.


International Journal of Economics and Financial 
Issues

ISSN: 2146-4138

available at http: www.econjournals.com

International Journal of Economics and Financial Issues, 2017, 7(2), 697-700.

International Journal of Economics and Financial Issues | Vol 7 • Issue 2 • 2017 697

Credit Default Swap and Liquidity

Khaldoun Maddallah Al-Qaisi1*, Rafat Mohd Soudki Al-Batayneh2

1Faculty of Business, Amman Arab University, Jordan, 2Department of Finance, Faculty of Business, Amman Arab University, 
Jordan. *Email: khaldoun_21@yahoo.com

ABSTRACT

The recent global economic downturn that erupted in the mid 2007 saw an increase of the Credit Default Swaps (CDS) by hundred basis points and 
severe liquidity crunch in the financial sector of the United States. The recession phase highlighted the importance of the liquidity for the investors 
and underlined the importance of understanding the connection between the liquidity of the market and the credit markets. In depth, this study tries 
to understand the relation between the liquidity risk in the CDS market and the credit risk. Along the same line of this study, a study conducted on 
the different Swiss and German companies revealed that credit risk is not the direct originator of the liquidity risk, but it created by a negative credit 
shock. In addition, this paper focuses on the causes that intensified the global crisis of (2007) as well as the macro-prudential policies are highlighted 
that will prevent a similar type of crisis in the future.

Keywords: Credit Risk, Liquidity, Financial Crisis 
JEL Classifications: G31, G33

1. INTRODUCTION

In the year 1998, long-term capital management hedge fund 
collapsed in the Russian crisis and a decade later. In the year 
2007, the global economic crisis erupted and emphasized on the 
importance of the liquidity for the investors. This period seen an 
increase in the Spread of Credit Default Swaps (CDS) by hundred 
basis point (bp) and one bp is hundredth of a percentage point 
(Hertrich, 2014). This created severe illiquidity in the market 
and many investor as well as hedge funds had to close their 
trading positions, which triggered a fire sale. Fire sale stands for 
a position, in which securities, mostly unwanted ones, have very 
less financial values sold to the known clients who just have no 
idea about what those securities are. This incidence emphasized 
on the values of the liquidity in the credit market and the risk 
models in the turmoil phase.

For investors, policy makers and the researcher in the field 
of financial market research, it is very important to know the 
importance of the CDS spreads and the size of the CDS market 
for measuring the financial health, stability and health of the 
sector. It is important to understand the intensifying factors that 
played a major role in the recent crisis. Cifuentes et al. (2005) 

in their article relates to the relationship between credit risk and 
liquidity. They add that the recent crisis felt the perfect need to 
put an importance to the restricted use of the CDS and importance 
of having a liquid position in the market. Moreover, the article 
focuses on the rationality of treating the liquidity as a weak 
exogenous in the time series sense when compared to credit risk 
or the vice versa. Finally, they reveal that around 39% of the Swiss 
and German companies felt that credit risk is a weak endogenous 
for liquidity while around 4.5% of the companies suggested that 
the vice-versa is true.

The trading scenario was changing just before the global crisis and 
the market for the derivatives expanded rapidly. The liquidity of the 
markets dealing with derivatives assumes to have a higher value 
than the liquidity of the underlying assets and likely corporate 
bonds. Underlying assets mean the value of the securities depends 
on the underlying assets. If the underlying asset is corporate bonds 
then the amount that the investor will get is the value underlying 
holds at the date of the maturity. The global crisis made people 
knowledgeable about the actual scenario leading to the value of 
the underlying assets. To gauge the liquidity of the assets and other 
securities different models, both advanced financial and statistical 
used to deal with the issue of liquidity.



Al-Qaisi and Al-Batayneh: Credit Default Swap and Liquidity

International Journal of Economics and Financial Issues | Vol 7 • Issue 2 • 2017698

This study focuses on the non-us markets and conducted very 
recently, just after the market crash of (Chen, Lesmond and Wei 
2007). In addition, it is the first one to focus on the reasons of the 
changes in the bid ask spread, the impact of the financial markets 
in general and CDS market in particular as well as the liquidity 
related to it (Hamilton, 1994). Section 2.1 of the study covers 
the literature review, and Section 2.2 discusses in detail the risk 
measures required. Section 3 is the methodology used to determine 
the co-relation between both risks. While Section 4 presents the 
samples and the subsamples used and Section 5 is the empirical 
section divided in three parts. In this study, different financial 
jargons are used such as CDS, bid-ask price, trade volume, 
quotations and others.

