What Drives Movements in Sovereign CDS Spreads?

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What Drives Movements in Sovereign

CDS Spreads?

By

JOSEPH A. BOGACZYK, ASEEM GROVER, AND MEILINDA HUANG

Abstract

We primarily aim to find the macroeconomic factors that drive the movements

in Sovereign CDS prices using data from 35 countries around the world. We

found no strong evidence of movements in sovereign CDS spreads related to

three country-specific macroeconomic factors: exports, imports, and CPI.

Since the onset of the financial crisis and the subsequent recovery from

recession, we find that 61 percent of the variation in sovereign CDS spreads is

explained by a single component. Similarly, a single component account for

54 percent of the variation in local stock market returns. We also find that

sovereign CDS spreads are almost equally related to the US stock market,

global equity premium, and local stock markets.

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The Great Recession of 2008 has prompted the need for a deeper understanding of the

forces that affect the valuation, risk and return of fixed income securities and their derivatives

such as credit default swaps. Credit default swaps (CDS) have been identified as sources of risk

for the institutions that use them and as potential contributors to systemic risk. CDS contracts

were also the underlying reason for the government bailout of AIG to avoid a chain reaction of

financial institution failures.

With the outbreak of sovereign debt crisis in the European Union (EU) at the end of

2010, sovereign credit default swaps (SCDS), which are CDS contracts on sovereign debt are

back in the limelight. The renewed prominence of credit default swaps have continued to make

international regulators nervous about the risks these CDS pose to an already volatile situation.

Prices of CDS on debt of Southern European countries such as Greece and Portugal have soared

as the sovereign debt crisis has worsened. The research done by Professor Longstaff et al1

(2010) showed that the sovereign credit risk was driven primarily by global macroeconomic

forces external to the country in question. We aim to find out whether export volumes, import

volumes, and consumer price index (CPI), three fundamental macroeconomic factors that

implicitly drive a country’s deficit and in turn its credit risk, are significant drivers of movements

in sovereign CDS spreads of the particular country. We also focus on whether the significant

explanatory variables from the Longstaff study are still significant in the post-recession world.

Credit Default Swaps

Sovereign CDS are analogous to insurance: in exchange for a fee paid to the seller, they

provide protection to buyers from losses that may be incurred on sovereign debt resulting from a

credit event. Credit events include failure to pay interest or principal on, and restructuring of, one

or more obligations issued by the sovereign. For our research, we used five-year sovereign credit

default swaps because they are the most actively traded contracts and therefore have the most

pricing data available.

The Sovereign CDS markets are generally small compared with the underlying

government debt outstanding, leading some to think that the impact of SCDS market would be

1 Longstaff, Francis, Pan, Jun, Pedersen, Lasse, and Singleton, Kenneth. “How Sovereign is Sovereign Credit

Risk?” American Economic Journal. April 2011: 75-103.

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limited on the financial system. However, since SCDS contracts are intertwined globally with

counterparties spread across America and Europe: the fall of a few counterparties could cause a

domino effect leading to a global collapse. Sovereign CDS spreads therefore are highly impacted

by liquidity risk. Moreover, SCDS notional amounts on underlying reference obligations are

large relatively to underlying government debt for many emerging and European economies.

This not only underlines the importance of the SCDS market, but also implies that the SCDS

markets are far more liquid than the sovereign bond market. For our study, highly liquid SCDS

markets mean that the SCDS data was easily available for a large number of countries aiding us

in making our analysis and estimates more accurate.

Key Findings

Our analyses uncover three important and interesting developments. First, similar to the

Longstaff et al paper, we find a high level of commonality in sovereign CDS spreads. This

commonality, however, has shrunk since the research was prepared in 2011. For our set of 35

countries, the first principal component explains 60.9% of the variation in sovereign CDS

spreads during the 2008–2014 sample period. In comparison, for the same sample period, the

first principal component of local returns for these countries describes only 53.9% of the

variation in local stock returns. Therefore, while sovereign credit risk seems to be more

interlinked with global factors and events, local equity returns are also impacted considerably by

global factors.

Second, contrary to the Longstaff et al paper, we find that in the aftermath of the financial

crisis, sovereign credit risk is driven as much by global market factors and risk premiums, as by

local economy specific fundamentals such as local stock market returns and local exchange rate.

