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DETERMINANTS OF EMERGING MAREKTS
DOLLAR-DENOMINATED SOVEREGIN BONDS SPREADS
Bashar A. Zakaria
B.S. Engineering, University of Jordan, Amman, 1989
M.B.A., California State University, Sacramento, 2000
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SUMMER
2011
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DETERMINANTS OF EMERGING MAREKTS
DOLLAR-DENOMINATED SOVEREGIN BONDS SPREADS
A Thesis
by
Bashar A. Zakaria
Approved by:
__________________________________, Committee Chair
Yan Zhou, Ph.D.
__________________________________, Second Reader
Tim Ford, Ph.D.
___________________________
Date
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Student: Bashar A. Zakaria
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
____________________________, Graduate Coordinator _____________________
Jonathan D. Kaplan, Ph.D.
Date
Department of Economics
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Abstract
of
DETERMINANTS OF EMERGING MAREKTS
DOLLAR-DENOMINATED SOVEREGIN BONDS SPREADS
by
Bashar A. Zakaria
This thesis uses data from twelve emerging markets economies (EMEs) to explain
the determinants of EMEs dollar-denominated sovereign bonds spreads by using an
econometric model that estimates the fair value of sovereign debt. This model employs
macroeconomic fundamentals and high-frequency Variables (HFVs). A cointegration
technique was used to find the relationship between EMEs spreads and macroeconomic
variables (i.e., real GDP growth, change in terms of trade, and investors’ risk aversion).
Afterwards, two HFVs were introduced—commodity index and U.S. 10-year Treasury bond
yield—to examine short-term deviation of spreads from the equilibrium by using an error
correction model. The model predictive value is evaluated by examining the predicted
value of the model vs. the actual value through back testing of in-sample and out-of-sample
data points. The best specification model produced close to 62 percent hit ratio coming
from trading triggers that are at least one standard deviation away from the mean.
__________________________________________, Committee Chair
Yan Zhou, Ph.D.
________________________
Date
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ACKNOWLEDGMENTS
I would like to thank my dedicated professors Dr. Yan Zhou, Dr. Tim Ford, and Dr. Esen
Onur for their excellent guidance and feedback that made the completion of this thesis
possible. I would like also to thank my lovely wife Alia, and my beautiful daughters
(Fadwa, Farah, and Dania) for giving me much of their time to complete this thesis. It
wasn’t possible without their support. I would like to thank my colleagues in the
investment community for their generous support—access to databases, reports, etc.
Finally, I would like to extend a special gratitude to Dr. Warren Trepeta for being a great
mentor and a solid motivator for more than eight years in my professional life.
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TABLE OF CONTENTS
Page
Acknowledgments ......................................................................................................
v
List of Tables ..............................................................................................................
viii
List of Figures .............................................................................................................
ix
Chapter
1. INTRODUCTION ..................................................................................................
1
2. LITERATURE REVIEW……………...................................................................
7
2.1. Long-term Series Approach…………………………………………......... ..7
2.2. Panel Regressions ………………………………..……………………
8
2.3. Time-series Analysis…………………...……………………………..
10
2.4. Survey of Previous Work……………………………………………...
12
3. EMPIRICAL MODEL AND DATA……. ……………………………………...
17
3.1. Empirical Model.……………………………………………………….
17
3.2. Selection of Variables.……………………..…………………………. .......19
3.3. The Data Set……………………………………………............................. 26
3.3.1 Dependent Variable…………………………………………..
27
3.3.1 Explanatory Variables………………………………………..
27
4. ESTIMATIONS AND RESULTS………………………………...…………….
30
4.1. Estimation of Results …..……………………………………………...
30
4.1.1. Macroeconomic Variables……………………………………
30
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4.1.2. High-frequency Variables ……………….…...........................
30
4.2. Empirical Results……............................................................................
32
4.2.1. Estimating Coefficients……....................................................
32
4.3. Aggregated Results………………..……...…………… ……………..... .....34
4.3.1. Country Results………….……….…………………………..........34
4.3.2. Model Coefficients…………….……………………………..........35
4.4. Testing of the Model……........................................................................
39
4.4.1. In Sample Testing…….……….……………………..........39
4.4.2. Out-of-sample Testing….…….…………………….......... 40
4.4.3. Looking for Strong Signals…………………………..........41
5. CONCLUSION........................................................................................................
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5.1. Summary of Findings...………………......................................................
43
5.2. Suggestion for Future Research………….................................................
44
References .....................................................................................................................
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LIST OF TABLES
Page
Table 3.1. Variables Sources and Definitions………………………………………..
29
Table 4.1. Standard unit root tests, null hypothesis is unit root……..……………..
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Table 4.2. EM sovereign bonds cointegration tests……....…………..………………
31
Table 4.3. Standard unit root test for High-frequency variables, null hypothesis
is unit root……………………………………………………….………
32
Table 4.4. Estimated error correction term………………… ……………………….
33
Table 4.5. EM bond index spread over the last 12 months—market and
Estimated…………………………………………………….……………
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Table 4.6. EM Bonds—Actual vs. Estimated and the Deviation …………...............
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Table 4.7. A change in the variable leads to an X-amount of bp change in
Spreads…………….……………………………….…………….............
Table 4.8. EM bond index spread forecast evaluation—hit rate…..…………............
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LIST OF FIGURES
Page
Figure 1.1. Brazil vs. U.S. 10-year Sovereign Spreads..… ………………….…….....
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Figure 1.2. Emerging Markets Bond Volatility vs. the U.S. 10-years…….………......
