Who Provides Liquidity in the Market for Credit Default Swaps? · Liquidity is traditionally...

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Who Provides Liquidity in the Market for Credit Default Swaps?

Peter Feldhüttera, Monika Gehde-Trappb, and Yalin Gündüzc

Abstract

We study liquidity provision in the CDS market 2009-2014. We distinguish between dealers,

hedge funds and asset managers (HF/AMs), and end-users. We find that net sellers of

protection earn a bid-ask spread, i.e. are liquidity providers. End users always demand

liquidity. In a third of the sample period dealers demand liquidity and HF/AMs provide

liquidity. Overall, there is almost no correlation between dealer transaction costs, an often-

used measure of market liquidity, and end user transaction costs. We find that dealers provide

less liquidity when they are more restricted.

JEL classification: G12, G13, G14, G18, G28

Keywords: Credit default swaps, liquidity, dealers, asset managers, hedge funds; a Copenhagen Business School, Solbjerg Plads 3, A4.02, 2000 Frederiksberg, Denmark. Phone: +45 38 15 37 53.

Email: [email protected]. b University of Hohenheim, 70599 Stuttgart, Germany. Phone: +49 711 459 24 74 0.

Email: [email protected]. c Deutsche Bundesbank, Wilhelm Epstein Str. 14, 60431 Frankfurt, Germany. Phone: +49 69 9566 8163.

Email: [email protected]. Support from the Center for Financial Frictions (FRIC), grant no. DNRF102, is gratefully acknowledged. The views represent the authors’ personal opinions and do not necessarily reflect the views of the Deutsche Bundesbank or its staff.

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1. Introduction

The financial crisis led to many regulatory changes in fixed income markets, not least

the Volcker rule limiting dealers' market making activities. A major concern of regulators,

academics, and investors is that this regulation has caused markets to become more illiquid

and there is a growing literature on this important question. However, there are conflicting

results and the literature is so far inconclusive.

Liquidity is traditionally measured by transaction costs that investors pay when trading

with dealers, i.e. the bid-ask spread, and the literature finds that transaction costs have gone

down, leading some to conclude that liquidity has not decreased in recent years (Adrian et al.

(2017), Aquilina and Suntheim (2017), Anderson and Schultz (2017), Trebbi and Xiao (2017)

and others). Amihud's price impact measure also shows that liquidity has not decreased,

maybe because it proxies for transaction costs as argued by Schestag et al. (2016). In contrast,

dealers' inventory and measures relating to their inventory have gone down suggesting that

fixed income markets have become more illiquid (Bao et al. (2017), Schultz (2017),

Bessembinder et al. (2017) and others).

The existing literature focuses on the liquidity provision of dealers, potentially

dictated by the data, since most papers investigate the US corporate bond market using the

TRACE database and this database identifies dealers but not non-dealers. In this paper, we

focus on the supply of liquidity by hedge funds and asset managers (HF/AMs). This group has

been identified as potentially stepping in to provide liquidity as dealers may be more reluctant

to do so, but to the best of our knowledge we are the first to directly test this. We investigate

the market for credit default swaps (CDSs) in the period 2009-2014 and use transactions data

from DTCC. The data has identities of all counterparties and therefore we can separate the

liquidity provision of dealers from that of HF/AMs.

We sort counterparties in the CDS market into three groups; G16 dealers (dealers),

hedge funds and asset managers (HF/AMs), and remaining investors (end-users). For each

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group we calculate an average monthly spread that they are paying or receiving. We find in

the time series that when a group (dealers, HF/AMs, or end-users) on aggregate is a net seller

of default protection then this group earns the spread, while net buyers of default protection

pay the spread. Furthermore, we find in a panel regression that there is a clear relation

between an individual counterparty selling default protection and earning the spread.

Therefore, investors demanding liquidity in the CDS market are net protection sellers.

We find that end-users are always net buyers of protection from both dealers and

HF/AMs during our sample period. Thus, end-users pay the spread to both dealers and

HF/AMs and we can clearly identify them as investors consistently demanding liquidity. The

flip-side of this result is that both dealers and HF/AMs are liquidity providers to end-users.

When we compare the liquidity provision of HF/AM to dealers, a different picture emerges.

In most of the sample HF/AMs buy protection from dealers and pay the spread, but in a third

of the sample HF/AMs sell protection to dealers and earn the spread.

When we compare the spread of dealers with the spread of end-users the correlation is

only 13.31% and statistically insignificant. Thus, the traditional way of calculating transaction

costs of dealers as a measure of market liquidity is not very informative of the transaction

costs of liquidity demanders. To illustrate this point, February 2010, the month with the

highest costs of trading viewed from end users at the same time saw dealers paying a spread

on average. Of course, this is because HF/AMs stepped in and provided liquidity to both

dealers and end-users.

