECB and the member

national central banks (NCBs) of the Eurosystem.

In an attempt to cover this gap, the European Parliament and the European Council

approved in 2014 the Directive 2014/65/EU - Markets in Financial Instruments Directive

II (MiFID II) and Regulation (EU) No 600/2014 Markets in Financial Instrument Regu-

lation (MiFIR) to enhance pre and post trade transparency of both equity and non-equity

instruments and derivatives including fixed income bonds. MiFID II and MiFIR, will be

applicable for all European markets from 3 January 2018. As we mentioned above, there

are no comprehensive databases available on the European corporate bonds, and so it is

not clear what kind of information we could expect and how the transactions and liq-

uidity measures would look like for the European corporate bond market after January

2018. This paper attempts to cover this gap by investigating the transactions on European

corporate bonds reported by German institutions to the German Federal Financial Super-

visory Authority, (Bundesanstalt für Finanzdienstleistungsaufsicht, short BaFin), through

the German Securities Trading Act, (Wertpapierhandelsgesetz, short WpHG) from 2008

to 2014. In particular we investigate the following questions:

a) What is the state of the art and the evolution of liquidity of the European/German

corporate bond market in the last decade?

b) How do the transactions data compare with TRACE data?

c) What lessons can be learned from US studies on corporate bonds based on TRACE that

can be applied in the European context?

In order to shed light on these questions, we make use of our access to the regulatory

dataset of BaFin. We start our analysis with the presumption that it is not appropriate to

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extrapolate from the available evidence in the U.S. corporate bonds as the structure and

composition of the two markets are entirely different, and the cross-sectional heterogeneity

of the individual markets within the Eurozone poses an additional challenge.8

There are many differences between the US and European bond markets that bear

mention. First, the US market is a large, relatively homogeneous market with trading

largely concentrated in one financial center, while the European market is quite diffuse,

with no clear locus. Since both markets trade OTC rather than on an exchange, this

is important to keep in mind, especially while assembling a database, because there is

no central location for trading. Second, since the TRACE effort was initiated in 2002,

there has been a central database in the US with mandatory reporting to the Financial

Industry Regulatory Agency (FINRA), which is, in turn, supervised by the Securities and

Exchange Commission (SEC) of the U.S. The requirement that all trades by broker-dealers

be reported to TRACE within 15 minutes of execution is quite strictly enforced, and the

database is generally considered to be extremely reliable, although it may still contain

some errors that need to be eliminated by researchers in their analysis. At present, there

is no similar source of data available in Europe, although it is expected that the gradual

implementation of MiFID II would remedy this lacuna. Third, the U.S. has a long history

of corporate bond trading that has historically been extremely important in the capital

markets since U.S. corporations rely on public debt markets to obtain leverage. In the

European setting, however, corporations typically rely on bank finance, rather than the

public debt markets, with the possible exception of the United Kingdom. Finally, the

U.S. market consists of mostly U.S. dollar denominated bonds, while there are multiple

currencies in vogue in Europe, except within the Eurozone; however, several European

corporations issue bonds denominated in other currencies, such as the U.S. dollar, the

8The lack of reliable data for the European corporate bond market implies that there is hardly anystudy that uses a reasonably large, reliable database for its analysis. As we document in the literaturereview section below, the few papers that cover this market are based on small, typically hand-collecteddatasets, whose reliability is difficult to assess.

4

U.K. pound sterling, the Swiss franc, and the Japanese yen.

From January 2018, MiFID II will require all firms in the European Union, to publish

details of their OTC transactions in non-equity instruments almost in real time (commonly

referred to as OTC Post Trade Transparency). While the scope of this directive is vast, it

remains to be seen how well and how broadly the directive will be implemented. Suffice it

to say that even in the U.S., the implementation of TRACE took place over several years,

just for the corporate bond market, and its extension to other fixed income markets is still

work in progress. It is fair to assume that the dissemination of a reasonable sample of such

data will take several years. It is therefore necessary to obtain a preliminary assessment

of the market, pending the availability of such detailed data as well as document some

patterns before the implementation of MiFID II, so that later studies on the effect of post

trade transparency on the European/German Corporate bond market could be assessed in

this light.

This paper provides such an assessment, pending the availability of a reliable database

a few years hence. Morevoer, it could be the basis for a proper analysis of the impact of

corporate bond purchases in the ECBs Corporate Asset Purchase Programs (CAPPs) since

June 2016. So far, to our knowledge, there are no studies of the impact of this program

on market microstructure. Our paper would be a benchmark to assess the impact of this

specific program by the ECB, a parallel to which has not been adopted in the corporate

bond market by the US Federal Reserve System (FED).

The paper is organized as follows. Section 2 provides a literature review on (i) studies

with U.S. corporate bond markets, in particular with TRACE data, (ii) existing studies

with European corporate bond markets, and (iii) studies with European sovereign bond

markets. Section 3 describes the corporate bond market structure in Europe and how the

changing regulations in financial markets affected this landscape. Section 4 introduces our

dataset and describes our approach to obtaining samples for which we provide descriptive

5

statistics. In Section 5 our liquidity measures from the literature are utilized in order to

analyze different aspects of German corporate and financial bonds. Section 6 compares

the liquidity attributes in the European market with those of the U.S. market through the

usage of TRACE data. Section 7 concludes.

2 Literature Review

2.1 European Corporate Bond Market

A number of papers deal with the pricing of Euro corporate bonds in relation to credit

or liquidity risk and other risk factors. Besides the different focus, none of them can

draw from a dataset as complete as ours. Some studies use transaction data, e.g. Dı́az

and Navarro (2002) is using 1993-1997 data of trades on three Spanish bond platforms

and Frühwirth et al. (2010) closing prices from transactions on German exchanges. Other

articles (Houweling et al. (2005); Van Landschoot (2008); Castagnetti and Rossi (2013);

Klein and Stellner (2014)) rely on yield quotes, e.g. from Bloomberg, at daily or lower

frequencies or consider corporate bond indices (Aussenegg et al. (2015)).

Two papers shed light on the market microstructure of specific trading platforms for

corporate bonds: Fermanian et al. (2016) models the request-for-quote (RFQ) process on

the multi-dealer-to-client platform Bloomberg FIT based on a fraction of the RFQs received

by BNP Paribas in the years 2014 and 2015. However their focus is on the behaviour of

clients and dealers rather than the market as a whole and the data are used to calibrate

the theoretical model. No statistics on trading volumes or liquidity measures are provided.

Linciano et al. (2014) study the liquidity of Italian corporate bonds that are listed on two

platforms contemporaneously (DomesticMOT or ExtraMOT and EuroTLX) and find a

mixed impact of such fragmentation. Also their analysis is restricted to the platforms and

neglects the major market share of OTC trades.

6

A different approach is taken in Bundesbank (2017), mainly considering the market

size of bonds of non-financial corporations in the Eurozone in terms of the total amount

outstanding. The report analyzes the market in the context of the low-interest-rate envi-

ronment and the relation to supply and demand factors, looking at issued amounts and

yields and spreads of corporate bond indices.9 With respect to impacts of ECB bond pur-

chases under the CSPP Grosse-Rueschkamp et al. (2017) shows that there was an increase

in bond financing in eligible companies. Again, there are no statistics provided on trade

volumes and liquidity measures.

Biais et al. (2006) investigates liquidity based on a dataset of interdealer trades from

2003-2005 in a set of euro- and sterling denominated bonds listed in the iBoxx index by

looking to quoted and effective bid-ask spreads. Furthermore the paper considers informa-

tional efficiency and compares to early literature on the US TRACE database. However,

it refers to the market before the global financial crisis and the sovereign financial crisis

and the consequent changes on market regulation.

Our study in contrast is based on a broader set both in terms of transactions and the

underlying bond universe and makes a direct comparison to the US TRACE database.

Finally in our evaluation of liquidity we do not only rely on quoted spreads and prices but

employ a range of liquidity measures that have proven more suitable for OTC markets.