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Figure 3.1. Real GDP Growth vs. 5-Year CDS Spreads…….………........................... 20
Figure 3.2. Terms of Trade vs. 5-Year CDS Spreads……… ………………….……... 21
Figure 3.2.1. Chile’s CDS Spread vs. Copper Spot Prices…….………....................... 22
Figure 3.3. Emerging Market Sovereign Bonds Spreads vs. Risk Aversion Index........ 23
Figure 3.4. Commodity Prices vs. Emerging Markets Sovereign Bonds Spreads…...... 24
Figure 3.5. U.S. Treasury Yields vs. Emerging Markets Sovereign Bonds Spreads....... 25
Figure 4.1. Market Spread Deviation from the Estimated Spreads (Market – Estimated) 36
Figure 4.2. Market vs. Estimated Spreads for In Sample Testing................................... 41
Figure 4.3. Market vs. Estimated Spreads for Out-of-sample Testing............................ 42
Figure 5.1. South Africa’s Sovereigns vs. Corporate Bonds Spread Movements…....... 45
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Chapter 1
INTRODUCTION
This thesis examines the impact of macroeconomic variables along with highfrequency variables (HFVs) on emerging markets economies (EMEs) dollar-denominated
sovereign bonds spreads. While some EMEs have been issuing debt in local currency,
this study is focusing on dollar-denominated sovereign debt because it aims at
neutralizing the impact of local currency valuation against hard currencies.
Understanding the drivers of sovereign bond spreads allows countries to focus on “what
matters” to better their debt metrics, improve their credit profile, lower their borrowing
cost, and eventually attract sizable foreign direct investments which eventually could
translate into lower unemployment and better government revenues. This research found
that higher real GDP growth is negatively related to spreads, and an improvement in
terms of trade leads to a spread tightening (lower spreads) in six out of nine. An increase
in risk aversion has led to higher spreads, especially in countries with higher spread
volatility. U.S. Treasury yields impact on spreads varied over time. Finally, commodity
prices are associated with a spread tightening.
Sovereign bonds are issued by sovereign countries and explicitly guaranteed by
the full faith and credit of the issuer. Like all bonds, sovereign bonds are usually rated by
at least one of the three main rating agencies (i.e., Moody’s, Standard & Poors, or Fitch).
Sovereign bonds rating, similar to consumers’ FICO score, reflects the probability of a
default, which is defined as the issuer inability and (or) unwillingness to pay back the
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bond’s par value and (or) the coupon payment in full and on time. Willingness to pay is
typically hard to measure, unlike the ability to pay that can be assessed by examining the
sovereign balance sheet (e.g., foreign exchange reserves, current account balance, debt
outstanding, government revenues, etc.). Willingness to pay lies in the hands of the
political leadership of the country. If a country, for some reason, concludes that its
finances won’t be affected in case of a default, then the political leadership might elect to
do so. Case in point is when Ecuador defaulted on its external debt in 2007/08; the
political leadership thought that the previous administration along with international
bankers ruined the country’s finances.
The pricing of sovereign bonds is quoted in spread terms and measured in basis
points (100 basis points (bp) = 1%), which is the risk premium, or additional yield,
required by investors to hold bonds issued by EMEs that are perceived to be more likely
to default than bonds issued by developed economies1. Thus, the difference between the
sovereign bond yield and the yield of a matching maturity risk-free bond (in most cases
U.S. Treasury bills or bonds) constitutes the sovereign bond spread. Moreover, the
change in sovereign spread is a function of not only the yield of the sovereign bond, but
also the yield of the risk-free bond.
Figure 1.1. shows an example of sovereign bonds spreads between the Brazilian
dollar-denominated 10-year bond and the U.S. 10-year Treasury bond. The Brazilian
sovereign bond was chosen due to its size in the EMBI, close to 20%, in addition,
Brazilian bonds are amongst the most liquid in the sovereign bond universe because
The recent economic crisis of 2008/09 has proved the opposite where EMEs didn’t default at all. Instead,
developed issuers were the ones seeking help form the IMF.
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Brazil is an active sovereign bond issuer with a size and maturity that appeal to sovereign
debt investors. That makes Brazilian spreads a good proxy of emerging market sovereign
spreads. The spread between Brazilian 10-year and U.S. 10-year was 200 bp on
6/30/2008 and widened on 12/22/2008 to 455 bp. The change in spread, in this example,
has two driving forces: The first driving force is higher Brazilian yield (coupon / current
price) as a result of lower Brazilian bond price. As the market price of Brazilian bond
dips lower, the current yield gets higher, which would cause higher spreads relative to the
U.S. 10-year.
Figure 1.1 Brazil vs. U.S. 10-year Sovereign Spreads
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Spread =
200 bp
Yield in %
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Spread =
455bp
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Brazil
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U.S.
2
Brazil
U.S.
3
0
6/30/2008
12/22/2008
Second driving force is lower U.S. 10-year yield (or higher bond prices) that makes the
spread wider.
Dollar-denominated sovereign bonds’ spread movements reflect the market price
based on investors sentiment and assessment of the issuer’s default probability. As the
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perceived risk of a sovereign bond gets lower due to fundamental reasons (e.g., lower
external debt-to-GDP, higher foreign exchange reserves, improving terms of trade,
current account surplus, etc.), or non-fundamental reasons (e.g., implicit guarantee of
payment by a wealthier nation, market favoring the asset class, optimism of long-sought
reforms materializing sooner than anticipated, observable improvement in political
stability, or even due to supply and demand dynamics, etc.), the price of sovereign bonds
gets higher resulting in a compressed spread (or tighter spread) while other things are
held constant. Being able to accurately identify variables that impact spreads and then
predict spread movements is the key success factor for sovereign bond portfolio
managers. Understanding and accurately anticipating spread movements on a tactical or
strategic basis would allow investors to long or short risky bonds on a timely fashion to
maximize alpha (excess returns over the benchmark returns). This is exactly what
happened in early December of 2008 when the yield of U.S. 10-year Treasury bond and
30-year Treasury notes and bonds have reached low to mid 2 percent--a level that has not
been seen ever. What happened?
In the previous spread example, the U.S. yield curve has shifted lower while the
Brazilian curve has shifted higher (in other words the price of Brazilian bonds went down
and the price of U.S. bonds went up). After the unfolding of the global liquidity crisis that
came after the blowup of Bear Sterns and Lehman Brothers’ bankruptcies, there was a
significant round of deleveraging where international investors along with hedge funds
portfolio managers sold off risky assets and bought safe assets (i.e., U.S. bonds). That
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explains the lower U.S. yields (or higher U.S. bond and note prices) and the higher yields
(or lower prices) of Brazil and the emerging market bonds in general.
Emerging market countries issue bonds to finance infrastructure projects, current
account deficits, or even sometime budget deficits. Borrowing cost is highly volatile.