2. LITERATURE REVIEW AND THEORY

2.1. Liquidity and Liquidity Risk
The term liquidity means the ease at which particular assets can 
convert into cash. Assuming a situation where the investors prefer 
more liquid assets and those assets priced at a higher price, and 
the trading costs associated with it lowered and bid – ask spreads 
of the assets has to split up. Liquidity itself has a risk associated 
with it and liquidity risk related to the probability that the asset 
cannot trade when liquidity is stochastic (Cont and Wagalath, 
2013). Liquidity risk tends to be high when the probability of the 
tradability of the assets becomes less. This becomes at an alarming 
position when the probability reaches one and the market becomes 
illiquid. So, when the market is liquid and the liquidity risk is low 
then the bid ask spread found to be small and stable.

2.1.1. Literature review and theoretical background
The risk component, which is the same for all the market makers, is 
only dependent on the market structure called exogenous liquidity 
risk. On the other hand, the liquidity risk which varies with the size 
of the trading position is within the control of the market maker is 
called endogenous risk. The exogenous liquidity risk often cited 
as the bid-ask spread. In this study, both risks cited exclusively 
and used interchangeably. The bond market scenario has changed 
a lot, and the current trend has used the credit derivatives market 
from where the investors can take positions regarding various 
securities and shed off the risky positions. This helped in the 
emergence of the new problem in the fixed income analysis of 
the bonds that spread of the corporate bonds into credit risk 
and liquidity component. In the recent financial risk, the lack of 
liquidity of the investors made the events take a serious turn, and 
it literally shook the world (Cifuentes et al., 2005). The spread of 
the corporate bond is determined as the gap between the duty free 
interest rate given in the default free interest rate and the yield to 
maturity rate of the bonds.

2.1.2. The relationship between liquidity risk and credit risk
The loss which triggered by the default of a debtor is called 
credit risk, and the risk is maximum when the probability 
becomes one. No such empirical theories clearly states how the 
risk factors interact dynamically with each other. In the Merton 
model, corporate bond uses as an underlying and a relation draws 
between the credit and liquidity risk. Whenever the credit risk 
increases, the liquidity risk also increases at the same time. In 

the recession, the liquidity shortages accompany by the rising 
CDS spreads, and the similarity between the bonds and CDS have 
made a positive relation between liquidity and illiquidity risk. 
Whereas another model states that in the case of short selling, the 
illiquid assets may often make higher prices than liquid assets 
depending upon the investment horizon and risk-taking capacity 
of the short seller.

Liquidity itself finds to be a multi-dimensional concept and 
cannot observe directly, so it measures by a variable associated 
with the bid-ask spread. The cost incurred relates to the taxes 
and fees associate with the trading as well as the associated 
costs relate to it. The absolute bid price is determined as the 
difference between the highest and the lowest of the bid prices. 
Upon comparing different liquidity measures of the US Treasury 
securities, it is found that the bid ask spread is found to be the 
best proxy as per the liquidity risks. On the other hand, another 
model shows that alternative liquidity measures and bid ask price 
are highly co-related and alternative liquidity measures include 
the effective spread of the trading volume. Bonds, which have a 
rating of say AAA, known to be default free, and they often trade 
at a positive spread, which remains quite high as compared to the 
yield on treasury bonds. This paper discusses thoroughly how the 
difference in the value of the spread can take a significant turn 
and affect the liquidity position of the entire market (Bongaerts, 
De Jong and Driessen 2012).

2.2. Risk Measures
Credit risk or the CDS mid-rate considered to have several 
advantages and calculated as the mean value of the risk and the 
bid price of the each company.

MIDT=AC+BC/2 (1)

The CDS mid-rate MIDT(AC+BC) is the corresponding of CDS risk 
(highest) and CDS bid prices of an organization at a given time.

CDS spreads normally trades on the standardized items and 
provide a pure pricing of the risk of the underlying assets. On the 
other hand, the bond spreads severely affected by the gap of chosen 
risk free benchmark and contractual agreements (Breitenfellner 
and Wagner 2012). Another merit of CDS spreads is that they are 
effective indicator of assessing the credit risk and respond quickly 
to the changes in the credit run than credit spreads as a result of 
the short sale restrictions and the funding issues associated with 
it. The researcher shows that about one-quarter of the corporate 
credit spread could define as a default prone or risky in nature 
due to the difference in the market spread against other different 
bonds (Hertrich, 2014).