We also provide evidence on why dealers demand liquidity at times. We calculate

restrictedness measures of dealers suggested in the literature (Chan-Lau and Sy (2006),

Laeven and Levine (2009), Demirgüc-Kunt and Huizinga (2010), Bao, O’Hara and Zhou

(2017)) and relate this to their liquidity provision. In periods where dealers demand liquidity

(i.e. pay the spread and are net buyers of protection) they are on aggregate more restricted. In

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a panel regression we find that the spread dealers are paying is related to their restrictedness.

Thus, dealers that are more restricted supply less liquidity.

There is a growing literature related to our paper. The most closely related paper is

Choi and Huh (2017). They argue that if some customers provide liquidity, dealer transaction

costs are a downward biased measure of transaction costs of customers demanding liquidity.

They find consistent evidence in the US corporate bond market and hypothesize that asset

managers are stepping in to provide liquidity. Since their data does not have non-dealer

identities, they cannot test this. Our data has identities of all counterparties and we can

directly test this hypothesis. Furthermore, they cannot estimate transaction costs of customers

demanding liquidity as we do. Goldstein and Hotchkiss (2017), Bao, O’Hara, and Zhou

(2017), Bessembinder, Jacobsen, Maxwell and Venkataraman (2017), Anderson and Stulz

(2017) and Stulz (2017) study the propensity of dealers in the corporate bond market to pre-

arrange trades rather than commit capital and take a bond into inventory. We show in the CDS

market that dealers may in fact demand liquidity. This result is in contrast to the finding in

Choi, Shachar, and Shin (2017) who find that dealers in the US corporate bond market

provided liquidity even in the financial crisis. Bongaerts, De Jong and Driessen (2011) find

that a liquidity premium is earned by protection sellers in the CDS market which is consistent

with our finding that protection sellers provide liquidity.

A parallel strand of literature to which our paper contributes is the research on CDS

market liquidity in general. While early research has often assumed that CDS prices reflect a

pure measure of credit risk (Norden and Weber (2004), Blanco et al. (2005), Longstaff et al.

(2005)), recent studies (Bongaerts et al. (2011), Gehde-Trapp et al. (2015), Tang and Yan

(2017) and others) have shown that illiquidity plays a significant role in CDS markets. Most

recently, Junge and Trolle (2015) show that liquidity risk accounts for 24% of CDS spreads.

Biswas et al. (2015) made use of the transaction-level single name CDS dataset from the

DTCC and estimated an effective half-spread of 14 bps for trades between dealers and end-

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users and 12 bps for interdealer trades. Based on transactions of index CDS, Collin-Dufresne

et al. (2017) reach much lower transaction costs – effective half spreads at a range of 0.66-

3.04 bps. Our paper is the first to provide evidence for time-varying liquidity provision in the

CDS market by the dealers, asset managers and hedge funds.

The next section describes our data and provides descriptive analysis. In Section 3 we

investigate who provides liquidity when. Section 4 relates the liquidity provision of dealers to

their restrictedness and Section 5 concludes.

2. Data Collection and Descriptive Results

2.1. Sample selection

Our initial sample, provided by the Trade Information Warehouse (TIW) of the

Depository Trust and Clearing Corporation (DTCC), consists of all single-name CDS

transactions where the underlying is a German reference entity between January 1, 2004 and

October 31, 2014. The DTCC estimates the coverage of their TIW to be as high as 99% of

single-name CDS in notional amounts, and about 95% in number of contracts (Gehde-Trapp

et al., 2015). Each transaction has information on price, trade size, protection buyer,

protection seller, underlying, currency, trade date, reporting date, and other contract

characteristics including whether the CDS is Standard European Corporate (with a contract-

specific upfront payments and standardized coupons of 25 bps, 100 bps, 500 bps, or 1000 bps)

or European Corporate (non-standard trades with no upfront payments and contract specific

coupons). Transactions can indicate new trades, terminations, or assignments. In the first step,

we remove all terminations and assignments since no price information is available for these

transaction types, and all new trades that do not specify modified modified restructuring

(MM, the standard for European underlyings). In the second step, we remove all compression

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trades.1 Third, we omit all trades prior to January 2009 (since coverage of data earlier than the

initiation of the TIW is based on backloadings), all non-corporate underlyings, and all

underlyings with fewer than 100 transactions. In the fourth step, we compute an effective

CDS premium for all Standard European Corporate contracts using the ISDA CDS Standard

Model. 2 Last, we remove outliers as follows: for each underlying reference entity and

transaction date, we download Bloomberg bid and ask quotes for all available maturities. We

linearly interpolate bid quotes and ask quotes to obtain a full CDS term structure on the bid

side and on the ask side for each date. We then compare each transaction price with the

maturity-matched bid and ask quote, and drop the observation if it exceeds (falls below) the

matched ask (bid) quote by max[20 bps, 50% of the quote]. These steps leave us with a total

of 172,132 transactions on 65 underlying reference entities between 504 protection buyers and

375 protection sellers.

