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D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2018.006  Vol. 18 (2018) 99−114 

Submitted November 25, 2018  ISSN (online) 2450-7067 
Accepted December 27, 2018 ISSN (print) 1234-3862 

Rumiana Górska* 

Decomposition of Sovereign CDS Spread using  
the Concept of Factorization 

A b s t r a c t. Sovereign CDS (Credit Default Swap) is a derivative that provides insurance of 
repayment of the government’s loans and may be considered as a market indicator of the insol-
vency risk of a country. The aim of the study is to identify factors affecting the sovereign CDS 
spreads of selected European countries for the period from 2008 to 2016. Factor analysis shows 
that there are two common factors that have explained about 92% of the variation of the CDS 
spreads. Next, the decomposition of the spreads presents the influence of these factors on CDS 
spreads of surveyed countries.  

K e y w o r d s: CDS spread; factor analysis; global risk; insolvency risk. 

J E L Classification: G14; G15. 

Introduction 
 CDS (Credit Default Swap) is a derivative that provides insurance of re-
payment of the loan. One of the parties of the transaction receives a specified 
remuneration and agrees to pay the debt to the other side of the transaction, if 
the debtor is unable to pay it back. Sovereign CDS is a derivative that provides 
insurance of repayment of the government’s loans. It is often considered as 
a market indicator of the insolvency risk of a country. The CDS contract price 
(or CDS spread) is calculated as a percentage of the debt, payable annually. 
The higher the risk of repayment of debt, the higher the insurance premium 
(risk premium) required by the issuer of the CDS contracts and the higher the 

                                                
* Correspondence to: Rumiana Górska, Warsaw School of Economics, Collegium of Eco-

nomic Analysis, Institute of Econometrics, 6/8 Madalinskiego Street, 02-513 Warsaw, Poland, 
e-mail: rgorska@sgh.waw.pl. 



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DYNAMIC ECONOMETRIC MODELS 18 (2018) 99–114 

100 

CDS spread. CDS spreads for each country therefore reflect the confidence in 
the issuer of the sovereign bonds. Variation in the levels of CDS quotes for 
each country shows the differences in the market assessment of the risk of 
investments in Treasury securities of this country. For example, on 29.11.2013 
sovereign CDS spreads for five-year bonds in basis points was: for Poland 
82.48; Germany 23.56; Spain 153.5; Portugal 341.4. Investors holding bonds 
of these countries can insure themselves against the insolvency of the given 
country, paying annually a percentage of the value of the bond (for Poland 
0.8248%, Germany 0.2356 %, Spain 1.535 %, Portugal 3.414 %). CDS quotes 
for Greece in March 2012 (during the Greek debt crisis) reached 25422.80 
basis points. 
 Sovereign CDS spread depends both on the so-called economic funda-
mentals (i.e. macroeconomic variables such as level of indebtedness, debt 
structure, the cost of servicing the debt, debt to GDP relation, the rate of GDP 
growth etc.), as well as global factors (global financial market conditions, mar-
ket liquidity, investor expectations etc.). 
 The aim of this study is to identify factors affecting the sovereign CDS 
spreads of selected European countries: Germany, France, Great Britain, Italy, 
Spain, Portugal, Czech Republic, Hungary and Poland for the period from 
2008 to 2016.  

1. Literature Review 
 Publications on the issues related to the information capacity of CDS 
spreads securing repayment of government debt focus on different aspects of 
the problem. Some authors (Varga, 2008; Komarek et al., 2013; Arce et al., 
2013; Coudert and Gex, 2013) examined the relationship between the CDS 
market and the government bonds market and came to the conclusion that 
prices in the market CDS and yields in the Treasury bond market reflect the 
same information about the credit risk of that country. Differences in the levels 
of spreads in the pricing of risk in both markets were associated with the fol-
lowing factors: counterparty risk, the lack of liquidity or liquidity problems, 
transaction costs, flight-to-quality and the debt repurchase decisions by the 
European Central Bank. Moreover, the CDS market preceded the bond market 
in the pricing of risk. In addition, during periods of turbulence in the financial 
markets, the role of the CDS markets increases.  
 Another group of publications includes studies of the relationship between 
the CDS spread and selected macroeconomic variables, market indicators or 
information about political events. Coronado et al. (2012) examined the rela-
tionship between the CDS market and the stock market in eight European 
countries for the period 2007–2010. The first of these markets represented the 
credit risk of the country and the second, the market risk. The authors came to 