Figure 1.2. shows the yield on a U.S. 10-year constant maturity vs. the JP Morgan
Emerging Markets Bond Index (EMBI), both expressed in percent, over the last 13 years.
The average yield on the U.S. 10-year constant maturity is 4.53 percent with a standard
deviation of 0.9 percent while the average yield on EMBI is 9.83 with a standard
deviation of 2.81--more than double the yield and three times the standard deviation of
the U.S. 10-year Treasury. The Asian crisis, the Russian Default, and the dot com bubble
were all events that have contributed to this significant difference in volatility and yield.
According to Rocha, Siqueira, and Pinheiro (2006), emerging market countries since then
have shown significant improvements in many economical, financial, and regulatory
fronts that made them a favored destination for many money managers who were looking
for higher yields. Moreover, there are external factors (interest rate, global economic
growth, global risk appetite, and quest for a higher yield) that are equally important
according to Cantor and Packet (1996). The previous factors significantly improved
international money managers’ risk appetite and sent them looking for higher yielding
EMEs bonds. International money managers’ assessment of EMEs sovereign bond risk
was confirmed by rating agencies who upgraded the credit rating of many EMEs faster
than upgrading developed economies ratings. It’s important to identify the main drivers
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of the sovereign spreads because they are the key for emerging market countries as they
constitute a floor for the cost of external borrowing.
Figure 1.2. Emerging Markets Bond Volatility vs. the U.S. 10-years
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Percent
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2
US 10-year Constant Maturity
Dec-09
Dec-08
Dec-07
Dec-06
Dec-05
Dec-04
Dec-03
Dec-02
Dec-01
Dec-00
Dec-99
Dec-98
Dec-97
0
JP Morgan EMBI
The remainder of this thesis is organized as follows. Chapter 2 surveys relevant literature,
Chapter 3 presents the data used, Chapter 4 describes the empirical model and the
estimation of results, and Chapter 5 concludes.
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Chapter 2
LITERATURE REVIEW
2.1. Long-term Series Approach
A few studies attempted to understand the behavior of very long time series of
EMEs bond spreads against riskless sovereign bond—a AAA-rated sovereign bond like the
U.S. Treasury. These studies relied on a long-series of annual data. However, long time
series have little explanatory value in the twelve to 24 month horizons due to recent event
risk, low frequency releases, and data availability and reliability issues for some countries.
Using over 600 years of data, Reinheart and Roggoff (2008) showed that serial
default is a nearly universal characteristic of risky sovereign debt markets. Countries tend
to struggle to graduate from developing to developed economies. This graduation process
requires capital flows, local credit market development, a developed yield curve, as well as
boom and bust cycles. Major defaults episodes, according to the paper, are typically spaced
some years and perhaps decades apart. This is indeed one of the major caveats of models
using long time series. They are mostly useless to predict market prices in the one to two
year horizon. Accordingly, crises frequently originate from the financial centers with
transmission through interest rates shocks and commodity prices. Indeed, as shown below
these last findings are quite helpful in specifying shorter term version of spread valuations.
Mauro, Sussman and Yafeh (2000) compared the behavior of bond yields against
the riskless yield in the 1990s and 1870-1913. They found that sharp changes in spreads in
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the 1990s tend to be mostly related to global events. Unlike Reinhart and Rogoff (2008),
the authors found that fundamentals where more important in earlier years. Mostly,
country-specific events drove spreads in the last two centuries. The authors used event
study to test behavior changes in the spreads around specific events where meaningful
capital flows and flight took place. The main statistical techniques applied were principal
components analysis, beta comparisons against benchmarks, and GARCH models.
A key limitation of these methodologies is the frequency of those defaults that took
place over several years or decades apart. Reinhart and Rogoff (2008) claim that this low
default frequency gives policy makers and investors a false since of confidence while
trying to convince themselves that "this time is different." For investors, however, this is a
key difficulty. Holding short positions in some bond markets may trigger a significant
underperformance against the benchmark. This is usually the core of the reasoning behind
some herd behavior across credit market investors as well as the contagion impact amongst
different markets.
2.2. Panel Regressions
A panel is a dataset that combines time-series information for a cross-section of
individual countries. This method tends to show slightly more robust results than capital
flows due to a higher data frequency (quarterly vs. annually). These models bundle several
countries together by applying panel regression econometrics with fixed effects to estimate
spread sensitivity to domestic and external factors. Panel regressions attempt to explain the
relationship between sovereign spreads and domestic and external macroeconomic
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indicators. Domestic macroeconomic indicators include statistics that highlight GDP,
savings and investments, revenues and expenditures, current account, inflation, foreign
exchange reserves, private vs. public local debt, etc. External macroeconomic indicators
include the country’s external position—external debt to GDP, trade balance, amortization
amount, liquidity ratio, net external borrowing requirement, and external vulnerability
indicator. The coefficient estimates that are obtained from panel regressions provide an
average set of long-run coefficient driving spreads for all the countries. Then, the same set
of long-run coefficients is applied to the different country fundamentals and arrives at the
country specific estimates of the long-run equilibrium spreads.
One of the advantages of the panel regression model is that it accommodates
countries with a different data set starting point. Moreover, they allow the researcher to
benefit from time series as well as cross-sectional data, and that provides a larger data set
when data are limited.
Panel regressions fair values are usually stale and of little help in the six to twelve
month horizons. The frequency, availability, and possible revisions of the data release
could be problematic for some countries. For example, some countries release economic
data on an annual basis and others on quarterly basis. Some countries don’t pay great
attention to the importance of timely releases, so some series won’t get updated to reflect
the most recent year or a quarter. Issues regarding data availability could severely impact
the researchers’ ability to accurately model sovereign spreads due to the limited data points
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available. Another potential limitation to this approach is the transparency which casts a
shadow of doubt on the reliability of data releases.
2.3. Time-series Analysis
Time-series uses higher frequency data, usually monthly or weekly data. Time
series analysis is usually the most robust framework. In these models spreads are usually
explained by monthly, weekly and, sometimes daily factors, which solve the shortcoming
of the panel regression method. Growth and credit metrics are the key macro factors.
