Post on 13-Feb-2017
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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