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the conclusion that the stock market leads the CDS market until 2010, the time 
of the first appearance of the debt crisis. Following that period, the reaction of 
the CDS market quickened and new information was included in the quota-
tions of CDS for the first time. Aizenman et al. (2013) examined the pricing 
of risk for selected European countries based on their fiscal situation (debt, 
deficit, taxes) for the years 2005–2010. They considered two groups of coun-
tries: the central EU countries (Germany, France, Great Britain) and the pe-
ripheral EU (Greece, Ireland, Italy, Portugal, and Spain). In 2010 (a period of 
increased risk) the risk of peripheral countries was valued at a higher level 
than was consistent with their fiscal situation. The authors explained this ef-
fect through the mechanism of the pessimistic self-fulfilling forecasts – the 
expectations being that the adaptation of the peripheral countries to the new 
situation will be more difficult. A panel study showed that the situation of 
public finances and other economic variables are important determinants of 
a country’s risk. Büchel (2013) examined the effect of public information and 
announcements made by the European Central Bank Governing Council 
members, European Union officials and National Representatives in the pe-
riod 2009–2011 to the CDS and bond markets. Information from the repre-
sentatives of Germany, France and the European Union authorities had an im-
mediate effect on both markets, while the expression of representatives of 
smaller countries had no influence. 
 The third group of publications relates to the contagion effect (risk trans-
fer) between countries. Kalbaska and Gątkowski (2012) identified the in-
creased risk of contagion between European countries after 2007 on the basis 
of studying the dynamics of the CDS market in the period 2005–2010. Spain, 
Ireland, and Greece had the biggest impact on the destabilization of the CDS 
markets. The most resilient country to contagion was the United Kingdom, 
and the least resistant, Portugal. The aforementioned studies were conducted 
using econometric methods, such as the analysis of stationarity and cointegra-
tion, VECM, VAR and GARCH models, IRF analysis, and Granger causality 
tests.  
 The studies, closest in terms of the methodology of the presented here 
study was made by Badaou et al. (2013) and Fabozzi et al. (2016). These au-
thors used a factor analysis framework to decompose CDS spreads. Badaou et 
al. (2013) identified the following components: the insolvency of the country 
(55.6% of the variation) and the liquidity of the market (44.32% of the varia-
tion). The authors concluded that increases in CDS spreads observed in times 
of crisis are mainly caused by the surge of liquidity, and not by an increase in 
the intensity of the risk of insolvency. Fabozzi et al. (2016) decompose CDS 
spreads using independent component analysis (a technique similar to princi-
pal component analysis) of so-called “old” EU members. 



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 In Poland, the issues of sovereign CDS was discussed by Kliber (2011), 
(2016) and Bieńkowski et al. (2011). Kliber (2011) explored the relationship 
between CDS spreads for three countries – Poland, Czech Republic, and Hun-
gary. The study showed that there is a variable relationship between these in-
struments. During the Hungarian crisis, the relationship between prices of 
Polish and Hungarian CDS and that of the Czech and Hungarian CDS weak-
ened, and during the Greek crisis, they rapidly increased. This means that 
global phenomena play a greater role than regional ones. Kliber (2016) also 
verified the impact of the ban on uncovered sovereign CDS trade in Europe 
on the interdependencies between the sovereign CDS market and other sectors 
of financial markets. The analysis of two European markets: Swedish and 
Hungarian shows that relationships of financial markets with the sovereign 
CDS was much weaker in the case of safe and developed Swedish market, so 
it is less prone to crisis transmission than Hungarian market. Tests made by 
Bieńkowski et al. (2011) on the susceptibility of the Polish currency market 
to internal and external instability showed, that the CDS spread belongs to the 
factors that cause the greatest disruption in the Polish currency market (the 
other factors are variables associated with the trade balance and the presence 
of a crisis in the peripheral European countries). 
 This paper extends the literature by presenting the decomposition of the 
CDS spread of different European countries, representing both “old” and 
“new” EU members, as well as the “peripheral” and the “central” countries 
using factor analysis and the factorization framework.  