Otherwise these models' explanatory factors are market variables such as U.S. Treasury
yields, commodity prices, and currency valuation. Time series econometric fit and
performance are usually the best but the least appealing from a macroeconomic perspective
(explained later in this thesis).
Recently, market practitioners have been attempting to use macro-based models to
explain emerging market sovereign bond spreads using available and up-to-date time series
to overcome the problems that they have faced with data availability and frequency.
Average credit ratings from the three major rating agencies along with growth and credit
metrics all have been used. GDP growth has a lagging trickle effect on many other sectors
in the economy. Slower GDP growth translates into worsening debt and fiscal ratios, as
many debt and fiscal indicators are expressed as a percent of GDP. As the denominator
shrinks or exhibits slower growth patterns, then the numerator, as a percent of the
denominator, gets larger, and that would trigger the rating agencies to revise the credit
outlook for a country and possibly follow it with a downgrade. Markets are typically
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quicker in reacting to such developments. Sovereign spreads will reflect a lower credit
rating for countries that the market participants are not confident of their growth prospects.
As a result, funding cost, in terms of sovereign bond spreads, will increase accordingly.
Short-term factors like U.S. Treasury yields, currency valuation, inflation rate,
dedicated money flow, and commodity prices were also used because of higher data
frequency. Such higher frequency releases resulted in time series models that are more
intuitive once compared to other less frequent ones. The usual specification of these models
is expressed by a cointegrating equation according to Sueppel (2005). The long-term
equilibrium relationship between the countries’ spreads, macro fundamentals, and global
investors’ level of risk aversion (possibly through a proxy such as swap rates or UST
yields) can be represented by the following equation.
Log Spread   Log B  Log GDP  Log REER   Log IS   u
Where GDP is GDP growth rate (year over year annualized), REER is the real effective
exchange rate, IS is EMEs investors’ sentiment, and u is the difference between market
spreads and what is indicated by its fundamentals (the error term). B is the regression
equation intercept.
The drawback of time-series models is over reliance on market data releases that
are often backward looking. Actual spreads might diverge from modeled ones because
actual spreads tend to price in anticipated data releases.
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2.4. Survey of Previous Work
Westphalen (2001) tried to find the determinants of sovereign bond spreads
changes by identifying variables that are expected to influence sovereign spreads and then
testing their statistical significance. The data set covered the period from March 1995 to
April 2001 for 26 countries and included 215 non-optionable U.S. dollar or hard currency
denominated bonds. Credit spreads were calculated based on the difference between the
sovereign bond yield and the corresponding benchmark risk-free yield. Finally, distance to
default was measured by debt service as a percent of export and exports as a percent of
GDP. Debt service as a percent of exports, changes in 10-year risk-free rate, changes in the
slope of the yield curve, changes in the twelve month historical volatility of the local stock
market, and the return of the MSCI world stock index were the explanatory variables. The
estimation was done by using pooled generalized least squares. Signs of the coefficients
were mostly inline with expectations and R-squared came at 15.9 percent, which implies
that the model leaves a large part of the changes in sovereign spreads unexplained.
Hong (1998) employed an empirical analysis to determine emerging markets
sovereign bond spreads. Two sets of explanatory variables were utilized. The first one
included liquidity and solvency variables (e.g., external debt as a percent of GDP, reserves
as a percent of GDP, current account as a percent of GDP, debt service as a percent of
exports, growth rate of imports, GDP growth rate, growth rate of exports, and net foreign
assets). The second set included macroeconomic variables (e.g., terms of trade, inflation
rate, nominal exchange rate, real oil price, 3-month U.S. Treasury bill rate, bond maturity,
amount of bonds outstanding, etc.). As for the first set of variables, they were all found to
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be statistically significant with the right predicted sign except for Current Account/ GDP
(CGDP) and GDP Growth (GGDP). The second set of variable showed a negative
relationship between terms of trade and sovereign bond spreads and a direct relationship
between inflation rate and higher sovereign bond spreads, both were statistically
significant. As for Nominal Exchange Rate, it showed a positive relationship with spreads,
which is against what was expected. When it comes to external shocks, Real Oil Price
(ROP) and the U.S. 3-months T-bill rate both happened to have a direct relationship with
spread, but none was found to be statistically significant. Maturity and amount outstanding
explanatory variables had an inverse relationship with bond spreads and both were
statistically significant. The R-squared came in at 64.9 percent.
Ferrucci (2007) attempted to quantify the portion of change in sovereign bond
spreads that’s explained by a change in the underlying macroeconomic fundamentals2
while controlling for external factors (i.e., liquidity and market risk), then compared
predicted spreads vs. actual spreads. He used a reduced-form model (resulted from generalto-specific approach) where both the default probability and the recovery rate are
exogenous. The models aimed at explaining the long-run determinants of sovereign bond
spreads with some short-run dynamic behavior. Data from J.P.Morgan’s EMBI and EMBI
Global secondary market spreads were used (1995 – 1997) for 27 countries. This study
concluded that market spreads broadly reflect fundamentals, but non-fundamentals factors
play a more important role in explaining spreads. Moreover, it concluded that markets
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External debt/GDP, fiscal budget/GDP, openness, amortization/reserves, interest payment/external debt,
current account/GDP short-term external debt/external debt, yield of 30-day US T-bill, yield of US 10-year
bond, log of yield spread between low and high-rated US corporate bond, log of US S&P 500 equity index.
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don’t take into account macroeconomic fundamentals when pricing sovereign risk. The
study found a divergence between predicted and actual spreads due to market mispricing
and market imperfections. Unaccounted for qualitative factors may have been able to
explain the divergence in the pricing of the sovereign debt, but they were not included in
this study.
Hilscher and Nosbusch (2007) examined the variation in sovereign bonds spreads
across countries and over time to determine how much can be explained by
macroeconomic fundamentals3. Their focus was the relationship between the variations in
macro fundamentals vs. the variation in the spreads. They used daily spread data for 32
countries that cover the period from 1993 to 2004 for dollar denominated and highly liquid
sovereign bonds with average maturity of twelve years. Hilscher and Nosbusch (2007)
were able to explain up to 48 percent of the time variation in the EMBI spreads using both
a reduced-form and a simple structural model. A significant part remains unexplained
despite using a large number of variables in their estimation. They assumed a linear
relationship between macro variables and credit risk and used the terms of trade as a proxy
of the country’s economy. Terms of trade (ToT) turned out to be the most important
determinant of spreads (a measure of repayment capability) followed by debt/GDP ratio
within the country series but not across countries (a measure of country’s liability).