We supplement the CDS transaction and quote data with a number of data sources to

measure how restricted a G16 dealer is at any point in time. We gather accounting

information on bank consolidated financial statements from Bankscope, and exchange rates

from ThomsonReuters Datastream. From Bloomberg, we download stock prices, market

capitalization, and option-implied volatility. We use this information to construct individual

riskiness measures in Section 4.1.

2.2. Market participants

We classify all market participants into the following groups: G16 dealers (Bank of

America-Merrill Lynch, Barclays, BNP Paribas, Citibank, Credit Suisse, Deutsche Bank,

Goldman Sachs, HSBC, J.P. Morgan, Morgan Stanley, The Royal Bank of Scotland, Société

Générale, UBS, and Wachovia/Wells Fargo Bank, Nomura starting from August 2011, Crédit 1 According to the definition provided by the Bank for International Settlements, “Compression aggregates derivatives contracts with similar risks or cash flows into fewer trades. It is a process for tearing up trades that allows economically redundant derivative trades to be terminated early without changing each participant’s net position”(Bank for International Settlements, 2016; Gündüz et al., 2017). We opt for removing them from our sample since no new price discovery occurs. 2 http://www.cdsmodel.com/cdsmodel/

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Agricole starting from April 2012), hedge funds and asset managers (HF/AMs), end users3,

and the clearinghouses ICE Clear Credit / ICE Clear Europe and LCH Clearnet. In 128,507 of

the transactions, G16 dealers buy protection, while they sell protection in 131,804

transactions, and 90,626 transactions are between two G16 dealers. This corresponds to

52.6% of transactions. 4 In 14,046 transactions with these G16 dealers, the ICE is the

protection buyer, and in 14,026 transactions the ICE sells protection to G16 dealers. We do

not observe any transactions between the clearinghouse and a non-G16 dealer, since only G16

dealers are clearinghouse members. We drop all transactions between G16 and the

clearinghouses.

2.3. Liquidity measures: effective spread and net protection sold

We measure liquidity from the perspective of dealers, HF/AMs, and end-users

separately. Our intuition for this is that we want to measure for each group a) whether they

provide liquidity or ask for liquidity at a specific date, and b) to whom they are providing

liquidity / from whom they ask for liquidity. We use two liquidity measures: the effective

spread (ES) and the net amount of protection sold (NPS). We transform all non-EUR values

into EUR-values using the exchange rate at the transaction date.

To compute ES, we first select a specific group i, CDS contract c, and month t. We

then compute for contract c and month t the volume-weighted average premium SP(i,j,c,t) at

which group i sells protection to group j and the notional volume-weighted average premium

BP(i,j,c,t) at which group i buys protection from group j. If group i only buys from or sells

protection to group j in a specific CDS contract in a specific month, we do not consider

contract c in month t. Otherwise, we compute the gross spread as the SP(i,j,c,t)- BP(i,j,c,t).

E.g., assume that two G16 dealers sell protection with a notional volume of 10 mn EUR and

20 mn EUR on a specific contract to two HF/AMs at 100 and 110 bps, and one G16 dealer

3 “end users” includes insurance companies, pension funds, smaller and public banks, and non-financial firms. 4 Evidence of concentration in the CDS market is given by, e.g., Brunnermeier et al. (2013), Peltonen et al. (2014), and Kenny et al. (2016).

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buys protection from an HF/AM at 105 bps. The average gross spread from G16 dealers to

HF/AMs then equals 1/3*100 bps + 2/3 * 110 bps – 105 bps. We then take this gross spread

relative to the volume-weighted mid premium 0.5*SP(i,j,c,t)+ 0.5*BP(i,j,c,t). In the last step,

we compute ES as the transaction-volume weighted average relative spread across all traded

contracts in this month.

Second, we determine NPS for group i vs. group j in month t by the aggregate notional

amount of protection sold by group i to group j, minus the aggregate notional amount of

protection bought from group i to group j.

2.4. Descriptive results

Table 1 shows summary statistics of the liquidity variables.