2. Methodology 
 In general, the factor analysis idea is based on the presentation of a large 
number of observable, correlated variables in terms of a potentially lower 
number of unobservable, "hidden" variables, called factors. Observable vari-
ables are modeled as a linear combination of a small number orthogonal fac-
tors that are sources of common variability of the primary variables and 
a unique variance of each variable. Common factors may be interpreted as 
a source of a systematic risk and random component reflects the unsystematic 
(specific) risk. Factor analysis is often used as a data reduction method or 
a data classification method. 
 The first goal of the presented research is to identify common risk factors 
affecting CDS spreads of the countries surveyed, as well as factors specific to 
each country. Factor analysis as a method allowing a reduction in a large num-
ber of interrelated variables down to a few factors, without a significant loss 
of information contained therein seems to be a suitable for the purpose.  
 Each primary variable is presented as a linear combination of common and 
specific factors in the following way:  



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103 

𝑍"# = 𝑤##𝐹"# + 𝑤#(𝐹"( + ⋯+𝑤#*𝐹"* + 𝑉"# 
… 
𝑍", = 𝑤,#𝐹"# + 𝑤,(𝐹"( + ⋯+𝑤,*𝐹"* + 𝑉"# (1) 
… 
𝑍"- = 𝑤-#𝐹"# + 𝑤-(𝐹"( + ⋯+𝑤-*𝐹"* + 𝑉"- 

where:  
Zti – standardized i-th variable at a moment t, i =1,2,…,n, 
Ftl – l-th common factor at a moment t, l=1,2,…,k, 
wil – factor loading l-th common factor to i-th variable, 
Vti – specific factor for the i-th variable at a moment t, 
n – number of primary variables, 
k – number of common factors. 

 Variance of the each variable is decomposed according to the following 
formula: 

𝑣𝑎𝑟(𝑍,) = 𝑤,#( + 𝑤,(( + ⋯+𝑤,*( + 𝜓,( = 1, (2) 
where 𝜓,( – variance corresponding to specific factor. 
 Correlation between p-th and q-th variables can be obtained as follows: 

𝑟56 = ∑ 𝑤5,𝑤6,*
,8# ,  (3) 

where rpq – correlation coefficient between p-th and q-th variable. 
 In matrix notation, a set of primary variables as present in the following 
way:  

𝐙 = 𝐅𝐖𝐓 + 𝐕, (4) 
where: 

Z – primary variables matrix (t × 𝑛), 
F – factor values matrix (t × 𝑘), 
W – factor loadings matrix (n × 𝑘), 
V – specific factors matrix (t × 𝑛). 

 The decomposition of the covariance matrix of the primary variables is 
conducted as follows: 

𝐑 = 𝐖𝐖𝐓 +𝚿,  (5) 
where: 

R – variance-covariance matrix of a primary set of variables, 
𝚿 – covariance matrix for specific factors. 

One of the ways to define the number of common factors is based on eigen-
values of variance-covariance matrix after factors extracted. In this study the 
minimum value of eigenvalues to be retained is 1. 



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104 

After common factors are identified, factor loadings are calculated. 
 The next step is the decomposition of the CDS spread and measurement 
of the impact of the risk factors using the concept of factorization. The idea of 
factorization was introduced by Ho (1999), who used it for the decomposition 
of the total rate of return in the model CAPM for the bond market. 
 CDS spreads are presented according to the following formula: 

𝑆", = 𝑤#,𝐹"# + 𝑤(,𝐹"( + ⋯+𝑤*,𝐹"* + 𝑉",  for i=1,2,…,n (6) 
where:  

Sti – CDS spread of a given country at a moment t,  
Ftl – l-th common factor at a moment t, l=1,2,…,k, 
Vti – standardized specific factor at a moment t, 
n – number of primary variables, 
k – number of common factors. 

 All variables are standardized. 
 Proposed two-step methodology allows to measuring of the response of 
individual country to the change of common risk factors and obtaining a his-
torical decomposition of CDS spreads.  

3. Characteristic of the Data 
 Empirical analysis is performed for a set of 9 primary variables – these are 
CDS spreads for the following countries: Germany, France, Great Britain, It-
aly, Spain, Portugal, Czech Republic, Hungary and Poland for the period from 
2008 to 2016 year. The data have daily frequency. The factor analysis as well 
the decomposition of the CDS spreads does not involve any macroeconomic 
data. The purpose is to identify factors ruling the evolution of spreads only 
based on the data concerning spreads.  
 Table 1 presents descriptive statistics of the data. 

