E ect of Central Clearing on Counterparty Risk

Eect of Central Clearing on Counterparty Risk, Liquidity and other determinants of corporate Credit Default Swaps Joséphine Molleyres

∗

March 1, 2018

Abstract Over-the-counter (OTC) trades are connected with a large amount of uncertainty regarding liquidity and counterparty risk. This paper shows that the recent implementation of regulated central counterparty clearing houses (CCPs) in the Credit Default Swaps (CDS) market reduces the impact of counterparty risk and liquidity on single name corporate CDS spreads listed in the S&P500. The results show evidence for the ecient risk management of CCPs and that transparency is increased in the CDS market. CDS spreads are determined by rmspecic and global factors. The determinants of CDS spreads are dependent on whether they are traded OTC or centrally. Using a novel technique to measure equity price uctuations from high-frequency data show that the rm-specic stock market determines CDS spreads much stronger when CDS are traded OTC than when they are centrally cleared. Equity log-returns have a signicant negative impact on CDS spreads, whereas equity volatility is positively related. The signicant negative eect of the market-wide stock market climate on CDS spreads is independent of the trading place. The term spread has a negative eect and the market wide volatility a positive deterministic potential on CDS spreads only when they are traded OTC, whereas there is no eect when CDS spreads are centrally cleared. Interestingly, OTC traded CDS spreads are much better explained than centrally cleared ones.

Keywords: Credit default swaps, central counterparty clearing houses, credit risk behavior.

JEL Classication: G18, G32 WWZ University of Basel, [email protected] ∗

Department

of

Finance,

CH-4001

Basel,

1 Introduction Central clearing in the Credit Default Swaps (CDS) market is a very new development. CDS were almost exclusively traded over-the-counter (OTC) until 2010. According to the Bank for International Settlements (2004-2017) only 1% of the total value of single-name CDS were traded over CCP by end of 2010 and remained around 1.5% by end of 2012.

OTC trades are

connected with a large amount of uncertainty regarding liquidity and counterparty risk, dened as the risk that following a credit event the CDS seller is unable to make the nal payment due under the CDS contract. The lack of transparency in the CDS market was strongly criticized especially during the recent nancial crisis starting 2007.

This made authorities implement

new reforms on OTC derivatives trades in order to increase transparency. As a result, the Dodd-Frank Act was signed in July 21, 2010, which introduced mandatory clearing through regulated central counterparty clearing houses (CCPs) for all suciently standardized derivatives. The swaps clearing mandate was eective from March 2013 on. The European Commission subsequently implemented similar rules.

1.1

Central clearing of Credit Default Swaps

Central counterparty clearing houses (CCPs) are highly regulated intermediaries that act as the buyer to every seller and vice versa. They have the goal to manage risks eciently in order to prevent default contagion and mitigate counterparty risk. CCP traded quotes are still negotiated OTC but are required to be posted. This strongly improves transparency in the CDS market. Because CCPs are buyers to every seller and vice versa, they have no direct susceptibility to value changes of the cleared contracts.

A CCP

can only be attained by losses, if a counterparty is unable to make the nal payment to the CCP due under the CDS contract in the event of a default of the underlying reference entity. Adequate risk management by CCPs requires to correctly identify the market value of the positions.

This might

be especially complicated for OTC derivatives with uncertain market values. To avoid such scenarios and to assure market stability, CCPs have minimum standards requirements for its participants in order to watch their credit risk. Additionally, CCPs collect collateral against current and future potential counterparty exposures on a daily or more frequent basis, depending on the current market conditions. CCPs require two types of collateral. One, in form of variation margins, which cover current counterparty exposures and absorb short term losses.

A second, in form of initial margins, which

compensate for potential future exposures that could emerge from valuation

1

changes. Members are also required to contribute to an additional default fund through which large losses can be covered, when existing margins are insucient. Table 1 provides a summary about the risk management of the major CCPs in the CDS market. CCPs are pooling risks among all clearing house members so that systemic risk is reduced. In contrast, decentralized dealers often renounce to initial margins and sometimes waive collateralization of certain types of counterparty exposures, including sovereigns and nonnancial companies as reported by the International Swaps and Derivatives Association (2010b).

According to Heller and Vause (2012), some market

participants have suggested that greater use of CCPs will signicantly raise the collateral needs of dealers, what will boost the eective trading costs and as a consequence, weaken the eciency of the market. CCPs are more expensive because they collect initial margins from its members, but do not provide initial margins to them. In contrast, in a decentralized OTC market, whatever collateral is supplied by one counterparty is received by another, so the net collateral is always equal to zero.

While clearing single-name

CDS centrally is meant to enhance risk management, clearing over multiple CCPs is costly. In their theoretical analysis, Due and Zhu (2011) show that adding a CCP for a specic class of derivatives reduces netting eciency and leads to an increase in average exposure to counterparty default risk, arguing that the collateral required by clearing in that specic market is too expensive.

According to Due and Zhu (2011), central clearing can reduce the

overall volume of counterparty credit exposures when bilateral positions are moved to the same CCP. If for example a bond and a CDS that hedges this bond position are cleared in the same clearing house, hedging eects reduce collateral requirements.

If those positions are cleared in dierent clearing

houses, the hedging eects that reduce collateral requirements in a bilateral netting agreement are lost. Currently in practice, CCPs show a tendency to specialize in clearing particular segments or classes of OTC derivatives, what limits the possible overall reduction in counterparty exposures. CCPs do not disclose the specic counterparties of the single trades, neither the agreed quotes.

However, the ISDA SwapsInfo collects data from

DTCC and Bloomberg of the percentage of centrally cleared trading volume of newly issued single-name CDS, measured by notionals relative to the total reported volume of issued contracts per day.

The data shown in Figure 1

reveal that the percentage of centrally cleared CDS was almost inexistent before September 2013, strongly increased in the subsequent half-year and since March 3, 2014, practically all single-name CDS were cleared centrally. Very little existing literature empirically analyzes the impact of imple-

2

3

min. net capital requirement of e37M for clearing members or $50M for Futures Commission Merchants to maintain at all times

Participation requirements

CDS (CDX NA, iTraxx, European single names CDS), interest rates, equity index, agricultural and energy derivatives

ICE Clear Europe

min. of $50M of adjusted net capita

CDS (CDX, iTraxx, corporate single names (North American and European) 34 sovereign single names

ICE Clear Credit

min.

13

Default fund

Members clearing single name CDS

Default Fund Contribution of

calculated under the expected shortfall methodology to a 5-day condence interval of 99.7%. cash and a broad range of government securities are accepted

Initial margins

e10M per clearing member

is collected daily and must be met in cash

Variation margins

29

22

min. CDS Guaranty Fund Contribution of e15M

The amount of original margin required is driven by the historical price uctuations for the given contract. Calibrated to cover the expected cost of closing out a defaulting member's position in normal market conditions to a 99% condence interval.

intra-day margin calls, both scheduled and unscheduled, as determined by market circumstances

No min. rating requirements. New and existing members are subject to regular risk monitoring

CDS (iTraxx, CDX NA, single names, options), commodity, equity, forex, listed rates, repo trades, cash bond trades

Market segment

LCH.Clearnet SwapClear

This table provides an overview of the risk management practices of the main central counterparties. Sources are the IntercontinentalExchange Inc. and the LCH.Clearnet as of March 1, 2018.

Table 1: CDS risk management of the main central counterparty clearinghouses

Figure 1: Centrally cleared trading volume of CDS

This gure shows the percentage of centrally cleared trading volume of CDS measured by notionals relative to total reported volume between June 28, 2010 and May 9, 2017.

menting CCPs in the corporate credit default swaps market. Loon and Zhong (2014) uses a sample of voluntary cleared single-name contracts and nd that central clearing reduces counterparty risk, increases CDS spreads, weakens the relation between the CDS spreads and the dealer's credit risk, suggesting a lowering of systemic risk, and increases liquidity and trading activity, inducing an increase in transparency. The analysis is based on an event-study, which compares the level of the variables in the pre- and post-period. In this paper, the existing literature is improved by analyzing how the deterministic potential of economic factors on CDS spreads alternate if CDS are traded OTC or cleared centrally. Existing literature is further improved in order to explain credit risk, i.e. CDS spreads, by including rm-specic variables as the volatility and jump risks of the equity market as well as global factors as potential determinants. Those variables were shown to be important drivers of credit risk in e.g. Collin-Dufresne, Goldstein, and Martin (2001), Elton, Gruber, Agrawal, and Mann (2001), Ericsson, Jacobs, and Oviedo (2009), Zhang, Zhou, and Zhu (2009). It is highly relevant for regulators to analyze whether the implementation of CCPs has the desired risk-reducing eects.

1.2

Credit risk: CDS vs. credit spreads

CDS spreads are a favorable measure of credit risk.

Contemporary litera-

ture, as e.g. in Chen and Kou (2009) and Zhang, Zhou, and Zhu (2009), uses corporate CDS spreads in order to measure credit risk instead of the credit spreads, dened as the bond yield minus the risk-free interest rate.

4

CDS

serve to transfer the risk of a credit event. The CDS protection buyer pays a periodic rate to a protection buyer in order to hedge bonds hold from a third company and in the case of a credit event the protection buyer is compensated by the seller by a predened sum, that in general is the dierence between the par value of the bond or loan and its market value after default. CDS spreads therefore reect market perceptions about the nancial health of a company and are important signals concerning nancial stability. The valuation of risky debt is of high interest in nance. Due (1999) and Hull and White (2000) explain the conception of casual relations between CDS spreads and bond spreads.

They show that in a frictionless market, CDS

spreads are equal to the bond yield over the risk-free interest rate. Because of several advantages, CDS spreads are a more direct measure of credit risk than bond credit spreads are. First, CDS are purely pricing credit risk and are directly observable. Credit spreads measured with bond spreads in contrast have to be computed with a risk-less benchmark.

The choice of the

risk-free benchmark is shown to be very sensitive in Houweling and Vorst (2005) and Zhu (2006). Second, CDS are typically traded on standardized terms. The bond market is more attained by market and institutional factors because they are attained by tax eects, as shown in Elton, Gruber, Agrawal, and Mann (2001), and by market microstructure eects and dierences in contractual agreements, as maturity, coupon, seniority, embedded option, guarantees, etc. Furthermore, funding and short-sale restrictions are absent in the CDS market.

Longsta, Mithal, and Neis (2005) and Chen,

Lesmond, and Wei (2007) nd that liquidity issues are also strongly aecting the bond spreads, which are not necessarily reecting the default risk of the underlying asset.

Third, the CDS spreads respond more quickly to

changes in credit quality of the underlying asset in the short-run compared to the bond market.

This is shown by Hull, Predescu, and White (2004),

Blanco, Brennan, and Marsh (2005) and Zhu (2006). In Molleyres (2016) a time-varying analysis show that the CDS spreads are also leading the price discovery process between CDS and credit spreads in times of nancial turmoil. The size of the single-name CDS market is still highly relevant, even though it continuously declined from 2012 on, as shown in Figure 2. The gross notional amount of single-name CDS outstanding was over 15 trillion USD in the beginning of 2010 and slowly declined to 8.5 trillion USD in end of 2014. Analogously, the number of single name CDS trades slowly declined from around 2.1 million beginning of 2010 to 1.3 million in end of 2014. The data is provided by the Trade Information Warehouse through ISDA SwapsInfo. The decline in CDS volume is due to the success of industry compression

5

eorts as reported in the ISDA Research Notes (2013), which was initiated in order to reduce complexity in the markets in October 2008. Participants in the credit derivative market have reduced the number of trade on a number of technology, media and telecommunications companies as reported by the nancial data providers Markit and Creditex.

Figure 2: Single name CDS volume traded

This gure shows the Single name CDS gross notional amount and the counts of trade between January 2010 and January 2015.