Furthermore, we find that all five categories of explanatory variables: local macroeconomic

indicators, local economic variables, global financial market variables, global risk premium

measures, and global market liquidity variables have a significant impact on the variation of

sovereign CDS spreads. More specifically, the most significant variables that affect CDS credit

spreads are local stock market returns, local exchange rate, US stock market returns, and global

equity & volatility premiums.

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Finally, we find that the three local macroeconomic variables: export volumes, import

volumes, and CPI are not significant in explaining the variation of the sovereign CDS spreads.

The Data

For our study, we used the CDS pricing data obtained from the Bloomberg system, which

collects CDS spread quotes data from all the leading broker dealers and important data sources

such as CMA. Our study focuses on data from January 2008 to January 2014. We found the

Bloomberg data during this period to be robust and in line with CDS prices on Markit, another

widely used CDS data source on the street. We obtained robust CDS data for 63 countries for the

aforementioned period. Next, we looked for corresponding monthly data for our selected

macroeconomic indicators (export volume, import volume, consumer price index) for the set of

63 countries.

In all, we were able to collect monthly CDS data and data for all the other variables for

35 different countries in the sample. For each country, the underlying debt security on which the

CDS contract is issued is the five-year highest grade international debt of the sovereign. In line

with the market norm, all CDS spreads are quoted in basis points2. Due to our mix of advanced

and developing countries, we see a wide variation in the value of CDS spreads across countries.

Table 1 provides summary information for the sovereign CDS spreads.

This variation is particularly pronounced from 2008 – 2010 because of the global

financial crisis. For instance, the lowest quoted spread is 3.81 basis points for Sweden while the

highest quoted spread is 25,412 basis points for Greece. A wide range of values can also be

observed in the standard deviations during this period. Specifically, the highest variation can be

seen in the case of Greece, where the CDS spreads range from 17.68 to 24,412 basis points

during the sample period.

2 Basis point is 0.01 of a percentage point

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Table 1—Descriptive Statistics for Sovereign Credit Default Swap Spreads

Commonality in Sovereign CDS Spreads

We perform two main types of analyses to examine the commonality in sovereign CDS

spreads among the 35 countries. First, corresponding with the Longstaff et al study, we conduct

principal components (PC) analysis of the changes in sovereign CDS spreads and local stock

market returns. Second, we apply a k-means cluster analysis to the changes in sovereign CDS

spreads to classify groups of countries with high degrees of associations with each other.

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The results of the PC analyses are reported in Table 2 for both the changes in sovereign

CDS spreads and local stock market returns for the 2008-2014 sample period. We find that there

is substantial commonality among sovereign CDS spreads with the first principal component

explaining 60.9% of the variation in the spreads. Moreover, the first five principal components

explain 78% of the variation in the sovereign credit spreads. For comparison, we also compute

the first five principal components of local stock returns and find that the first component

explains 53.9% of the variation in the same period. The findings show that since 2010 the

percentage difference between the two factors’ first principal component have compressed

significantly. For instance, Longstaff et al’s PC analyses using the full 2000-2010 sample period

for CDS changes and stock returns showed a 17.8% (63.92 - 46.16 percent) gap between the first

components, whereas our results showed a 7% (60.9 – 53.9 percent) gap between the first

components. Even the variation between first components for Longstaff et al’s 2000-2006

(8.12%) and 2007-2010 (13.36%) subperiods are higher than our variation for the 2008-2014

period. One possible explanation may be that since the world has emerged from the global

financial crisis, credit spreads and stock returns of sovereigns have become less affected by

global factors, and more by the fundamental strength of their respective economies. This would

be a significant deviation from the high level of commonality contrast shown in previous data.

Table 2—Principal Components Analysis Results

Figure 1 illustrates the top three principal components for the 35 countries. Consistent

with Longstaff et al’s research, we believe the primary source of variation across most of the

CDS spreads can be explained by the strong correlation between the sovereigns and the US stock

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market returns. We believe Greece, Ireland and Portugal differ from the majority because all

three European countries have experienced severe debt crises recently and are therefore more

impacted by their internal fiscal problems and less impacted by the US macroeconomic

conditions.