<Insert Table 1 about here>

Panel A of Table 1 shows that dealers on average provide liquidity when trading either

with HF/AMs or with end users. Specifically, the average effective spread when trading with

end-users is 5.99% and 3.11% when trading with HF/AMs. Thus, it is clear that on average

dealers provide liquidity to the rest of the market. However, the 25% percentile of only

0.21% for trades of dealers with HF/AMs indicates that there are times where dealers do not

provide liquidity to HF/AMs, something we explore this further in the next section. We also

see that HF/AMs provide liquidity to end users because HF/AMs earn the spread; however the

spread HF/AMs are charging end users are on average around half of that of dealers (2.63%

vs. 5.99%).

The average volume-weighted CDS premium in our sample is 136.2bps and with an

effective spread of 5.99% between dealers and end users, this implies an average roundtrip

cost of 8.15bps. This is substantially below the 28bps reported in Biswas, Nikolova, and

Stahel (2015). However, Biswas, Nikolova, and Stahel (2015) estimate transaction costs of a

liquidity demander and by definition their cost measure is always positive. We estimate costs

of dealers as a group without assuming that dealers are always liquidity providers. If dealers

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in some trades against end users demand liquidity, then our calculated effective spread will be

lower than the transaction cost a liquidity demander is facing.5

The summary statistics of net amount of sold protection in Panel B of Table 1 shows

that G16 dealers are net sellers of protection to HF/AMs and end-users. On a monthly basis

dealers sell 610.21 mn EUR of protection to end users and 150.80 mn EUR to HF/AMs. The

25% percentile of 3.02 mn EUR for dealers vs. HF/AMs again indicates that dealers at times

demand liquidity from HF/AMs. We also see that HF/AMs are net sellers of protection to

end-users although to a much smaller extent than dealers; the average net protection sold by

HF/AMs is only 42.62 mn EUR compared to 610.21 mn EUR sold by dealers.

2.5. The relation between the effective spread and protection selling

The summary results above suggest that protection sellers earn the spread and we next

test this formally at the individual counterparty level. For every counterparty and every month

we calculate the effective spread charged and the net protection sold against dealers, HF/AMs,

and end-users as groups. In other words, we compute the ES and NPS for each member m of

group i vs. all members of group j. We demean the resulting time series to adjust for level

differences between group members, and run a panel regression with year fixed effects to

adjust for time series variation in ES and NPS. The results are displayed in Table 2.


Table 2 shows that ES and NPS are positively associated for all group combinations: all

estimates are positive and statistically significant, at least at the 10% level. The economic

significance is also substantial: an increase in net selling by 10 mn EUR increases the spread a

dealer earns from HF/AMs by 18 bps, and from end-users by 73 bps.

5 See Biswas, Nikolova, and Stahel (2015) and Choi and Huh (2017) for a further discussion.

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3. Who provides liquidity when in the CDS market

3.1. Time series analysis of liquidity provision

We now consider the time series of liquidity provision in the CDS market. Figure 1

displays the time series of ES and NPS for the different group combinations.

< Insert Figure 1 about here>

Panel A of Figure 1 shows the spreads that end-users are charged, and the net amount

of protection they purchase from the combined group of dealers and HF/AMs. The spread and

net protection bought is always positive (with a small outlier for the effective spread in June

2010). Splitting up the liquidity provision to end-users between dealers and HF/AMs, Panel

B and C show that both dealers and HF/AMs basically always provide liquidity to end-users.

The direct liquidity provision to end users is much larger for dealers than for HF/AMs; for

example, the maximum amount of net protection sold is around 1.4 bn EUR for dealers while

it is only around 0.1 bn EUR for HF/AMs. Consequently, total net protection sold of dealers

to end users in Panel B resembles that of dealers and HF/AMs to end-users in Panel A.

Overall, it is clear that end-users always demand liquidity from the rest of the market.

While both G16 dealers and HF/AMs are net providers of liquidity to end users, who

are the overall main providers of liquidity to the market? We provide evidence on this in

Panel D and F of Figure 1. From early 2011 on, dealers conform to their expected role by

providing liquidity (to HF/AMs and end-users jointly), as the positive ES and NPS indicate.

But from mid-2009 to late 2010, dealers were liquidity demanders: they pay up to 5.9% of the

mid spread, and buy up to 400 mn EUR of net protection. As Panel B shows that end-users do

not provide liquidity to dealers, this only leaves HF/AMs to step in and provide liquidity to

dealers. Panel E shows the extent to which this is the case: Effective spreads and net sold

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protection from HF/AMs’ perspective vs. the dealers is positive from mid-2009 to mid-2011.

Hence, G16 dealers demand liquidity from HF/AMs for almost two years.