1.3

Pricing corporate credit risk

Structural models provide a theoretical framework to price credit risk and to identify the determinants of credit spreads. They are built on the initial contributions of Black and Scholes (1973), who show that corporate bonds can be valued as options. First and main contributions to structural models were made by Merton (1974) who considers the capital structures of companies by dening equity as call option on assets and corporate debt as default-free debt minus a put option. He nds that the value of corporate debt essentially depends on the rate of return on risk-free bonds, on various provisions and restrictions contained in the indenture and on the probability of the rm's default, but doesn't take into account the probability of a default before maturity, the eect of stochastic interest rates and that bonds may have dierent coupons. (1974) model were developed.

Many extensions on the basic Merton

For example, Black and Cox (1976) intro-

duce a default barrier, in Geske (1977) the compound option technique is used to value a corporation's risky coupon bonds, Longsta and Schwartz

6

(1995) incorporates default and interest rate risk, Leland and Toft (1996) include stochastic default boundaries and dynamic capital structure, Due and Lando (2001) allow for periodic and imperfect accounting reports, and in Collin-Dufresne and Goldstein (2001), rms adjust outstanding debt levels in response to changes in rm value.

Chen (2010) include macroeconomic

conditions and business cycle variations and Collin-Dufresne, Goldstein, and Helwege (2010) market-based jump risks in structural models.

They show

that macroeconomic variables help to explain the variations in credit spreads. Structural models are shown to perform poor applied to the real world and very little is known about the actual drivers of credit spreads. Most of the structural models generate values that are lower than the historical credit spreads. This so called "credit risk puzzle" is illustrated in an early work of Jones, Mason, and Rosenfeld (1984). Eom, Helwege, and Huang (2004) nd that the Merton (1974) and Geske (1977) models both underestimate credit spreads, and Longsta and Schwartz (1995), Leland and Toft (1996) and Collin-Dufresne and Goldstein (2001) models over predict credit spreads on average. As structural models typically model the rm value process, they are much more appropriate to "tting" the observed credit spreads than to oer insights into the magnitude of the fundamental drivers of credit spreads. In order to analyze the credit risk drivers, it is more appropriate to implement regression analyses. In Elton, Gruber, Agrawal, and Mann (2001), more exible regression analysis attempt to explain credit spreads by nding the determinants of the spread between corporate and government bonds and show that rm-specic default risk factors have extremely small deterministic potential. Collin-Dufresne, Goldstein, and Martin (2001) also nd that expected default loss variables have only very limited explanatory power. Their regression analysis is performed on credit spreads, dened as the corporate bond rates over the risk-free government rates. Elton, Gruber, Agrawal, and Mann (2001), Collin-Dufresne, Goldstein, and Martin (2001) and Ericsson, Jacobs, and Oviedo (2009) nd that global factors are important drivers of credit spreads. In order to better reect rm-specic default risks to explain credit spread variation, novel research proposes the use of volatility and jump risk measures extracted from high-frequency equity return data.

That there is a strong

relation between the stock and the bond market is shown at the aggregate level by e.g.

Keim and Stambaugh (1986), Fama and French (1993) and

Campbell and Ammer (1993). That this relation is found at the individual corporate level is shown by e.g. Blume, Keim, and Patel (1991) and Cornell

7

and Green (1991).

Huang and Huang (2012)

1

are the rst incorporating

a jump-diusion rm value process in the structural model. They apply a new calibration approach based on historical default data in order to explain corporate-Treasury yield spreads for investment grade bonds. This technique is shown to provide credit spreads closer to reality.

Bu and Liao (2014)

extend the Merton model by allowing for time-varying volatility and jumps in structural credit risk modeling. Their empirical analysis show that this extension better explains the time variation in actual credit spreads, proxied by CDS spreads, especially for small rms or when the market is exposed to turbulence as during the recent 2007/2008 nancial crisis.

Chen and

Kou (2009) present similar results. Zhang, Zhou, and Zhu (2009) nd in a regression analysis that a very large part of corporate credit spread variation can be predicted by volatility and jump risks of the individual rms.

1.3.1 Quadratic variation of jump-diusion processes The realized volatility and jump risk measures are constructed from highfrequency 5-minute equity return data in order to better capture the underlying asset dynamics. The methodology is in line with the one in Christensen and Podolskij (2006), Christensen, Oomen, and Podolskij (2010) and Christensen, Oomen, and Podolskij (2014) and uses a non-parametric method to consistently estimate the components of quadratic variation of the price range. The univariate equity log-price

p

is assumed to be a member of the

class of jump-diusion semimartingales which satises:

Z

t

Z

σu dWu +

µu du +

pt = p0 +

t

0

0

Nt X

Ji

(1)

i=1

µu is the drift, σu the diusion and Ji the jump functions for all u ≤ t < ∞. Wu is a standard Brownian motion and N a nite-activity simple 2 counting process . The high-frequency equity data pt available through the sampling period [0, t] of a trading day t is dened to have a intraday return over [ti−1 , ti ] with 0 ≤ ti−1 ≤ ti ≤ t of:

where

rti ,∆i = pti − pti−1 where

∆i = ti − ti−1 .

1 Huang

2 Having

and Huang (2012) was rst submitted in 2003. data with a 5-minute frequency over one trading day, N = 78.

8

(2)

Following Barndor-Nielsen and Shephard (2004, 2007), and BarndorNielsen, Graversen, Jacod, Podolskij, and Shephard (2006) two general measures for the quadratic variation process can be extracted: The realized variance (RV ), which measures the total variation included by the diusive and jump component, and the bipower variation (BV ), which can be used to sep-

0 = t0 < t1 < ... < tn = t → 0 as the sampling frequency n → ∞, the realized

arate these parts. For any sequence of partitions such that max1≤i≤n {∆i }

variance and the bipower variation converge to:

RVtn

=

[nt] X

p 2 → − ri∆,∆

σu2 du

0

i=1

BVtn

t

Z

+

Nt X

Ji2 ,

(3)

i=1

Z t [nt]−1 1 X p = 2 |ri∆,∆ ||r(i+1)∆,∆ | → − σu2 du. µ1 i=1 0

(4)

The asymptotic dierence between the realized variance and the bipower variation equals zero if there is no jump and strictly positive in the presence of a jump. In order to detect jumps, Christensen and Podolskij (2006) propose a non-parametric test of

H0 √

z= where

QQnt

as:

n (RVtn − BVtn ) d √ → − N (0, 1) v1 QQnt

(5)

is a consistent estimator with quad-power quarticity computed

from the data directly as:

QQnt

[nt]−3 1 X |ri∆,∆ ||r(i+1)∆,∆ ||r(i+2)∆,∆ ||r(i+3)∆,∆ | = 4 µ1 i=1 p

P

≥ 0, meaning that the test is oneH0 . The H0 is rejected if z exceeds

with

α

as chosen signicance level. In order to rely

2 i=1,...,N Ji sided, and positive outcomes go against

Under

− Ha , RVtn − BVtn →

the critical value

z1−α ,

(6)

on the economic intuition that jumps in nancial markets are rare and large and by following the Monte Carlo evidence in Huang and Tauchen (2005) and Tauchen and Zhou (2011), the signicance level is set to

α = 0.999.

The

t-statistic in Equation (5) can be interpreted as Hausman (1978) test. With the ltered realized jumps one can estimate the continuous (RV and jump (RV

(C))

(J)) components of the realized volatility for each day directly:

9

p 
 p
RV (C)nt = RVtn ∗ 1 − I z > Φ−1 + BVtn ∗ I z > Φ−1 α α

(7)

p
RV (J)nt = RVtn − BVtn ∗ I z > Φ−1 α ]

(8)

where I(.) is an indicator function that takes the value 1 if the jumps are signicant with respect to Equation (5).

1.4

Hypotheses

The hypotheses tested in this empirical paper contribute to the existing literature by examining the impact that central clearing of corporate CDS spreads has on counterparty risk, liquidity and on the deterministic power on CDS spreads of factors that inuence the creditworthiness of the rm.

Hypothesis 1: Central clearing reduces counterparty risk. Centrally cleared CDS should be less attained by counterparty risk, i.e. by the default risk of the major CDS sellers, if the risk management of the CCPs is ecient. In that case, CCPs are less risky than any other counterparty of a bilateral agreement. The price of a CDS protection is determined by the sellers' credit quality, which should be higher in a centralized environment than in a single OTC position.

Hypothesis 2: Central clearing aects CDS liquidity. CDS liquidity should determine CDS spreads stronger in a less transparent OTC environment. When CDS are cleared centrally, trade transparency is increased by providing traders with pricing information, the fear and emergence of liquidity throats decreases and risk-sharing among traders is improved. In a less transparent OTC market, liquidity frictions and associated emerging risks cannot be diversied so that the impact of liquidity on CDS spreads has to be much bigger. Clearing CDS could increase trading activity because hedgers want to take advantage of the reduction in counterparty risk and therefore increase their demand for credit protection. On the other side, higher trade transparency can discourage dealers from competing aggressively as in an OTC market. Informed traders might reduce their trading positions in cleared reference entities to less transparent OTC traded credit derivatives or credit-sensitive assets, which provide better opportunities.

10

Hypothesis 3: Corporate CDS spreads are determined by the indicators of development of creditworthiness of the rm, its deterministic powers are independent of the type of trade. Corporate CDS spreads describe the credit risk of the underlying rm. Therefore, CDS spreads are driven by rm-specic as well as by global factors, which determine the creditworthiness of the rm. If Hypotheses 1 and 2 hold, counterparty risk and CDS liquidity should be the only variables that might have alternating deterministic potentials on CDS spreads when comparing CDS spreads which were traded OTC with CDS spreads that were cleared centrally.

Other CDS non-specic variables should have the same

deterministic potential on CDS spreads when traded OTC and cleared centrally. The changes in CDS spreads are unaected by the amount of collateral posted, this can only aect the level of CDS spreads, as it is shown in Loon and Zhong (2014).

2 The Data The data used for this empirical study spans from January 4, 2010 to December 31, 2014.

The majority of the data is on a daily basis, some data

used for robustness purpose are on a quarterly basis.

2.1

CDS Data

The CDS mid bid and ask data consist of daily 5-year single-name CDS spreads of 29 corporate reference entities listed in the S&P500 from January 4, 2010 to December 31, 2014. The rms are listed in Table 2. The mid, bid and ask spreads are obtained from Markit through Bloomberg, except for 3

the ones of four rms , which are extracted from Eikon Thomson Reuters. The CDS spreads are denominated in USD. The ve years maturity is chosen because it is by far the most liquid maturity in the CDS market. The CDS data is constructed out of daily closing quotes of corporate single names CDS from the ocial books of record of the market makers. After a rigorous curvebased cleaning process is applied to the CDS data, the daily CDS composite spreads better reect the daily market trading than one individual CDS price.

3 Namely

General Electric, Apple, Chevron, and Coca-Cola. The CDS bid and ask spreads of Coca Cola are linearly interpolated where data is missing. The missing data points are well spread over the whole time sample.

11

The descriptive statistics of the corporate CDS mid spreads in Table 2 and its time series plots in Figure 3 indicate large dierences in the levels and standard deviations of the CDS spreads with large uctuations over time.

2.2

CDS determinants

The data potentially determining the CDS spreads consists of global variables and rm-specic variables.

2.2.1 Global CDS Drivers Counterparty risk: Counterparty risk (CPR) is dened as the risk that a CDS protection seller is unable to make the payment due under a CDS contract following a credit event of the underlying reference entity. Counterparty risk should have a negative eect on CDS spreads when traded OTC, because it diminishes the quality of the CDS protection.

In theory, coun-

terparty risk should be priced in the CDS spreads when traded OTC. When CDS are traded over a CCP, counterparty risk should have no impact on CDS spreads due to the clearing houses' risk management.

On the other

side, Due and Zhu (2011) show that having more than one CCP for a specic class of derivatives, as it is the case for CDS, reduces the netting efciency and leads to an increase in average exposure to counterparty default risk. The counterparties of OTC traded CDS cannot exactly be attributed in this data set.