For the second component, positive weights are given to European Union (EU) countries

whereas negative weights are given to mostly Asian and Latin American countries. Therefore,

we believe this principal component represents a spread between EU and non-EU countries. The

third component is more difficult to interpret because there is no clear pattern between countries

that are positively and negatively weighted. However, as mentioned before we believe that

Portugal and Ireland are the most heavily weighted due to both countries suffering significant

financial turmoil in the last few years. Portugal, in particular, has had an ongoing fiscal deficit

crisis and was severely dependent on a bailout from the EU central bank in 2011. Ireland’s

financial crisis began in 2009 with acceptance of an EU central bank euro rescue package in late

2010.

Figure 1. Principal Components of Monthly Changes in CDS Spreads for 35 Countries

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Cluster Analysis

PC analysis aims to reduce the dimensionality of data to a few principal components

while explaining as much as the volatility as possible. Cluster analysis, on the other hand, shows

how many clusters are present for a given correlation level. Cluster analysis gives a more

qualitative overview of the correlation structure which aims to highlight similarity between

countries, whereas PCA is more quantitative in that the principal components are weighted

combinations of countries.

We used the k-means clustering algorithm on the monthly changes in sovereign CDS

spreads to examine which countries tend to move together. We tested the clustering with 4, 6, 7,

8, 9, and 10 clusters. We believe that a cluster analysis with 7 clusters gives cluster outputs that

can reasonably explain correlations in the sovereign CDS markets.

The results of the cluster analysis differ slightly than what was observed in the PC

analysis. As shown in Table 3, while the world was divided into EU vs. rest of the world (except

for a few outliers: Portugal, Greece, and Ireland), cluster analysis gives us more distinction in

what countries are alike. In particular, Portugal and Greece, which were impacted by severe

internal debt crises, are in a completely separate cluster, with their credit spreads dependent more

on aid from institutions such as the European Central Bank and International Monetary Fund,

than on other countries in the EU or elsewhere. Likewise, Ireland, which was severely afflicted

by the financial crisis, moves independently depending on how quickly it gets its fiscal house in

order.

As expected, most Western European countries have high correlations with each other.

However, Slovenia and Slovakia, countries in central Europe, gravitate towards Western

European countries rather than with countries in Eastern Europe. Most surprisingly, the two

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Asian stalwarts, China and Japan are more similar to the Western European countries than they

are to other countries in Asia or the Europe, Middle East, and Africa (EMEA) region. The reason

Slovakia, Slovenia, China, and Japan gravitate towards the core Western European economies is

because these Western European markets are among the largest export markets for each of those

four countries.

Next, we see that Colombia, Mexico, Philippines, South Africa, South Korea, Turkey and

Vietnam cluster together and are most likely tied together by their relatively stable governments

and moderately advanced financial markets (except for Vietnam).

In another cluster, Bulgaria, Croatia, Estonia, Latvia, Lithuania, and Romania, all count

Russia, as well as each other, as their largest trading partners. It is no surprise then that their

CDS spreads are correlated with each other. However, Iceland and Hungary are two surprise

inclusions in this group that cannot be explained by our theory of CDS spreads of trading

partners moving together.

One of the bigger revelations of this cluster analysis is that the markets do not perceive

the credit riskiness of United Kingdom (UK) to be aligned with the rest of the Europe. We

believe this is because of the conservative government in power, which is more aligned in its

policies with the United States of America than Europe.

Venezuela is also an outlier that appears to be in the same cluster group as UK, but we

believe this is an anomaly and find no explanation of why or how this could be possible. We

suspect Venezuela would gravitate towards its largest trading partners—US and other Latin

American countries—if they were included in the cluster analysis.

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Table 3—Cluster Analysis for 7 Groups

Figure 2. Cluster Analysis for 7 Groups using Stata

The Variables

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Based on the high level of commonality we find in SCDS spreads, we look for variables

that drive these spread movements. Our variables can be grouped into two ways: local/global and

financial/economic. Additional details regarding the definitions, sources, and timing for the

variables we chose are provided in the.

Local Financial Market Variables – Defaults are associated with unhealthy economies,

we include the local stock market return (denominated in units of the local currency), percentage

changes in the exchange rate of the local currency against the dollar, and percentage changes in

the dollar value of the sovereign’s holdings of foreign reserves, to measure information about the

state of the local economy.

Local Economic Variables – We gain a more complete view of the sovereigns’ economic

picture by including three local economic variables to our analysis: Imports, Exports, and CPI.

Countries in our sample vary widely in the extent to which their economy is driven by imports or

exports so by including both we account for those differences. CPI provides a measure of the

price level in a particular country.

