i

Effect of US Macroeconomic Surprises on the Term

Structure of Emerging-Market Sovereign Credit

Default Swaps

Abstract

This paper discusses how the term structure of the credit default swap (CDS) spread in

emerging markets affects the real economy and returns of the stock index. The study

sampled data from January 2001 to August 2013. We suggest that the term structure of

CDS spread implies different short-term and long-term expectations. When markets

have pessimistic expectations for the future, the term structure of CDS spread is higher.

Countries with higher term structure of CDS spread decrease their GDP growth rate by

0.0062% on average. Higher term structure of CDS spread implies higher risk and

investors require higher expected returns of the stock index, which were 0.0029%,

0.0104%, and 0.0202% in 1, 3, and 6 months, respectively, in our study. Our suggested

strategy of buying high and selling low could earn 1.35% returns in the next month. We

also observed that the mean and variance of the term structure decreases with good US

macroeconomic news and increases with bad news.

Keywords: Credit Default Swap, Term Structure, Spillover Effect, EGARCH Model,

Financial Crisis

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1. Introduction

In recent years, numerous crises, such as the technology bubble in 2000,

subprime crisis in 2008, Eurozone debt crisis and bankruptcy in Greece in 2010, and

Brexit in 2017, occurred, causing financial markets to become more volatile. This

study attempted to determine some of the signals to indicate scenarios or patterns

that trigger crisis to enable investors to hedge or speculate. Research has indicated

that the credit default swap (CDS) is a suitable derivative for gauging the possibility

of upcoming market deterioration. Moreover, the interest rate yield curve contains

information regarding expected future economic conditions. For example, when the

interest yield curve is inverted, an economic depression occurs after less than 2

years. We observed that CDS spread quotes had varying maturity times, and thus

investigated whether any relevant information was hidden in the CDS spread curve.

Studies have observed that the United States dominates the worldwide

economy; for example, when the US economy stocks excessive capital, the capital

flows to other countries, which either trade or exchange the stock; however, when

the US economy deteriorates, the capital flows back into the United States rapidly

and sometimes severely affects the economies of other countries, especially those of

developing countries because of their fragile economic conditions. Therefore, this

study analyzed CDS data from 23 emerging markets: six countries from the Asia–

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Pacific region, five from the Americas, and 12 from European, Middle Eastern, and

African regions. The CDS spread slope is defined on the basis of a study by Han et

al. (2017), with a difference between 5-year and 1-year CDS spread, at a monthly

frequency. Good and bad US macroeconomic news indices were calculated based on

a study by Kim et al. (2015) using the EGARCH model to capture the asymmetric

effect on the variance of the CDS spread slope and adding the US macroeconomic

news indices as exogenous variables to observe how they affect the CDS spread

slope. Good US macroeconomic news reduced the level and variance of the CDS

spread slope, revealing a bright future for CDS buyers because they no longer

must pay high premiums if they want long-term debt protection, thereby flattening

the CDS spread slope. Bad US macroeconomic news increased the level of the CDS

spread slope and reduced its variance. Bad news is usually announced during

recession, causing CDS buyers to pay higher premiums for long-term debt

protection.

The study further analyzed the relation between the CDS spread slope and the

real economy. If the CDS spread slope represents the difference between long-term

and short-term premiums, a steeper slope implies that in the long-term, the economy

has a tendency to worsen and the GDP growth rate will decline. In the same year,

the CDS spread slope and GDP growth were negatively correlated, which matched

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the hypothesis of this study. According to our empirical results, as the CDS spread

slope increases by 1 bp, the simultaneous and subsequent GDP growth rate declines

at 0.0062% and 0.0035%, respectively. The CDS spread slope cannot forecast

changes in the GDP growth rate. Therefore, we assumed that the CDS spread slope

can only reflect the GDP growth rate.

The study further analyzed the forecast ability of the CDS spread slope for the

stock index. If a higher CDS spread slope reflected pessimism in the future, the

stock index tended to decrease. We regressed the CDS spread slope to its stock

index market return and found a steeper CDS spread from positive stock index

return, which was 0.0029%, 0.0104%, and 0.0202% at 1 month, 3 months, and 6

months, respectively. This result did not match our hypothesis. Norden and Weber

(2009) demonstrated that stocks led the CDS spread, but there was no significant

evidence to prove that CDS spread led the stock market. We suggested that the CDS

spread slope and stock index adjusted the price with simultaneous default risk,

thereby causing the expectation return to be positive. The stock market falls to a

relatively low base but tends to increase in the following several months. Moreover,

higher CDS spread slopes indicated higher default risks in the future; therefore,

investors require higher expected returns to compensate for their risk-taking

behavior. Finally, we constructed a portfolio, dividing sample countries into three

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groups by CDS spread slope, and bought high-slope and sold low-slope portfolios

and calculated the returns the following month. The portfolio brought 1.35% returns

per month with a t statistic of 5.04. The return was adjusted through the capital asset

pricing model (CAPM), Fama–French model, and Carhart model, and received a

significant positive alphas through these factor model adjustments, which were

1.50%, 1.60%, and 1.67%, respectively.

Finally, we focused on the Eurozone CDS spread slope in 2010–2013. During

this period, Greece announced bankruptcy, 1-year CDS spread reached 40,000 bp,

and 5-year CDS spread reached 25,000 bp. Kalbaska and Gątkowski (2012)

observed that the CDS spread in 2005–2010 had a contagion effect; we assumed the

CDS spread slope may have a similar effect. The sampled data were cut after 2010,

during the outset of the Eurozone debt crisis. The CDS spread slope of Greece

reduced the CDS spread slope 0.0044 bp in other emerging European countries.

During that period, the CDS spread slope of Greece turned negative, thus turning the

effect negative.

The study sample period included the financial crisis and Eurozone debt crisis,

and we shrank our sample period after 2008 to conduct robustness checks. The

results were identical with the empirical results of the entire sample period. Good

US macroeconomic news reduced the level and variance of the term structure of the

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CDS spread, whereas bad news increased the level and variance of the term

structure of CDS spread in many emerging countries. A higher slope of the CDS

spread reflected a lower GDP growth rate in the same year and positive stock index

return 1 to 6 months later.

The paper is organized as follows. Section 2 reviews the extant literature

regarding the term structure of the CDS spread; Section 3 describes the data

sampled in this study; Section 4 details the methods used in this study; Section 5

presents the empirical results of this study; and Section 6 concludes the paper.

2. Literature Review

A CDS is a credit derivative; CDS sellers provide protection of loss from

default if the reference entity cannot afford the interest or principal before the

maturity date approaches. The counterparty, CDS buyers, pay premiums continually

during the contract period to receive protection. The amount that CDS buyers are

required to pay is calculated as the CDS spread multiplied by the notional amount.