4

Because there were only 14 nancial institutions

selling

CDS OTC according to the International Swaps and Derivatives Association (2010a), it is convenient to use a common factor of the default probabilities of those banks as a proxy for counterparty risk. Those 14 banks, known as the G14, count as the most active global derivatives dealers, covering 90.4 % of the credit derivatives outstanding during the whole analyzed time period. According to the OCCs Quarterly Report (2010), CDS represent 97% of all credit derivatives notionals, and therefore 88% of the CDS are traded by the G14 banks. Therefore the counterparty risk proxy remains valid when CDS are cleared centrally because those banks still were the major market markers during that time. The average default probability of the CDS sellers used as proxy for counterparty risk is an average over all major CDS dealers, i.e. sellers, of the CDS implied default probabilities provided by Bloomberg. The data is on a daily basis. As a second proxy for counterparty risk, the time series of the rst two principal components of the CDS spreads of the major

4 The banks selling CDS are Bank of America,

Barclays, BNP Paribas, Citigroup, Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC, JPMorgan Chase, Morgan Stanley, Royal Bank of Scotland, Société Générale, UBS, and Wells Fargo. 12

Table 2: Descriptive statistics of the corporate CDS spreads

This table shows the descriptive statistics of the corporate CDS spreads in basis points. The sample period is from January 2010 to January 2015. Company Consumer Goods (CG)

Coca-Cola Home Depot McDonald's NIKE Procter & Gamble The Walt Disney Walmart Energy (ENRG)

Chevron Exxon Mobil

avg

stdv

max

min

obs

KO HD MCD NKE PG DIS WMT

103.62 48.65 29.15 62.98 40.25 31.76 36.96

37.03 17.02 10.86 13.88 9.87 12.00 12.50

271.16 87.56 53.44 113.50 63.35 65.80 60.06

55.00 18.73 11.38 44.50 17.62 14.52 12.01

1256 1235 1234 1236 1219 1232 1234

CVX XOM

30.17 25.30

12.15 9.76

68.50 52.00

13.75 12.00

1113 1188

AXP GS JPM TRV

73.97 157.69 90.77 73.47

26.37 73.32 28.09 32.30

161.75 419.94 185.32 178.99

32.67 62.84 46.66 26.44

1246 1258 1258 1254

JNJ MRK PFE UNH

31.63 49.07 48.78 83.26

11.11 14.07 18.27 36.86

52.70 79.15 91.15 164.64

12.11 20.34 16.49 24.54

1241 1249 1242 1243

MMM CAT GE BA UTX

28.48 74.97 127.17 56.87 48.54

9.77 24.02 58.90 17.97 21.50

53.27 151.14 337.38 102.32 121.00

14.22 33.85 44.34 18.70 18.25

1240 1243 1258 1229 1248

AAPL CSCO IBM INTC MSFT

30.17 59.89 38.59 40.03 35.51

12.15 21.73 6.09 10.55 7.10

68.50 124.58 58.17 77.25 56.00

13.75 31.13 24.17 25.00 0.00

1113 1227 1237 1229 991

DD

52.77

12.99

103.72

23.59

1249

VZ

62.05

10.57

98.81

36.16

962

Financials (FIN)

American Express Goldman Sachs JPMorgan Chase & Co. The Travelers Companies Health Care (HC)

Johnson & Johnson Merck & Co. Pzer UnitedHealth Industrials (IND)

3M Caterpillar General Electric The Boeing United Technologies

Information Technology (IT)

Apple Cisco Systems IBM Intel Microsoft

Materials (MAT)

DuPont

Telecommunication (TEL)

Verizon Communications

13

Figure 3: Corporate CDS Spreads

This gure shows the log CDS spreads in basis points of all reference entities between January 2010 and January 2015.

14

CDS dealers in rst dierences are used. The data is on a daily basis. The rst principal component catches 67.76% of the variations in CDS spreads of the major CDS sellers and the second principal component explain additional 14.65%.

Both together therefore explain 82.40% of the variation of

the CDS spreads in rst dierences of the major dealers. The rst principal component (CPR1) has the highest importance, while the second principal component (CPR2) is additionally included to tests its signicance. The signs cannot be clearly interpreted. Among others, Arora, Gandhi, and Longsta (2012), Arce, Mayordomo, and Pena (2013) and Molleyres (2016) also use the dealers' CDS spreads as proxy for counterparty risk.

Market wide stock market climate:

As proxy for the overall state of

the US economy, the daily MSCI US is used and obtained from Bloomberg. An increase in the MSCI US induces a good performance in the US corporate market and should signal higher future protability and therefore lower the CDS spreads. The log-returns of the MSCI US are denoted by

Risk free rate:

rUS .

To consider for the risk-free interest rate (RF), the US

government bond redemption yield is used, with a maturity of 5 years in order to match the CDS maturity. As shown in Merton (1974) and Longsta and Schwartz (1995), a higher risk-free rate should increase the risk-neutral drift of the rm value process and so reduces the probability of default, what should lead to a reduction of the CDS spreads. The daily risk-free interest rates are extracted from Bloomberg.

Slope of the yield curve:

The slope of the yield curve, i.e.

the term

spread, is dened as the dierence between the 10-year and the 3-month Benchmark Treasury yields. The daily data is provided by Bloomberg. The term structure is interpreted as an indicator of expectations of future short rates, and as an indicator of the overall health of the economy and/or uncertainty.

A high slope means improved economic growth and is therefore

negatively related to CDS spreads.

E.g.

Collin-Dufresne, Goldstein, and

Martin (2001) and Annaert, De Ceuster, Van Roy, and Vespro (2013) use similar proxies for their analysis.

Market wide volatility:

The market wide volatility is proxied by the

Merrill Option Volatility Expectations Index (MOVE). The MOVE reects a market estimate of future Treasury bond yield volatility and is the bond market's equivalent of the Chicago Board Options Exchange Volatility Index (VIX). The MOVE extracted from Bloomberg on a daily basis is a yield curve

15

weighted index of the normalized implied volatility on 1 month Treasury options by using a weighted volatility average of the 2, 5, 20 and 30 year 5

Treasury yields .

An increase in market wide volatility should lead to an

increase in uncertainty in the markets and therefore increase CDS spreads. Collin-Dufresne, Goldstein, and Martin (2001) and Ericsson, Jacobs, and Oviedo (2009) use the VIX to describe the market wide volatility. To address the market wide volatility in the credit spread environment, this analysis considers it as benecial to use the bond market's equivalent. For robustness purpose, this analysis is also performed with the VIX. The daily data is from Bloomberg.

Bond market overall liquidity:

To account for the overall liquidity in

the bond market, the bid-ask spreads of the 5 year maturity Treasury yield, denoted by

∆RF B/A ,

are used. The daily data is provided by Bloomberg.

2.2.2 Firm-Specic Drivers CDS liquidity: The rm specic

liquidity (CDSB/A ) is captured by the

bid-ask spreads of the CDS quotes with 5 year maturity.

The rm spe-

cic liquidity is expected to determine the CDS spreads negatively, then a CDS with lower liquidity is less attractive than one with high liquidity. That derivatives also contain liquidity premia is shown in the theoretical asset pricing model of Bongaerts, de Jong, and Driessen (2011). Bid-ask spreads are the most frequently used proxies for liquidity in nancial literature, as in e.g. Venkatesh and Chiang (1986), Stoll (1989) or Annaert, De Ceuster, Van Roy, and Vespro (2013). They indicate inventory holding, order processing and information asymmetry costs.

Equity:

The equity log-returns (rE ) are used to proxy for the general per-

formance of the company. Higher equity returns increase the rm's value and are therefore expected to lower the CDS spreads.

Implied equity Volatility:

Higher equity volatility (rvC) generally im-

plies higher asset volatility and therefore means that the rm is more likely to be below the default boundary. This should elevate CDS spreads. Zhang, Zhou, and Zhu (2009) nd a strong positive dependence between equity volatility and corporate CDS spreads.

5 weighted

with 20%, 20%, 40% and 20%

16

Jump components of realized volatility:

Higher equity jumps (rvJ)

are linked to higher equity returns and should therefore reduce the CDS spreads. According to Zhu (2006) and Zhang, Zhou, and Zhu (2009) there is a strong positive relationship between credit spreads and jump intensity and volatility, which include the more extreme movements in asset returns of the specic rm. Jumps in nancial asset prices are rare events, accounting for a very small proportion of the total price variation.

Credit rating:

A higher credit rating reects a better credit quality, and

is therefore negatively related to the CDS spreads. Bloomberg estimates a current analyst credit rating, which is provided on a daily basis. The rating scale is between 1 and 5, where 5 is the strongest ranking.

Dividends: in dividends

The fraction of net income a rm pays to its shareholders 6

(DIV) is also expected to be negatively related to the CDS

spreads, then higher dividends imply higher earnings of the company, what induces a lower default probability. Zhang, Zhou, and Zhu (2009) also nd dividends to be an important credit spreads driver. The data is provided by Bloomberg on a quarterly basis. Dividends are also used as determinant of CDS spreads in Zhang, Zhou, and Zhu (2009).

Return on Equity:

Following the existing literature in e.g. Zhang, Zhou,

and Zhu (2009) or Chiaramonte and Casu (2013), the normalized return on equity (ROE), dened as the returns on common equity based on net 7

income excluding one-time charges used.

from Bloomberg on a quarterly basis is

Higher returns on equity are improving the rms balance sheet and

are therefore expected to decrease CDS spreads.

Leverage ratio:

The company's nancial leverage ratio (LEV) is calcu-

lated by dividing its total debt by the common stockholders' equity. quarterly data is provided by Bloomberg.

The

In the structural framework, a

rm's default is triggered when the leverage ratio gets to unity. An increase in leverage should therefore lead to higher credit spreads. Zhang, Zhou, and Zhu (2009) also use the leverage ratio as determinant of CDS spreads.

6 Calculated

as: [Total Common Dividends]*100 / [Income Before Extraordinary Items] - [Minority and Preferred Dividends]. 7 The ROE is calucalted as: ([Trailing 12 Month Normalized Income] / [Average of Current and Prior Period (Common Equity)]) *100

17

3 Empirical evidence: Eect of central clearing CDS on counterparty risk and liquidity Central clearing in the CDS market especially has the purpose to reduce counterparty risk. If the risk management of CCPs is eective, CDS spreads are only aected by counterparty risk, i.e. the risk of default of the major CDS sellers, which are also the major CDS dealers active during the investigated period, while CDS are traded OTC and there is no deterministic power when CDS are cleared centrally.