The data of the study has a senior single of 5-year CDS risk and 
bid prices quoted in bps and determined in Euros, which figured 
out on August 24, 2007 to 2010, 01 June. This period marks 
the entire tenure of the financial crisis from the collapse of the 
Lehman Brothers to the end of the financial crisis. In addition, this 
paper consists of 5-year maturity CDS only as the CDS contracts 
thought to be the most liquid of all the contracts (Bolton and 
Oehmke, 2013). The focus here is on the short-term relationship 



Al-Qaisi and Al-Batayneh: Credit Default Swap and Liquidity

International Journal of Economics and Financial Issues | Vol 7 • Issue 2 • 2017 699

between the liquidity risk and the credit risk, and the imbalances 
are present in the liquidity risk supply and demand impact 
liquidity. On the other hand, in the cross sectional regression 
analysis, there is a tremendous amount of positive co relation 
between the default and liquidity components of the bond yield 
spread of the securities.

3. DATA AND METHODOLOGY

The collected data is from the database of the Credit Market 
Analytics, which holds the maximum number of credible data of 
the most active and the largest buy side investors: Asset managers, 
hedge funds and the global investment banks. Even if the CDS 
markets operates as an over the counter market, the using data 
from the large number of investors and majority of being blue chip 
companies makes the data more credible and helps to mitigate the 
problem. One can observe that daily the CDS risk and bid prices 
are comparatively stable over the sampling period. Around spring 
2008, just after the global investment bank bear Stearns signed 
a deal with J P Morgan for a merger agreement. On March 16, 
2008 just after Lehman Brothers filed for bankruptcy, the bid- risk 
prices exhibited large amount of spikes. These incidences were 
one-off incidence and the prices showed volatility in those cases 
whereas in normal cases the bid-risk prices were low and stable 
(Hertrich, 2014).

This study is made to identify whether at the time of time series 
the credit risk changes are weakly exogenous with the liquidity 
risk changes. Various finance models are used, namely vector 
autoregression (VAR) model. The stationary test of Kwiatkowski 
shortened as KPSS model, which tests whether the mid-rate and 
the bid risk spread are stationary. This particular method is helpful 
in case of examining the causality in the stochastically trending 
variables. In a while, the researcher has moved forward from KPSS 
model to the Granger causality analysis in a bivariate VAR. It is 
assumed in the relative models that the mid-rate and bid ask spread 
are to be weakly stationary. The VAR allows only for a maximum 
of seven lags as this removes any sort of serial correlations. The 
optimal number of lags calculates by lowering the value of the 
Akaike criterion of information.

Before calculating the Granger causality test, the properties 
and the model assumptions are checked, which helps to test the 
residuals for conditional heteroscedasticity, auto co relation and 
the non-normality method by using the related multivariate test 
statistics. Other methods for calculating the multivariate tests 
like autoregressive conditional heteroscedastic-LM models and 
multivariate Jarque–Bera test used exclusively to reach at the 
desired conclusions (Hertrich, 2014).

4. EMPIRICAL FINDINGS

Different advanced time series properties like bid ask spread 
stationarity, mid-rate stationarity, bid ask spread and mid-rate auto 
co relation used in the study. Bid ask spread stationarity looks for a 
time trend in the mid-rate and bid ask spread. It is found that as per 
the KPSS test, the bid ask spread are non-stationary whereas the 
changes are highly stationary. As such and until date, it is found to 

be no agreement on the fact that CDS bid ask spread be stationary 
or not. Another hypothesis also comes into play that mid-rate and 
bid ask spread both bounded from zero, and the traditional unit 
root is rejecting the null hypothesis of the unit root.

The mid-rate stationarity helps in determining the difference 
between the mid-rate levels of the KPSS tests and a null hypothesis 
is included as an intercept. The findings of these test helps in 
according with the time series properties of the CDS spreads, 
and the data presented in the study is taken from 9th August 
2007 to 29th March 2010 included German and Swiss companies 
(Hamilton, 1994). The companies that taken in the study are, ABB, 
Roche, Swiss Re and many others across different sectors namely; 
insurance, chemicals, electric, healthcare and others. The ratings 
of the bonds vary as per the companies are holding the bonds and 
normally they are in the range of CCC to AAA. In the mid-rate 
segment, the average is around 93% of the bonds has a default risk 
component and 65% has a risk to the total bond spread whereas 
35% of the total of the corporate bonds known to have a liquidity 
risk component.