Eichengreen and Mody (2000) analyzed 1,000 sovereign bonds issued by 37 emerging
market countries with a specific maturity, face value, and coupon issued from 1991 to
1996. They examined issue and pricing decisions jointly to minimize selectivity bias.
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Debt/GDP, terms of trade, GDP growth rate, foreign exchange reserves/GDP, and country default history.
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Maximum-likelihood probit model and a regression using the estimated Inverse Mills Ratio
were used in this analysis. It was found that observed changes in fundamentals4 explain
only a fraction of the spread compression in the period leading up to the late 1990s
emerging market crisis. Moreover, this study found that Investors tend to price bonds on
the basis of incomplete knowledge of countries’ economic and financial circumstances,
which is conducive to market volatility. In addition, it was found that the same explanatory
variables have quite different effects on different types of borrowers (Latin American vs.
East Asian) whereas, overtime, spreads are clearly influenced by shifts in market sentiment
rather than by shifts in fundamentals. More specifically, it was found that higher debt-toGNP both reduces probability of bond issuance and increases the spread. Debt rescheduling
leads to a higher issuance and wider spreads. As U.S. yields rise, emerging market
countries tend to issue less, and this decline in issuance limits supply and increase prices
(reduces spreads).
This thesis will contribute to the existing literature by blending a set of macroeconomic
fundamentals along with HFVs5 to explain sovereign bonds spreads for twelve emerging
markets countries. This thesis will use a macroeconomic indicator (i.e., change in terms of
trade) and a market sentiment indicator (i.e., Citi Global Risk Aversion Index)—two
explanatory variables that have not been used in a cointegration and error correction
models before. Macroeconomic indicators, which are mostly used in the literature to
explain sovereign spreads, come in a very low frequency, usually on annual or quarterly
4
Total external debt/GDP, debt service/exports, reserves/GNP, GDP growth rate, budget deficit/GDP,
recent credit rating, a dummy variable that captures whether the country restructured its dept before or not.
5
High frequency variables, HFVs, (or according to Sueppel (2005) fast fundamentals) are “parameters that
are available to the market at large and whose levels help to predict future changes in the credit rating.” In
this study, HFVs are variables that literature has shown to impact spreads and with daily data frequency.
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basis, and they fail to capture market sentiment and higher market volatility during times of
risk-off trades where investors flock to quality assets. Finally, this research uses a
commodity index that contains both hard and soft commodities unlike what other literature
sources have traditionally been using—mostly oil prices like in Sueppel (2005). This
should improve on exiting results because countries included in this study export a variety
of hard and soft commodities. The results of this research add to the existing work and
were able to predict spread movements in nine out of twelve countries.
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Chapter 3
EMPIRICAL MODEL AND DATA
3.1. Empirical Model
As it was mentioned earlier, sovereign bonds’ spreads are impacted not only by
fundamentals factors or macroeconomic indicators, but also by non-fundamental or
macroeconomic factors. Thus, to model sovereign spreads, various explanatory variables
can be put together in an equation to explain the relationship.
CDSi,t = f (ICCRi,t, RPi,t) …………………..(1)
The dependent variable is emerging market Credit Default Swaps spread (CDSi,t), a
timely market proxy of sovereign bonds spread. The explanatory variables are individual
country credit risk (ICCRi,t) and investors’ demanded risk premium (RPi,t). ICCRi,t
captures macroeconomic fundamentals like GDP growth (GDPGi,t), and change in terms
of trade (CToTi,t).
ICCRi,t = f (GDPGi,t, CToTi,t) …………..…(2)
RPi,t is a function of investors’ risk aversion index (RAi,t) measured by Citi Global Risk
Aversion Index (RAi,t).
RPi,t = f (RAt)………………..………….(3)
By Replacing equations (2) and (3) into equation (1), CDSi,t becomes a function of
macroeconomic indicators, as well as investors’ risk aversion index.
CDSi,t = f (GDPGi,t, CToTi,t, RAt)…………………..(4)
For simplicity, spreads’ are assumed to have a functional form –the f-function in equation
17
18
(4) above– is characterized for a Cobb-Douglas function6. This implies the notion that
macroeconomic fundamentals and the investors’ risk aversion are likely non-linear and
compound each other.
CDSi,t = i . GGDPxi,t . CToTγi,t . RAλi,t………………(5)
When expressed in a logarithmic form, equation (5) becomes.
Log CDSi,t = Log I + x Log GDPGi,t+ γ Log CToTi,t+λ Log RAt……(6)
To validate this theoretical model, unit root tests for the twelve countries in this study
were conducted followed by Johansen’s cointegration test which implies that a set of
variables is cointegrated if each of the individual series presents a unit root but that a
linear combination of them is stationary. Finally, the cointegrating equation—the longterm equilibrium relationship between the country’s spreads, its macroeconomic
fundamentals (GDPG and CToT) and global investors’ level of risk aversion (RA)— is
presented using the following format.
Log CDSi,t= Log i+ x Log GDPGi,t+ γ Log CToTi,t+λ Log RAt+ ui,t..............(7)
ui,t is the difference between observed market spreads and the spread that’s indicated by
the model (i.e., the error term).
Error correction mechanism (ECM) states that if sovereign spreads, macro
fundamentals (GDPG and CTOT) and investors’ global risk aversion are cointegrated,
6
The Cobb–Douglas production function is used to represent the relationship between inputs and outputs.
In its general form, the function can be represented by z=C.x a.yb; where z is an output and x and y are
inputs. “C”, “a” and “b” are constants.