These results show that dealer spreads can at times be a poor measure of spreads paid

by end-users. To illustrate this, consider the month where end users pay the highest spread,

February 2010. In this month end-users pay an average monthly spread of 20% as Panel B

shows. At the same time, G16 dealers pay an average spread of 4% (Panel D). Effectively,

both end users and dealers demand liquidity from HF/AMs, who charge a considerable price

for their liquidity provision as Panel C shows. In this case, dealers’ spread is simply not a

meaningful measure of market liquidity as seen from liquidity demanders’ point of view.

To examine the relation between end user liquidity and dealer liquidity more broadly,

we estimate bivariate correlation coefficients for effective spreads. Table 3 displays the

estimation results.

< Insert Table 3 about here>

Table 3 shows that the spread end users pay is basically uncorrelated with the spread

G16 dealers earn; the correlation is 13.31% and statistically insignificant. This is because

dealer spreads to a large extent are determined by the relative liquidity provision between

dealers and HF/AMs: when dealers demand liquidity from HF/AMs dealer spreads are low

and HF/AM spreads are high and vice versa (the correlation is -66.98% and highly

significant). The relative liquidity provision between dealers and HF/AM is uncorrelated with

the transaction costs of end-users (the correlation is -12.80% and statistically insignificant).

Thus, end-user transaction costs are statistically unrelated to dealer transaction costs.

Overall, our results show that in the CDS market transaction costs of dealers are not

informative for the transaction costs of liquidity demanders.

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4. Liquidity provision and dealer restrictedness

In the previous section, we provided evidence that G16 dealers demand liquidity in

specific time periods. We now explore why there is time variation in dealers’ liquidity

provision. In particular, we examine whether dealers provide less liquidity, and effectively

start demanding liquidity, when they are more restricted.6

We use a broad range of measures of dealer restrictedness to be confident that our

results are robust. For our first two restrictedness measures, we use banks’ balance sheet

information and capital market data. First, we compute a dealer’s z-score (ZS, the ratio of

equity-to-assets plus ROA to standard deviation of ROA) as in Laeven and Levine (2009) and

Demirgüc-Kunt and Huizinga (2010). Second, we compute a dealer’s distance to capital

(DTC) as suggested by Chan-Lau and Sy (2006). The intuition for these variables is that

liquidity provision is costlier for risky banks.

Our third and fourth restrictedness measures derive directly from the CDS market.

First, we compute the transaction volume-weighted average CDS mid spread for the contracts

that a dealer trades within the month. This measure has a similar intuition as the bond

downgrade in Bao et al. (2016). Next, we compute the average CDS mid spread of the

individual dealer. To avoid endogeneity issues, we use Bloomberg mid spreads (average

between quoted bid and quoted ask spread). We standardize all explanatory variables to

obtain comparable coefficient estimates.

As dependent variables, we use dealers’ effective spread and net protection sold, and a

dummy variable that captures whether dealers’ ES and NPS are negative. Panel A of Table 4

displays the results for univariate time-series regressions. There, we use averages across

6 Bao et al. (2016) and Bessembinder et al. (2016) distinguish dealer that are affected by the Volcker rule vs those that are not to identify the impact of regulation on liquidity provision. This is not feasible in our setting: with the exception of Nomura, all G16 dealers fall under the Volcker rule.

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dealers, weighted by dealer transaction volume. Panel B of Table 4 displays results for

univariate panel regressions. There, we de-mean all variables and use year fixed effects to

account for heterogeneity between dealers and over time.


Table 4 provides clear evidence that restricted dealers provide less liquidity. In the

time series regression, we mostly find a negative and statistically significant association

between dealer liquidity provision and restrictedness. Also, dealers are significantly more

likely to demand liquidity (ES<0, NPS<0) when they are more restricted. One puzzling result

is that two estimates for the impact of restrictedness on ES are significantly positive. This

could be due to dealers’ optimal price for providing liquidity: If all dealers are restricted, the

least restricted dealers may still provide liquidity since the spread they can earn by doing so is

extraordinarily high. This interpretation is consistent with the consistently negative impact of

the explanatory variables on NPS in Panel A, and the negative impact on ES and NPS in

Panel B.

5. Conclusion

We study liquidity provision in the market for Credit Default Swaps and distinguish between

G16 dealers, hedge funds and asset managers (HF/AMs), and end-users. We find that net

sellers of protection earn a bid-ask spread, i.e. provide liquidity to the market. End-users

always demand liquidity and both dealers and HF/AMs consistently provide liquidity to end

users. Thus, end users are demanding liquidity at all times in the market. In the majority of

2009-2014 dealers provide liquidity to HF/AMs, but in 2009-2011 HF/AMs provide liquidity

to dealers and the average spread that dealers earn vs. HF/AMs and end-users is negative.