Bid risk spread analyzes the changes in the mid-rate and the bid 
risk spread taking into consideration a very large lags and the null 
hypothesis serially rejected in the co related time series. The CCF 
determines the direction, and the co relation of the two time series 
observed (Campbell and Taksler, 2003). Even the CCF is helpful in 
making important lags and leads of the mid-rate changes or the bid 
risk spread changes, and the stationary difference exhibits largely 
in the stationary difference. It is found in the study that the time 
difference is responsible for the change in the mid-rate changes 
and the bid risk price changes. This particular theory thoroughly 
used in the study and deductions have been made based on the 
above models. Also from the study, it is observed that in case of the 
companies from Switzerland, a major co relation co efficient found 
among them but not at all a healthy sign of operations. Although 
the epicenter of the global financial crisis was in the United 
States, but the repercussions stroked throughout the world and 
Switzerland is not an exception. Switzerland also felt the severity 
of the crash, which affected all of the companies (Hertrich, 2014). 
Other studies are focused mainly on the economy and companies 
of us, but this study focuses exclusively on the non-US operations. 

Table 1: Granger causality test for Swiss companies
Company ΔBAS GrC ⇏ ΔMID ΔMID GrC ⇏ ΔBAS
ABB 0.330 1.006
CS 1.192 2.228
HOLCIUM 1.159 3.061
SwissRe 2.022 2.517
UBS 1.410 1.187
(Source: Ang, Goetzmann and Schaefer 2010)

AQ1

Table 2: Granger causality test for German companies
Company ΔBAS GrC ⇏ ΔMID ΔMID GrC ⇏ ΔBAS
BASF 0.615 2.084
Dialmer 0.330 1.379
DBank 0.500 1.184
Lufthansa 1.065 1.802
(Source: Corò, Dufour and Varotto 2013)

AQ1



Al-Qaisi and Al-Batayneh: Credit Default Swap and Liquidity

International Journal of Economics and Financial Issues | Vol 7 • Issue 2 • 2017700

In addition, the timing of the study took place during 2007-2010, 
in the turmoil phases.

Another finding in this study is that a liquidity enhanced capital 
asset pricing model or a value at risk model. This inculcates the fact 
that liquidity should be treating as an endogenous and the control 
for the interaction among the liquidity with the credit risk as against 
the standard price method that used until date. Different models 
applied taking the real life scenario and the data of the study took 
from the recession period when the economy was showing sign of 
recession in mid-2007 to the end of 2010 where the economy was 
showing signs of improvement (Hull, 2006). The recession began 
with the collapse of the century old Investment bank Lehman 
Brothers and many business houses and hedge funds close down 
their operations. Bear Sterns is one such investment bank that 
acquired by another big giant company called J P Morgan and 
ultimately the two companies merged.

5. CONCLUSION

Many economists cited the collapse of the markets caused by the 
illiquid market and the CDS factors. This study has thoroughly 
researched to the cause of particularly the bonds and the effect it 
presented on the overall market. Illiquid market is always a matter 
of concern to particularly the investors and the governments as 
well. The investors do not get their return at the right time and 
the government is unable to pay off the dues and spend on the 
public. The findings of the study show that how the policies the 
government regulatory body takes should align with the risk taking 
ability of the entire market. In addition, solution is providing as to 
how a further 2007 like incidence can avert from coming.

Different views are discussing as regards to the mathematical and 
financial models with their application in the corporate bonds. 
So, the effects of applying standard testing procedures over time 
series analysis and the alternative methods of the application 
in the alternative casualty tests in the discrete time series are 
discussing. This study will be helpful in averting another 2007 
like scenario and the government regulatory body investors will 
be extra cautious in investing in a particular bond.

REFERENCES

Bolton, P., Oehmke, M. (2013), Strategic conduct in credit derivative 
markets. International Journal of Industrial Organization, 31(5), 
652-658.

Bongaerts, D., De Jong, F., Driessen, J. (2012), An Asset Pricing Approach 
to Liquidity Effects in Corporate Bond Markets.

Breitenfellner, B., Wagner, N. (2012), Explaining aggregate credit default 
swap spreads. International Review of Financial Analysis, 22, 18-29.

Campbell, J.Y., Taksler, G.B. (2003), Equity volatility and corporate bond 
yields. The Journal of Finance, 58(6), 2321-2350.

Cifuentes, R., Ferrucci, G., Shin, H.S. (2005), Liquidity risk and 
contagion. Journal of the European Economic Association, 3(2-3), 
556-566.

Cont, R., Wagalath, L. (2013), Running for the exit: Distressed selling 
and endogenous correlation in financial markets. Mathematical 
Finance, 23(4), 718-741.

Hamilton, J.D. (1994), Time Series Analysis. Vol. 2. Princeton: Princeton 
University Press.

Hertrich, M. (2014), Does credit risk impact liquidity risk? Evidence 
from credit default swap markets. International Journal of Applied 
Economics, 12(2), pp. 1-46.

Hull, J.C. (2006), Options, Futures, and Other Derivatives. Upper Saddle 
River, NJ: Pearson Education India.

Author Queries???
AQ1: Kindly cite Tables 1 and 2 in text part