While the literature on European Corporate bond market is extremely limited, the

presence of a single currency in several countries as well as the availability of data on quote

and transactions from the interdealer platform Mercato dei Titoli di Stato (MTS) have

allowed for several papers on the European sovereign bond markets of which we also want

to give a brief overview here. Prior to the global financial crisis there are several papers

that focus on the impact of market liquidity on bond yields; see for example Coluzzi

et al. (2008), Beber et al. (2009), Favero et al. (2010), Dufour and Nguyen (2012) and

9An earlier study in this direction is Pagano and Von Thadden (2004) in the context of the monetaryunification of the Eurozone.

7

Bai et al. (2012). More recent work has highlighted the effects of the sovereign crisis and

ECB interventions on bond yields, credit risk, market liquidity, and arbitrage relationships

between fixed income securities, see for example Corradin and Rodriguez-Moreno (2014);

Ghysels et al. (2014); Mesters et al. (2014); Eser and Schwaab (2016); Boissel et al. (2017);

Corradin and Maddaloni (2017); Gutkowski (2017); O’Sullivan and Papavassiliou (2017)

and Pelizzon et al. (2014, 2016, 2017). The peculiarities of all these works is that they refer

to the MTS intraday database that cover very well the Italian sovereign bond markets and

provide a good proxy of the other sovereign bond markets as France, Spain, Belgium, etc.

However, the largest sovereign bond market in Europe, the secondary market for German

Bunds is far under-represented with only a fraction of less than 1% of the trades covered

by the MTS market. The presence of all these papers on the European Sovereign bond

markets on one side indicates how relevant it is to perform analysis specific to the European

bond market, that being characterized by a single currency, provides a perfect laboratory

to perform studies on sovereign risk that the US or other countries are not providing. On

the other side it shows how relevant it is to have reliable data to perform analysis of this

market. Unfortunately, the MTS database does not cover transactions on the European

corporate bond market.

2.2 U.S. Corporate Bond Market

Since the inception of TRACE in July 2002, there is a growing number of empirical

studies that analyze the corporate bond market as a whole, focusing on its liquidity and

trading activity, which can represent an issue due to the fact that all transactions are exe-

cuted OTC. Among the first to analyze the corporate bond market after the introduction of

post-trade transaparency are Bessembinder et al. (2006) and Edwards et al. (2007). Both

papers focus on the effect that post-trade transparency has on corporate bond transaction

costs, finding that bid-ask spreads significantly reduced after the introduction of TRACE.

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Edwards et al. (2007) further provide an analysis of trading costs in the cross-section of

bonds, showing that those better rated, recently issued, close to maturity are more liquid.

In a more recent paper, Bao et al. (2011) focus on the link between illiquidity and pricing

in the US corporate bond market, showing that a significant part of the variation of yield

spreads can be explained by movements in corporate bond prices. Along the same lines,

Lin et al. (2011) find a robust link between liquidity risk and corporate bond returns.

Liquidity can be an issue especially during periods of financial distress, either at the market

or the security level. The two most prominent papers analyzing US corporate bond market

liquidity during the financial crisis are Friewald et al. (2012) and Dick-Nielsen et al. (2012a):

both show that trading costs spiked during the recent financial crisis, having a significant

impact on yield spreads, especially those of bonds with high credit risk. Jankowitsch et al.

(2014) focus instead on the effects of financial distress on liquidity at the security level,

analyzing recovery rates of defaulted bonds.

A more recent group of papers focuses on the impact of the Volcker rule on the US corpo-

rate bond market liquidity. As pointed out by Duffie (2012b), the restriction imposed by

the regulators on dealers’ trading activity can significantly impact the level of the bid-ask

spreads in the market. Bessembinder et al. (2016) study corporate bond liquidity and

dealer behavior in the period 2006-2016, finding that trade execution costs have not in-

creased significantly over time, while dealer capital commitment was significantly reduced

after the crisis. Bao et al. (2016) analyze the illiquidity of stressed bonds, focusing on

rating downgrades as stress events. They find that, after the introduction of the Volcker

Rule, stressed bonds are significantly more illiquid, due to Volcker-affected dealers low-

ering their liquidity provision. Finally, in a recent working paper, Choi and Huh (2017)

demonstrate that customers often provide liquidity in the post crisis US corporate bond

market. Therefore, average bid-ask spreads, relying on the assumption that dealers provide

liquditiy, underestimate trading costs that investors demanding liquidity pay.

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3 Market structure and Financial Market Regulation

3.1 Market structure

While the corporate bond market appears to be an ideal laboratory to study liquidity,

two constraints have hampered empirical research in corporate bond liquidity so far. First,

the corporate bond market is largely a dealer market. For Europe, no central data source

existed for all the transactions occurring in the market. This has limited a lot the studies

of this or, more precisely, these markets. Indeed, the European corporate bond market is

structurally fragmented in several sub-markets and platforms, and in this respect Germany

is not an exception.

From the market microstructure point of view the fixed income markets could be clas-

sified into two main categories: the voice market with dealers at its core and the electronic

platforms with inter dealer platforms at its core. The voice market is the most traditional

one for fixed income trading, and it has been organized around dealers (large banks and

securities houses) and their network of clients. Transactions are largely bilateral via the

telephone. As stressed in Duffie (2012a), the matching process of buyer and seller requires

a large amount of intermediation on this market as well as search costs. This market is

known as a quote-driven market, i.e. executable prices are offered in response of a counter-

parties request to trade. Usually a trader often contacts more than one dealer to figure out

the best price. The market is also characterized by a segmentation between the inter-dealer

segment (i.e. D2D) and the dealer-customer segment (i.e. D2C) in which dealers trade

with their customers (usually asset managers, insurance companies, pension funds, etc).

In this case end-users never trade among each other. The inter-dealer market is organized

either with bilateral trade by phone or multilaterally (and anonymously) via voice brokers.

This structure of the market has made the bond trading largely more opaque than

trading in other asset classes. Therefore quotes and transaction prices for the same bonds

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at the same time could vary across dealers. The aim to reduce this discrepancy is at the

core of MiFID II pre and post trade transparency.

In the late 1990s, the fixed income markets have started to experience a shift versus

electronic communication networks first affecting the D2D sovereign bond market and then

expanded to D2C. This market is order-driven, i.e. executable prices are offered in advance

of any request to trade.

The D2C market took two forms: the single-dealer platform (SDP) and the Multi-

dealer platforms (MDP). They are both the electronic version of voice market based on

request for quote. Usually, SDP is the electronic version of bilateral D2C, i.e. proprietary

trading systems offered by a single dealer to its customers where customers request quotes

to the dealer electronically. The MDP allows the customer to request quotes to trade from

a number of dealers simultaneously. MDPs also allow automated record keeping helping

to identify the best execution price. In both these markets quotes could be indicative or

executable.

For a survey of the ongoing developments that are affecting the market structure and

functioning of the fixed income markets because of electronic trading see BIS (2016). Re-

garding the European corporate bond market, a survey of Greenwich Associates (Greenwich

Associates (2014)) indicates that around 50% of trading volume is conducted electronically

in European Investment grade corporate bond market and 20% of high-yield markets.

Our data indicates whether a transaction has been executed through an electronic

platform or voice. While there are several platforms indicated, the large majority of the

transactions reported, both in terms of amount traded and number of trades, are from

OTC trades. This supports the relevance of an analysis of the BaFin database and its

importance for the later investigation of MiFID II post trade data.

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3.2 Financial Market Regulation: MiFID II

MiFID is the Markets in Financial Instrument Directive developed first in 2004 (MiFID

I) and applied across the European Union. It has the aim to regulate financial markets in

order to increase competitiveness and eventually support the creation of a single market

for financial services in Europe. The final objective is to increase the harmonization of

protections of investors across the different European national financial markets. The

main focus of MiFID I was the regulation of equity markets in terms of authorization,

transparency, regulatory reporting and rules for admission.