If CDS are mainly cleared across

CCPs, the concentration of credit, liquidity and operational risks will increase in these institutions and CCPs might become potential sources of systemic risk. Clearing members will have to apply credit limits to CCPs, what diminishes liquidity in the market. The risk management of CCPs may therefore also aect CDS liquidity and change its eect on CDS spreads. As seen in Figure 2, the volume of single name CDS continuously declined from 2012 on, inducing a decline in CDS liquidity. To analyze the eects of an implementation of central clearing houses on the CDS spreads and in order to test Hypotheses 1 and 2, the CDSspecic factors counterparty risk (CPR) and CDS liquidity (CDSB/A ) are allowed to be state dependent. The dependence of further global drivers and rm-specic factors are not attained by the CDS trading place in theory. Therefore, the following threshold panel regression that allows to switch between the central clearing (CC) and the over-the-counter (OTC) regime is used:

∆CDSit =γ1 ∆CP Rt ∗ 1(OT Ct ) + γ2 ∆CP Rt ∗ 1(CCt ) + γ3 ∆CDSB/A,it ∗ 1(OT Ct ) + γ4 ∆CDSB/A,it ∗ 1(CCt ) + β1 rE,it + β2 ∆rvCit + β3 ∆rvJit + β4 ∆ratingit + δ1 rUS,t + δ2 ∆M OV Et + δ3 ∆RFt + δ4 ∆RFB/A,t + δ5 ∆termt + εit

(9)

∆ denotes the rst dierences of the variables. The indicator func1(.) enable to switch between the two regimes. 1(OTCt ) is equal to 1 if CDS are in average traded over-the-counter at time t, 0 otherwise. If at time t where

tions

CDS are cleared centrally in average, 1(CCt ) is equal to 1, 0 otherwise. The two regimes are determined by the percentage of centrally cleared trading volume, measured by notionals relative to total reported volume. The data in Figure 1 reveal that single name CDS change from being traded OTC to

18

being traded over CCPs between September 1, 2013 and March 3, 2014. The regimes can be clearly distinguished, then the percentages of centrally cleared trading volume have a mean (standard deviation) of 1.05 (3.59) in the OTC regime, and a mean (standard deviation) of 78.43 (6.78) in the regime of central clearing. The augmented Dickey-Fuller test shows that the percentage of centrally cleared trading volume is stationary in the OTC regime with a p-value of 0.001. Even though stationarity is rejected by the augmented Dickey-Fuller test in the regime of central clearing (p-value of 0.480), the data in Figure 1, the mean and standard deviation conrm that the implementation of central clearing by regulatory reforms in this regime is unambiguous after March 3, 2014. Therefore, one can assume that CDS are traded OTC in average between January 4, 2010 and September 1, 2013, so that 1(OTCt )=1 then, and 1(OTCt )=0 otherwise. Similarly, it is realistic that CDS are traded over CCPs in average between March 3, 2014 and December 31, 2014, i.e. 1(CCt )=1, and 1(CCt )=0 otherwise. Whereas counterparty risk (CPR) and CDS liquidity (CDSB/A ) are dened as threshold variables, the remaining explanatory variables are not state dependent in this analysis. To get results robust to cross-equation correlations and heteroscedasticity, White standard errors are estimated by clustering over the rms

i.

Counterparty risk is de-

ned as the default risk of the major CDS sellers, which also are the major CDS dealers active during the entire period. Counterparty risk is measured as the average of the CDS implied default probabilities of those 14 major 8

CDS dealers . The threshold panel regressions are estimated over all rms, excepting JPMorgan Chase and Goldman Sachs because they are part of the major CDS dealers. Equation (9) is also estimated for subsets of rms, which are dened by their sectors, as in Table 2. The results of Equation (9) are in Table 3. The panel regression results over all rms in Table 3 show that counterparty risk has a signicant deterministic potential on the CDS spreads in the OTC market. Counterparty risk is not aecting CDS spreads when they are cleared centrally.

In order words, corporate single name CDS are not

attained by the credit risk of the major CDS dealers when CDS are cleared through CCPs, while they are strongly attained when CDS are traded OTC. This strongly support the eectiveness of risk management of the central counterparty clearing houses and arms Hypothesis 1. Breaking down the analysis on a sector level highlights the same result. Using a 5% signicance

8 The banks selling CDS are Bank of America,

Barclays, BNP Paribas, Citigroup, Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC, JPMorgan Chase, Morgan Stanley, Royal Bank of Scotland, Société Générale, UBS, and Wells Fargo.

19

level, the eect of counterparty risk on CDS spreads vanishes throughout all sectors when CDS are cleared centrally, whereas counterparty risk has a highly signicant deterministic power on CDS spreads while traded OTC. Only in the nancial sector, counterparty risk also has a weak signicant effect (signicance level of 10%) when CDS are cleared centrally. Those results induce that contagion eects are present in the CDS market. A high default probability, i.e.

high credit risk, of the major CDS sellers has a spill over 9

eect on the credit risk of single rms while CDS are traded OTC . Contagion eects disappear when CDS are cleared centrally, except for some weak contagion eects that persist in the nancial sector.

The positive sign of

the coecient is unexpected, because higher counterparty risk indicate lower quality of the CDS protection and therefore lower the CDS spreads in theory. If CDS are traded OTC, an increase in the default probability of the major CDS sellers by 1 basis points (bp) strongly increases corporate CDS spreads by 586 bp in average. CDS spreads of nancial and industrial corporations are aected twice as much by counterparty risk as the average rm, whereas the eect on consumer goods and IT corporations is only around 300 bp. Liquidity has a highly signicant impact on CDS spreads when they are traded OTC. Lower liquidity, i.e. higher CDS bid-ask spreads, increases CDS spreads, as expected from theory. An increase in the CDS bid-ask spreads by 1 bp, increase the CDS spreads by 0.157 bp. When CDS are cleared centrally, the eect of liquidity is only signicant at the 10% level. Whereas liquidity is positively related to the CDS spreads in the central clearing regime, the eect is closely to zero and non-signicant at the 5% level, which is why the result is not further heeded. The results are less consistent when analyzing the dierent sectors separately. In the nancial and telecommunication sectors, liquidity never has an eect on CDS spreads, whereas the CDS spreads in the industrial and energy sectors always are determined by liquidity. The consumer goods, materials and health care sectors show a negative relation between liquidity and CDS spreads in the OTC regime, where the eect is non-signicant when CDS are cleared centrally. The IT sector is only slightly aected by liquidity in the central clearing regime at the 10% signicance level only. In general, Hypothesis 2 is conrmed. CDS liquidity has a much stronger eect on CDS spreads when they are traded OTC than when they are cleared centrally. This arms that CCPs improve transparency in the CDS market.

9 Strong evidence of contagion eects in the CDS market is found in earlier work of Jorion and Zhang (2007) and Allen and Carletti (2006). For further investigations, it might be interesting to test whether those contagion eects are diminished with the implementation of central clearing in the CDS market.

20

Counterparty risk is shown to have the same eect on sovereign singlename CDS spreads in Molleyres (2018). Sovereign CDS spreads are driven by counterparty risk only if CDS are traded OTC, whereas counterparty risk has no eect on sovereign CDS spreads when they are cleared centrally. Liquidity is shown to be not attained by the trading place of sovereign CDS.

21

Table 3: Threshold regressions

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The panel regressions are performed on all rms as well as on subgroups dened by sectors. The sample period is from January 2010 to January 2015. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. all ∆ CPR*1(OTC) ∆ CDSB/A *1(OTC) ∆ CPR*1(CCP) ∆ CDSB/A *1(CCP) ∆ RVC ∆ RVJ

rE ∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

R2adj obs

FIN

586.361 (3.77) 0.157∗∗∗ (4.55)

∗∗∗

IND

1196.042 (3.37) 0.115 (1.15)

∗∗∗

CG

1191.825 (3.29) 0.546∗∗∗ (5.05)

∗∗∗

335.287∗∗∗ (3.02) 0.104∗ (1.77)

-437.974 (-0.88) -0.057∗ (-1.67)

-1447.467∗ (-1.65) -0.045 (-0.41)

-1278.635 (-1.48) 0.321∗∗∗ (3.15)

-324.009 (-0.69) -0.088 (-1.61)

29.045∗∗ (2.4) 8.644 (0.99) -16.366∗∗∗ (-7.85) 2.115∗∗∗ (2.92) -3.32∗∗ (-2.05) 0.046∗∗∗ (4.69) -3.165∗ (-1.8) 1.298 (0.8) -15.589 (-0.8)

21.575 (1.13) -31.482∗ (-1.78) -28.388∗∗∗ (-4.38) -2.037 (-0.7) -4.619 (-1.39) 0.099∗∗∗ (3.98) -8.789∗ (-1.84) 1.578 (0.52) -10.156 (-0.19)

63.252∗∗ (2.03) 36.974 (0.92) -34.634∗∗∗ (-8.34) 4.318∗∗ (2.21) -5.39∗ (-1.78) 0.076∗∗∗ (4.17) -6.489∗∗ (-2.07) 1.223 (0.41) -43.202 (-1.25)

16.252 (1.36) 1.375 (0.16) -8.515∗∗∗ (-3.12) 1.897∗ (1.64) -2.444 (-1.5) 0.036∗∗∗ (3.7) -1.51 (-0.87) 1.244 (0.8) -8.091 (-0.36)

6.93% 28764

15.28% 2292

15.41% 5517

2.96% 7512

22

continued )

Table 3: Threshold regressions (

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The panel regressions are performed on all rms as well as on subgroups dened by sectors. The sample period is from January 2010 to January 2015. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. IT ∆ CPR*1(OTC) ∆ CDSB/A *1(OTC)

315.897 (2.8) -0.007 (-0.14)

∆ CPR*1(CCP) ∆ CDSB/A *1(CCP) ∆ RVC ∆ RVJ

rE ∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

509.754 (2.55) -0.067 (-0.4)

∗∗

ENRG

MAT

401.566 (2.91) 0.112∗∗ (2.17)

424.741 (2.61) 0.414∗ (1.82)

∗∗∗

∗∗∗

HC 424.424∗∗∗ (3.58) 0.112∗∗ (2.52)

-168.225 (-0.24) -0.137∗ (-1.8)

94.867 (0.06) -0.199 (-0.63)

940.6 (1.01) -0.216∗∗∗ (-2.65)

474.316 (0.38) 0.199 (1.11)

156.466 (0.23) 0.052 (1.03)

11.776 (1.47) -2.947 (-0.34) -4.962∗∗∗ (-2.69) 2.965∗∗ (2) -3.165∗∗ (-2.47) 0.032∗∗∗ (3.88) -1.676 (-1.15) 1.631 (1.26) -25.811 (-1.55)

20.749 (0.83) 80.923∗∗ (2.56) -4.885 (-0.6) -0.57 (-0.08) -7.118∗∗ (-2.29) 0.075∗∗∗ (3.44) -5.452 (-1.47) 3.083 (0.88) 44.078 (0.92)

48.302∗∗∗ (2.67) 10.749 (0.62) -6.422 (-1.34) 0.52 (0.31) -2.245 (-1.24) 0.01 (0.83) -0.634 (-0.3) 2.129 (1.18) -10.895 (-0.48)

67.732∗∗ (2.46) -28.934∗ (-1.94) -1.107 (-0.15) 1.452 (0.6) -6.976∗∗∗ (-3.26) 0.051∗∗∗ (3.45) -2.69 (-1.02) 5.45∗∗ (2.52) 20.814 (0.69)

5.598 (0.62) 16.545 (1.63) -3.132 (-1.08) -0.613 (-0.54) -2.259 (-1.55) 0.024∗∗∗ (2.78) -1.97 (-1.12) 0.81 (0.51) 6.003 (0.31)

3.85% 5257

5.70% 917

4.03% 1816

10.49% 1214

3.90% 4239

R2adj obs 3.1

TEL ∗∗∗

Robustness

To test the robustness of the estimates of Equation (9) in Table 3, dierent proxies are used for counterparty risk. all rms individually.

Equation (9) is also estimated for

A two-stage least squares regression analysis is also

23

performed to test the results' robustness.

3.1.1 Counterparty risk proxies For robustness purpose, Equation 9 is re-estimated by using as another proxy for counterparty risk the time series of the rst two principal components of the CDS spreads of the major CDS dealers in rst dierences, which also count as major CDS dealers in the whole period. The rst principal component catches 67.76% of the changes in CDS spreads of the major CDS dealers, whereas the second principal component explain additional 14.65%. The time series of the rst and second principal components therefore explain 82.40% of the variations in credit risk of the main CDS dealers.

The rst

principal component (CPR1) has the highest importance, while the second principal component (CPR2) is additionally included to tests its signicance. The signs cannot be clearly interpreted. The results in Table 4 show very similar results to the ones above in Table 3 where the default probability of the major dealers is used as proxy for counterparty risk. An increase in the time series of the rst principal component by 1 bp increases corporate CDS spreads by 0.02 bp in average, if CDS are traded OTC. When CDS are cleared centrally, the credit risk of the major CDS sellers have no signicant impact on CDS spreads.