18
19
then according to Granger Representation Theorem, these explanatory variables can be
modeled in an error-correcting relationship. The error-correction mechanism prevents the
integrated variables from drifting apart without a bound. The ECM model, similar to
Sueppel (2005) model, can be expressed in the following ECM formulas:
l
Y
i,t
 a
i,j
  Z i ,s , j Yi,t - s b u
 D x 
.......... ......( 8 )
i , j i ,t 1
i, j t
i , j ,t 1
j 1
For Y'i,t = Log CDSi,t, LogGDPGi,t, Log CToTi,t, Log RAt
and x't = {HFV1,t…HFVn,t}
HFVs are high-frequency variables available to the market at large. Their levels help
to predict future changes in the CDS spreads and reflect current market conditions. The
coefficients ai,j and bi,j are 3 x 1 vectors, Zi,s,1, …Zi,s,l are 3 x 3 matrices coefficients of
lagged changes in variables (log spreads, log GDPG, log CTOT and log RA), while Di,j is
a 3 x n matrix—the coefficient of HFVs. The term ui,j,t-1 represents how much the system
was out of equilibrium in the previous period, and εi,j,t-1 is the error term. The coefficient
bi,j measures the speed of adjustment or the proportion of the error is corrected each
period.
3.2. Selection of Variables
The model relies on three macro variables and two HFVs to explain the evolution
of spreads. The first one is Real GDP Growth (GDPG) expressed in constant prices in
U.S. dollar at the official exchange rate.
19
20
Figure 3.1 shows that higher GDP growth is inversely related to sovereign CDS
spreads. Higher GDP results in lower debt ratios, lower external financing needs, higher
ability to service and payback debt, higher revenues, budget surpluses, lower default risk,
and higher income per capita (an important factor to get a higher credit rating), Hilscher
and Nosbusch (2007). Figure 3.1 shows GDP growth to be inversely related to CDS
spreads. GDP growth is a lagging indicator that summarizes the economic performance
of a national economy up to the most recent quarter.
Figure 3.1. Real GDP Growth vs. 5-Year CDS Spreads
250
380
360
340
240
Real GDP (Billions of US $)
300
230
280
260
220
240
220
200
210
180
CDS Spreads (in Basis Points)
320
160
200
140
120
GDP (LHS)
Dec-10
Sep-10
Jun-10
Mar-10
Dec-09
Sep-09
Jun-09
Mar-09
Dec-08
Sep-08
Jun-08
100
Jan-00
190
CDS (RHS)
The second is change in terms of trade (CToT). CToT is the annualized percent
change in the average price of merchandise exports relative to the average price of
merchandise imports. Improving terms of trade could potentially lead to a trade surplus,
current account surplus, higher revenues, lower debt, appreciating real effective exchange
20
21
rate, and higher economic growth. Expected relationship with spreads is also negative—
Figure 3.2. The impact of CToT varies among countries depending on their openness of
the economy (i.e., exports + imports as a percent of GDP) and their reliance on
commodity exports. As the value of exports increase relative to imports, that leads to
trade balance surpluses, which would lessens external financing needs, lowers sovereign
bond issuance, and tightens spreads Ferrucci (2007). For example, Chile’s sovereign
bond issuance became very limited after copper prices, Chile’s major export commodity,
significantly increased. Moreover, Chile’s CDS spreads went noticeably lower between
2005 and 2007 and between 2009 and 2011 as copper prices appreciated noticeably
(Figure 3.2.1.).
Figure 3.2. Terms of Trade vs. 5-Year CDS Spreads
250
40
Correlation = -0.31
Terms of Trade
20
150
10
100
0
50
CDS in in Basis Points
30
200
-10
CDS (LHS)
Jan-11
Jan-10
Dec-08
Jan-08
Jan-07
Jan-06
Jan-05
Jan-04
-20
Jan-00
0
Terms of Trade (RHS)
The third is Risk Aversion (RA). The Citi Global Risk Aversion Index measures
21
22
risk aversion in global financial markets. It’s an equally weighted index of emerging
market investors’ risk appetite and sentiment that incorporate implied foreign exchange,
EMEs equity indices volatility, and swap volatility.
The index is expressed in a rolling historical percentile and ranges between zero
(low risk aversion) and one (high risk aversion). This indicator gained a special
importance after the collapse of Bear Sterns and Lehman Brothers where contagion,
liquidity squeeze, solvency, and counterparty risk7 were all elevated.
Figure 3.2.1. Chile's CDS Spread vs. Copper Spot Prices
(Normalized levels)
800
700
January 2005 = 100
600
500
400
300
200
100
CDS
5/7/2011
1/7/2011
9/7/2010
5/7/2010
1/7/2010
9/7/2009
5/7/2009
1/7/2009
9/7/2008
5/7/2008
1/7/2008
9/7/2007
5/7/2007
1/7/2007
9/7/2006
5/7/2006
1/7/2006
9/7/2005
5/7/2005
1/7/2005
0
Copper
Source: Bloomberg
According to Sy (2001), high global investor risk aversion leads to wider EM sovereign
7
Counterparty Risk is a risk that arises when a seller of CDS fails to fulfill the contractual obligation to the
buyer due to illiquidity, insolvency, or lack of collateral.
22
23
bonds spreads. As risk aversion increases, emerging market sovereign bonds spreads
increases (Figure 3.3.)
HFVs include monthly change in commodity prices and U.S. 10-year bond yield.
Monthly change in commodity prices is measured by Standard and Poors (S&P)
Enhanced Commodity Official Close Index. Sueppel (2008) used what he described as
fast fundamentals, oil price, U.S. 10-year Treasury yield, and euro-dollar exchange rate,
because they impact credit rating, funding cost, and exports volume respectively.
Figure 3.3. Emerging Market Sovereign Bonds Spreads vs. Risk Aversion Index
1,600
1.00
0.90
1,400
Basis Points
0.70
1,000
0.60
800
0.50
0.40
600
0.30
400
RAI (0 = low, 1 = high)
0.80
1,200
0.20
200
0.10
Market Spreads (LHS)
Dec-10
Dec-09
Dec-08
Dec-07
Dec-06
Dec-05
Dec-04
Dec-03
Dec-02
Dec-01
Dec-00
Dec-99
Dec-98
0.00
Dec-97
0
RAI (RHS)
Source: Bloomberg, JPMorgan, and Citi
In this study, U.S. 10-year Treasury was used as an indicator of funding cost and
as a global benchmark of risk-free yield. Euro-dollar exchange rate was not used, and oil
prices were replaced by a commodity index. This index contains soft and hard
commodities (i.e., agricultural, industrial metals, precious metals, gas, and oil). The
23
24
Majority of the countries in this study are net exporters of one or more types of
commodity that are covered by this index. This is another contributing factor the existing
literature. The theory here is that as the country’s production of one or more commodity
goes higher and as the world demand for exported commodities grow larger, commodity
prices climb higher, which translate into, ceteris paribus, a higher balance of payment
surplus, current account surpluses, lower external financing needs, higher foreign
exchange reserves, and stronger ability to service and (or) payback debt. Figure 3.4.
shows a negative relationship between S&P Enhanced Commodity Index and
J.P.Morgan’s EMBI.