As a revision of MiFID I, MiFID II has been proposed in 2011 and adopted by the

European Parlament in April 2014 and the Regulation on Markets in Financial Instruments

(MiFIR) by the Council of the European Union in May 2014. MiFID II builds on MiFID

I and sets new rules that take into account the developments in the trading environment

since the implementation of MiFID in 2007. Moreover, in light of the financial crisis, it

aims to improve the functioning of financial markets making them more efficient, resilient

and transparent (see ESMA (2015)).10

The MiFID II regime substantially expands the pre-trade and post-trade transparency

regime for financial instruments traded in the European Union. The MiFID I transparency

requirements are limited to equities admitted to trading on regulated markets. MiFID II

extends the scope of the transparency framework to cover an expanded range of financial

instruments. This includes not only shares but also depositary receipts, exchange traded

funds, certificates and similar instruments (“equity-like instruments”), as well as bonds,

structured finance products, emission allowances and traded derivatives (“non-equity in-

struments”). MiFID II also covers an expanded range of trading venues the MiFID I

10More specifically, the objective of MiFID is to bring more transparency in any business or asset class,protecting customers by bringing transparency in non-equity instruments and OTC markets, avoiding darkpools, moving from dark pool to regulated platforms (FXall, deal matching, FXT, etc.), aligning the reg-ulation across EU, increasing competition across financial markets and introducing reinforced supervisorypowers.

12

venues, namely regulated markets (“RMs”) and multilateral trading facilities (“MTFs”),

plus a new category of trading venues classified as organised trading facilities (“OTFs”).

MiFID II aims to boost trading onto these venues. Under MiFID II an expanded set of

pre-and post-trade transparency obligations will also be imposed on systematic internalis-

ers (“SIs”)11 and other investment firms trading in over-the-counter (“OTC”) financial

instruments.12

Concerning data dissemination, operators of trading venues must make pre-trade and

post-trade transparency information available to the public separately and on a reasonable

commercial basis. Moreover, they must ensure non-discriminatory access. The information

for non-equities must be made available by SIs free of charge as close to real time as possible

and in any case within 15 minutes after the execution of the relevant transaction from 3

January 2017 until 1 January 2020. After 1 January 2020 this limit will be reduced to 5

minutes.

From 2014 ESMA has produced several regulatory technical and implementing stan-

dards documents that address the technical issues for the implementation of the MiFID II.

There are significant concerns on how this implementation will be realized. Furthermore,

the industry is worried about the cost of fulfilling the requirements as well as the fact

that mandatory increased transparency could potentially hamper liquidity. Without data

regarding the liquidity of the market before the implementation of MiFID II, it would be

impossible to assess empirically whether pre- and post trade transparency in the European

bond market increased liquidity.

The need to this new regulation comes largely from the current trading structure of non-

equity (largely fixed income) securities. As stressed in the previous section, they present

11A “systematic internaliser” means an investment firm which, on an organised, frequent systematic andsubstantial basis, deals on own account when executing client orders outside a regulated market, an MTFor an OTF without operating a multilateral system (see ESMA, Technical Advice, 19 December 2014).

12For more information, see http://www.hoganlovells.com/en/knowledge/topic-centers/mifid-ii

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http://www.hoganlovells.com/en/knowledge/topic-centers/mifid-iihttp://www.hoganlovells.com/en/knowledge/topic-centers/mifid-ii

a market structure largely based on voice transactions and request to quote also in the

electronic markets.

4 Description of data set

4.1 Data description

Our dataset is based on transaction reporting obligations of German banks mandated by

the German Securities Trading Act (Wertpapierhandelsgesetz, “WpHG”). Section 9 of the

act, further detailed out in the respective regulation (Wertpapierhandel-Meldeverordnung,

“WpHMV”), requires credit or financial services institutions, branches of foreign institu-

tions and central counterparties (only Eurex Clearing AG, in practice) domiciled in Ger-

many to report to the German Federal Financial Supervisory Authority (Bundesanstalt fr

Finanzdienstleistungsaufsicht, “BaFin”). The requirement is to report “any transaction in

financial instruments which are admitted to trading on an organised market or are included

in the regulated market (regulierter Markt) or the regulated unofficial market (Freiverkehr)

of a German stock exchange.” Through the WpHG dataset we also capture close to the

full set of transactions of EU and non-EU institutions at German exchanges.13

To the best of our knowledge this dataset has only been used in a set of studies in

the context of institutional herding in the German equities market (Kremer and Nautz

(2013a,b); Boortz et al. (2014)). Since these studies do not provide a comprehensive de-

scription of the dataset, we initially provide a detailed description of the dataset and the

filtering steps we have applied.

The transactions dataset contains security information, information on the transaction

13Building societies (Bausparkassen) are excluded from the reporting requirement. Moreover, non-German EU banks do not have to report trades in Mifid-securities since they are already reporting thesein their home countries. A non-binding English translation of the law is provided at https://www.bafin.de/SharedDocs/Veroeffentlichungen/EN/Aufsichtsrecht/Gesetz/WpHG_en.html

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https://www.bafin.de/SharedDocs/Veroeffentlichungen/EN/Aufsichtsrecht/Gesetz/WpHG_en.htmlhttps://www.bafin.de/SharedDocs/Veroeffentlichungen/EN/Aufsichtsrecht/Gesetz/WpHG_en.html

(for instance, time, price, size, exchange code or indicator for OTC trades) and the par-

ties involved (identifier for the reporting institution and, where applicable, identifiers of

client, counterparty, broker or intermediaries). We append this information with security

characteristics from the Centralised Securities Database (CSDB), which is operated jointly

by the members of the European System of Central Banks (ESCB), together with other

security information from Thomson Reuters Datastream. For a smaller subset of bonds we

verify the security characteristics with information from Bloomberg and add time-series of

daily price quotes.

Our raw dataset contains all reportings in “any interest-bearing or discounted security

that normally obliges the issuer to pay the bondholder a contracted sum of money and to

repay the principal amount of the debt” as indicated by a CFI-code starting with “DB”

(with “D” for debt instruments, and “B” for bonds). This definition exludes any convertible

bonds (“DC”), bonds with warrants attached (“DW”), medium-term notes (“DT”), money

market instruments (“DY”), asset-backed securities (“DA”), mortgage-backed securities

(“DG”), or other miscellaenous debt instruments (“DM”). Our bond dataset (“DB”) that

covers the full set of transactions over the period from January 2008 to December 2014,

therefore initially includes any type of sovereign, guaranteed, secured, unsecured, negative

pledge, junior/subordinated and senior bonds reported through WpHG.

With this sample selection we will be using a more narrow definition of the corporate

bond market than Bundesbank (2017) and the capital market statistics of Deutsche Bun-

desbank, which include other debt-type securities not classified as bonds. Of the market

size of 145 billion EUR amount outstanding of German corporate bonds at the end of 2014

reported in the capital market statistics, we capture 41 billion EUR, i.e. roughly one-third.

In addition, we remark that our initial sample includes non-German bond securities (traded

by German financial institutions) as well.

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4.2 Data Filtering

Our initial dataset went through a careful filtering, in order to ensure soundness of the

final sample. We describe below the general procedure, mentioning also considerations for

other uses of the data than ours, as we also list additional filters that are specific to our

paper. Table 1 gives an overview of the share of observations lost in the whole cleaning

process. Starting from ca. 28 million initial data points we discard 62.5% and of these

only 5.8% are attributable to our specific filters.

Insert Table 1 here.

In a first cleaning step we remove entries with invalid ISINs or time-stamps. Moreover,

we observe the error code that the BaFin assigns each line, which takes the integer values

from 0 (no errors) to 3 (serious errors - junk). We drop observations with error code 3. On

average this step only filters out 0.2% of observations and we observe the data quality to

be improving from 2009.

Second, we notice that the initial filtering of our dataset for bond type securities relied on

the CFI-code provided by the reporting institutions. However, to ensure robustness of our

data we also remove all observations from ISINs where the CFI-code recorded by CSDB is

not starting with “DB”, thus double-checking our sample selection. This removes another

5.0% of our initial observations.

Third, trades are reported in the currency used in the trade and need to be converted to

EUR. Here we keep only trades in the main currencies: ”EUR”, ”AUD”, ”CHF”, ”GBP”,

”USD”, ”CAD”, ”JPY”, ”DKK”, ”NOK” and ”SEK”.