Table 1.  Descriptive statistics of the data 
Country Mean Median Min Max Standard deviation 

DE 24,88 20,94 6,64 92,50 16,67 
FR 49,43 38,02 11,25 171,56 32,46 
GB 48,48 42,82 11,66 165,00 27,70 
IT 162,05 121,39 39,50 498,66 100,22 
ES 154,34 113,97 37,00 492,07 100,51 
PT 343,22 219,23 37,00 1521,50 315,49 
CZ 80,22 69,24 33,00 350,00 46,87 
HU 271,31 248,00 76,00 661,24 122,96 
PL 119,31 92,78 43,50 421,00 66,50 

Note: DE – Germany, FR – France, GB – Great Britain, IT – Italy, ES – Spain, PT – Portugal, CZ – Czech 
Republic, HU – Hungary, PL – Poland. 



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Table 2 reports correlation coefficients matrix. 

Table 2. Correlation coefficients matrix 
Country DE FR GB IT ES PT CZ HU PL 

DE 1,00 0,80 0,90 0,56 0,55 0,51 0,92 0,86 0,83 
FR 0,80 1,00 0,55 0,86 0,85 0,85 0,69 0,57 0,81 
GB 0,90 0,55 1,00 0,33 0,33 0,22 0,92 0,90 0,75 
IT 0,56 0,86 0,33 1,00 0,93 0,87 0,51 0,41 0,76 
ES 0,55 0,85 0,33 0,93 1,00 0,86 0,47 0,37 0,73 
PT 0,51 0,85 0,22 0,87 0,86 1,00 0,42 0,27 0,63 
CZ 0,92 0,69 0,92 0,51 0,47 0,42 1,00 0,96 0,87 
HU 0,86 0,57 0,90 0,41 0,37 0,27 0,96 1,00 0,83 
PL 0,83 0,81 0,75 0,76 0,73 0,63 0,87 0,83 1,00 

 CDS spreads are highly correlated, thus factor analysis is suitable statisti-
cal procedure for this data set. It allows a reduction of the data dimension and 
identification the interrelationships among the primary variables and latent, 
common factors.  

3. Results 
 At the first step of the study factor analysis revealed that two common 
factors have explained about 92% of the variation of the CDS spreads. These 
factors are unobservable, latent variables, but they may have some economic 
interpretation. Figure 1 presents common factors, labeled as F1 and F2.  

 
Figure 1. Common factors affecting CDS spreads  

 The first factor reached the maximum values at the turn of 2008–2009 
years during the global financial crisis. Next, this factor increased in 2011. 
A hypothesis that this factor reflects a global risk will be investigated. 

-3
-2
-1
0
1
2
3
4

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2



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Financial crisis from 2008 year influences the financial markets worldwide 
and contributed to the European sovereign-debt crisis in 2010 year. It can be 
expected that the first factor has influenced CDS spreads of all of the countries 
surveyed. 
 The second factor has different characteristics. It reached maximum val-
ues in 2011–2012 years when the Eurozone sovereign debt crisis exacerbated. 
This crisis has started in late 2009 and remained a critical factor at the sover-
eign credit market. Prior to the crisis the credit risk of Eurozone countries was 
considered to be very low; however, after the onset of the crisis, the credit risk 
has increased dramatically. It can be expected that second factor has influ-
enced the countries with the biggest debt problems. 
 Next question is how these two common factors affect CDS spreads of the 
counties. Factor loadings obtained during the factor analysis show the influ-
ence of the factors on the CDS spreads. In order to obtain a clearer picture of 
these influences orthogonal varimax rotation was applied. Varimax rotation is 
orthogonal rotation that maximizes the sum of the variances of the squared 
loadings. It is achieved if any given variable has a high loading on a single 
factor but low loadings on the remaining factors. Factor loadings for each pri-
mary variable after varimax rotation are shown in Table 3.  

Table 3.  Rotated factor loadings, share of explained variation and unique variances 

Country Common factors Uniqueness F1 F2 
GB 0.9519 0.1111 0,0816 
CZ 0.9504 0.1691 0,0681 
PL 0.9361 0.3025 0,0322 
DE 0.8647 0.4088 0,0852 
HU 0.7303 0.5972 0,1100 
FR 0.4754 0.8308 0,0838 
ES 0.2220 0.9204 0,1036 
IT 0.2468 0.9216 0,0898 
PT 0.1367 0.9211 0,1329 

Variation explained 4.3214 3.8914  
Share of variation 0.4802 0.4324 0,0874 

Note: CZ – Czech Republic, PL – Poland, GB – Great Britain, DE – Germany, HU – Hungary, FR – France, 
ES – Spain, IT – Italy, PT – Portugal. Factor loadings higher than 0,8 is hig. 