With different lengths of maturities, the spreads vary. The magnitude of CDS

contracts has increased rapidly since 1997; the gross notional amount reached the

peak of USD 62.2 trillion in 2007 from USD 180 billion in 1997. Because of the

financial crisis in 2008, the amount of CDS contracts decreased to USD 27 trillion

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in 2009.

The mechanism of finance has changed because of the innovation of the CDS.

Ismailescu and Phillips (2015) observed that the credit spread of sovereign bonds

decreased by initiating sovereign CDSs, especially for countries with high default

risk. Das et al. (2014) observed that corporate bond markets become less efficient

and do not improve their liquidity after initiating CDSs. The CDS spread also

implies the anticipation of the financial condition of the markets, including the

probability of default and the proportion that creditors can acquire when the entity

defaults. Greatrex (2015) observed that the CDS market anticipates negative

earnings surprises when prices are adjusted prior to the announcement date of the

actual earnings. Chng and Wang (2014) noted that CDS trading became more

informative for an increasing number of firms when the global financial crisis

approached. CDS spreads reflect the credit rating of a country and the financial

condition of a firm. Some studies have investigated the relation between CDSs and

other financial products. Lee et al. (2016) observed significantly stronger stock

return momentum when past stock and CDS returns were in congruence compared

with entities whose past stock and CDS returns disagreed. Norden and Weber (2009)

observed that stock returns lead CDS spread; however, CDS spread does not lead

stock returns. Forte and Lovreta (2015) observed that the stock market has stronger

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dominance during a crisis, but the contribution of the CDS market toward price

discovery is equal or higher than that of the stock market. Hassan et al. (2017)

observed that CDS spread drove the value of the Turkish lira against the US dollar

in the postcrisis period.

The United States has become the most influential country in the world; some

scholars have observed that effects of US macroeconomic news spill over to other

countries or financial products. Dooley and Hutchison (2009) observed that

emerging markets respond strongly to deteriorating situations in the US financial

system and real economy. Nikkinen and Sahlström (2015) demonstrated that the

implied volatility increases before the US macroeconomic news is announced and

decreases after stock market announcements in both the United States and Finland.

Gurgul and Wójtowicz (2014) observed that US macroeconomic news affects large,

medium, and small stocks differently in Poland. Kilian and Vega (2011) investigated

the spillover effect of US macroeconomic news to energy prices and observed no

compelling evidence at daily or monthly horizons. Based on research about how the

effects of US macroeconomic news spills over to other countries, some papers have

investigated the spillover effect on CDSs. Baum and Wan (2010) observed that not

only the first moment but also the second moment of traditional factors of

macroeconomic uncertainty, such as risk-free rate and treasury term spread, have

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significant explanatory power for the CDS spread. Candelon et al. (2011) focused on

news about credit ratings in the Eurozone. Greece, which is a relatively large

economy, was downgraded to a near speculative grade rating, and the spillover

effect across the Eurozone was systematic. Kim et al. (2015) used the EGARCH

model to capture the spillover effect from the United States, Eurozone, and China.

Good news from three major economies reduced the CDS spread and volatility,

whereas bad news increased the CDS spread; however, the effect on volatility

differed. Bad news from China and the Eurozone generally increased the volatility

of other sovereign CDS spreads; however, bad news from the United States

decreased the volatility and had a calming effect instead.

Some papers have discussed the term structure of CDS spread, which is defined

as the difference between the long-term and short-term spread. Han et al. (2017)

defined the CDS spread slope as the difference between long-term and short-term

CDS spread. Calice and Zeng (2018) defined the term structure through another

method—the log difference between long-term and short-term CDS spread. Pan and

Singleton (2008) explored the nature of default arrival and recovery that is implicit

in the term structure of sovereign CDS spreads through reduced-form model.

Augustin (2012) investigated the relation between the term structure of sovereign

CDS spread and risks, and observed that when the CDS spread slope is positive,

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global shocks are the dominant force underlying changes in the price of sovereign

credit risk. When the CDS spread slope is negative, the importance of domestic

shocks increases. Han et al. (2017) investigated the term structure of the US

corporate CDS spread. They observed that the flat term structure of CDS spread

forecast decreases in default risk and increases in future earnings surprises; they also

negatively predict future stock returns. Calice and Zeng (2018) analyzed a sample of

29 countries and observed a steeper term structure of CDS spread for countries

predicting currency appreciation against the US dollar. They also claimed that the

level of sovereign CDS spread reflects global risk, whereas the term structure of

sovereign CDS spread reveals the specific risk in that country.

3. Data

3.1 CDS spread and term structure

CDS data were sourced from Markit, a common CDS database. Mayordomo et al.

(2014) compared five CDS databases and observed that Markit gathered composite

quotes, with continual daily quotes. The sample period for the study data was

monthly from January 2001 to August 2013. The list of emerging markets followed

the constituents of the MSCI emerging markets index, excluding countries with low

CDS quotes, such as Taiwan. The PIIGS countries were added in our sample set.

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Data from 23 sample countries were analyzed in the study: six countries were from

the Asia–Pacific region (China, Indonesia, South Korea, Malaysia, the Philippines,

and Thailand), five countries were from the Americas (Brazil, Chile, Colombia,

Mexico, and Peru), and 12 countries were from European, Middle Eastern, and

African regions (Czech Republic, Egypt, Greece, Iceland, Italy, Morocco, Poland,

Portugal, Qatar, Russia, South Africa, and Spain). The following data filters were

used in the study: a) government sector–represented derivatives of sovereign debt; b)

US dollar–denominated quotes because CDSs were mostly traded in the United

States and the US dollar–denominated sovereign debts were more liquid than local

currency–denominated bonds; c) old or full restructuring and senior unsecured debt

tiers because data were the most sufficient. The study used daily quotes and used the

previous quote if the value was missing and transferred it into monthly data to

ensure data completeness.

Han et al. (2017) defined the term structure of CDS spread as 5-year spread minus

1-year spread, whereas Calice and Zeng (2018) defined the term structure of CDS

spread as log of 5-year spread minus log of 1-year spread. This study adopted the

definition used by Han et al. (2017). Figure 1 presents the time series data of CDS

spread and slope from various countries. The CDS spread slope of China was less

volatile, and moved upward during the financial crisis and the European debt crisis.