Global Financial Market Variables – As globalization increases, sovereigns’ economic

relationships with other countries become more meaningful. A government’s creditworthiness

and ability to repay their debt depends on local variables as well as on the state of the global

economy. As a measure of changes in the state of the global economy, with some granularity

regarding different asset classes, we include several measures from the US equity and fixed

income markets. We assumed that since the US is the largest economy in the world and also the

financial epicenter of the world, changes in the US financial markets directly impact the

economies and financial markets of many other sovereigns.

We include the S&P 500 index monthly return as the equity market variable. Monthly

changes in the 5 year constant maturity Treasury (CMT) are a measure variation in the US fixed

income markets. Changes in the CMT yield can signal changes in US economic growth and

ultimately changes in the global business cycle. Other US fixed income variables we include are

the spreads of US investment-grade and high-yield corporate bonds. We calculate the changes in

the spreads between the five-year BBB- and AAA-rated bonds and between the five-year BB-

and BBB-rated bonds. This allows us to segment the different levels of credit risk.

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Global Risk Premiums – With increasing globalization comes increased correlations

between global risk and defaults. Based on this, we adopt the approach of using risk premium

estimates from other global markets as explanatory variables. To indicate the variation in the

equity risk premium, we use monthly changes in the earnings-price ratio for the S&P 100 stock

market index. Another risk premium proxy we include is monthly changes in the spreads

between implied and realized volatility for index options. This illustrates the premium for

exposure to the volatility risk of an option position. Specifically, we compute volatility premium

by subtracting the 20 Day Standard Deviation of the S&P 500 from the VIX.

Global Investment-Flow Variables – The flow of investment capital around the world

provides liquidity to local markets and in turn also influences SCDS prices. As investors

diversify their holdings globally sovereigns benefit from the improved access to global sources

of capital. Investment flows are also a measure of investor sentiment and confidence showing

where investors are willing to expose themselves to sovereign credit risk. We included the net

new flows (inflow minus outflow) into mutual funds investing primarily in bonds and equity.

This data was obtained from the Investment Company Institute.

Spreads of Other Sovereigns – To capture other external economic factors that might

influence the SCDS spread for a given country, we grouped the countries geographically and

computed averages within the region and outside of the region. We divide the countries into four

regions: Latin America, Asia, Europe, and the Middle East/Other. We regress the changes in

these regional spreads on the other explanatory variables and use the residuals from these

regressions as additional explanatory variables.

Regression Analysis

For our regression analysis, we calculated the monthly changes in the CDS spread and

regressed them against the explanatory variables described in the previous section. The results of

the t-statistics (Halbert White (1980) heteroskedasticity-consistent estimate of the covariance

matrix) and adjusted R2

for each regression can be seen in Table 4.

The results indicate that the three macroeconomic variables (Export Volume, Import

Volume, and CPI) affect Sovereign CDS spreads in only a handful of cases. Exports are

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significant (at the five-percent level) for only 7 of the 35 sovereigns in our sample. CPI and

Imports are only significant for 5 and 4 countries, respectively.

The significant coefficients for CPI were almost all negative (except China) indicating

that an increase in the consumer price index decreases the sovereign credit spreads. For the

export volume coefficients, six of the seven significant coefficients were positive, suggesting that

the sovereign credit spreads increase as a country’s exports increase. This is contrary to expected

behavior, where we would expect the company’s credit risk to decrease as it exports more to the

world. We suspect the exports in these cases are increasing because the countries have seen their

currencies depreciate and not because the country’s growing faster or improving its productivity.

In the case of import volumes, we found three negative significant coefficients and one positive

significant coefficient. We see no emerging pattern or common threads to tie all the negative

significant import volume coefficients together.

Next, we look at the local variables across our sample sovereigns. As expected, we find

that the state of the local economy is an important measure of the sovereign credit risk. The t-

statistics in Table 4 show us local stock market returns are significant for 20 sovereigns. With the

exception of Ireland, Norway, Sweden, and United Kingdom, all the other sovereigns CDS price

changes were negatively correlated with the local stock market—indicating that the credit risk of

the sovereign goes down when the bulls dominate the local economy.