 First common factor (F1) affects in the highest degree Great Britain, 
Czech Republic, Poland, and Germany. Second common factor (F2) affects 
mostly Portugal, Italy, and Spain. Hungary and France are under the influence 
of both common factors. Unique variance of the countries’ CDS is between 
3,22% (Poland) and 13,29% (Portugal). 
 As CDS spreads of nine European countries evolved under the influence 
of two common factors, it is important to gave some economic explanation of 



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107 

these factors. Interpretation of the first common factor could be facilitated by 
comparing it to the Volatility Index (VIX), known as “fear index”. The VIX 
Index is designed to produce a measure of constant, 30-day expected volatility 
of the U.S. stock market, derived from real-time, mid-quote prices of S&P500 
Index call and put options. On a global basis, it is one of the most recognized 
measures of volatility, widely reported by financial media and closely fol-
lowed by a variety of market participants as a daily market indicator.  
 Figure 2 presents first common factor (F1) and volatility index. Correla-
tion coefficient between two variables equals 0,75.  

 
Figure 2. First common factor and index VIX  

Second common factor affects at the largest extent countries with a highest 
indebtedness problems during the Eurozone debt crisis. Table 4 reports Gov-
ernment debt to GDP ratio for the surveyed countries. Countries are sorted in 
ascending order (in 2012 year). 

Table 4. Government debt to GDP ratio (percentage) 
Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 

CZ 28,7 34,1 38,2 39,8 44,5 45 42,2 40 36,8 
PL 46,3 49,4 53,1 54,1 53,7 55,7 50,3 51,1 54,2 
GB 35,4 50,1 64,6 71,4 75,1 78,6 80,5 82,9 82,6 
HU 71,6 77,8 80,5 80,7 78,2 76,6 76,6 77,7 76 
DE 65,1 72,6 81 78,7 79,9 77,5 74,7 71 68,2 
ES 39,5 52,8 61 69,5 85,7 95,5 100,4 99,4 99 
FR 68 78,9 81,6 85,2 89,5 92,3 94,9 95,6 96,6 
IT 102,4 112,5 115,4 116,5 123,3 129 131,8 131,5 132 
PT 71,7 83,6 96,2 111,4 126,2 129 136 128,8 129,9 

-2

0

2

4

6

8

2008 2009 2010 2011 2012 2013 2014 2015

F1 VIX



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Countries with the highest Government debt to GDP ratio after 2012 year are 
Portugal and Italy. Countries with the lowest ratio are Czech Republic and 
Poland. 
 In summary, based on the factor loadings and additional analysis, we can 
interpret two common factors as the global risk factor, and the Eurozone in-
solvency risk factor. These findings are in line with results presented by Ang 
and Longstaff (2013). They used the CDS for US Treasuries, US states, and 
major Eurozone countries. They found that the systemic risk component is 
related to global financial factors such as the VIX index. In the pricing model, 
they proposed the use of two types of credit events, systemic and sovereign-
specific.  
 Presented in this paper results show that the source of volatility for CDS 
spreads shifted from the global risk factor in 2008 to the Eurozone insolvency 
risk factor in 2011. 
 The final step of presented research is a historical decomposition of CDS 
spreads, using the factorization framework. It allows to investigate which fac-
tor and to what extent affects the CDS spread of the given country. Conse-
quently, it enables identification of investors’ perception of the risk connected 
with investment in sovereign debt securities of this country. Historical decom-
position is obtained using the equation (6), for k=2 common factors. Standard-
ized CDS spread of each country is presented as a sum of three components, 
which indicate influence of two common factors and one specific to each 
country factor.  

 
Figure 3. Decomposition of CDS spread of Great Britain  

-2

-1

0

1

2

3

4

5

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

GB



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109 

 Figures 3 to 6 show decomposition of CDS spreads for the first group of 
counties, correlated to the first common factor – Great Britain, Czech Repub-
lic, Poland and Germany.  