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The CDS spread slope of South Korea was similar to that of China. The CDS spread

of Greece was steady before the year 2010; however, during the European debt

crisis, the 1-year spread dramatically increased to 40,000 bp, which turned the CDS

spread slope negative. The CDS spread of Russia also became volatile during 2009;

however, the 1-year spread was more sensitive than the 5-year spread, which was

different from that of China and South Korea, and the CDS spread slope also

became negative. Brazil’s 2002 election resulted in an unstable economy and thus

depreciated substantially. Therefore, the 1-year and 5-year CDS spread rose over

4000 bp and also made the CDS spread slope negative. The term structure of CDS

spread in Mexico was volatile, at nearly 300 bp.

Table 1 presents the statistics of the CDS spread slope for each region. Panel A

of Table 1 presents the statistics of the CDS spread slope in the Asia–Pacific region.

The minimum and maximum values were observed in data from Indonesia, which

were −91 bp and 375 bp, respectively. The most volatile CDS spread slope was

observed in the Philippines, which had a standard deviation of 93.61 bp. The CDS

spread slope in South Korea was the most stable, with a standard deviation of 20.48

bp. The CDS spread slopes in Asia indicate positive skewness and positive kurtosis,

except those of Malaysia, the Philippines, and Thailand. Panel B of Table 1 presents

the statistics of CDS spread slopes in the Americas. The standard deviations were

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greater than those of the Asia–Pacific region, and Brazil had the most volatility, with

a minimum CDS spread slope of −693 bp, maximum CDS spread slope of 698 bp,

and a standard deviation of 231.69 bp. Therefore, the economy in the Americas was

less stable than that in the Asia–Pacific region, which has made severe changes in

the CDS term structure. Most of the CDS spread slopes indicated positive skewness

(except Brazil) and positive kurtosis (except Colombia). Panel C of Table 1 presents

the statistics of the CDS spread slope in European, Middle Eastern, and African

regions. The CDS spread slope of Greece was the most volatile in the whole sample,

with a minimum of −16,261 bp, maximum of 71 bp, and standard deviation of

4951.98 bp. The standard deviations of other countries, such as Iceland, Portugal,

and Russia (whose CDS spread slopes were more volatile than others) were 113.66,

104.12, and 107.19 bp, respectively.

3.2 US macroeconomic news index

Macroeconomic news from the United States was collected and categorized

into good and bad news indices, following the method used by Kim et al. (2015).

First, US macroeconomic news was collected from briefing.com, and the following

indicators were selected: trade balance, unemployment rate, GDP growth, nonfarm

payrolls, and leading indicators. Second, the aggregate forecast value incorporated

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the average forecast values from three institutions. Third, if the news indicator was

expressed as a percentage, such as unemployment rate, the absolute difference

between the forecast and announced value is used. If the news indicator was

expressed in numeric form, such as nonfarm payrolls, the log difference between the

forecast and announced value was used. Fourth, each news indicator was

standardized for comparison and divided by its standard deviation over the sample

period. Fifth, the news variables were separated into good and bad news indices. If

the announced value was greater than the forecast value, it was considered good

news. However, if the announced value was smaller than the forecast value, it was

considered bad news. If the announced value was equal to the forecast value, it was

considered neither good nor bad news. However, the lower the unemployment rate

is, the more prosperous the economy is. Therefore, a lower announced value of

unemployment rate was considered good news. For each macroeconomic indicator,

the average was considered the good and bad news index in United States.

3.3 Stock index return

Stock index can be a proxy of a country’s economy because it is considered

investors’ expectation about the economy. If investors forecast economic growth, the

stock return tends to increase. There are numerous indices in a country, and the main

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indices that are used by foreign institutions were selected in this study. For example,

the Bangkok Set Stock Index from Thailand, Bovespa Index from Brazil, and

EGX30 Index from Egypt were chosen in this study. Data about stock returns were

sourced from Bloomberg and investing.com. The whole stock index is tabulated in

Table 2.

3.4 GDP growth rate

Similar to the stock index return, GDP growth rate reflects the real economy of

a country. Yearly GDP growth rate data were collected for the study from the World

Bank.

3.5 Control variables

The VIX is the most popular index that captures investors’ sentiments about the

US market. When investors are worried, the VIX increases immediately. For

example, the VIX increased to 59.89 points in October 2008, and 42.96 points in

September 2011, which matches the two most severe events in the sample period,

financial crisis, and European debt crisis. Daily VIX data were collected from the

Taiwan Economic Journal (TEJ) database.

The USD index was adopted as a control variable because we filtered CDS

contracts to be USD-denominated. CDS contracts denominated in USD are the most

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common and liquid. The appreciation and depreciation of the US dollar change

quotes of CDS spread. During the sample period, the USD index increased by

120.59 in January 2002 and decreased by 72.72 in January 2008. The USD index

data were collected at a daily frequency from the TEJ database.

4. Empirical Methodology

The study used the EGARCH model, which was adopted by Booth et al. (1997),

Braun et al. (1995), and Kim et al. (2015). The EGARCH model was derived from

the GARCH model, a heteroskedasticity model that assumes that positive and

negative effects are equivalent; however, the EGARCH model captures asymmetric

effects on variance from good and bad news. To analyze how good and bad news

affects the mean and variance of the term structure of CDS and spread slope in each

country, the good and bad news indices were considered exogenous variables. Three

control variables were added, which were momentum calculated as cumulative

returns from the previous 12 months and the VIX and USD index returns. The

regression was calculated as follows.

𝑆𝑙𝑜𝑝𝑒𝑡 = 𝛼 + 𝛼𝑙𝑆𝑙𝑜𝑝𝑒𝑡−1 + 𝛼𝑔𝐺𝑜𝑜𝑑𝑁𝑒𝑤𝑠𝑡 + 𝛼𝑏𝐵𝑎𝑑𝑁𝑒𝑤𝑠𝑡 +

∑ 𝛼𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑡𝑘𝐾

𝑘=1 + 𝜀𝑡 (1a)

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𝑙𝑛ℎ𝑡 = 𝛽 + 𝛽ℎ𝑙𝑛ℎ𝑡−1 + 𝛽𝜀1𝜀𝑡−1

√ℎ𝑡−1 +𝛽𝜀1

|𝜀𝑡−1|

√ℎ𝑡−1+

𝛽𝑔𝐺𝑜𝑜𝑑𝑁𝑒𝑤𝑠𝑡 + 𝛽𝑏𝐵𝑎𝑑𝑁𝑒𝑤𝑠𝑡 (1b)

where Slopet−1 is the lagged CDS spread slope in each country, lnht−1 is the

lagged error parameter, 𝜀𝑡−1

√ℎ𝑡−1 is the lagged conditional variance, and

|𝜀𝑡−1|

√ℎ𝑡−1 is the

asymmetric component.