Overall, we find almost no correlation between dealer transaction costs, an often-used

measure of market liquidity, and liquidity demanders’ (in this case end users’) transaction

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costs. Finally, we find that dealers’ willingness to provide liquidity is related how restricted

they are in terms of capital and distress.

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References

Acharya, V. and Bisin, A. (2014), Counterparty risk externality: centralized versus over-the-counter markets, Journal of Economic Theory 149:153–182

Acharya, V. and Shachar O., and Subrahmanyam M. (2011), Regulating OTC derivatives. In: Regulating Wall Street: the Dodd–Frank Act and the new architecture of global finance, ed. VV Acharya, T Cooley, M Richardson, I Walter, pp. 367–426. Hoboken, NJ: Wiley

Aquilina, M. and F. Suntheim (2016), Liquidity in the UK corporate bond market: evidence from trade data, FCA occasional papers in financial regulation

Badaoui, S. and Cathcart, L. and El-Jahel, L. (2013), Do sovereign credit default swaps represent a clean measure of sovereign default risk? A factor model approach, Journal of Banking & Finance 37:2392-2407

Bank for International Settlements (2016), Statistical Release: OTC Derivatives Statistics at end-December 2015. Monetary and Economic Department, Basel.

Bao, J. and O’Hara, M. and Zhou, X. (2017), The Volcker Rule and market-making in times of stress, Forthcoming in Journal of Financial Economics

Bessembinder, H. and Jacobsen, S. and Maxwell, W. and Venkataraman, K. (2017), Capital commitment and illiquidity in corporate bonds, Forthcoming in Journal of Finance

Biswas. G., S. Nikolova, and C. Stahel (2015), The transaction costs of trading corporate credit, Working Paper

Blanco R. and Brennan, S. and Marsh, I. (2005), An Empirical analysis of the dynamic relation between investment-grade bonds and credit default swaps, Journal of Finance 60:2255-2281

Bongaerts, D. and De Jong, F. and Driessen, J. (2011), Derivative pricing with liquidity risk: theory and evidence from the credit default swap market, Journal of Finance 66:203–240

Brunnermeier, M., and Clerc, L., and El Omari, Y., and Gabrieli, S. and Kern, S. and Memmel, C. and Peltonen, T. and Podlich, N., and Scheicher, M. and Vuillemey, G. (2013), Assessing contagion risks from the CDS market, European Systemic Risk Board Occasional Paper no. 4

Chan-Lau, J. and Sy, A. (2006), Distance-to-default in banking: A Bridge too far?, IMF Working Paper WP/06/215

Choi, J., Shachar O., and S. Shin, 2017, Dealer liquidity provision and the breakdown of the law of one price: evidence from the CDS-bond basis, Management Science (forthcoming)

Choi, J. and Y. Huh (2017), Customer liquidity provision: implications for corporate bond transaction costs, Working Paper

15

Cont, R. and Kokholm, T. (2014), Central clearing of OTC derivatives: Bilateral vs. multilateral netting, Statistics and Risk Modeling 31:3–22

Demirgüc-Kunt, A. and Huizinga, H. (2010), Bank activity and funding strategies: The Impact on risk and returns, Journal of Financial Economics 98:626-650

Dick-Nielsen, J. and Rossi, M. (2016), The Cost of immediacy for corporate bonds, Working Paper, Copenhagen Business School and Texas A&M University

Du, W, and Gadgil, S. and Gordy, M. and Vega, C. (2016), Counterparty risk and counterparty choice in the credit default swap market, SSRN Working Paper

Duffie, D. (2012), Market-making Under the Proposed Volcker Rule, Report requested from the author by SIFMA

Duffie, D. and Garleanu, N. and Pedersen, L. H. (2005), Over-the-counter markets, Econometrica 73:1815-1847

Duffie, D. and Garleanu, N. and Pedersen, L. H. (2007), Valuation in over-the-counter markets, Review of Financial Studies 20:1865–1900

Duffie, D. and Scheicher, M. and Vuillemey, G. (2015), Central clearing and collateral demand, Journal of Financial Economics 116:237–256

Duffie, D. and Zhu, H. (2011), Does a central clearing counterparty reduce counterparty risk?, Review of Asset Pricing Studies 1:74–95

Feldhütter, P. (2012), The Same bond at different prices: Identifying search frictions and selling pressure, Review of Financial Studies 25:1155-1206

Friewald, N. and Jankowitsch, R. and Subrahmanyam, M. (2017), Liquidity and transparency in the structured product market, Forthcoming in Review of Asset Pricing Studies.