Fourth, we remove so-called technical lines. These lines are created in some reporting

systems e.g. when a trade is on hold while a broker is gathering more of a security he has

committed to sell. Technical lines are detected when the reporting entity field is identical

to the client field. Discarding them removes 37.6% of the initial number of observations.

16

Fifth, even after accounting for technical lines, the same transaction can still be recorded

in multiple lines. This happens e.g. when both counterparties are obliged to report, when

a central counterparty is involved or when an intermediary is used. Since our focus is on

trading activity, we keep only one observation for each transaction. We identify duplicates

as trades on the same day in the same security at the same price and for the same absolute

volume. While also the parties involved in the trade are reported, their reporting style

can be inconsistent. We thus neglect the information on the involved parties to avoid false

negative duplicate detections from our filtering. Another crucial variable is the intraday

time of the trade. Unfortunately it is possible that for the same trade different times are

reported, e.g. when one counterparty of an OTC transaction needed additional time to

conclude their side of the trade. As a compromise between false positive and false negative

duplicate detections we consider two lines duplicate only when their intraday time difference

is at maximum ten minutes. By discarding duplicates we drop another 18.5% of the initial

or 32.8% of the remaining observations.

Finally, we apply a price filter. We first remove trades reported at prices of less than 1

EUR or more than 500 EUR (per 100 EUR face value) and then apply a weekly price

median filter, filtering out trades that deviate by more than 10% from the weekly median

price. For computational reasons we do not apply a price reversal filter since we find for a

smaller subset of actively traded bonds that only a very small number of trades would be

flagged.

There are two additional fields in the dataset, which indicate whether a deal is on

behalf of a client or not and whether the deal affects the balance sheet of the reporting

institution. These fields are useful when one is interested in the inventory or balance sheet

of the reporting institutions.

17

4.3 Market-level description

As an initial description of our dataset and the size of the market that we are covering,

we provide summary statistics of the number of different bonds traded, the number of

transactions and the traded volume in Table 2. These statistics are reported for all trades,

trades that took place on exchanges or regulated platforms and transactions that were

concluded over-the-counter (OTC).14 In the panels of Table 2 we apply a further round of

filtering and detail out the different categories of bonds that we are interested in.

Insert Table 2 here.

The column full sample in Panel A describes the filtered sample after the steps described

in the previous section and Table 1. We apply two more filtering steps: First, in the column

complete CFI code we consider only bonds where the 1st, 2nd and 3rd attributes (type

of interest, guarantee, redemption) in the CFI code derived from CSDB are well-defined

(i.e. non-“X”). This corresponds to dropping another 8.8% of trades. Second, we select

bonds with either fixed or zero coupon rate and a fixed redemption date and which are not

classified otherwise as hybrid or structured products and we term these vanilla bonds. Our

initial sample to reach vanilla bonds therefore consists of 6.7 million trades in 81, 664 bonds

for a traded volume of ca. 12.5 trillion EUR. Of the 81, 664 bonds in our sample 49, 264 are

traded on exchanges or platforms and 63, 679 OTC. This also implies that for ca. 31, 299

bonds we observe trades both OTC and on exchanges and that for similarly large amounts

of bonds there is only either exchange or OTC trading present in our sample. The larger

market is clearly OTC with the amount traded roughly ten times as large as the one on

exchanges in about three times the number of transactions.

14Since not all observations carry an exchange- or OTC-flag, the number of trades and traded volumereported for OTC trades and exchange trades do not sum up to those reported for all trades. This isespecially prevalent in 2008, when over 30% of observations are not correctly categorized and no longer asignificant issue from 2010 on. From 2012 on all trades carry a correct flag.

18

Panel B distinguishes vanilla bonds by bond securisation type as inferred from the

2nd attribute of the CFI code. The column secured/guaranteed refers to vanilla bonds

either secured through assets or guaranteed by a non-government entity (attribute “S”

or “G” respectively), Treasury-type bonds are issued or guaranteed by a federal or state

government (attribute “T”), e.g. German Bunds and KfW-issued bonds are also part of

this category. Unsecured bonds do not carry a guarantee or security (attribute “U”). The

largest share of trading volume is due to government bonds with 11.2 trillion EUR, while

secured or guaranteed bonds make up for 792 billion EUR.

Unsecured bonds account for a total trading volume of 438 billion EUR and in panel

C we distinguish unsecured vanilla bonds into straight bonds and certificates. While there

is a large number of certificates, they make up for only 30 billion EUR of traded volume.

The set of straight bonds is further distinguished in Panel D into corporate bonds (i.e.

non-financials) and financial bonds (i.e. issued by financial corporations such as banks,

insurance corporations and financial auxiliaries). Even though there are only 817 corporate

bonds in our sample, they make up for a traded volume of 57 billion EUR compared to 332

billion EUR traded in 10, 853 financial bonds. It is worth noting that at all these levels of

distinction the OTC market is dominant compared to exchanges both in terms of number

of trades and traded volume.

Insert Figures 1-2 here.

Panel (a) of Figure 1 shows the monthly trading volume in vanilla bonds during our

sample period (where the dominant contribution are treasury-type bonds). The monthly

volume moves around roughly 150 billion EUR, with slightly larger volumes in the be-

ginning of 2008 and visible dips in activity in December. About 60-100 billion EUR per

month are due to trading in German bonds.

In panel (b) of Figure 1 we show the monthly volume in straight bonds and in the

19

panels of Figure 2 for corporate (panel (a)) and financial bonds (panel (b)) respectively.

The graphs show that monthly volumes in financial bonds have decreased from ca. 6 billion

EUR monthly in 2008 to ca. 3 billion EUR monthly in 2010 and stagnated since, whereas

for corporate bonds activity has increased from ca. 200 million EUR monthly in 2009 to

roughly 750 million EUR in 2013. Different from the sample of vanilla bonds, for straight

bonds we do not observe end-of-year effects. While trading volume in financial bonds is

largely due to German-issued bonds, a large share of corporate bond trading activity is

due to non-German issues.

In Table 3 we quantify this observation and distinguish trading activity across our

sample by bond issuer country. Panels A through E refer to vanilla, unsecured, straight,

corporate and financial bonds, respectively. A large share of foreign issues is especially

present for vanilla bonds in Panel A, mostly from treasury-type bonds. Especially Italian

and French issues contribute with roughly 1.5 and 1.0 trillion EUR of traded volume,

respectively. In the other panels we find that German bonds represent the dominating

share of trading volume. The only exception is for corporate bonds in panel D where of

the total volume of 57 billion EUR significant shares are also due to French (6.4 billion

EUR), US (3.8 billion EUR), UK (2.1 billion EUR) and other Eurozone country issues.

Insert Table 3 here.

We believe our sample to be highly representative of the German (corporate) bond

market, but only to a lesser extent for the European market. Therefore we focus on

German corporate and financial bonds in our liquidity analysis from Section 5 on.

4.4 Bond-level description

In Table 4 we provide basic summary statistics of bond characteristics and bond-level

trading activity. Our main sample of vanilla bonds is very heterogenous in its composition

20

of corporate and sovereign bonds, certificates and other security types, which reflects in the

statistics. Thus the median amount outstanding in vanilla bonds in Panel A is less than

one million EUR (dominated by certificates, for which often an amount outstanding is not

available) and the mean is 441 million EUR due to large issues mostly in sovereign bonds.

Similarly the average maturity is almost three years while the median is much lower at

1.38 years.

Focusing on straight bonds in Panel C, we still find that the distribution of the amount

outstanding is skewed by big issues, with a mean of 489 million EUR and a median of 25

million EUR. Panels D and E for corporate bonds and financial bonds respectively reveal

that this is mostly due to differences between financial and non-financial bonds, since for

non-financial corporates the mean of 489 million EUR is quite close to the median of 413

million EUR. Comparing to a mean and median of 147 and 20 million EUR for financial

bonds reveals that we are looking at a sample of rather consistenly big issues in corporate

bonds while for financial bonds there is still considerable heterogeneity in issue size.