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The exchange rate, which plays an important role in determining a country’s current

account deficit and therefore would be expected to have a role in a country’s creditworthiness,

was found to be significant for 13 sovereigns. Of these, 11 of the coefficients are positive in sign,

indicating that the sovereign’s credit riskiness increases as the sovereign’s currency depreciates

relative to the US dollar. Surprisingly, we find that a country’s currency reserves were only

significant for four sovereigns. Of these, two coefficients were negative, suggesting that as a

country’s currency reserve increased, its risk of default declined. Two positive coefficients for

South Africa and Venezuela, are most likely an indication of their fragile internal economic

conditions. South Africa’s recent currency depreciation is an indication of the state of the

economy. Venezuela, on the other hand, has long been marred with political instability and

skyrocketing inflation.

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The Longstaff et al study on 26 sovereigns had previously shown the US stock market

returns and US high-yield spreads were the most significant global financial variables. As can be

seen in Table 4, we find that in our regressions only the US stock market returns were

significant. We found the US stock market returns to be significant for 19 countries. Hence, the

US stock market return and the local market returns are equally significant in explaining

variation in sovereign credit default spreads. The sign of coefficients for the US stock markets is

negative for all but 8 European countries (Belgium, France, Greece, Hungary, Italy, Portugal,

Slovenia, and Ukraine). It’s interesting to note that none of regressions for these eight countries

showed US stock market returns to be a significant variable. However, the same set of eight

countries showed the local stock market returns to be significant. This indicates that CDS

spreads for these countries are more influenced by the local stock markets than they are by the

US stock markets. The US treasury, investment grade, and high-yield variables were only

significant for two, six, and three sovereigns respectively, indicating a low explanatory power

over the sovereign CDS spreads.

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Among the global risk premiums, we found a strong relation between sovereign spreads

and global equity and volatility premiums. The equity risk premium is significant for 19

countries, volatility risk premium is significant for 17 countries, while the term premium is

significant for only seven countries. The coefficients for equity risk premium are all negative

except for Belgium, Germany, Greece, Ireland, Italy, and Portugal. This signals that these

countries were more likely influenced more by local and regional factors than by the US

economy. The coefficients for volatility premium are all negative in sign, indicating that the less

volatile the global financial markets, the less variation in sovereign credit spreads.

For the global investment-flow variables, we see in Table 4 that the global equity-flows

were significant for five sovereigns, while the global bond-flows were significant for four of the

countries. The significant coefficients for global-equity variables are positive in all cases except

one (Ukraine). On the other hand, the significant coefficients for global bond-flow variables are

all negative in sign. This suggests that while an increase in capital invested in global bonds

causes sovereigns credit spreads to decrease, the opposite is true for the increase in capital

allocated to global equity investments. While, there is no concrete theory to tie these two

contrarian behaviors together, we suspect that the sovereign credit markets realize that since the

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financial crisis the movements in global equity investments have been driven by search for

higher returns, with investors swiftly moving capital from one market to the other in hopes pf

higher yields. We think that because of this reason, the market believes that these investments

are short term and does not see the an increase in global equity investment as any reason to think

that countries have more access to long term capital and are therefore less likely to default.

Sovereign global and regional spreads also exhibit strong relationships with the sovereign

credit spreads. The regression shows that the regional CDS spread is significant for 12 countries.

All these coefficients are positive—suggesting that regional factors (e.g. liquidity crunch, credit

bubbles, contagions risks) have an impact on countries in those regions. On a similar note, the

coefficients for the global credit spread are significant for 16 of the countries, with all significant

coefficients being positive.

With the exception of Belgium, Greece, Ireland, Portugal, and Slovenia, the adjusted R2s

for all the regression runs of all the other sovereigns regressions are over 50 percent, indicating

that our model and chosen explanatory variables explain the variation in sovereign CDS spreads

well. The median and mean values of the adjusted R2s are 68.5 and 71 percent, respectively.