Gehde-Trapp, M. and Gündüz, Y. and Nasev, J. (2015), The Liquidity premium in CDS transaction prices: Do frictions matter?, Journal of Banking & Finance 61:184-205

Gündüz, Y., and Ongena, S. and Tümer-Alkan, G., and Yu, Y. (2017), CDS and credit: Testing the Small Bang Theory of the financial universe with micro data, Deutsche Bundesbank Discussion Paper 16/2017.

Heller, D., and Vause, N. (2012), Collateral requirements for mandatory central clearing of over-the-counter derivatives, BIS Working Paper No. 373

Kenny, O. and Killeen, N. and Moloney, K. (2016), Network analysis using EMIR credit default swap data: micro-level evidence from Irish domiciled special purpose vehicles (SPVs), IFC Bulletins

Laeven, L. and R. Levine, Bank governance, regulation and risk taking, 2009, Journal of Financial Economics, Volume 93, Issue 2, August 2009, Pages 259-275.

16

Longstaff, F. and Mithal, S. and Neis, E. (2005) Corporate yield spreads: Default risk or liquidity? New evidence from the credit default swap market, Journal of Finance 60:2213-2253

Loon Y., and Zhong, Z. (2014), The Impact of central clearing on counterparty risk, liquidity, and trading: evidence from the credit default swap market, Journal of Financial Economics 112:91–115

Loon Y. and Zhong, Z. (2016), Does Dodd–Frank affect OTC transaction costs and liquidity? Evidence from real-time CDS trade reports, Journal of Financial Economics 119:645–72

Mayordomo, S. and Posch, P. (2016), Does central clearing benefit risky dealers?, Journal of International Financial Markets, Institutions and Money 42:91-100

Norden, L. and Weber, M. (2004), Informational efficiency of credit default swap and stock markets: The impact of credit rating announcements, Journal of Banking & Finance 28:2813-2843

Peltonen, T. and Scheicher, M. and Vuillemey, G. (2014), The Network structure of the CDS market and its determinants, Journal of Financial Stability 13:118–133

Schestag, R. and Schuster, P. and Uhrig-Homburg, M. (2016), Measuring Liquidity in Bond Markets, Review of Financial Studies 29:1170-1219

Tang, D. and Yan, H. (2017), Understanding transactions prices in the credit default swaps market, Journal of Financial Markets 32:1-27

Trebbi, F. and Xiao, K. (2015), Regulation and Market Liquidity, Working Paper, University of British Columbia

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Figure 1: Time series of effective spread and net protection sold

The table displays the time series of effective spread (ES) and net amount of protection sold (NPS). ES (dashed line, left vertical axis) is in percentage points, NPS (solid line, right vertical axis) in million EUR. Both statistics are computed as described in Table 1. The first three panels display liquidity provision to end users by G16 dealers and HF/AM jointly (Panel A), G16 dealers only (Panel B), and HF/AMs only (Panel C). Panel D shows liquidity provision of G16 dealers vs. HF/AMs and end users, Panel E shows liquidity provision of HF/AM vs. G16 dealers, and Panel F shows liquidity provision of HF/AMs vs. G16 dealers and end users.

Panel A: ES and NPS of Dealers and HF/AMs vs. End-Users

Effective spread Net amount of protection sold

Panel B: ES and NPS of Dealers vs. End-Users


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Panel C: ES and NPS of HF/AMs vs. End-Users


Panel D: ES and NPS of Dealers vs. HF/AMs and End-Users


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Panel E: ES and NPS of HF/AMs vs. Dealers

Panel F: ES and NPS of HF/AMs vs. Dealers and End-Users


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Table 1: Summary statistics of liquidity measures

This table displays summary statistics of our liquidity measures. On a monthly basis, we compute liquidity provision from one group vs. another, either the effective spread or the net amount of protection sold. Panel A shows statistics for the monthly effective spread, where the effective spread is the average difference between the buy and sell price divided by the mid price and the price is CDS premium. Panel B shows the statistics for the net notional amount of protection sold.

N Mean St. Dev. 25th percentile Median 75th percentile

Panel A: Effective spread (ES) in percent

Dealer to HF/AM 70 3.11 3.37 0.21 3.08 4.03

Dealer to End-User 70 5.99 4.28 2.89 4.82 10.35

HF/AM to End-User 69 2.63 3.00 1.18 2.60 4.83

Panel B: Net amount of protection sold (NPS) in mn EUR

Dealer to HF/AM 70 150.80 67.35 3.02 160.15 310.70

Dealer to End-User 70 610.21 107.69 280.76 602.95 916.82

HF/AM to End-User 69 42.62 20.87 24.10 39.85 53.99

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Table 2: Cross sectional relation between ES and NPS

The table displays results of a regression analysis of effective spread (ES) on net amount of protection sold (NPS). On a monthly basis, we compute ES and NPS for each member m of group i (row names) vs. the aggregate of group j (column names). ES is in percentage points, NPS in million EUR. We demean the member-specific time series to adjust for member fixed effects, and include year-fixed effects. P-values are included in round brackets. We compute standard errors clustered by group member and date. Adjusted R-squared are in percentage points. ‘*’ indicates statistical significance at 10% level and ‘**’ at the 5% level.