In average, the second

principal component is signicantly determining CDS spreads positively over both regimes. Breaking down the analysis on single sectors give less distinct results than in Table 3. The nancial sector's CDS spreads are attained by counterparty risk in both sectors. Counterparty risk is signicantly reduced through central clearing in the industrial, consumer goods, IT, telecommunication, materials and health care sectors. Conversely, the energy sector is most attained by counterparty risk if CDS spreads are traded over CCPs. The impacts of liquidity on CDS spreads are highly conform to the ones in Table 3 for all analyses and therefore highly emphasize the robustness of the results.

24

Table 4: Threshold regressions: Robustness

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The panel regressions are performed on all rms as well as on subgroups dened by sectors. The sample period is from January 2010 to January 2015. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. ∆ CPR1*1(OTC) ∆ CPR2*1(OTC) ∆ CDSB/A *1(OTC) ∆ CPR1*1(CCP) ∆ CPR2*1(CCP) ∆ CDSB/A *1(CCP) ∆ RVC ∆ RVJ

rE ∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

R2adj obs

all

FIN

IND

CG

∗∗∗

0.02 (5.78) 0.034∗∗∗ (8.39) 0.141∗∗∗ (4.22)

∗∗∗

0.069 (5.38) 0.033∗∗∗ (2.91) 0.117 (1.17)

0.04 (5.22) 0.064∗∗∗ (7.62) 0.456∗∗∗ (4.49)

0.014∗ (1.93) 0.029∗∗∗ (4.01) 0.1∗ (1.77)

0.016 (0.98) 0.04∗∗∗ (2.77) -0.064∗ (-1.95)

0.133∗∗∗ (3.53) -0.025 (-0.85) -0.065 (-0.62)

0.03 (1.19) 0.035 (1.53) 0.304∗∗∗ (3.2)

-0.013 (-0.63) 0.06∗∗∗ (3.27) -0.084∗ (-1.73)

17.824∗ (1.78) 7.467 (0.9) -10.678∗∗∗ (-6.6) 1.046 (1.55) 1.559 (1.47) 0.027∗∗∗ (4.06) 0.898 (0.89) -23.045∗ (-1.65) -1.678 (-1.56)

12.093 (0.79) -18.2 (-1.19) -15.457∗∗∗ (-2.69) -3.108 (-1.06) 5.115∗∗ (2.05) 0.066∗∗∗ (3.13) -2.231 (-0.78) -5.08∗∗ (-2.2) -41.841 (-1.05)

43.102 (1.54) 37.249 (0.95) -18.462∗∗∗ (-5.58) 1.34 (0.76) 1.932 (0.96) 0.048∗∗∗ (3.54) -1.843 (-1.02) -3.502∗ (-1.7) -52.394∗ (-1.78)

7.367 (0.72) 1.747 (0.23) -4.643∗∗ (-2.09) 1.447 (1.27) 1.756 (1.32) 0.02∗∗∗ (2.79) 2.518 (1.53) -1.22 (-0.97) -13.794 (-0.78)

13.82% 28764

32.35% 2292

25.02% 5517

8.73% 7512

25

∗∗∗

continued )

Table 4: Threshold regressions: Robustness (

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The panel regressions are performed on all rms as well as on subgroups dened by sectors. The sample period is from January 2010 to January 2015. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. IT ∆ CPR1*1(OTC) ∆ CPR2*1(OTC) ∆ CDSB/A *1(OTC) ∆ CPR1*1(CCP) ∆ CPR2*1(CCP) ∆ CDSB/A *1(CCP) ∆ RVC ∆ RVJ

rE ∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

R2adj obs

0.006 (2.29) 0.026∗∗∗ (6.94) -0.017 (-0.33) -0.034 (-0.98) 0.066∗∗ (2.18) -0.149∗ (-1.93) 2.34 (0.32) -3.124 (-0.39) -2.307 (-1.37) 2.179 (1.6) -0.283 (-0.28) 0.02∗∗∗ (3.07) 0.92 (0.78) -0.054 (-0.05) -27.52∗ (-1.85) 9.34% 5257 ∗∗

TEL

ENRG

MAT

HC

0.003 (0.49) 0.041∗∗∗ (6.76) -0.054 (-0.33) 0.107 (1.53) 0.029 (0.47) -0.383 (-1.22) 25.228 (1.04) 76.04∗∗ (2.45) -0.809 (-0.1) -0.817 (-0.11) -2.604 (-0.89) 0.053∗∗ (2.55) -2.089 (-0.78) 0.8 (0.24) 54.31 (1.18) 10.85% 917

0.006 (1.3) 0.023∗∗∗ (4.59) 0.113∗∗ (2.25) -0.092∗∗ (-2.18) 0.085∗∗ (2.31) -0.266∗∗∗ (-3.43) 41.524∗∗ (2.39) 10.446 (0.6) -3.267 (-0.72) -0.492 (-0.34) -0.021 (-0.01) 0.003 (0.26) 1.237 (0.62) 0.604 (0.38) -13.171 (-0.68) 7.79% 1816

0.027 (1.98) 0.018∗ (1.88) 0.353∗ (1.84) 0.049 (0.69) 0.023 (0.34) 0.275 (1.64) 53.673∗∗ (2.08) -19.174 (-1.28) 6.553 (0.9) -0.416 (-0.18) -3.014 (-1.48) 0.036∗∗∗ (2.72) -0.249 (-0.12) 2.663 (1.29) 10.827 (0.33) 18.63% 1214

0.01∗∗∗ (3.07) 0.027∗∗∗ (6.04) 0.081∗∗ (2.01) 0.034 (1.16) 0.021 (0.73) 0.047 (0.96) -1.689 (-0.22) 9.225 (0.96) 0.024 (0.01) -0.859 (-0.8) 1.023 (0.78) 0.01 (1.46) 0.862 (0.62) -1.106 (-0.77) 2.782 (0.16) 11.76% 4239

26

∗∗

3.1.2 Threshold regression on single rms The threshold regression in Equation (9) is also estimated for all rms individually in Table 5.

The eect of counterparty risk on CDS spreads is

very consistent across the rms and are in line with the previous results in Table 3.

For 23 rms, the changes in counterparty risk have a signicant

deterministic potential on the changes in OTC traded CDS spreads, but no signicant impact when CDS are cleared centrally. The CDS spreads of 3M and Intel are not driven by counterparty risk at all. Microsoft's CDS spreads are aected by counterparty risk in the central clearing regime only.

The

CDS spreads of General Electric, Pzer and Procter & Gamble are always driven by counterparty risk. Liquidity is aecting CDS spreads of more corporations (17) in the OTC regime than in the regime with central clearing (8), what also support the previous results in Table 3.

3.1.3 Two-stage least squares regression Two-stage least squares are applied when there might be endogeneity problems in the explanatory variables, i.e. when the dependent variable's error terms are correlated with the independent variables.

In order to test the

robustness of the estimates in Table 3, Equation (9) is re-estimated by additionally dening the independent variables as instrumental variables.

In

a rst stage, the two-stage least squares regression nds the amount of the endogenous and exogenous variables that can be attributed to the instruments.

This is done by estimating an OLS regression of each variable on

the set of instruments. In a second stage, the original equation is regressed with all of the variables, replaced by the tted values from the rst-stage regressions. The results in Table 6 are highly consistent to the ones in Table 3 and therefore underline the models robustness.

27

28

CPR*1(OTC) CDSB/A *1(OTC) CPR*1(CC) CDSB/A *1(CC) RVC RVJ

R2adj obs

∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

rE

∆ ∆ ∆ ∆ ∆ ∆

0.0033 853

32.83 0.04∗ -143.49 -0.36∗ -6.6 -4.91 0.34 -0.49 -0.67 0.01∗∗ -0.58 0.09 -8.45

MMM

0.207069 1206

1376.57∗∗∗ 0.52∗∗∗ -1133.81 -0.23 3.78 -52.8∗ -26.28∗∗∗ -1.34 -4.8 0.1∗∗∗ -10.69∗∗∗ 0.57 -24.33

AXP

0.0342 1049

422.14∗∗∗ 0.17∗∗∗ 1293.95 -0.24∗∗ 6.12 -10.03 4.41 2.28 -5.75∗∗∗ 0.02 -6.25∗ 4.24∗ -58.45

AAPL

0.075838 1183

332.71∗∗∗ 0.54∗∗∗ 664.86 0.15 9.59 2.83 -12.63∗∗∗ 1.11 -1.45 0.03∗∗∗ -1.9 1.27 -13.87

BA

0.137805 1203

553.49∗∗∗ 0.27∗∗∗ 1915.32 0.65∗∗ -19.78 -3.99 -26.59∗∗∗ 4.9∗ -3.93 0.05∗∗∗ -4.36 2.58 -16.44

CAT

0.04442 1051

343.77∗∗ 0.18∗∗∗ 1039.4 -0.21∗∗ 46.44∗∗∗ 23.12 -9.34∗∗ -0.03 -4.01∗ 0.01 -2.24 3.48 -65.54

CVX

0.089773 1189

568.79∗∗∗ 0.02 -1020.13 0.13 25.96∗∗∗ 7.92 -7.38∗∗∗ 4.76∗ -4.85∗∗∗ 0.04∗∗∗ -2.59 2.77 -8.43

CSCO

0.01905 1081

993.45∗∗ 0.11∗∗∗ -603.42 -0.05 54.47 -18.73 -4.12 6.61 -10.19∗ 0.09∗∗ 2.56 9.83 -58.59

KO

0.099496 1214

424.74∗∗∗ 0.41∗∗∗ 474.32 0.2 67.73∗∗∗ -28.93 -1.11 1.45 -6.98∗∗∗ 0.05∗∗∗ -2.69 5.45∗∗ 20.81

DD

0.016081 765

462.57∗∗∗ 0.03 -762.58 0.25 52.85∗∗∗ -2.67 -1.26 1.53 -0.29 0.01 0.4 0.61 42.02

XOM

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The sample period is from January 2010 to January 2015. ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels.

Table 5: Threshold regressions on single rms

29

continued )

CPR*1(OTC) CDSB/A *1(OTC) CPR*1(CC) CDSB/A *1(CC) RVC RVJ

R2adj obs

∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

rE

∆ ∆ ∆ ∆ ∆ ∆

0.3712 1209

3687.08∗∗∗ 0.55∗∗∗ -6295.12∗ 0.03 248.37∗∗∗ 42.72 -97.98∗∗∗ 9.71 -16.79∗∗∗ 0.21∗∗∗ -21.27∗∗ 3.15 -161.04

GE

0.323275 1204

3772.95∗∗∗ 0.06 -4227.55 0.48 74.07∗ 113.21∗∗ -128.56∗∗∗ -18.62∗∗ -33.91∗∗∗ 0.11∗∗ -25.87∗∗∗ 25.66∗∗∗ -12.14

GS

0.081412 1200

326.82∗∗∗ 0.17∗∗∗ -447.73 0.13 21.12∗∗ 11.95 -11.13∗∗∗ 1.27 0.6 0.04∗∗∗ -2.95 -2.58 17.99

HD

0.013462 927

238.78 0.07 478 -0.17 -13.72 -27.72 -8.12∗∗ 2.89 -3.45 0.02 0.6 1.81 0.11

INTC

0.062932 1199

202.33∗∗ -0.07 -102.09 0.05 11.03 5.21 -15.1∗∗∗ 3.31 -0.94 0.03∗∗∗ -1.44 0.04 -12.67

IBM

0.039 1201

157.51∗∗ -0.12∗∗∗ -425.54 -0.17∗∗∗ -2.38 14.42 -6.16∗∗ 0.29 -1.39 0.01 -1.38 1.06 10.16

JNJ

0.324196 1223

2142.53∗∗∗ -0.21∗∗∗ -1514.73 0.39 72.91∗∗∗ -25.7 -47.62∗∗∗ -1.69 -24.09∗∗∗ 0.11∗∗∗ -18.46∗∗∗ 15.85∗∗∗ 33.77

JPM

0.04044 1198

162.68∗∗ -0.01 348.64 -0.18∗∗∗ 21.65∗∗ -11.92 -6.05∗∗ -0.19 -1.11 0.01∗ -0.25 0.53 -4.43

MCD

0.026922 622

751.67∗∗∗ -0.03 251.23 0.34∗∗∗ -32.13∗∗ 44.14∗∗∗ -3.32 1.5 -0.17 0.01 0.34 -1.23 170.52

MRK

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The sample period is from January 2010 to January 2015. ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels.