Corporate bonds also show a much higher maturity at issuance (10.2 or 7.3 years in

the mean or median respectively) while financials are shorter-lived with a mean maturity

of 4.48 years (median 4 years). The mean coupon paid for straight bonds is 5.38 percent,

consistent with figures for European sovereign bond indices as reported in Bundesbank

(2017) (see i.e. Figure on page 27).

Turning to indicators of trading activity, there are again signs of great disparity among

bonds. The mean number of trades per day for vanilla bonds is at 0.13 still higher than the

75% percentile at 0.03, indicating that trading activity is concentrated in a small number of

active bonds and a rather large fraction of rarely traded bonds. This view is supported also

by other measures. For instance, the median time between two trades is 24.5 trading days

or 35 calendar days (mean of 47.5 and 68.5 respectively) and the mean daily trading volume

is ca. 237,000 EUR compared to a median of 239 EUR, i.e. three orders of magnitude

21

smaller.

These trends are still present, though to a lesser extent, for the subsample of straight

bonds (panel C). The average daily number of trades is especially large for corporate bonds

(panel D) at 1.38 (compared to 0.17 for financials, see panel E), again driven by very active

corporate bonds: the median, 75% and 95% percentiles are 0.04, 0.35 and 9.13 respectively.

The mean of daily trading volume is ca. 0.1 million EUR for corporate bonds and roughly

half that for financials, again mostly driven by the most active bonds. While for the most

liquid straight bonds average intervals between trades are as low as one or two days, the

mean and median intervals are 68.1 and 25 calendar days respectively.

Finally, the total volume that we observe during the lifetime of a straight bond (in

our sample period from 2008 through 2014) is about 3.3 million EUR on average (roughly

double that for corporates and slightly less for financials) and thereby an order of magnitude

smaller than typical issue sizes.

Insert Table 4 here.

All of the bond-level evidence on trading activity points to a concentration of market

activity in a few frequently traded straight bonds, whereas a large fraction rarely sees any

activity. In Table 5 we quantify and confirm this hypothesis. For the 81,664 securities in

our sample, only 837 are traded on more than 200 days a year (i.e. virtually year-round)

and 1,507 securities are traded at least every other day (101 or more days a year), whereas

the vast majority of securities is traded on at most 50 days a year (but typically even much

less).

Naturally a large share of these very active titles are sovereign bonds, but we also

see 199 straight bonds in the highest rank of trading activity. Comparing the first lines

that reflect the more active titles of Panel A (securities from all countries) and Panel B

(German-issued securities), we find that the majority of active bonds are from Germany:

22

Out of 199 straight bonds that are traded on more than 200 days, 180 are German, and

162 of the 196 bonds that see between 151 and 200 trades per year. For these reasons, in

the next section on liquidity we will focus on German-issued straight bonds only, since for

these bonds we are in a much better position of observing a representative share of the

market.

Insert Table 5 here.

Finally, when comparing the columns for German corporate and financial bonds in

Panel B of Table 5, it is also worth pointing out that corporate bonds tend to be either

very liquid (78 bonds in the top bin) or rather illiquid (53 bonds in the bottom bin) while

only few bonds are traded from 51 to 200 days a year. This is not the case for financial

bonds, where the number of bonds in each bin decreases with the trading activity associated

with that bin, i.e. there are 102 very actively traded German financial bonds compared to

8,487 such bonds that trade 50 or less days a year.

5 Liquidity proxies, methodology

The previous section provided strong evidence in support of the conclusion that the

German corporate bond market is largely illiquid, with a large fraction of bonds that are

traded only a few times per year, a typical characteristic of bond markets compared to

equity or foreign exchange markets. Nevertheless, it is important to investigate the level

of liquidity of the most frequently traded bonds in order to a) investigate the evolution of

the liquidity of the most liquid bonds and b) compare the level and the dynamic of the

liquidity in the German market with the one of the US corporate bond market.

We investigate the liquidity measures described in the subsection below and discuss our

empirical observations in section 5.2.

23

5.1 Liquidity Measures

We employ a set of liquidity measures that capture mostly the costs associated with

price-impact and round-trip trades, following the presentation in Friewald et al. (2017).

While these measures are typically calculated on a daily basis for US TRACE data, here

all measures are calculated based on weekly data. This allows us to include more bonds

in our analysis that are actively traded but not typically on a daily basis. We fix notation

such that Liqit is the liquidity of bond i in week t and Nit is the number of trades in bond

i in week t.

• The Amihud measure, proposed in Amihud (2002), is our proxy of price impact. The

more a trade of a given size shifts the observed price, the higher the Amihud measure

and the less liquid the bond. The measure is obtained as the mean ratio of absolute

log returns to trade volumes:

Amihudit =1

N it

N it∑j=1

|ri,jt |V i,jt

(1)

where the index j spans all trades in bond i in week t while ri,jt and Vi,jt are the (log)

return and transaction volume associated with the trade j. The measure is given in

units of basis points per million EUR and we require at least 4 transactions per week

in order to calculate it.

All following measures capture the liquidity component that is associated with the cost

of a round-trip trade and are given in units of basis points:

• Price dispersion was introduced in Jankowitsch et al. (2011). The idea is that the

lower the volatility of prices around the consensus price, the more liquid the bond,

since agents are more likely to trade the bond at its fair value. It is calculated as the

root mean squared (weighted) difference between traded prices P i,jt and the market

24

valuation P it proxied by the volume-weighted average trade price.

PriceDispit =

√√√√ 1∑N itj=1 V

i,jt

N it∑j=1

(P i,jt − P it )2 Vi,jt (2)

with P it =1∑Nit

j=1 Vi,jt

∑N itj=1 P

i,jt As for the Amihud measure we require a minimum of

4 transactions per week.

• Roll is the Roll measure that relates the autocorrelation of returns to the bid-ask

spread, developed in Roll (1984). It is obtained as twice the square root of the

negative auto-covariance of returns.

Rollit = 2

√−Cov(ri,jt , r

i,j−1t ) (3)

We require a minimum of 8 transactions per week in order to compute the Roll

measure.

• Imputed round-trip cost, developed in Feldhütter (2011) and applied for OTC markets

in Dick-Nielsen et al. (2012b), proxies bid-ask spread by comparing the highest to

the lowest price of a set of transactions with identical volumes. These transactions

are assumed to belong to a round-trip trade and the highest (lowest) of their prices

thus to correspond to the prevailing ask (bid) price:

ImputedRTCostit =1

Bit

Bit∑b=1

1− minPi,bt

maxP i,bt(4)

where Bit is the number of sets with trades of identical size and Pi,bt is the set of prices

that belong to the set b. We require a minimum of 4 transactions per week and at

least 2 transactions of the same size in order to compute the imputed round-trip cost.

25

• Quoted bid-ask spread is the difference between the best ask and bid price obtained

from quotes on Bloomberg:

QuotedSpreadit =1

Dt

Dt∑d=1

Qi,askt,d −Qi,bidt,d (5)

where Dt is the number of trading days in week t and Qi,askt,d is the ask quote for bond

i on day d of week t and idem for the bid. We require quotes on both the bid and

ask side for at least one day per week for the quoted bid-ask spread.15

• Effective bid-ask spread, proposed in Hong and Warga (2000), is the most restrictive

of our measures. It is the difference between the average sell and the average buy

price, normalized by their midprice:

EffSpreadit =2(P̄ i,sellt − P̄

i,buyt )

P̄ i,sellt + P̄i,buyt

(6)

where P̄ i,sellt =1

N i,sellt

∑N i,selltj=1 P

i,jt is the average sell price and idem for the average

buy price P̄ i,buyt of bond i in week t. The trade sign (buy/sell) is inferred using

the algorithm of Lee and Ready (1991) by comparing to quotes from Bloomberg.

Therefore for this measure we not only require Bloomberg quotes but also 4 trades

which must include at least one buy and sell trade each. In our case it is possible to

obtain negative values for the effective bid-ask spread since we infer the trade sign

from daily data but average over one whole week and we discard the value in that

case.16

As we have seen in section 4 our dataset is representative mostly for German bonds but

15A bond for which there are quotes on one day typically also has quotes data available for the wholeweek, so that we do not require a minimum days filter.

16E.g. we could observe buys on a first day. The bond price falls and the bond is sold again at a lowerprice in the same week.