5. Empirical Results

5.1 Effect on CDS term structure from US macroeconomic news

First, we checked whether US macroeconomic news announcements affect the

term structure of CDS spread in emerging markets. The EGARCH model was used

to capture the effects of mean and variance from the US good and bad news indices.

The results are presented in Table 3. Panel A of Table 3 presents the results of the

Asia–Pacific region. The mean equation indicates that good news reduces the CDS

spread slope. The countries with significant results were Thailand (−2 bp), China

(−3 bp), Indonesia (−9 bp), and the Philippines (−5 bp), whereas bad news increased

the level of the CDS spread slope in Thailand (2 bp), Malaysia (3 bp), and South

Korea (3 bp). In the Philippines, both good news and bad news reduced the level of

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the CDS spread slope, and the average values were −5 bp and −2 bp, respectively.

The announcement of good news indicates that the economy is more favorable than

expected, and CDS buyers do not need to pay as high of premiums as they did

earlier, especially for long-term CDSs, which led the term structure of CDS spread

to become narrower. If bad news is announced, it can be explained with two

conditions. First, the announcement of bad news indicates that the economy is less

favorable than expected, and CDS buyers must pay more premiums than they did

earlier to ensure protection, especially for long-term CDSs, which lead the term

structure of the CDS spread to widen. In the variance equation, good news

decreased the variance of the CDS spread slope in Thailand, Indonesia, and the

Philippines. When good news was announced, the CDS spread slope changed

dramatically in the shock time; investors knew that the US economy was more

favorable than expected and the variance of the CDS spread slope stabilized.

However, bad news increased the variance in Indonesia and the Philippines and

decreased the variance of in Thailand and China. On some occasions, bad news

indicates that the worst time has passed. Markets know that the panic will not last

forever; thus, the CDS spread slope stabilizes. Therefore, it can be assumed that

good news from the United States makes the premium of CDS contracts with

different maturities stable, and bad news makes the premium of CDS contracts

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either stable or volatile in the Asia–Pacific region.

Panel B of Table 3 presents the results from the Americas region. In the mean

equation, good news increased the level of the CDS spread slope in Brazil (3 bp)

and Colombia (0 bp) and reduced the level of the CDS spread slope in Peru (−2 bp).

However, bad news increased the level of the CDS spread slope in Colombia (1bp),

Peru (3 bp), and Mexico (2 bp). The empirical results are similar with those in Panel

A, and the effect of bad news from the United States on the level of the CDS spread

slope was observed consistently in the Americas. In the variance equation, good

news from the United States decreased the variance of the CDS spread slope in

Colombia, Peru, and Mexico, whereas bad news from the United States increased

the variance of the CDS spread slope in Brazil, Colombia, Chile, Peru, and Mexico.

The effect of good and bad news on the variance of the CDS spread slope is stronger

and more consistent in the Americas.

Panel C of Table 3 presents the results from European, Middle Eastern, and

African regions. In the mean equation, the effects of good and bad news from the

United States were inconsistent. For example, a positive effect of good news

occurred in Egypt (5 bp) and Morocco (1 bp), whereas a negative effect occurred in

the Czech Republic (−1 bp) and Greece (−13 bp). A positive effect of bad news

occurred in Greece (7 bp) and Spain (0 bp), whereas a negative effect occurred in

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Egypt (−4 bp) and the Czech Republic (−2 bp). The number of countries undergoing

negative effects from good news was more than those undergoing positive effects,

that is, four versus two. The number of countries undergoing positive effects from

bad news was more than those undergoing negative effects, that is, six versus three.

Therefore, good news from the United States decreases the level of CDS spread

slope, whereas bad news from the United States increases the level of CDS spread

slope. The effects of good and bad news were also inconsistent in the variance

equations. Good news reduced the variance of the CDS spread slope in six countries

and increased the variance of the CDS spread slope in five countries. Bad news

reduced the variance of the CDS spread slope in two countries and increased the

variance of the CDS spread slope in five countries. In conclusion, good news

decreases the level and variance of the CDS spread slope, whereas bad news

increases the level and variance of the CDS spread slope. These findings are similar

with those of Kim et al. (2015).

5.2 Effect on GDP growth rate from CDS term structure

Based on the findings from Section 5.1, macroeconomic news from the United

States affects the term structure of CDS spread in emerging markets. Next, we

wanted to confirm how the CDS spread slope reflects the real economy. Table 4

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presents the relation between the term structure of CDS spread and the GDP growth

rate.

𝐺𝐷𝑃𝑖,𝑡 = 𝛽0 + 𝛽1𝑆𝑙𝑜𝑝𝑒̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 + 𝛽2𝐶𝐷𝑆1̅̅ ̅̅ ̅̅ ̅

𝑖,𝑡 + 𝛽3𝑉𝐼𝑋𝑡 + 𝛽4𝑈𝑆𝐷𝑡 +

𝐹𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡 + 𝜖𝑖,𝑡 (2)

where Slope̅̅ ̅̅ ̅̅ ̅i,t

indicates the average CDS spread slope per year in each country.

The average 1-year CDS spread, VIX return, USD index return, year effect, and

fixed effect were added as control variables. By adding 𝐶𝐷𝑆1̅̅ ̅̅ ̅̅ ̅i,t as a control

variable, the greater CDS spread slope can be interpreted in two ways. When the

short-term spread is fixed, the higher slope can be derived from the greater

long-term spread, which implies deterioration in the future. When the long-term

spread is fixed, the higher slope can be derived from lower short-term spread, which

implies that the short-term economy is expected to be better than one with a flatter

slope. In column 1, the GDP growth rate per year in each country is set as a

dependent variable. The coefficient of the CDS spread slope was significantly

negative, at −0.0062 with a t statistic of −3.81. That is, on average, the GDP growth

rate in emerging markets declines 0.0062% when the CDS spread slope increases 1

bp. If the CDS spread slope is greater, it implies that investors must pay more

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premiums to receive longer protection, and that the economies of emerging markets

are expecting a recession. These findings are similar with those of Han et al. (2017),

who analyzed US corporate CDSs and observed that corporate CDSs with flatter

spread slope had more standardized unexpected earnings in the next 3 months to 1

year.