Table 5: Threshold regressions on single rms (

30

continued 2 )

CPR*1(OTC) CDSB/A *1(OTC) CPR*1(CC) CDSB/A *1(CC) RVC RVJ

R2adj obs

∆ rating ∆ term ∆ MOVE rUS ∆RF ∆RF B/A

rE

∆ ∆ ∆ ∆ ∆ ∆

0.0396 893

25.89 -0.44∗∗∗ -3437.69∗∗∗ 0.08 27.14∗∗ 14.36 -3.94 4.75∗ -0.91 0.04∗∗∗ 0.45 -0.01 -83.41∗

MSFT

0.128628 473

548.91∗∗ -0.05 167.79 0 41.4∗∗ 7.25 -1.03 3.5 -14.54∗∗∗ 0.06∗∗∗ -7.85∗ 8.5∗∗ -285.89

NKE

0.033879 1207

390.99∗∗∗ 0.03 1634.59∗∗ -0.01 -10.97 11.95 0.52 -0.48 -2.93∗ 0.02∗∗ -2.21 2.09 -3.82

PFE

0.032467 1176

222∗∗ -0.04 -1614.53∗∗ 0.18 -20.85∗ 4.62 -7.32∗∗ 1.15 -0.45 0.03∗∗∗ -2.22 -0.39 -21.17

PG

0.103342 1086

937.27∗∗∗ -0.1 -1632.29 0.17 56.96∗∗ -10.55 -29.82∗∗∗ -3.36 -5.15 0.1∗∗∗ -6.27 2.85 27.53

TRV

0.08043 1209

675.28∗∗∗ 0.35∗∗∗ -137.65 0.03 20.99∗∗ 16.98 -2.46 -3.99 -3.87 0.06∗∗∗ -3.45 0.11 13.67

UNH

0.154472 1069

400.52∗∗∗ 0.46∗∗∗ 666.5 0.24 14.52 3.7 -19.41∗∗∗ 7.29∗∗ -4.69∗∗ 0.02∗∗ -1.54 3.61∗ -38.39

UTX

0.05783 917

509.75∗∗ -0.07 94.87 -0.2 20.75 80.92∗∗∗ -4.88 -0.57 -7.12∗∗ 0.08∗∗∗ -5.45 3.08 44.08

VZ

0.04515 1194

201.98∗∗∗ 0.16∗∗∗ -634.83 -0.28∗∗∗ 7.12 15.69 -4.3 1.28 -1.11 0.03∗∗∗ -2.74∗ -0.35 4.16

WMT

This table shows the results of regressing the changes in corporate CDS spreads on the changes in all explanatory variables, by dening counterparty risk and CDS liquidity as threshold variables. The sample period is from January 2010 to January 2015. ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels.

Table 5: Threshold regressions on single rms (

Table 6: Threshold two-stage least squares regressions

This table shows the results of panel regressing the changes in corporate CDS spreads on the changes in all explanatory variables with the two-stage least squares technique, by dening counterparty risk and CDS liquidity as threshold variables. The sample period is from January 2010 to January 2015. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. ∆ ∆

CPR*1(OTC) CDSB/A *1(OTC)

586.361

∗∗∗

(3.77) ∗∗∗ 0.157 (4.55)

∆ ∆

CPR*1(CCP) CDSB/A *1(CCP)

-437.974 (-0.88) ∗ -0.057 (-1.67)

∆

RVC

29.045

∗∗

(2.4)

∆

RVJ

8.644 (0.99) ∗∗∗

rE

∆ ∆ ∆

-16.366

(-7.85) ∗∗∗

rating

2.115

(2.92) ∗∗ -3.32

term

(-2.05) ∗∗∗ 0.046

MOVE

(4.69) ∗ -3.165

rUS

(-1.8)

∆RF

1.298 (0.8)

∆RF B/A

-15.589 (-0.8)

2 Radj J-Statistics (prob) obs

6.93% 24.01% 28764

31

4 Empirical evidence: The determinants of corporate CDS spreads In order to investigate in the dierences between trading CDS OTC and over CCPs, it is important to analyze whether CDS spreads are driven by the same economic variables in both regimes. This analysis enables to investigate whether the exposure of CDS spreads is sensitive to where trades take place. A panel regression analysis is performed in the OTC and central clearing regime separately by regressing the changes of the variables that that were 10

found to have deterministic potential on CDS spreads in theory

on the

changes in CDS spreads:

∆CDSit =β1 ∆CDSB/A,it + β2 rE,it + β3 ∆rvCit + β4 ∆rvJit + β5 ∆ratingit + δ1 rUS,t + δ2 ∆M OV Et + δ3 RFt + δ4 RFB/A,t + δ5 ∆termt + it Where

∆

denotes the rst dierences of the variables.

(10)

To get results

robust to cross-equation correlation and heteroscedasticity, White standard errors clustered over the rms

i

are estimated. By analyzing the drivers of

the changes in CDS spreads, the x amount of collateral posted for each CDS contract are canceled out and have no impact on the changes in CDS spreads. The regression estimates in the OTC regime are in Table 7, the estimates in the central clearing regime in Table 8. Four dierent panel regressions are performed on both regimes, which investigate in the relevance of the selected deterministic variables and compare the results between both regimes. Regression (A) in Tables 7 and 8 includes all explanatory variables, (B) omits the volatility and jump risk measures, (C) omits all rm-specic drivers and (D) omits all global determinants. Comparing the adjusted R

2

of Regressions (C) and (D) to the ones of

Regression (A) in Tables 7 and 8 show that rm-specic and global variables are both important to explain CDS spreads. Changes in equity volatility and its jump components explain a portion of the changes in CDS spreads, as the 2 adjusted R of Regression (B) reveal compared to Regression (A). Nevertheless, its predictive power is much smaller than it is found to be in Zhang, 2 Zhou, and Zhu (2009), where the adjusted R could be strongly increased by

10 Existing

literaute which uses similar CDS drivers are e.g. Collin-Dufresne, Goldstein, and Martin (2001), Zhu (2006), Zhang, Zhou, and Zhu (2009), Ericsson, Jacobs, and Oviedo (2009), Chiaramonte and Casu (2013), Annaert, De Ceuster, Van Roy, and Vespro (2013) or Arce, Mayordomo, and Pena (2013) 32

30% by including equity volatility and its jump components as determinants of the CDS spreads in levels. When CDS are traded OTC, rm-specic variables explain more of the variability of the changes in CDS spreads with a 2 2 R of 7.54% than global variables (adjusted R of 5.77%). In contrast, when CDS are traded over CCPs the global variables explain slightly more of the 2 variability of the changes in CDS spreads with an adjusted R of 1.6% than 2 rm-specic variables (adjusted R of 1.5%). Those results imply that factors, which are directly related to the default probability of the underlying rms, are driving credit risk stronger when CDS are traded OTC than when they are cleared centrally.

In both regimes, including rm-specic as well

as global factors, as in Regression (A), improves the explanatory power of 2 the variation in changes in CDS spreads with an adjusted R of 9.19% when 2 trading OTC and an adjusted R of 2.22% when trading over CCPs. The variability of changes in CDS spreads can be explained three times better by the determinants while they are traded OTC than when they are cleared centrally. Those ndings already induce the rejection of Hypothesis 3. Considering the previous results in Section 3, where CDS spreads are shown to be strongly driven by counterparty risk in the OTC regime only, one should 2 expect a lower adjusted R in the OTC regime when counterparty risk is excluded from the analysis. To the author's knowledge, this nding is not explainable with the today's existing literature. The results induce that there has to be an additional unknown factor that inuences CDS prices, which are cleared over central counterparty houses. It would be interesting to test whether this eect in the corporate market is present in newer data from 2015 on. That CDS spreads which are cleared centrally can be less explained than ones that are traded OTC could also be found for sovereign CDS spreads between 2010 and 2017, as shown in Appendix A.1. A detailed data description of the appropriate selection of deterministic variables and background information are elucidated in Molleyres (2018). The estimates of Regression (A) in Tables 7 and 8 show that the eect of liquidity on corporate CDS spreads is negative while CDS are traded OTC, as expected from theory. A 1 bp decrease in corporate CDS bid-ask spreads decreases the CDS spreads by 0.13 bp.

The eect is non-signicant when

CDS are cleared centrally. That liquidity has no impact on the magnitude of CDS spreads while CDS are cleared centrally induces that the liquidity management of CCPs is ecient and that concerns about liquidity throats are relieved when CDS are cleared centrally. Those ndings are highly consistent with the ones in Table 3 and also approve Hypothesis 2. This eect could also be stated for sovereign CDS spreads, as shown in Ap-

33

pendix A.1. Equity log-returns have a signicant negative impact on changes in CDS spreads, as predicted from theory. When CDS are traded OTC, a 1% increase in equity log-returns results into a 36.68% decrease in CDS spreads. If CDS are traded over CCPs, the eect of equity log-returns on CDS spreads is still signicantly negative, but approximately 4 times smaller. Equity returns, proxied by the country-specic MSCI log-returns, have a similar impact on sovereign CDS spreads, as shown in Appendix A.1. The continuous component of the realized volatility of the equity prices strongly increases corporate CDS spreads while they are traded OTC. A 1% increase in equity volatility increases the CDS spreads by 38.3%. The volatility jumps are non-signicant when CDS are traded OTC. When corporate CDS are cleared centrally, equity volatility has no signicant deterministic power, whereas large jumps in equity volatility have a signicant impact. If a 1% volatility jump occurs, the centrally cleared CDS spreads increase by 15.3%.

In other words, small equity volatility changes have no signicant

impact, whereas only large jumps are able to inuence CDS spreads when CDS are cleared centrally. Those results are in line with the ones on equity returns, i.e. clearing CDS centrally diminishes the impact of the rm's equity on CDS spreads. The market wide stock market climate negatively inuences the CDS spreads, as expected from theory. The eect is very robust over both regimes and therefore unaected by the type of trade. An increase in the US equity log-returns by 1% decreases CDS spreads by 4.5% for OTC trades, and by 4.8% for trades over CCPs. The eect of the global stock market conditions on sovereign CDS spreads is less consistent, but for the major countries, a decrease of the MSCI world increases CDS spreads as shown in Appendix A.1.

The eect is stronger

when sovereign CDS are traded OTC than when they are cleared centrally. Other global variables, as the term spread and the market wide volatility only have deterministic potential when CDS are traded OTC. An increase in term spread by 1% declines CDS spreads by 6.7%, where an increase in the market wide volatility by 1 bp increases CDS spreads by 7 bp. Both eects vanish when CDS are traded centrally. One would expect a signicant impact of the rm's rating on CDS spreads from theory. The data show that the credit rating of the rm has no signicant eect on the CDS spreads. The risk-free interest rate and its liquidity neither have a signicant impact

34

on CDS spreads. Summarizing the results, corporate CDS spreads are determined by the indicators of development of creditworthiness of the underlying rm. Nevertheless, the determinants are strongly varying between both regimes. Hypothesis 3 is therefore rejected.

4.1

Robustness

To test the robustness of the estimates of Equation 10 in Tables 7 and 8, Regression (A), dierent proxies are used as explanatory variables, which are also often seen in existing literature. Furthermore the two-stage least squares regression analysis is applied to test the results' robustness.