 
Figure 4. Decomposition of CDS spread of Czech Republic  

 
Figure 5. Decomposition of CDS spread of Poland  

-2

-1

0

1

2

3

4

5

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

CZ

-2

-1

0

1

2

3

4

5

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

PL



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Figure 6. Decomposition of CDS spread of Germany 

 
Figure 7. Decomposition of CDS spread of Portugal  

 Historical decomposition of CDS spreads of these countries revealed that 
CDS spread of these countries evolve mainly due to the influence of the first 
factor. The influence of the second factors is lower. But at the period of 

-2

-1

0

1

2

3

4

5

6

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

DE

-2

-1

0

1

2

3

4

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

PT



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insolvency crisis (2011 year) Poland and Czech republic are more influenced 
by the second factor than Great Briatan and Germany. 
 Figures 7, 8 and 9 present the decomposition of the CDS spread for the 
second group of countries, the most affected by the second common factor – 
Portugal, Italy and Spain. 

 
Figure 8. Decomposition of CDS spread of Italy  

  
Figure 9. Decomposition of CDS spread of Spain  

-2

-1

0

1

2

3

4

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

IT

-2

-1

0

1

2

3

4

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

ES



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 The CDS spreads of these countries are influenced mainly by the second 
factor.  
 And last, France and Hungary – countries that are under the influence of 
both common factors. Figures 10 and 11 show the decomposition of CDS 
spread of these countries.  

 
Figure 10. Decomposition of CDS spread of France  

 
Figure 11. Decomposition of CDS spread of Hungary  

-2

-1

0

1

2

3

4

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

FR

-2

-1

0

1

2

3

4

2008 2009 2010 2011 2012 2013 2014 2015

F1 F2 Specific Sum

HU



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 Historical decomposition revealed the evidence that the sovereign CDS 
spreads are under combined influence of two common factors and one specific 
factor – time-varying and different for each country.   
 Summarising, first factor (F1) may be interpreted as a global risk factor, 
appearing during the crisis in 2008. It affected most of the countries, but in 
the larger extent influenced CDS spreads of Great Britain, Czech Republic, 
Poland and Germany. The second factor (F2) can be interpreted as an insol-
vency risk factor, it appeared in 2011–2012 years and influenced mainly the 
countries, which had debt problems – Portugal, Spain, Italy and to a lesser 
extent France and Hungary.  
 Comparative analysis shows that CDS spread of Poland’s government 
debt reacts similarly to those of Czech Republic, Great Britain, and Germany. 
This leads to the conclusion that Poland treasury securities are considered by 
investors in a similar way as tree mentioned-above countries.  

Conclusions 
 The presented analysis confirms that sovereign CDS spread is a measure 
of risk and it depends both on global risk factors (global financial market con-
ditions, investor expectations etc.) as well as macroeconomic fundamentals 
(eg. level of indebtedness of the country). The factor analysis shows that two 
common factors explained about 92% of the variation of the CDS spreads of 
the surveyed countries. The results of decomposition of CDS spreads confirm 
that Great Britain, Poland, Czech Republic, and Germany are countries most 
influenced by the first common factor, interpreted as a global risk factor, ap-
pearing during the crisis in 2008. Portugal, Italy, and Spain are countries under 
the impact of the second common factor interpreted as an insolvency risk fac-
tor appearing in 2011–2012 years. France and Hungary are the countries in-
fluenced by both common factors. 

References 
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Europe, Journal of Monetary Economics, 60(5), 493–510,   
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Dekompozycja spreadów kontraktów CDS na obligacje skarbowe 
przy zastosowaniu koncepcji faktoryzacji 

Z a r y s  t r e ś c i. CDS (Credit Default Swap) na obligacje skarbowe to instrument pochodny, 
który stanowi ubezpieczenie spłaty długu rządowego i może być uważany za rynkowy wskaź-
nik ryzyka niewypłacalności danego kraju. Celem badania jest identyfikacja czynników wpły-
wających na spready CDS wybranych krajów europejskich w okresie od 2008 do 2016 r. Na 
podstawie przeprowadzonej analizy czynnikowej stwierdzono, że istnieją dwa wspólne czyn-
niki, które wyjaśniają około 92% zmienności spreadów CDS. Następnie, dekompozycja sprea-
dów CDS pokazuje wpływ tych czynników ryzyka na spready CDS badanych krajów. 

S ł o w a  k l u c z o w e: CDS spread; analiza czynnikowa; ryzyko globalne; ryzyko niewypła-
calności. 