Next, we analyzed the effect of the CDS spread slope extending to GDP growth

rates next year. In column 2, the dependent variable is GDP growth rate in the

following year to control the GDP growth rate of the current year. The results

remain significantly negative, at −0.0035 bp with a t statistic of −2.21. Moreover, to

confirm that the CDS spread slope can forecast changes in economic conditions, the

change in GDP growth rate was substituted as the dependent variable as

∆𝐺𝐷𝑃 𝑔𝑟𝑜𝑤𝑡ℎ𝑖 = 𝐺𝐷𝑃 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡+1 − 𝐺𝐷𝑃 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡, and the result appears in

the third column of Table 4. The coefficient of the CDS spread slope was

nonsignificantly positive, at 0.0008 bp with a t statistic of 0.42. Therefore, the CDS

spread slope can only effectively reflect the current economy but cannot forecast

changes in the economic conditions of emerging markets.

5.3 Effect of stock index return from the term structure of CDS spread

To understand whether the CDS spread slope has a similar effect on stock index,

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we ran a panel regression with the CDS spread slope to generate the cumulative

returns from 1, 3, and 6 months and 1 year later. The 1-year CDS spread, VIX return,

USD index return, and momentum were the control variables, with a cumulative

return for the previous 12 months. The regression was calculated as follows: i

represents each country; t represents the time period; and j is 1, 3, 6, or 12, which

are the cumulative returns.

𝐼𝑛𝑑𝑒𝑥𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡+𝑗 = 𝛽0 + 𝛽1𝑆𝑙𝑜𝑝𝑒𝑖,𝑡 + 𝛽2𝐶𝐷𝑆1𝑖,𝑡 + 𝛽3𝑀𝑂𝑀𝑖,𝑡 + 𝛽4𝑉𝐼𝑋𝑡 +

𝛽5𝑈𝑆𝐷𝑡 + 𝐹𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡 + 𝜖𝑖,𝑡 (3)

Table 5 presents the results. The dependent variables of each column are

1-month, 3-month, 6-month, and 1-year cumulative return. The coefficients of the

CDS spread slope were 0.0029%, 0.0104%, 0.0202%, and 0.0087, respectively. The

CDS spread slope forecast the stock index returns up to 6 months effectively. The

coefficient direction of the CDS spread slope was inconsistent with the results from

Section 5.2. The greater the CDS spread slope was, the more favorably the stock

index performed but the lower the GDP growth rate was. We thought that the greater

CDS spread slope was during the panic period, where the base period of the stock

index is relatively low because it reflects current expectation of default risk, the

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more positive returns would be for the next 1–6 months. Actually, when the CDS

spread slope became higher, it represented an increase in the probability of default.

Investors required higher expected returns as compensation because of the higher

risks involved. Moreover, the R squared was greater in the regression of the 6-month

cumulative return, and we thought that the CDS spread slope had stronger

explanatory power for future stock index returns.

5.4 Portfolio strategy

The data presented in Table 5 indicate that the relation between future stock

return and the CDS spread slope is positive in emerging markets. Therefore, we

wanted to utilize this relation to construct a portfolio strategy. The countries were

divided into three groups according to the CDS spread slope in every month; we

longed the high-slope group and shorted the low-slope group and calculated the

return the following month. The results are presented in Table 6. The average of the

raw return of the first (highest) group was 1.3092% with a t statistic of 2.92 and of

the third (lowest) group was −0.0454% with a t statistic of −0.1. The buy high–sell

low strategy gained a profit of 1.3547% per month with a t statistic of 5.04. The

returns were adjusted based on risk factor, using the CAPM, Fama–French

three-factor model, and Carhart four-factor model. After adjustment, each strategy

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had an excess profit of 1.5024% through CAPM, 1.600% through Fama–French

model, and 1.6672% through Carhart four-factor model. In each risk adjustment, the

coefficient of the highest group was significantly different from zero, whereas the

coefficient of the lowest group was not. Moreover, all the buy high–sell low

strategies yielded positive returns, which agrees with the findings of Section 5.3.

6. Conclusion

US macroeconomic news affects the CDS spread slope in emerging markets

and the PIIGS countries. Good news that is more favorable than expected reduces

the mean and variance of the CDS spread slope; bad news that is less favorable than

expected increases the mean and variance of the CDS spread slope. If the CDS

spread slope is flat, GDP growth increases in the same year. However, the CDS

spread slope can only reflect GDP growth rate and cannot forecast future changes.

The CDS spread slope can also estimate trends of future stock index return.

However, the steep CDS spread slope induces positive returns of stock index at 6

months, and the effect is stronger in the long-term to compensate for taking larger

risks. The effect persists even when the economy is in a bad state; for example, the

financial crisis or European debt crisis.

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References

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2007). Real-time price

discovery in global stock, bond and foreign exchange markets. Journal of

international Economics, 73(2), 251-277.

Augustin, P. (2012). The term structure of CDS spreads and sovereign credit risk.

Balduzzi, P., Elton, E. J., & Green, T. C. (2001). Economic news and bond prices:

Evidence from the US Treasury market. Journal of financial and Quantitative

analysis, 36(4), 523-543.

Baum, C. F., & Wan, C. (2010). Macroeconomic uncertainty and credit default swap

spreads. Applied Financial Economics, 20(15), 1163-1171.

Booth, G. G., Martikainen, T., & Tse, Y. (1997). Price and volatility spillovers in

Scandinavian stock markets. Journal of Banking & Finance, 21(6), 811-823.

Braun, P. A., Nelson, D. B., & Sunier, A. M. (1995). Good news, bad news, volatility,

and betas. The Journal of Finance, 50(5), 1575-1603.

Calice, G., & Zeng, M. (2018). The Term Structure of Sovereign CDS and the

Cross-Section Exchange Rate Predictability.

Candelon, B., Sy, M. A. N., & Arezki, M. R. (2011). Sovereign rating news and

financial markets spillovers: Evidence from the European debt crisis:

International Monetary Fund.

Chng, M. T., & Wang, P. (2014). Rating downgrade and the price impact of CDS spread

on stock return. Review of futures markets, 21(3), 283-323.

Das, S., Kalimipalli, M., & Nayak, S. (2014). Did CDS trading improve the market for

corporate bonds? Journal of Financial Economics, 111(2), 495-525.

Dooley, M., & Hutchison, M. (2009). Transmission of the US subprime crisis to

emerging markets: Evidence on the decoupling–recoupling hypothesis. Journal

of International Money and Finance, 28(8), 1331-1349.

Forte, S., & Lovreta, L. (2015). Time‐Varying Credit Risk Discovery in the Stock and

CDS Markets: Evidence from Quiet and Crisis Times. European Financial

Management, 21(3), 430-461.

Greatrex, C. A. (2015). The credit default swap market's reaction to earnings

announcements.

Gurgul, H., & Wójtowicz, T. (2014). The impact of US macroeconomic news on the

Polish stock market. Central European Journal of Operations Research, 22(4),

795-817.