4.1.1 VIX as market wide volatility measure Existing literature, as in e.g. Collin-Dufresne, Goldstein, and Martin (2001) and Ericsson, Jacobs, and Oviedo (2009), often uses the Chicago Board Options Exchange Volatility Index (VIX) as market wide volatility measure instead of the MOVE. The robustness of the results in Tables 7 and 8 is tested by estimating Equation (10) with the substitution of

∆VIXt .

∆MOVEt

by

The results shown in Table 9 (E) are very consistent to the ones

estimated with the MOVE. If CDS are traded OTC, the changes in the VIX inuence changes in CDS spreads very similarly as the changes in MOVE do.

When CDS are traded over CCPs, the estimates with the VIX index

remain signicant and the coecient

δ2

is almost equal to the one in the

OTC regime. A 1% increase in the VIX increases the CDS spreads by 9 to 10 bp. The MOVE however does not remain signicant in the central clearing regime. The coecients of the remaining variables are consistent to the estimates in Tables 7 and 8. Estimating Equation (10) with the VIX instead of the MOVE index explain the variability of the changes in CDS spreads 2 2 worse in the OTC regime (R of 8.62% vs R of 9.19%), but slightly better 2 2 in the regime with central clearing (R of 2.52% vs R of 2.22%). Because 2 the R of Equation (10) perform better in average with the MOVE than with the VIX, the former is favored.

The results underline the previous results

in Tables 7 and 8, that the variability of the changes in CDS spreads can be much better explained in the OTC regime than in the central clearing regime.

35

Table 7: CDS spread drivers: OTC regime

This table shows the results of the panel regressions (A), (B), (C), (D) on the changes in CDS spreads in the OTC regime (1/2010-9/2013). Explanatory variables are divided in rm-specic and macro-nancial variables. The former group includes changes in the CDS bid-ask spreads, changes in equity volatility measures, its jump component, equity returns, and changes in credit rating, the latter group consist of the changes in the term-spread, MOVE, MSCI US returns, changes in the risk-free interest rate and in its bid-ask spreads. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. (A)

∆

CDSB/A

∆

RVC

∆

RVJ

0.13

∗∗

(2.57) ∗∗∗ 38.281

rating

(D)

∗∗∗

0.135

(2.62)

(2.67) ∗∗∗

46.528

(2.98) 12.603 -37.451

(0.59) ∗∗∗ -47.929

∗∗∗

(-10.3)

2.248

2.253 (1.31) ∗∗∗ -6.933

(-11.21) ∗∗ 3.922

∆

term

(1.33) ∗∗∗ -6.748

∆

MOVE

(-2.95) ∗∗∗ 0.068

0.074

(4.63) ∗∗ -4.831

(4.59) ∗∗ -5.103

(5.19) ∗∗∗ -13

(-1.96)

(-1.96)

rUS ∆RF ∆RF B/A 2 Radj obs

∗∗∗

8.553

(-10.21)

∆

0.134

(C)

(2.69) (0.4) ∗∗∗ -36.679

rE

(B)

(-3.04) ∗∗∗

(2.29) -11.198

∗∗∗

(-4.43) ∗∗∗

0.097

3.399

3.319

(-2.77) ∗ 4.954

(1.42)

(1.37)

(1.92)

-7.086

-5.545

8.997

(-0.29)

(-0.22)

(0.35)

9.19%

8.93%

5.77%

7.54%

22808

22810

25222

23169

36

Table 8: CDS spread drivers: central clearing regime

This table shows the results of the panel regressions (A), (B), (C), (D) on the changes in CDS spreads in the central clearing regime (3/2014-1/2015). Explanatory variables are divided in rm-specic and macro-nancial variables. The former group includes changes in the CDS bid-ask spreads, changes in equity volatility measures, its jump component, equity returns, and changes in credit rating, the latter group consist of the changes in the term-spread, MOVE, MSCI US returns, changes in the risk-free interest rate and in its bid-ask spreads. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. (A)

(B)

(C)

∆

CDSB/A

-0.035

-0.034

(-1)

(-0.98)

∆

RVC

9.994

∆

RVJ

15.279

rE

(-3.43)

∆

rating

∆

term

∆

MOVE

rUS ∆RF ∆RF B/A 2 Radj obs

-0.038 (-1.1) ∗

16.947

(1.19) ∗

(1.81) ∗∗∗ -9.688

(D)

(1.78) ∗

14.037 -10.018

(1.7) ∗∗∗ -13.551

∗∗∗

(-3.52)

(-4.71)

0.82

0.878

1.143

(0.62)

(0.65)

(0.88)

-1.175

-1.308

-1.954

(-0.61)

(-0.68)

0.012

0.013

(-1.04) ∗ 0.016

(1.51) ∗ -4.466

(1.63) ∗ -4.585

(1.83) ∗∗ -6.464

(-1.83)

(-1.86)

(-2.46)

-0.939

-0.878

-0.701

(-0.53)

(-0.49)

(-0.4)

-447.446

-471.14

-429.786

(-1.09)

(-1.14)

(-1.04)

2.22%

2.15%

1.60%

1.50%

5179

5179

5530

5294

37

4.1.2 Balance sheet variables Chiaramonte and Casu (2013) and Zhang, Zhou, and Zhu (2009) e.g.

use

balance sheet variables as rm-specic drivers of the CDS spreads. In order to further test the robustness of Equation (10), the CDS spreads are allowed to be determined by the dividends, the leverage ratio and the return on equity instead of using the equity log-returns:

∆CDSit =β1 ∆CDSB/A,it + β21 ∆LEVit + β22 ∆DIVit + β23 ∆ROEit + β3 ∆rvCit + β4 ∆rvJit + β5 ∆ratingit + δ1 rUS,t + δ2 ∆M OV Et + δ3 RFt + δ4 RFB/A,t + δ5 ∆termt + it Where

∆

denotes the variables in rst dierences.

clustered over the rms

i

(11)

White standard errors

are estimated, in order to get results robust to

cross-equation correlation and heteroscedasticity. The disadvantage of using balance sheet variables is that they are only available on a quarterly basis, whereas the remaining data is on a daily frequency. This leads to a large loss of information. In order to match the lower frequency of the balance sheet variables with the higher frequency of the remaining variables in Equation (11), two dierent MIDAS approaches (see Ghysels, Sinko, and Valkanov (2007)) are applied. In a rst step, Equation (11) is estimated on a daily basis, where the balance sheet data of each quarter is kept on for the remaining quarter. The results in Table 10 (F) support the validity of the non-balance sheet variables in Tables 7 and 8, showing very similar coecients. Balance sheet variables are not considered as relevant drivers of CDS spreads. This is mainly due to the fact that the daily endogenous and non-balance sheet exogenous variables behave white noise around zero during one quarter, whereas balance sheet variables are constant during a quarter. Only the leverage ratio in the regime of central clearing has a negative signicant impact on CDS spreads. In both regimes, the variability of the changes in CDS spreads cannot be 2 better explained by including balance-sheet variables (R of 6.62% in the 2 OTC regime and R of 1.83% in the central clearing regime). In a second step, Equation (11) is estimated on a quarterly basis, by only using the rst value in each quarter of the data in Equation (11) that is on a daily basis. This approach has the disadvantage that a large amount of information gets lost. Having quarterly data in the OTC regime results in having only 15 data points per rm and only 5 data points per rm in

38

the central clearing regime. The results in Table 10 (G) show inconsistent results to the previous ones in Tables 7 and 8. The leverage ratio and the rm's rating signicantly inuence the CDS spreads only if CDS are cleared centrally.

The term spread negatively inuences the CDS spreads in both

regimes with a factor of -14.9 if CDS are traded OTC and with a factor of -17.7 if CDS are cleared centrally, whereas the other explanatory variables are non-signicant. Due to the large loss of information when using quarterly data, the analysis on a daily basis is favored.

4.1.3 Two-stage least squares regression Two-stage least squares are used in the case where some explanatory variables are endogenous to the dependent variable of the regression. This is the case when the regressions' error terms are correlated with the explanatory variables. In order to test the robustness of the estimates of Equation (10), the equation is estimated again by dening the exogenous variables as instrumental variables. In a rst step of a two-stage least squares regression, the amount of the endogenous and exogenous variables that can be attributed to the instruments is found, by estimating an OLS regression of each variable on the set of instruments. In a second stage, the original equation is regressed with replacing all the variables by the tted values from the rst-stage regressions. The results in Table 11 are highly consistent to the ones in Tables 7 and 8 and therefore underline the previous models robustness.

39

Table 9: CDS spread drivers: Robustness checks (1)

This table shows the results of the panel regression (10) on the changes in CDS spreads in the OTC regime (1/2010-9/2013) and in the central clearing (CC) regime (3/2014-1/2015). Explanatory variables are divided in rm-specic and macronancial variables. The former group includes changes in the CDS bid-ask spreads, changes in equity volatility measures, its jump component, equity returns, and changes in credit rating, the latter group consist of the changes in the term-spread, VIX, MSCI US returns, changes in the risk-free interest rate and in its bid-ask spreads. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. (E) OTC

∆ ∆ ∆

CDSB/A RVC RVJ

0.135

∗∗∗

-0.037

(2.67) ∗∗∗ 40.916

(-1.08)

(2.69)

(0.91) ∗ 14.744

10.535 (0.5) ∗∗∗

rE

CC

-34.133

7.57

(1.79) ∗∗

-6.508

(-10.11)

(-2.47)

2.353

0.516 (0.39)

term

(1.42) ∗∗ -5.312

-0.326

VIX

(-2.08) ∗∗ 0.098

(-0.17) ∗∗∗ 0.093 (3.11)

rUS

(2.11) ∗ -4.241

∆RF

(-1.84) ∗ 4.278 (1.76)

(-0.77)

∆RF B/A

-20.079

-468.497

(-0.75)

(-1.14)

8.62%

2.52%

23031

5251

∆ ∆ ∆

rating

2 Radj obs

40

-1.592 (-0.66) -1.378

Table 10: CDS spread drivers: Robustness checks (2)

This table shows the results of the panel regression (11) on the changes in CDS spreads in the OTC regime (1/2010-9/2013) and in the central clearing (CC) regime (3/2014-1/2015). The regression is performed on a daily basis (F) and on a quarterly basis (G). Explanatory variables are divided in rm-specic and macro-nancial variables. The former group includes changes in the CDS bid-ask spreads, changes in equity volatility measures, its jump component, leverage ratio, dividends, return on equity, and in credit rating, the latter group consist of the changes in the term-spread, MOVE, MSCI US returns, changes in the risk-free interest rate and in its bid-ask spreads. t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. (F) OTC

∆

CDSB/A

∆

RVC

∆

RVJ

0.125

∗∗

∆

LEV DIV

∆

ROE

∆

rating

∆

term

∆

MOVE

rUS ∆RF ∆RF B/A 2 Radj obs

CC

OTC

CC

-0.036

-0.116

-0.04

(2.44) ∗∗∗ 49.327

(-1.03)

(-0.81)

(-0.28)

13.944

5.149

0.782

(3.19)

(1.6) ∗ 15.805

(0.13)

(0.02)

7.082

20.354

(0.25) ∗∗ 0

(0.83)

0

(1.84) ∗∗ -0.001

-0.001

(-1.52)

(-2.2)

(2.48)

(-0.32)

9.465 (0.42)

∆

(G)

0

0

0

-0.004

(0.39)

(-0.55)

(-1.1)

(-1.25)

-0.001

0.01

0.002

0.004

(-1.01)

(1.14)

-1.317

0.391

(0.4) ∗ 7.625

-4.122

(-0.76) ∗∗∗ -11.318

-1.844

(1.68) ∗∗ -14.887

(-4.5) ∗∗∗ 0.09

(-0.97)

(-2.21)

0.014

0.008

-0.003

(5.6) ∗∗∗ -12.157

(1.62) ∗∗ -6.682

(0.15)

(-0.23)

-13.209

-4.39

(-2.75) ∗∗ 5.26

(-2.54)