26

less so for bonds issued in other jurisdictions. We therefore focus purely on German-issued

bonds and as a filter for sufficient liquidity in terms of trading activity we calculate liquidity

measures on a weekly basis for straight bonds that have at least 4 trades per week in at

least 40% of weeks during their lifetime in our sample period. In addition we verified using

Datastream and Bloomberg that for each bond not more than one redemption date was

present, the bond is not marked as floating, index-linked, convertible, covered or secured

nor as a certificate or government bond or originating from a coupon stripping program.

By applying this filter we ended up with 292 straight bonds of which 92 are corporate

bonds and 200 are financial bonds and a number of trades equal to 780,942.

For these most active bonds we have also applied a price reversal filter that discards

observations where the log return to both the preceding and the following price is more

than 10% in opposite directions (i.e. the price reverts). Of the more 0.78 million initial

observations, only 101 are dropped due to the additional filter. We take this as a confir-

mation that our previous filtering procedures lined out in section 4.2 are already sufficient

to eliminate the vast majority of errors. Finally for our later analysis we also truncate all

liquidity measures at the 1% and 99% percentiles.

As mentioned in the description of our liquidity measures, the measures of liquidity

that we employ require different minimum amounts of data and are thus not available at

any instant of time. Figure 3 illustrates this for all 292 straight bonds in our set of liquid

German bonds, and in the panels (a) and (b) of Figure I1 we further distinguish between

corporate and financial bonds, in each case for a selection of our measures. The number

of bonds that are active (i.e. already issued but not yet matured) at any instant in time

oscillates around ca. 100, more so in 2008 and from mid of 2012 and less so in the period

of 2009 to 2011.17 Price dispersion is a rather parsimonious measure and requires only a

17The observation from Panel (a) of Figure 6 that there are more liquid financial bonds at the beginningof our sample in 2008 and a larger number of corporate bonds to the end of our sample is also clearlyreflected in Figure I1.

27

minimum of 4 observed trades per bond and week in the WpHMV dataset. The number

of bonds associated with the quoted bid-ask spread is the only one that does not depend

on our transactions dataset, it rather requires valid bid and ask quotes that we obtain

from Bloomberg, in order to calculate the quoted bid-ask spread and mid-prices that are

used to infer the direction of trades (buy or sell) necessary to compute the effective bid-ask

spread. The effective bid-ask spread requires both at least 4 observed trades and quotes

from Bloomberg to infer the initiator of the trade as well as at least one buy and one sell

trade and thus has the smallest number of valid observations. Year-end effects are clearly

discernible in all transaction-based measures of liquidity.

Insert Figures 3 and I1 here.

5.2 Empirical evidence

This section provides summary statistics for the most liquid German bonds for the pe-

riod January 2008 till December 2014. Panel A of Table 6 provides the summary statistics

for our sample of 292 German liquid straight bonds. Comparing this to Panel C of Table

4 that reports the results for the full sample of straight bonds, namely 11670 bonds, we

see that coupon rate is lower for the most liquid bonds with a mean of 4.34% and a stan-

dard deviation of 2.05% (compared to 5.38% and 4.07% for the full sample, respectively).

Assuming that the level of the coupon is a good proxy of the riskiness of the bond given

the tendency to issue bonds at par we could easily guess that the most liquid bonds are

also those that are relatively less risky. The distribution of the coupon rate is just slightly

positively skewed with the 5th percentile having a coupon rate of 1.75% and the 95th

percentile 8.00% i.e. very close to a symmetric distribution. The maturity of the straight

bonds in our full sample is on average of 4.9 years, which is only slightly more than the

average of the most liquid bonds of 4.8 years. However the standard deviation of the full

28

sample is much larger at 4.4 years compared to 2.4 years for liquid bonds.

If we look at panel D and E of Table 4 displaying the descriptive statistics of all corpo-

rate and financial bonds we can observe how different these characteristics are depending on

whether we are considering corporate or financial bonds. While on one side the difference is

not that large when considering the coupon rate, maturity differs a lot. Indeed the average

maturity of corporate bonds is equal to 10.18 years with a standard deviation of 8.41 years

while the average maturity of financial bonds is 4.48 years with a standard deviation of

3.68 years. Maturity of corporate bonds has a strong positively skewed distribution and

long right tail with 5th percentile equal to 4 years, the 95th equal to 30 years and the 75th

percentile almost equal to the mean. The mean of the amount issued of all straight bonds

reported in Table 4 Panel C is equal to 171 million EUR while the amount for the German

liquid straight bonds, Panel A of Table 6, shows that the mean of the amount issued of the

German most liquid bonds is slightly lower at 166 million EUR. The standard deviation is

579 million EUR for all straight bonds and the 95th percentile is equal to 826 million EUR,

the one of German liquid bonds is 269 million EUR with the 95th percentile equal to 772

million EUR. This result is quite surprising because usually a larger amount outstanding

is associated with more liquidity. For what concerns the trading activity variables it is

interesting to see the difference between the results for the full sample of straight bonds

compared to the results for the most liquid German bonds. Indeed the average number

of trades per day for all straight bonds is equal to 0.25 while for the German most liquid

bonds it is equal to 4.58 with a standard deviation of 5.45 days and at the 95th percentile

we observe 15.28 trades on average per day. This confirms that the large majority of the

straight bonds are not liquid. Considering the trading activity of financial and corporate

bonds presented respectively in Panel D and E of Table 4, we can observe quite surprisingly

that trading activity is higher for corporate bonds, suggesting more liquidity, despite all

liquidity measures considered so far pointedly indicating financial bonds as more liquid.

29

The number of trades per day in corporate bonds is on average 1.38. For financial bonds,

the number of trades per day is significantly lower. If we consider trading activities of only

the most liquid German corporate and financial bonds by comparing panels B and C of

Table 6 we see that the corporate bonds in our sample are traded more often than the

financials by all of our measures. The most liquid German corporates on average trade 8.6

times a day, trade on average every 1.25 trading days and at an average daily volume of

566,000 EUR, compared to on average 2.7 trades a day in financials, trades happening on

average every 1.76 trading days and an average daily volume of 251,000 EUR. During their

active time in our sample, corporate bonds were traded for a total value of 339 million EUR

on average, almost three times the 116 million EUR for financial bonds and only partially

explained by the shorter maturities and smaller issue amounts of the liquid financial bonds

in our sample.

Insert Table 6 here.

We now consider the rating distribution of the sample of liquid bonds, reported in Table

7. The table shows that for a large fraction of these bonds the rating is not available in

Bloomberg, particularly the availability of ratings on corporate bonds is very low. We see

how the ratings available are mainly those allocated in the best classes. If we consider that

for a corporate or an institution it is not mandatory to publish their ratings it is easily

understandable why we have such a distribution concentrated on top investment grade

classes and very few public ratings available for worst classes. Comparing to the amount

of ratings information available for the sample of US bonds in Friewald et al. (2012) gives

an indication of how different the approach on this measure is in US compared to EU.

Insert Table 7 here.

Next we briefly describe the summary statistics of the liquidity measures described in

30

Section 5.1, whereas a detailed comparison with US TRACE data and the evolution of

liquidity over time are discussed in Section 6. The summary statistics of these liquidity

measures are reported in Table 8. The table shows that the Amihud measure (in units of bp

per million EUR) for German straight bonds corresponds to a mean of 74.30, a median of

30.66 and a standard deviation of 129.29 indicating that the distribution is not symmetric

rather largely right skewed with the fifth percentile equal to 5 and the 95 percentile is

equal to 387.90. In panels B and C that respectively report the same descriptive statistics

for corporate and financial straight bonds, it turns out that the large skewness is mainly

attributable to corporate bonds, that show strong dispersion and skewness with the 5th

percentile equal to 14.31 and the 95th percentile equal to 936.64, a mean of 202.92 and

a median of 70.77 bp per million EUR. Panel C shows that also German financial bonds

have an Amihud measure distribution which is right skewed but the median is not that

different from the mean and the interquartile range is definitely more limited than in the

corporate case.