Han, B., Subrahmanyam, A., & Zhou, Y. (2017). The term structure of credit spreads,

firm fundamentals, and expected stock returns. Journal of Financial Economics,

124(1), 147-171.

Paper #720310

27

Hassan, M. K., Kayhan, S., & Bayat, T. (2017). Does credit default swap spread affect

the value of the Turkish LIRA against the US dollar? Borsa Istanbul Review,

17(1), 1-9.

Ismailescu, I., & Phillips, B. (2015). Credit default swaps and the market for sovereign

debt. Journal of Banking & Finance, 52, 43-61.

Kalbaska, A., & Gątkowski, M. (2012). Eurozone sovereign contagion: Evidence from

the CDS market (2005–2010). Journal of Economic Behavior & Organization,

83(3), 657-673.

Kilian, L., & Vega, C. (2011). Do energy prices respond to US macroeconomic news? A

test of the hypothesis of predetermined energy prices. Review of Economics and

Statistics, 93(2), 660-671.

Kim, S.-J., Salem, L., & Wu, E. (2015). The role of macroeconomic news in sovereign

CDS markets: Domestic and spillover news effects from the US, the Eurozone

and China. Journal of Financial Stability, 18, 208-224.

Lee, J., Naranjo, A., & Sirmans, S. (2016). Related securities and the cross-section of

stock return momentum: evidence from credit default swaps (CDS).

Mayordomo, S., Pena, J. I., & Schwartz, E. S. (2014). Are all credit default swap

databases equal? European Financial Management, 20(4), 677-713.

Nikkinen, J., & Sahlström, P. (2015). Impact of Scheduled US Macroeconomic News on

Stock Market Uncertainty: A Multinational Perspecive.

Norden, L., & Weber, M. (2009). The co‐movement of credit default swap, bond and

stock markets: An empirical analysis. European Financial Management, 15(3),

529-562.

Pan, J., & Singleton, K. J. (2008). Default and recovery implicit in the term structure of

sovereign CDS spreads. The Journal of Finance, 63(5), 2345-2384.

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Table 1.

Descriptive statistics

Panel A

APA N Min Q1 Me Mean Q3 Max Sd Skew Kurt

Thailand 149 16 25 51 49 68 96 23.09 0.177 -1.186

Malaysia 149 8 22 50 47 63 122 24.51 0.179 -0.672

China 152 6 17 28.5 34 43 93 21.39 1.031 0.441

Indonesia 139 -91 88 108 121 149 375 77.19 0.792 2.771

Korea 150 2 18 32 35 47 93 20.48 0.811 0.134

Philippines 150 -11 88 120 160 261 356 93.61 0.384 -1.151

Panel B

AME N Min Q1 Me Mean Q3 Max Sd Skew Kurt

Brazil 152 -693 66 87 168 326 698 231.69 -0.482 2.419

Colombia 150 45 71 102 174 295 535 131.21 0.919 -0.504

Chile 139 8 16 43 47 62 165 33.02 1.147 1.309

Peru 139 43 68 92 154 175 655 139.59 1.883 2.912

Mexico 152 15 55 70 85 102 266 48.44 1.474 2.194

Panel C

EMEA N Min Q1 Me Mean Q3 Max Sd Skew Kurt

Egypt 137 27 57 83 107 142 403 64.99 1.402 2.467

Czech 150 2 7 15 24 40 79 20.58 0.862 -0.343

Greece 151 -16261 -12 7 -1843 10 71 4951.98 -2.475 4.309

Poland 152 2 14 29.5 40 55 149 33.18 1.324 1.212

South Africa 151 18 46 78 76 100 134 29.48 -0.174 -0.963

Russia 144 -352 40 90.5 94 125 528 107.19 -0.015 6.592

Qatar 144 6 22 35.5 39 52 98 21.58 0.448 -0.688

This table presents the statistic description of each emerging market, including the sample number of the

CDS spread slope, minimum, first quarter, mean, median, third quarter, maximum, standard deviation,

skewness, and kurtosis. Panels A, B, and C present the regions of APA (Asia–Pacific), AME (the

Americas), and EMEA (Europe, Middle East, and Africa), respectively. The unit of each panel is one

basis point.

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Table 2.

Stock index

APA EMEA

Country Index Country Index

Thailand Bangkok Set Stock Index Egypt EGX30 Index

Malaysia Kuala Lumpur-Stock Index Czech PX Index

China Shanghai Synthesis Index Greece ASE Index

Indonesia Indonesia JSX-Stock Index Poland WIG Index

Korea South Korea-KOSPI Index South Africa Johannesburg Stock Index

Philippines Manila Stock Index Russia Russian RTS Stock Index

Qatar QE Index

AME

Country Index

Brazil Brazil Bovesp Index

Colombia COLCAP Index

Chile Chile IPSA Index

Peru BVL Index

Mexico Mexico IPC Index

This table presents the stock index that was chosen to represent the stock market performance in

emerging markets from the regions of APA (Asia–Pacific), AME (the Americas), and EMEA (Europe,

Middle East, and Africa), respectively.

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Table 3.

EGARCH estimation of slope of CDS spread

Panel A

Mean Mu Ar1 Good Bad MOM VIX USD

Thailand 0.0067*** 0.9864*** -0.0002*** 0.0002*** -0.0014*** 0.0000*** 0.0011***

(8019.78) (10696.91) (-1999.63) (1807.5) (-9710.2) (122.16) (590.14)

Malaysia 0.0060*** 0.9870*** -0.0001 0.0003*** -0.0019** -0.0000 0.0015

(6.92) (81.57) (-0.73) (2.68) (-2.57) (-0.12) (1.13)

China 0.0034*** 0.9788*** -0.0003*** 0.0000 -0.0004** -0.0001*** 0.0014***

(10.01) (72.44) (-12.33) (1.11) (-2.18) (-4.64) (7.65)

Indonesia 0.0113*** 0.8569*** -0.0009** -0.0004 -0.0001 0.0016*** -0.0029

(12.94) (18.14) (-2.02) (-0.74) (-0.04) (3.17) (-0.77)

Korea 0.0011** 0.9932*** 0.0000 0.0003*** -0.0009*** -0.0001 -0.0003

(2.2) (57.74) (0.14) (3.7) (-8.21) (-1.55) (-0.54)

Philippines 0.0272*** 0.9970*** -0.0005*** -0.0002*** -0.0019*** 0.0010*** -0.0009***

(162593.96) (5624.55) (-12397.8) (-279.23) (-167.47) (10762.73) (-259.51)