Figure 3.4. Commodity Prices vs. Emerging Markets Sovereign Bond Spreads
1,200
1,400
1,000
1,200
600
US $
800
600
400
400
200
200
Market Spreads (RHS)
Feb-11
Mar-10
Aug-10
Apr-09
Sep-09
Oct-08
May-08
Jun-07
Nov-07
Dec-06
Jun-06
Jul-05
Jan-06
Feb-05
Mar-04
Aug-04
Apr-03
Sep-03
Oct-02
May-02
Jun-01
Nov-01
Dec-00
0
Jul-00
0
Jan-00
Basis Points
1,000
Correlation = -0.78
800
Commodity Prices (LHS)
Source: International Institute of Finance (IIF), Bloomberg, and JPMorgan
The U.S. 10-year Treasury bond yield is used as a proxy for global interest rates.
24
25
Sovereign bonds investors consider the U.S. 10-year Treasury as a safe haven or a
riskless asset similar to how individuals view their bank deposits being in safe hands
because they are federally insured.
Sovereign governments, institutional investors along with individual investors
treat the U.S. 10-year treasury as an investment safe haven because of its explicit
guarantee by the full faith and credit of the U.S. government. When investors’ confidence
in the EMEs gets lower, they choose to sell out of EMEs sovereign bonds and buy the
U.S. 10-year treasury. When the U.S. 10-year yield goes down (due to a price increase),
while keeping the EM sovereign bond yield constant, then the distance between the U.S.
10-year Treasury yield and the EM bond yield gets wider. The opposite is true. Figure
3.5. shows the relationship between U.S. 10-year Treasury yield and J.P.Morgan’s EMBI.
Figure 3.5. U.S. Treasury Yield vs. Emerging Markets Sovereign Bonds Spreads
7.0
1,100
6.5
1,000
6.0
900
US Treasury Yield (LHS)
Market Spreads (RHS)
Source: International Institute of Finance (IIF), Bloomberg, and JPMorgan
25
Feb-11
Mar-10
Aug-10
Apr-09
Sep-09
Oct-08
Nov-07
Jan-06
May-08
US $
100
Jun-07
2.0
Jun-06
200
Dec-06
2.5
Jul-05
300
Feb-05
3.0
Mar-04
400
Aug-04
3.5
Sep-03
500
Oct-02
4.0
Apr-03
600
May-02
4.5
Jun-01
700
Nov-01
5.0
Jul-00
800
Dec-00
5.5
Jan-00
Basis Points
Correlation = 0.25
26
3.3. The Data Set
Monthly data covering 2000:1 through 2008:11 were used. Given that not all
EMEs subscribe to the IMF’s special data dissemination standard (SDDS) that calls for a
specific macroeconomic indicators data releases, frequencies, and format, different data
sources were used (i.e., the IMF, Datastream, Bloomberg, major rating agencies, and the
International Institute of Finance (IIF), along with the World Bank, and the U.S. State
Department). Quarterly data were interpolated into monthly data by using Cubic Spline
Interpolation method8. Twelve EMEs were included in this study (Argentina, Brazil,
Colombia, Russia, Peru, Venezuela, Indonesia, South Africa, South Korea, Philippines,
Panama, and Mexico). Table 3.1. lists the all of the explanatory variable along with their
description, and data sources.
Country inclusion criteria used were based on various characteristics. First, the
country must be a member in the EMBI with a weight greater than five percent to insure
liquidity of the bonds and a narrow bid-ask spread. Second, the country must be an active
dollar-denominated sovereign bond issuer with an issue size of $250 million or larger.
Third, the country must have a frequent and reliable data releases and availability. Fourth,
Countries that recognize the IMF SDDS are preferred. Finally, the country sovereign
bond issuance must be an “investable” instrument (e.g., high liquidity with appealing
transaction cost (low bid-ask spread), registration of their bonds, and a maximum
transaction settlement of three days).
8
Newey-West standard errors have been used, as they are robust to the autocorrelation introduced by cubic
splines.
26
27
3.3.1.Dependant Variable
J.P.Morgan EMEs five year Credit Default Swaps (CDS) is the dependent
variable. It was chosen because it’s themes sovereign bond industry standard
that’s used to measure the riskiness of sovereign bonds and offers real time
intraday data releases. In a nutshell, CDS buyers shift the risk of sovereign bond
default to the CDS sellers by paying CDS premium. CDS is treated and used as a
real time sovereign bond risk parameter. As the perception of a default risk goes
down, because of improved fundamentals or market perception of risk, CDS
premium goes down.
3.3.2. Explanatory Variables:
Real GDP growth (GDPG), change in terms of trade (CToT), and Citi Global Risk
Aversion Index as a proxy for investors’ risk aversion (RA)9 are the macro explanatory
variables. All of the variables have monthly data except for the GDPG, but it was
interpolated into monthly readings. GDPG measures total economic activity within the
borders of a country. Higher GDP growth, while other measures stay constant, helps in
lowering many important ratios like Debt-to-GDP, External Financial Requirements, etc.,
which will convince rating agencies to upgrade a country and eventually lower its bond’s
spreads—Arora and Cerisola (2001). Expected relation with spreads: Higher real GDP
growth leads to tighter spreads—negative relationship (Figure 3.1). CToT compares
Measures risk aversion in global financial markets. It’s an equally weighted index of U.S. credit spreads,
U.S. swap spreads, and implied foreign exchange, equity and swap rate volatility. It’s expressed in a rolling
historical percentile and ranges between 0 (low risk aversion) and 1 (high risk aversion).