Next we consider liquidity metrics that capture the round-trip or bid-ask spread, start-

ing with the price dispersion measure. The price dispersion mean for German straight

bonds is equal to 34.3 bp and standard deviation equal to 22.6 bp, the median is 29.6

bp indicating that the distribution is slightly right skewed. The price dispersion mean for

corporate bonds equals 52.1 bp while the mean for financial bonds is 25.5 bp. Standard

deviation is higher for corporate bonds, equal to 24.6 bp while standard deviation for finan-

cial bonds is equal to 16.2 bp. Also for the Roll measure the mean is higher for corporate

German bonds than financial bonds, even though the difference is less pronounced than

the previous measures and the interquartile range shows less dispersion. Similarly going

on with the results in the table we see that also the effective bid-ask spread, the imputed

round-trip cost and the quoted bid-ask spread are higher for corporate bonds and lower

for financial bonds both in terms of the mean, indicating that the corporate bonds seem to

31

be less liquid, and in terms of standard deviation suggesting a higher degree of variability

affecting corporate bonds with respect to financial bonds. For both corporate and financial

bonds the distribution is right skewed, with longer right tails for corporate bonds measures.

Insert Table 8 here.

As we stated so far the different liquidity measures indicate a large dispersion of the

liquidity level across the different bonds. Table 9 reports the correlation, both in the

level and in the changes, among the different liquidity measures within our sample. As the

table shows, in general there is positive correlation between the different liquidity measures

which is consistent with the fact that theyre conceptually related and measuring the same

characteristic of a bond (i.e. the liquidity), even though correlations coefficients are small

e.g. for differences in the quoted bid-ask spread (in Panel B). The visual analysis of the

liquidity measures over our sample period, especially Figure I2, suggests that this is mostly

due to a change in quoting practices on Bloomberg in 2011. This also implies that one

needs to be careful if one wanted to base an analysis of liquidity on this measure alone.

If we first consider Panel A showing the correlation computed on the levels we see that

correlation coefficients are high, with the exception of the correlation of the quoted with

the effective bid-ask spread. Besides the reason given above, this can also be partially

due to the bias from the fact that the effective bid-ask spread can only be calculated for a

smaller set of bonds as indicated in Figure 3. All other measures are quite highly correlated

with the Amihud measure, which is against expectations if we consider that contrary to the

other measures the Amihud is the only measure not computed around the bid-ask spread.

If we compare these correlations with those reported in Schestag et al. (2016) on liquidity

measures in level for the US we observe that they are similar but the level is slightly

lower than the one reported by Schestag et al. (2016). Considering the correlation matrix

computed on differences we see that the positivity of almost all coefficients is confirmed

32

even though the general level of correlation appears to be lower.

Insert Table 9 here.

On top of investigating the level of liquidity of German bonds we also aim to look to

their dynamic as well. We report first the dynamic of the trade volume of the most liquid

bonds, distinguishing between corporate and financial bonds. Figure 4 shows the monthly

traded volume in corporate bonds and in financial bonds for the most liquid bonds issued

in Germany. Concerning corporate bonds we see that the path is highly volatile with some

sharp peaks and an increase after 2012 even if it is very volatile. The lower level of amount

traded during 2008-2010 suggests lower liquidity during the time of the subprime crisis and

a recovery until the beginning of 2013, even though the trend appears to become volatile

and overall decreasing from 2013. The figure shows an opposite trend for financial bonds,

with a sharp decline in the volumes traded starting from 2009 suggesting a decrease of

liquidity in the global financial post-crisis period. Decrease corresponds to the sovereign

financial crisis and that persist as a trend later on. In this case the trend is less volatile,

sharply decreasing after the 2009 peak and remaining at very low levels for the entire

following period.

Insert Figure 4 here.

We wonder whether this dynamic of the trade volume is on one side driven by a larger

amount outstanding in corporate bonds that become more liquid rather than an overall

increase of the amount of corporate bonds, and vice versa for financial bonds. If we

compare the total amount outstanding of financial bonds (Panel (b) of Figure 5) to the

total amount outstanding of the most liquid financial bonds (in Figure 6), we see that the

amount of the German financial bonds experienced a slight decrease but the decrease of

the amount of the most liquid bonds after the crisis is more than proportional to the total

33

decrease, which suggests that some financial bonds outstanding effectively reduced their

trading volume in the post-crisis period. On the contrary we see that the total amount

outstanding in corporate bonds (Panel (a) of Figure 5) increases in the post-crisis period

as well as the amount outstanding of the most liquid corporate bonds (Panel (b) in Figure

6). This might suggest that the increase in the amount outstanding in liquid corporate

bonds is due to an overall increase in the number of corporate bonds. Panel (a) of Figure

6 confirms this by showing that the number of liquid corporate bonds outstanding during

the crisis was lower and it started increasing from 2010, while it shows exactly the opposite

behaviour for the number of liquid financial bonds. So if on one hand the decrease of the

most liquid financial bonds might be partially due to financial bonds switching from the

“most liquid” group to the “less liquid”, on the other hand the increase of liquid corporate

bonds might not be due to an increase of liquidity but rather due to an overall increase of

the number and the amount outstanding in these bonds.

Insert Figures 5 - 6 here.

We will comment on the evolution of the liquidity measures in Section 6 below, where

we also compare them to the respective trends observed for the US from TRACE data.

6 Comparison to US TRACE

In this section, we compare our sample of German bonds with a corresponding sample

of the US corporate bond market obtained from TRACE enhanced for the period 2008-

2014.18 First, we present a comparison between the summary statistics of the two samples

used for the calculation of liquidity measures (see Tables 6 and 10). Second, we focus on

the liquidity measures for the German bonds and the US market, shown in Tables 8 and

18For a description of our preparation of the TRACE data, see Appendix A.

34

11, respectively. Third we comment on the common trends and differences revealed in the

time-evolution of liquidity, shown in Figures 7 - 9.

When looking at the sample statistics, we focus on the subsamples of corporate and

financial bonds in Tables 6 and 10. Starting from the corporate bond sample, it is important

to note that the number of liquid bonds in the German market is significantly lower than

the amount of bonds available in the US (92 vs 1,432). It is interesting to note that while

the coupon distribution is almost identical across the two markets, the US sample has a

significantly higher maturity (5.6 years vs. 14 years), which can be reconciled with the

fact that US corporations heavily issued long-term bonds in the period 2010-2014 in order

to take advantage of the low interest rates and the relatively strong market. This could

not happen in Europe, due to the sovereign debt crisis in 2011-2012. Not surprisingly

the amount issued is almost doubled for the US bonds at each point of the distribution.

The traded volume offers a similar pattern, except for the lowest percentiles, where the

German bonds seem to be traded more. This is due to the fact that the BaFin sample is

including only some few heavily traded bonds, while in the US sample we capture a wider

range of securities. An almost identical analysis can be applied to the sample of financial

bonds. Also there there are significantly less German bonds than there are US ones (200

vs 3,481), and the German financial bonds have shorter maturity, smaller issuing amount

and much lower traded volume than their US counterparts. Overall, the samples’ statistics

provide compelling evidence on the underdevelopment of the German market with respect

to the US: only few bonds are frequently traded, and they are much smaller than those

issued by US companies. This has profound implications for the estimation of transaction-

based liquidity measures: such an exercise in the German market is only feasible for the

most liquid segment of securities. The US market instead allows the application of such

measures to a wider cross-section of bonds: this caveat needs to be taken into account

when comparing the liquidity estimates below.

35

Insert Table 10 here.

When we compare the summary statistics for the liquidity measures of German bonds

(see Table 8) with those for their US counterparts (see Table 11), it is striking that while for

the US sample the liquidity statistics for corporate and financial bonds are quite similar,

this is not at all the case for German corporate and financial bonds. E.g. the imputed

round-trip cost is on average 62.3 and 63.1 basis points for US corporate and financial bonds

respectively, while German corporate bonds have an average of 103.9 basis points, much

higher than German financial bonds at 34.3 basis points. These trends are similar also

for the other measures of liquidity that we consider. Therefore we take care in separating

carefully between non-financial and financial issuances in our discussion. From the time-

evolution of liquidity measures presented below in combination with the pre-dominance of

financial bonds in the first part of our German sample and corporate bonds in the second

part thereof (see Panel (a) of Figure 6 and Figure I1) it is also clear that this effect is

exacerbated by a typically more liquid set of financial bonds in 2008 when they were also

more numerous, whereas corporate bonds dominate our German sample from 2012 on,

coinciding with a period when they became also more illiquid. We will comment on these

trends in more detail below where we look to the time-evolution of liquidity.