Variance Omega Alpha1 Beta1 Gamma1 Good Bad

Thailand 0.3321*** 0.2643*** 0.9999*** -0.1658*** -0.7947*** -0.3412***

(30674.94) (16264.64) (12297.9) (-39166.38) (-73751.27) (-2034.37)

Malaysia -0.6139** 0.2612*** 0.9506*** 0.3618** -0.2043 -0.0898

(-2.41) (2.84) (73.61) (2.11) (-0.88) (-0.41)

China -0.7317*** 0.2415*** 0.9434*** 0.3195*** -0.0386 -0.3367*

(-4.41) (4.37) (66.75) (6.14) (-0.19) (-1.83)

Indonesia -1.1054*** 0.0497 0.9269*** 0.2989*** -0.4073** 1.0779***

(-3.49) (0.62) (43.79) (3.15) (-2.00) (3.63)

Korea -1.1212* 0.1066 0.9312*** 0.5935*** -0.1327 0.4836

(-1.65) (0.89) (25.96) (4.52) (-0.37) (1.35)

Philippines -1.8589*** -0.1948*** 0.8682*** -0.4718*** -0.6208*** 0.9550***

(-7806.31) (-3555.41) (8213.22) (-3607.89) (-5807.73) (5468.91)

Table 3. (Continued)

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Table 3. (Continued)

Panel B

Mean Mu Ar1 Good Bad MOM VIX USD

Brazil 0.0073*** 0.8985*** 0.0003*** -0.0001 -0.0019*** -0.0004*** 0.0061***

(129.92) (29.18) (8.16) (-1.57) (-5.75) (-3.91) (4.28)

Colombia 0.0074*** 0.6484*** 0.0000*** 0.0001*** -0.0045*** 0.0003*** 0.0076***

(6432.54) (4237.96) (225.09) (176.38) (-41695.82) (6.58) (412.2)

Chile 0.0066* 0.9942*** 0.0001* 0.0002 -0.0022*** -0.0000 0.0029

(1.86) (77.24) (1.76) (1.34) (-2.88) (-0.42) (1.3)

Peru 0.0166*** 0.9804*** -0.0002*** 0.0003*** 0.0002*** 0.0001*** 0.0077***

(4077.94) (1345.96) (-5.09) (12.42) (10.59) (8.8) (32.95)

Mexico 0.0135*** 0.9956*** -0.0000 0.0002*** -0.0022*** 0.0003** 0.0095***

(11.74) (56.27) (-0.13) (3.17) (-4.13) (2.12) (7.05)

Variance Omega Alpha1 Beta1 Gamma1 Good Bad

Brazil -1.1956* 0.3117* 0.9171*** 0.9023*** -0.4762* 1.4347***

(-1.94) (1.73) (18.53) (6.69) (-1.91) (4.68)

Colombia -1.269*** -0.2757*** 0.9158*** -0.3533*** -1.6718*** 2.6618***

(-2768.7) (-60684.54) (2895.98) (-2452.51) (-2761.2) (7450.37)

Chile -2.2385*** 0.1220 0.8800*** 0.4410*** -0.1125 1.7611***

(-3.73) (1.27) (27.15) (2.87) (-0.37) (4.01)

Peru -0.3151*** 0.3736*** 0.9691*** -0.1842*** -0.3240*** 0.0164***

(-1696.54) (12673.14) (4239.65) (-84214.09) (-98585.69) (481.94)

Mexico -2.3942*** 0.4935*** 0.8247*** 0.3237*** -0.8257*** 0.9471**

(-10.23) (6.15) (331.08) (5.87) (-6.51) (2.22)

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Table 3. (Continued)

Panel C

Mean Mu Ar1 Good Bad MOM VIX USD

Egypt 0.0128*** 0.9518*** 0.0005*** -0.0004*** 0.0014*** 0.0008*** -0.0084***

(17263.19) (5914.78) (54.25) (-116.24) (453.08) (493.89) (-75.4)

Czech 0.0011*** 1.0000*** -0.0001** -0.0002*** 0.0003*** 0.0001*** 0.0006***

(21.5) (105.55) (-2.21) (-5.7) (7.68) (2.69) (3.67)

Greece 0.0009*** 0.7862*** -0.0013*** 0.0007*** -0.0032*** -0.001*** -0.01***

(302.41) (843.74) (-451.24) (105.12) (-361.77) (-11088.26) (-455.57)

Poland 0.0015*** 1.0000*** -0.0001*** 0.0001*** -0.0002*** -0.0001*** 0.0014***

(3.72) (99.07) (-9.61) (4.11) (-11.03) (-2.91) (9.57)

South Africa 0.0051*** 1.000*** -0.0001 0.0002 -0.0000 0.0002 0.0032**

(22.75) (505.83) (-0.49) (1.09) (-0.02) (1.11) (2.19)

Russia 0.0431*** 0.9983*** -0.0000 -0.0001 -0.0019*** -0.0004*** 0.0049

(19.31) (545.8) (-0.17) (-0.25) (-5.95) (-3.4) (0.88)

Qatar 0.0016*** 0.9965*** -0.0003*** 0.0000*** -0.0005*** 0.0003*** 0.0006***

(673622.57) (12016.73) (-10940.72) (12.25) (-6.96) (23.81) (53.08)

Variance Omega Alpha1 Beta1 Gamma1 Good Bad

Egypt -0.101*** 0.4325*** 0.9726*** -0.1885*** -0.6745*** -0.0829***

(-4312.91) (7011.76) (368476.1) (-18065.84) (-13180.52) (-1755.35)

Czech -2.9871*** 0.0113 0.853*** 1.1369*** 1.6761*** 0.8533*

(-2.94) (0.11) (16.37) (6.53) (3.58) (1.96)

Greece -3.9509*** -2.1687*** 0.8317*** 0.3197*** 3.5991*** 2.9578***

(-7673.95) (-96.68) (301281.49) (24.55) (159.53) (115.18)

Poland -2.5280*** 0.4263*** 0.8629*** 0.9679*** 0.9432** 0.6400

(-3.26) (3.9) (20.81) (5.13) (2.29) (1.56)

South Africa 0.1368*** 0.2454*** 0.9948*** 0.0296 -0.7683*** -0.0554

(3.61) (4.41) (54760.03) (1.2) (-7.15) (-0.59)

Russia -1.3164*** -0.0569 0.8666*** 1.3659*** -1.2516*** 0.2159

(-3.16) (-0.59) (27.84) (7.74) (-5.51) (0.55)

Qatar -1.7155*** 0.3975*** 0.9050*** -0.3993*** 0.0177*** 0.7522***

(-8603.66) (8143.42) (10514.32) (-8020.37) (10157.82) (14198.48)