(-1.3)

(-0.63)

-0.704

10.166

16.241

(0.29)

(0.08) (-0.59) ∗

-17.685

(-1.85)

(2.12)

(-0.4)

(1.04)

(1.44)

5.453

-399.062

-63.195

1242.81

(0.2)

(-0.96)

(-1.16)

(0.55)

6.62%

1.83%

8.06%

13.09%

21837

5179

420

411

41

Table 11: Two-stage least squares regressions (two regimes)

This table shows the results of panel regressing the changes in corporate CDS spreads on the changes in all explanatory variables with the two-stage least squares technique. The sample period is divided in the OTC regime (1/2010-9/2013) and the central clearing regime (3/2014-1/2015). t-statistics are in brackets, ∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels. OTC

∆

CDSB/A

∆

RVC

∆

RVJ

rE

∆

0.128

∗∗

(-0.93)

(2.82) 8.403

(1.64) ∗ 15.085

(0.4) ∗∗∗ -34.797

(1.71) ∗∗∗ -8.282

(-9.97)

(-2.95) 0.194 (0.15)

∆

term

∆

MOVE

(-3.36) ∗∗∗ 0.069 (4.68) ∗∗

-5.922

-1.738 (-0.9) 0.013 (1.53) ∗∗∗

-6.709

(-2.15) ∗ 4.144

(-2.63)

(1.73)

(-0.33)

-5.755

-366.014

(-0.23)

(-0.87)

∆RF B/A 2 Radj J-statistic (prob)

13.986

2.375 (1.46) ∗∗∗ -7.715

∆RF

-0.033

(2.56) ∗∗∗ 39.917

rating

rUS

CC

-0.59

9.25%

2.57%

81.91%

62.67%

23290

5382

obs

42

5 Conclusion Over-the-counter (OTC) trades are connected with a large amount of uncertainty regarding liquidity and counterparty risk, dened as the risk that following a credit event the Credit Default Swaps (CDS) seller is unable to make the nal payment due under the CDS contract. The lack of transparency in the CDS market was strongly criticized especially during the recent nancial crisis starting 2007. This made authorities implement new reforms on OTC derivatives trades in order to increase transparency. As a result, authorities introduced in July 21, 2010 mandatory clearing through regulated central counterparty clearing houses (CCPs) for all suciently standardized derivatives.

From March 2013 on, practically all single name CDS were cleared

through CCPs, which have the goal to manage risks eciently in order to mitigate counterparty risk and to increase transparency. Having more than one CCP which are partly clearing dierent asset classes, as it is the case nowadays, is strongly criticized in recent literature. The analysis show that central clearing strongly reduces the impact of counterparty risk and liquidity on single name corporate CDS spreads listed in the S&P500. The results show evidence for the ecient risk management of CCPs and that transparency is increased in the CDS market. CDS spreads are determined by rm-specic and global factors, which are responsible for the development of creditworthiness of the rm. The determinants of CDS spreads are by the majority inconsistent between CDS, which are traded OTC, and CDS, which are cleared centrally.

Using a novel technique to

measure equity price uctuations from high-frequency data show that the rm-specic stock market determines CDS spreads much stronger when CDS are traded OTC than when they are centrally cleared. The signicant eect of the market wide stock market climate on CDS spreads is independent of the trading place. The term spread and the market wide volatility only have deterministic potential on CDS spreads when they are traded OTC. OTC traded CDS spreads can be much better explained as centrally cleared ones. The results are very robust.

43

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49

Journal of Financial

A Appendix A.1

Empirical evidence: Determinants of sovereign CDS spreads

This section investigates in the deterministic power of implementing central clearing houses in the sovereign CDS market. The data is provided by Molleyres (2018) and consist of daily 5-year sovereign single names CDS spreads of Portugal, Italy, Spain, Ireland, France, Germany, Finland, Belgium, Austria, the Netherlands, the United Kingdom, the USA and Japan from June 28, 2010 to May 9, 2017. The explanatory variables of the sovereign CDS spreads are dened as the local stock market, proxied by the log-returns of the sovereign MSCI's, the percentage changes of the local currency against the underlying CDS currency, the local liquidity, proxied by the changes in the sovereign CDS bid-ask spreads, the market wide liquidity, proxied by the changes in LIBOR-OIS spreads, the global market volatility as the changes in VIX, the global stock market conditions, proxied by the log-returns of the MSCI world and funding costs, dened as changes in the spreads between daily 3 month AA nancial commercial paper interest rates and the 3 month treasury bill.

Further details and specication can be found in Molleyres

(2018). A regression analysis is performed on the regime where CDS are traded OTC and on the regime of CDS central clearance separately. The two regimes can be clearly distinguished as Figure 1 shows. the time series in the two regimes.

The vertical lines divide

The OTC trading regime is set to be

between June 28, 2010 and September 18, 2013, where the percentage of centrally cleared CDS trading volume has a mean (standard deviation) of 1.23% (3.90), and the regime with central clearing is set between March 3, 2014 and May 9, 2017, where the percentage is on an average (standard deviation) of 78.64% (6.49). In the OTC regime, the percentage of centrally cleared trading volume of the CDS is clearly stationary according to the augmented Dickey-Fuller test with a p-value of 0.001. The data in the regime with central clearing is stationary according to the augmented Dickey-Fuller test having a p-value of 0.13 when considering a critical value of 15%. The following regression is performed for each country on both regimes separately:

50

∆CDSt = α1 ∆BASt + α2 MSCIWorld t,LogR + α3 ∆VIXt

(12)

+ α4 ∆FDCt + α5 ∆LIBOISt + α6 ∆XRt + α7 MSCIt,LogR + t The Newey-West estimator is used to correct for possible heteroscedasticity and autocorrelation in the error terms

t .

Counter party risk is omitted as

explanatory variable of the regression analysis in order to compare the power of predictability of the variables that are driving CDS spreads independently of where the trades are settled. The results in Table 12 show that the adjusted R

2

is much higher in the

regime where sovereign CDS are traded OTC than in the one where trades are cleared centrally. Only exceptions are found in Portugal and the UK, where 2 the R remains practically constant. This is probably the case because those sovereign CDS spreads might have been traded OTC during that time due to the exceptional market conditions in both countries. The sovereign CDS spreads can therefore be better predicted in the OTC market. This eect is also seen in the corporate CDS market, as shown in Section 4, Tables 7 and 8. The impact of liquidity on the sovereign CDS spreads diminishes when CDS are traded centrally in all countries, except for Germany and the US, where liquidity has almost no deterministic power.

This support the nd-

ings in the corporate CDS market, as shown in Section 4, Tables 7 and 8. Exchange rates have a strong signicant eect on the spreads when CDS are traded OTC, except for in the US and in Japan. The eect of exchange rates diminishes when CDS are cleared centrally. The country specic MSCI constantly determines the CDS spreads in both regimes, except for in Germany and the US, where the eect vanishes when CDS are cleared centrally. That the rm-specic equity returns have a strong negative impact on the CDS spreads is also shown in the corporate market in Section 4, Tables 7 and 8. The eect of the global stock market conditions on sovereign CDS spreads is less consistent, but for the major countries, a decrease of the MSCI world increases CDS spreads. This eect, found for Portugal, Spain, Ireland, Finland, Belgium, Austria, the US and Japan, is stronger when CDS are traded OTC than when they are cleared centrally. The eect of the market wide stock market climate on corporate CDS spreads is more.

Their relationship is consistently negative as shown in Section 4,

Tables 7 and 8.

51

52

Adj. obs

R2

MSCILogR

∆VIX ∆FDC ∆LIBOIS ∆XR

0.27 766

-0.04 -8.93∗∗ 36.25 -10.42 -179.22∗∗∗

0.32 758

0.33 -2.74 12.95 86.16∗∗ -155.19∗∗∗

0.44∗ 36.13

0.16∗∗ -175.33∗∗∗

∆BAS

orld MSCIW LogR

ITL

PTG

R2

CCP

Adj. obs

MSCILogR

0.36 815

0.12 -2.43 1.41 417.1∗∗∗ -501.44∗∗∗

-1.94∗∗ 8.56 62.84 1021.38∗∗∗ -659.8∗∗∗

∆VIX ∆FDC ∆LIBOIS ∆XR

0.26 775

0.73∗ 37.97

0.32∗∗ -311.23∗

∆BAS )

orld MSCIW LogR

ITL

PTG

OTC

0.12 764

-0.01 -0.49 6.93 31.3 -17.87∗∗∗

0.41∗∗∗ 0.1 -3.02 80.39∗∗ -156.94∗∗∗

IRE

0.18 815

-1.05∗∗ 11.71∗ 134.77 865.69∗∗∗ -159.56∗∗∗

0.13 -51.92∗∗∗

0.37 767

FRA

0.14 767

0.18 -1.93∗∗ 13.3∗ 74.04∗∗ -53.11∗∗∗

0.29∗ 37.07

FRA

0.40 813

0.04 1.72 16.42 223.85∗∗∗ -168.38∗∗∗

0.19 0.29∗∗ -380.15∗∗∗ -15.54

IRE

0.18 72.57∗∗

ESP

0.47 815

-0.55 5.56 62.45 535.59∗∗∗ -399.94∗∗∗

0.51∗∗ -152.91∗

ESP

0.04 779

-0.04 -0.9∗∗∗ 1.52 -1.46 -3.97

0.06 -18.98

GER

0.31 815

0.05 1.34 22.43 99.23∗∗∗ -62.01∗∗∗

0.05 -19.85

GER

BEL

AUT

0.03 775

-0.07 0.26 -8.69∗∗ -9.9 -7.13∗

0.1 -17.35∗

FIN

0.29 815

-0.07 -0.73 8.85 51.59∗∗∗ -24.64∗∗∗

0.10 699

-0.05 -0.05 -2.77 14.53 -16.1∗∗∗

-0.13 -38.94∗∗∗

BEL

0.38 815

-0.51 2.04 27.05 372.26∗∗∗ -107.07∗∗∗

NDL

0.36 815

-0.09 2.29 24.16 110.05∗∗∗ -110.74∗∗∗

0.35∗∗∗ -14.25

NDL

0.08 779

-0.05 -0.68 -1.79 10 -8.27∗∗∗

0.05 773

0.03 -1.24∗∗ 2.15 20.74∗∗∗ -11.66∗∗∗

0.07 0.1∗ -23.17∗∗∗ 4.04

AUT

0.38 815

-0.12 -0.88 -37.49 108.06∗∗ -110.6∗∗∗

0.24∗∗ 0.63∗∗∗ 1.95∗∗ -50.34∗∗∗ -284.64∗∗∗ -83.33∗

FIN

0.17 779

0.05 -0.76 14.63∗∗ 77.28∗ -22.64∗∗

-0.06 7.61

UKD

0.17 815

-0.06 0.08 22.97 100.93∗∗∗ -85.2∗∗

0.10 -19.88

UKD

JPN

0.00 628

-0.03 -0.96 -2.36 5.15 -0.27

0.06 -0.91

USA

0.06 798

-0.01 -1.44∗ 5.90 0.29 31.45∗

0.01 755

-0.03 0.49 1.32 0.12 -32.87∗∗

0.28∗∗ -23.99∗∗

JPN

0.26 830

-0.20 -4.43∗∗∗ 5.25 -0.07 -51.46∗∗∗

-0.02 0.32∗∗ -68.69∗∗∗ -125.88∗∗∗

USA

This table reports the results of regressing the changes of local and global determinants on the changes in sovereign CDS spreads. The upper results are for the regime where CDS are traded OTC between June 28, 2010 and September 18, 2013 and the lower table shows the results in the regime where CDS are cleared centrally between March 3, 2014 and May 9, 2017.∗∗∗ ,∗∗ ,∗ report the 1%, 5%, 10% sign. levels.

Table 12: Regressions on sovereign CDS spreads: OTC vs central clearing