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Lastly, the local ratio in the last row of Table 4 measures the amount of total variation in

regression explained solely by the local variables. To calculate local ratio, we follow the

approach of Longstaff et al; namely, for each sovereign, we run a regression of the changes in

CDS spreads with the local variables and divide the R2 from this regression run by the R

2

obtained from the regression run of changes in each sovereign spread against all the explanatory

variables.3 As explained in the Longstaff et al paper

3, this ratio most likely overstates the

proportion of the total variation due solely to the local variables because the local variables from

sovereigns are not orthogonal to the non-local variables. Therefore, in line with the Longstaff et

al’s research, this local ratio should be viewed more as an upper bound than an exact explanation

of variation explained by local variables. As seen in Table 4, the local ratio varies across

sovereigns from a low of 0.07 for Greece to 0.94 for Hungary. Contrary to Longstaff et al’s

findings, which found nearly two-thirds of the local ratios to be less than 50 percent, we find that

83 percent of sovereigns have local ratios of over 50 percent. The median value for local ratio is

0.61 and the average value is 0.59. Hence, the upper bound for the total variation in regression

3 Longstaff, Francis, Pan, Jun, Pedersen, Lasse, and Singleton, Kenneth. “How Sovereign is Sovereign Credit

Risk?” American Economic Journal. April 2011: 75-103.

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being explained by the local variables is around 60 percent of the total variation explained by our

regression analysis.

Conclusion

In conclusion, we find that sovereign credit default spreads are still driven in a large part

by US equity markets, but not as much by high-yield or investment grade markets. We also find

that the local equity markets have become equally significant in driving sovereign credit spreads.

Other factors that have a large impact on sovereign CDS spreads are global equity and volatility

premiums, liquidity effects as explained by global and regional spreads.

Additionally, we discover that Export Volumes, Import Volumes, and Consumer Price

Indices are not significant in driving sovereign credit spreads. However, we believe that if data

availability were not a constraint, other fundamental macroeconomic variables cannot be ruled

out as drivers of sovereign CDS. We suggest future research be conducted on the effects of

variables such as Public Debt/GDP, Sustainable Current Account Deficit (SCAD), Non-

performing Bank Loans/Total Bank Loans on CDS spreads.

Finally, we find that the correlation of sovereign CDS spreads among countries is more

associated with countries that are significant trading partners rather than by neighboring

countries or countries in the same regions.

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Appendix

This Appendix provides additional details about the definition, timing, and sources of the data

used in the study. For all data, the value for the last trading day of the month or the last available

quotation during the month is used. All data are obtained from the Bloomberg system unless

otherwise indicated.

1. Sovereign CDS Spreads –These CDS spreads prices for five-year CDS contracts.

2. Local Stock Market Returns – The local stock market returns for the countries in the sample

are monthly total returns (including dividends). The indexes are either from MSCI or S&P IFC.

3. Exchange Rates – Exchange rates are in units of the local currency per US dollar.

4. Foreign Currency Reserves – The dollar values of sovereign foreign currency holdings. Data

for this was obtained from the Global Financial Data database.

5. US Stock Market Returns – The US stock market return used is for the S&P 500 Index.

6. Treasury Yields – Monthly changes in the Treasury yields are based on the 5 year constant

maturity Treasury (CMT) rates.

7. Corporate Yield Spreads – We get the changes in investment-grade yield spreads by

calculating the spread between monthly changes in the basis-point yield spread between BBB

and AAA industrial bond indexes. Similarly, changes in high-yield spreads were computed by

taking the spread between monthly changes in the basis-point yield of BB and BBB industrial

bond indexes.

8. Equity Premium – We use monthly changes in the price-earnings ratio for the S&P 100 index

as proxy for changes in the equity premium.

9. Volatility Risk Premium – We calculate the volatility risk premium by subtracting the 20 Day

Standard Deviation of the S&P 500 from the VIX.

10. Term Premium – The term premium is calculated as the difference between the 10 year and

the 2 year constant maturity Treasury (CMT) rates.

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11. Bond and Equity Flows – This data is obtained from the the Investment Company Institute

website.

12. Regional and Global Sovereign CDS Spreads – For each country, we compute the regional

CDS spread by taking the average of the CDS spreads for all of the other countries in that

country’s region. The global spread for each country is calculates by taking the average spread of

the other regions.

13. Exports – The export volume is the value of exports in the US dollars terms.

14. Imports – The import volume is the value of exports in the US dollars terms.

15. CPI – The consumer price index for the various countries (base years vary).

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Table A1 - Correlation Matrix of Sovereign CDS Spread Changes4

4 The table above displays the pairwise correlation coefficients of monthly changes in the sovereign CDS spreads for 35 countries.

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Table A1 Continued - Correlation Matrix of Sovereign CDS Spread Changes

What Drives Movements in Sovereign CDS Spreads?

Economy & Finance

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