Table 3 presents the regression of the CDS spread slope of each emerging market from US

macroeconomic news consisting of trade balance, unemployment rate, GDP growth rate, nonfarm

payrolls, and leading indicators. Good news is defined as announcements that are more favorable than

forecasts, and bad news is defined as announcements that are less favorable than forecasts. The control

variables include momentum calculated as cumulative return of previous one year, VIX return, and USD

index return. 𝑆𝑙𝑜𝑝𝑒𝑡 = 𝛼 + 𝛼𝑙𝑠𝑙𝑜𝑝𝑒𝑡−1 + 𝛼𝑔𝐺𝑜𝑜𝑑𝑁𝑒𝑤𝑠𝑡 + 𝛼𝑏𝐵𝑎𝑑𝑁𝑒𝑤𝑠𝑡 + ∑ 𝛼𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑡

𝑘𝐾𝑘=1 + 𝜀𝑡, 𝑙𝑛ℎ𝑡 = 𝛽 + 𝛽ℎ𝑙𝑛ℎ𝑡−1 +

𝛽𝜀1𝜀𝑡−1

√ℎ𝑡−1 +𝛽𝜀1

|𝜀𝑡−1|

√ℎ𝑡−1+ 𝛽𝑔𝐺𝑜𝑜𝑑𝑁𝑒𝑤𝑠𝑡 + 𝛽𝑏𝐵𝑎𝑑𝑁𝑒𝑤𝑠𝑡. The first subtable of each panel describes the coefficients of

the mean equation, and the second subtable describes the coefficients of the variance equation. Panels A,

B, and C present the regions of APA (Asia–Pacific), AME (the Americas), and EMEA (Europe, Middle

East, and Africa), respectively. The first row of each country presents the coefficient, and the second row

of each country presents the t statistic; *, **, and *** denote significance of 10%, 5%, and 1%,

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respectively. The unit of each panel is percentage.

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Table 4.

Regression for GDP growth rate

GDPt GDPt+1

Intercept 4.7571 4.7425 (0.76) (0.78)

Slope -0.0062*** -0.0035** (-3.81) (-2.21)

CDS1 -0.0028*** -0.00** (-4.1) (-3.33)

GDPt 0.2920*** (4.82)

VIX 0.0155 0.0673 (0.07) (0.30)

USD 0.059 0.0292 (0.11) (0.06)

Adj. R2 0.6618 0.6845

Year effect Yes Yes

Country effect Yes Yes

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This table presents the regression between the average CDS spread slope (bp) of each country per year

and the GDP growth rate (%) with the control variables: 1-year average CDS spread (bp), GDP growth

rate (%), VIX return (%), and USD index return (%). The dependent variable in the first column is the

GDP growth rate per year, the second column is the GDP growth rate in the next year, and the third

column is the difference between the GDP growth rate between the next year and the current year. The

average slope for each year is computed as an independent variable, and panel data regression was run

considering year effect and country effect. 𝐺𝐷𝑃𝑖,𝑡 = 𝛽0 + 𝛽1𝑆𝑙𝑜𝑝𝑒𝑖,𝑡 + 𝛽2𝐶𝐷𝑆1𝑖,𝑡 + 𝛽3𝐺𝐷𝑃𝑡 + 𝛽4𝑉𝐼𝑋𝑡 +

𝛽5𝑈𝑆𝐷𝑡 + 𝜖𝑖,𝑡 . The first row presents the coefficient, and the second row presents the t statistic; *, **,

and *** denote significance of 10%, 5%, and 1%, respectively. The unit of each panel is one basis point.

Table 5.

Regression on future stock return

1 month 3 months 6 months 1 year

Intercept 0.3582 2.2055 3.7654 7.2061* (0.34) (1.19) (1.34) (1.75)

Slope 0.0029** 0.0104*** 0.0202*** 0.0087 (2.21) (4.39) (5.55) (1.62)

CDS1 0.0013** 0.0047*** 0.0092*** 0.0053** (2.32) (4.69) (6.08) (2.32)

MOM 0.0055 -0.0185** -0.1224*** -0.1930*** (1.21) (-2.27) (-9.87) (-10.49)

VIX -0.0562*** -0.0054 0.0423** 0.0240 (-7.77) (-0.42) (2.13) (0.82)

USD -0.0250 -0.3034*** -0.2258 1.1168*** (-0.46) (-3.16) (-1.55) (5.24)

Adj. R2 0.1174 0.2762 0.3606 0.4006

Year effect Yes Yes Yes Yes

Country effect Yes Yes Yes Yes

This table presents the regression between the returns (%) of 1, 3, and 6 months and 1 year and the CDS

spread slope (bp); a moving window is used to compute the cumulative return for different periods. The

control variables include previous 1-year CDS spread (bp), momentum computed as 1-year cumulative

return (%), VIX return (%), and USD index return (%) considering year effect and country

effect.𝐼𝑛𝑑𝑒𝑥𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡+𝑗 = 𝛽0 + 𝛽1𝑆𝑙𝑜𝑝𝑒𝑖,𝑡 + 𝛽2𝐶𝐷𝑆1𝑖,𝑡 + 𝛽3𝑀𝑂𝑀𝑖,𝑡 + 𝛽4𝑉𝐼𝑋𝑡 + 𝛽5𝑈𝑆𝐷𝑡 + 𝜖𝑖,𝑡 , where

j = 1, 3, 6, 12. The first row presents the coefficient, and the second row presents the t statistic; *, **, and

*** denote significance of 10%, 5%, and 1%, respectively.

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Table 6.

Portfolio strategy

1(High) 2 3(Low) High-Low

Average Return 1.3092*** 0.5765 -0.0454 1.3547*** (2.92) (1.43) (-0.10) (5.04)

CAPM Alpha 1.0528** 0.3226 -0.3051 1.5024*** (2.41) (0.83) (-0.66) (5.55)

FF-3 Alpha 1.2064*** 0.4679 -0.2518 1.6000*** (2.67) (1.16) (-0.53) (5.72)

Carhart-4 Alpha 1.2726*** 0.5087 -0.2533 1.6672*** (2.8) (1.25) (-0.52) (5.97)

Countries are divided into three groups per month according to CDS spread slope. The average return is

computed in each group, and the high minus low portfolio is computed for the next month. The alpha

from the CAPM, Fama–French three-factor model, and Carhart four-factor model are computed. The first

row presents the excess return, and the second row presents the t statistic; *, **, and *** denote

significance of 10%, 5%, and 1%, respectively. The unit of each panel is one basis point.

Paper #720310