Insert Table 11 here.

We now compare the trend of some liquidity measures of US corporate and financial

bonds vs. German corporate and financial bonds. A common feature to all time series

is the sudden increase in illiquidity in the end of 2008 due to the financial crisis. The

European sovereign debt crisis of 2012 also shows in the graphs corresponding to German

bonds (Panel (a) respectively) whereas this effect is at most minor for US bonds. Most

strikingly all of our measures with the exception of the Roll measure show a divergence

of German corporate bonds, which become more illiquid than German financial bonds.

36

As suggested above, the figures we present allow for at least two alternative explanations.

First, corporate bonds might have become more illiquid (and also more numerous) in

general. Second, trading activity has expanded to newly issued bonds which are more

illiquid and, thus, while the liquidity of individual bonds may not have changed, the overall

liquidity level might be lower since our sample gradually includes more illiquid securities.

Distinguishing between these effects is one question that we intend to answer in future

work.

Moving to the details of the single measures, we start with Figure 7 and the evolution of

the effective bid-ask spread within our 2008-2014 time frame. Recall that effective bid-ask

spread requires the most information and is thus available for the least amount of bonds

in our sample. We consider it both a more reliable estimate given the higher degree of

information as well as sophistication for its calculation, but also a more restrictive one,

since it can only be computed for a smaller number of bonds. Therefore, the effective

bid-ask spread is based on the smallest sample of bonds and can result in a rather noisy

measure, as suggested by the spikes (especially for financial bonds) in Panel (a) of Figure

7. However, in further analysis we find that the observed trend is robust to the alternative

liquidity measures. In Figure 7, German and US bonds experienced a similar trend during

the 2008/2009 biennium, characterized by a sharp spike at the end of 2008 in conjunction

with Lehman Brothers bankruptcy. In both markets the effective bid-ask spread for finan-

cial bonds shows a more volatile trend with a stronger increase than corporate bonds in

2008. Furthermore, financial bonds also present higher volatility in 2012, which suggests

they were hit by the European sovereign debt crisis more heavily than corporate bonds.

Consistently, German financial bonds seem to be hit harder than US financial bonds as

well. From 2013 onwards, both German and US financial bonds show a decreasing trend in

terms of mean value and volatility, indicating an improvement of market conditions in the

post sovereign crisis period. On the other hand, corporate bonds (in particular the German

37

ones) experience increasing illiquidity on average after 2013. This suggests that corporate

bonds, even though behaving more steadily during downturns, appear to be particularly

affected in terms of liquidity reduction during the post sovereign crisis time window.

Insert Figure 7 here.

In order to confirm the findings of Figure 7, we present the same time-dynamics for

different liquidity proxies in Figures 8 and 9. Panel (a) therein shows the average price

dispersion of German corporate and financial bonds and US corporate and financial bonds

respectively. Price dispersion occurs when different sellers offer different prices for the same

item in a given market. In the literature, it is considered as the result of spacial difference

and search costs. As indicated above, there are clear peaks discernible for the financial

crisis at the end of 2008. After the European sovereign bond crisis in 2012, non-financials

corporate bonds are on average less liquid than financials, a trend which is much more

pronounced for German than for US bonds. Notably, financial bonds show a decrese in

the level of illiquidity, reflecting more stable market conditions. Interestingly, while US

financial bonds have been more affected in levels and volatility by the financial crisis, the

inverse shows for European bonds where corporates have been less liquid around the period

from late 2008 to mid 2009.

The Roll measure shown in Panel (b) of Figures 8 and 9 is a proxy for the round trip

costs and it is computed as twice the square root of the negative auto-covariance of returns.

As we can see in the graphs, the German bonds appear to be far more volatile than US

bonds, probably also due to our smaller sample size. Overall, it is clear that for US bonds

the trend in different liquidity measure is very similar. Financial bonds are more volatile

and show wide spikes during downturns, while corporate bonds are more stable during

the entire post financial crisis period. For German bonds the trends in liquidity are more

volatile. In the Roll measure case costs are on average higher for corporate bonds during

38

the 2008/09 biennium while for financial bonds the estimated round trip costs are higher

and more volatile from 2010 with the higher values reached in 2012 conjointly with the

European debt crisis. The Roll measure is the only among our liquidity proxies to indicate

an increase in trading costs after 2013 also for German financial bonds. However, given

that we are considering a still dark market and in combination with the caveat that even

for our regulatory dataset the time-precision of trades can not fully be relied on, the Roll

measure may not be the most suitable for our German bond dataset.

Panel (c) of Figure 8 and 9 also includes the trend of the imputed round-trip cost.

Imputed round-trip cost proxies the bid-ask spread by comparing the highest to the lowest

price of a set of transactions with identical volumes and indeed the similarities with the

trend of the effective bid-ask spread just analysed are evident. In the US market, the

trend for both financial and corporate bonds is confirmed, with financial bonds having a

more variable trend, particularly sensitive to distressed market periods, while corporate

bonds appear to be more robust during crisis. Both German corporate and financial

bonds show a strongly distressed path both during the financial crisis and during the 2012

European sovereign debt crisis, with corporate bonds reacting more strongly after the

Lehman collapse in 2008. German corporate bonds also show a notable increase both in

terms of mean and volatility in the period following 2013 while in the same period US

corporate bonds show stable or even improving liquidity.

Even though the Amihud measure shown in Panel (d) of Figures 8 and 9 is a proxy for

price-impact instead of round-trip costs as the measures discussed so far, we confirm the

same trends here that we already discussed before. Both corporate and financial bonds

in the US and Germany reacted strongly in the wake of the Lehman crisis and German

corporate bonds did even more so. 2011 brought an noticeable increase in illiquidity mostly

for German bonds and hardly so for their US counterparts and while German financial

bonds were rather stable in and after 2012, average liquidity continued to deteriorate for

39

German corporate bonds.

If we compare the levels of liquidity in the two markets over time (again with the caveat

that our samples are very different in both markets as already pointed out above), then

it appears that before the financial crisis price impact was lower in the German sample

compared to the US. At the latest in 2011, this trend inverted and trading German bonds

was on average more costly, and this effect was especially strong for German corporate

bonds. This message is less clear for the measures of round-trip costs that we considered

above. While pre-financial crisis levels of liquidity are lower for all of the measures we

considered above, at the end of our sample in 2014 costs for US and German financial

bonds seem comparable in the light of the effective bid-ask spread, lower for US financial

bonds when regarding the imputed round-trip cost and price dispersion measures and still

higher by the Roll measure. Again with the exception of the Roll measure, there is a more

clear trend for corporates, where German corporate bonds appear less liquid than their US

counterparts at the end of our sample period. Given that we only look the relatively most

liquid subsample of German bonds (in terms of trading activity), this effect might be even

stronger for a larger cross-section of bonds.

Insert Figures 8 and 9 here.

For the sake of completeness, in the Internet Appendix we also display the quoted

bid-ask spread as obtained from Bloomberg for European corporate bonds. Due to the

larger number of securities in our US sample, we were not able to obtain the same figure

for the American market. The trends in Figure I2 differ somewhat from those observed

above. For example the increased illiquidity due to the Lehman crisis is less sudden and

appears only for German financial bonds, whereas German corporates showed even heavier

reactions in all other measures above. We also see a rather sudden increase in the bid-ask

spread in early 2011, preceding the trends we observed in the other measures during the

40

European sovereign bond crisis. For these reasons we consider the quoted bid-ask spreads

rather unreliable, especially before 2012, and prefer them as an indicator of mid-prices

rather than liquidity. This last finding adds to the general picture we would like to draw

in this section, namely that liquidity is far more complex in the European market than