From/to Dealer HF/AM End-User All

Dealer - 0.0184** 0.0725** 0.0311**

- (0.0223) (0.0103) (0.0202)

Adj. R2 - 7.35 7.95 9.48

HF/AM - - 0.0319** 0.0152*

- - (0.0208) (0.0679)

Adj. R2 - - 5.57 8.83

End-User - - - 0.0691*

- - - (0.0634)

Adj. R2 - - - 5.83

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Table 3: Time series relation between effective spreads of different investor groups

The table displays time series correlation of monthly estimates of effective spread (ES) for different investor groups. ES are computed as in Table 1. P-values are included in round brackets and computed using Newey-West standard errors. ‘*’ indicates statistical significance at 10% level, ‘**’ at the 5% level, and ‘***’ at the 1% level.

Dealer vs. All

HF/AM vs. Dealer

HF/AM vs. End-User

HF/AM vs. All

Dealer vs. End-User

Dealer & HF/AM vs. End-User

Dealer vs. All 1.0000

-0.6698***

(0.0000) -0.5455***

(0.0000) -0.6382***

(0.0000) 0.1366

(0.2593) 0.1331

(0.2720)

HF/AM vs. Dealer

1.0000 0.4907*** (0.0000)

0.9452*** (0.0000)

-0.1326 (0.2740)

-0.1280 (0.2909)

HF/AM vs. End- User

1.0000 0.5122*** (0.0000)

0.0512 (0.6828)

0.0590 (0.6379)

HF/AM vs. All 1.0000 -0.2042* (0.0900)

-0.1968 (0.1025)

Dealer vs. End- User

1.0000 0.9998*** (0.0000)

Dealer&HF/AM vs. End-User

1.0000

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Table 4: Liquidity provision of G16 dealers and restrictedness

The table shows coefficient loadings for univariate regressions of G16 dealers’ effective spreads (ES) and net protection sold (NPS) on dealer restrictedness. In Panel A, all variables are volume-weighted averages across dealers (where the weight corresponds to the relative trading volume of a dealer in the CDS market). DTC is G16 dealers’ distance-to-capital, from Chan-Lau and Sy (2006). ZS is dealer’ z-score from Laeven and Levine (2009) and Demirgüc-Kunt and Huizinga (2010). CDS_D is dealer mid CDS spread in basis points. CDS_C is contract mid CDS spread. Panel A gives results for time series regressions of volume-weighted averages across dealers. Panel B gives results for panel regressions. In Panel A, p-values are computed using Newey-West standard errors. In Panel B, we de-mean the dealer-specific times series and use year fixed effects to account for heterogeneity between dealers and over time, and use standard error clustered by dealer and year. The number of observations is 70 in each regression in Panel A, and 982 in each regression in Panel B. ‘*’ indicates statistical significance at 10% level, ‘**’ at the 5% level, and ‘***’ at the 1% level.

Panel A: Time series regression

ES NPS ES<0 NPS<0

DTC -0.0016*** -5.3740*** 0.0003*** 0.0002***

(0.0029) (0.0000) (0.0001) (0.0000)

ZS 0.0036*** -2.9802*** 0.0006*** 0.0019

(0.0003) (0.0000) (0.0004) (0.2860)

CDS_D 0.0042*** -3.0300*** 0.0001 0.0004*

(0.0000) (0.0006) (0.6140) (0.0654)

CDS_C -0.0101** -1.5760*** 0.0010** 0.0077

(0.0345) (0.0000) (0.0148) (0.6600)

Panel B: Panel regression

ES NPS ES<0 NPS<0

DTC -0.0106** -1.1508** 0.0007*** 0.0012**

(0.0182) (0.0491) (0.0002) (0.0366)

ZS -0.0085** -2.6824*** 0.0050 0.0007*

(0.0034) (0.0005) (0.1820) (0.0884)

CDS_D -0.0012* -2.6794*** 0.0056*** 0.0075***

(0.0941) (0.0004) (0.00301) (0.0067)

CDS_C -0.0014*** -1.0320 0.0004 0.0034*

(0.0018) (0.6050) (0.1521) (0.0759)

Who Provides Liquidity in the Market for Credit Default Swaps? · Liquidity is traditionally...

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