9
27
28
countries’ exports to their imports in terms of value change year over year. Hilscher and
Nosbusch (2007) found that when country’s exports are gaining value faster than its
imports, while keeping volumes constant, that will translate into an improved balance of
payment, higher government revenues, a lower financing need, a higher GDPG, a higher
foreign exchange reserves, a stronger ability to service and payback debt, and eventually
get a credit rating upgrade.That said, the credit matrix would look much better, which
will improve countries’ ability to not only service debt, but also pay it back like what
Russia did after oil prices reached triple digits in 2008. Expected relation with spreads:
Improved ToT leads to tighter spreads as countries’ default probabilities tend to decrease
due to trade balance surpluses —negative relationship (Figure 3.2). RA measures
investors’ appetite for taking risk by buying EMEs sovereign bonds. Expected relation
with spreads: As RA gets closer to a reading of 1, sovereign spreads are expected to
decline (Figure 3.3)—a negative relationship according to Remolona, Scatigna, and Wu
(2007).
28
29
Bloomberg
U.S. 10-year Treasury Bond Yield
measured in percentage points
Note: A detailed description of the variables is provided late in this research
Standards and Poors (S&P) and Bloomberg
Monthly Change in Commodity Prices
S&P Enhanced Commodity Official Close Index
High-frequency Variables (HFV)
Citi Group and Bloomberg
International Institute of Finance (IIF)
Change in terms of trade (CToT)
Citi Global Risk Aversion Macro Index (RA)
Bloomberg and DataStream
JPMorgan and Bloomberg
Data Source
Real GDP growth (GDPG)
Explanatory
JPMorgan EMEs 5-year Credit Default Swaps
(CDS) measured in basis points (bp)
Dependent Variable
Table 3.1. Variables Sources and Definitions
US 10-year Treasury coupon divided by market price equals to
US 10-year Treasury yield. As the market price goes higher (lower)
the yield goes lower (higher)
This index contains soft and hard commodities
(i.e., agricultural, industrial metals, precious metals, gas, and oil).
Majority of the countries in this study is net exporters of one
or more type of commodity that’s covered by this index.
It’s an equally weighted index of emerging market sovereign
spreads, U.S. credit spreads, U.S. swap spreads, and implied FX,
equity and swap rate volatility. It’s expressed in a rolling historical
percentile and ranges between 0 (low risk aversion)
and 1 (high risk aversion).
The change in the value of country's exports vs. the
change in its imports. Positive readings imply that
the country's exports are gaining more value once
compared to its imports, which push its trade balance
more to the positive and result in current account surplus.
Countries real GDP growth as it was reported to the
IMF and the World Bank is nominal deflated by each country's
local CPI figures.
A highly reactive measure that market participants use
and rely on to measure investors’ general assessment
of sovereign bond default risk.
Definition
29
30
Chapter 4
ESTIMATION AND RESULTS
4.1. Estimation of Results
4.1.1. Macroeconomic Variables
Based on the Augmented Dickey-Fuller (ADF) test for macro variables, Table
4.1., the null hypothesis of a unit root in log spread, log GDPG, and log CTOT
cannot be rejected.
Cointegration Tests: Two tests were used to check if the model variables (log spreads, log
GDPG, log CTOT and log RA) are cointegrated. The trace test and the maximum
Eigenvalue test were used. As shown in Table 4.2, Trace and Eigenvalue tests both reject
Ho of no conintegration in nine out of twelve countries.
In general the tests provided evidence of cointegration between the variables.
Mexico, Panama, and Venezuela showed no cointegration, and they will be eliminated
from the country sample (i.e., the research will focus on the remaining nine countries).
4.1.2. High-frequency Variables
To test whether the HFVs are stationary in order to use them as control
variables in the ECM, the log differences in commodities and log levels in U.S.
Treasuries 10-year yields were calculated. The ADF test rejects Ho of a unit
root—Table 4.3.
30
31
Table 4.1: Standard unit root tests, null hypothesis is unit root
Log Spread ADF test
Log GDPG ADF test
Log CToT ADF test
t-stat.
P-value
t-stat.
P-value
t-stat.
P-value
Argentina
0.91
0.37
0.79
0.43
0.33
0.74
Brazil
0.58
0.56
0.91
0.37
0.56
0.58
Colombia
0.31
0.75
0.25
0.80
0.16
0.87
Indonesia
0.82
0.42
0.51
0.61
0.51
0.61
Malaysia
6.0E-03
0.98
0.11
0.91
0.85
0.40
Mexico
0.86
0.39
0.41
0.68
0.29
0.77
Philippines
0.5
0.62
0.81
0.42
0.72
0.47
Russia
0.97
0.33
0.56
0.58
0.51
0.61
South Africa
0.92
0.36
0.94
0.35
0.51
0.61
Peru
0.67
0.50
0.46
0.65
0.03
0.98
South Korea
1.1
0.27
0.29
0.77
0.73
0.47
Panama
0.87
0.39
0.82
0.41
0.86
0.39
Venezuela
0.89
0.38
0.77
0.44
0.81
0.42
Table 4.2: EM sovereign bonds cointegration testsa
Test:
Trace test
Max. Eigenvalue Test
Ho:
No cointegration
No cointegration
H1:
One cointegration
One cointegration
Statistic:
Argentina
Brazil
Colombia
Indonesia
Mexico
Philippines
Russia
South Africa
Peru
South Korea
Panama
Venezuela
Trace test
p-value
ME Stat. pvalue
0.0811
0.3781
0.1103
0.5361
0.0389
0.5163
0.0501
0.1431
0.1532
0.2231
0.0039
0.0041
0.1212
0.3171
0.2624
0.2871
0.2761
0.3464
0.2711
0.0552
0.5521
0.2132
0.0157
0.0261
a based
on cointegration with four series: log spread, log GDP, log CTOT and log RAI
Null hypothesis rejected at 5%
31
32
Table 4.3: Standard unit root tests for High-frequency variables,
null hypothesis is unit root
ADF testa
Log 1st diff. level
t-Stat
Commodity Index
0.02
***
Log level
10y UST(log)
0.19
*
a
Augmented Dickey-Fuller test level, test equation with intercept, maximum
lag.
Unit root is rejected at 1%, 5%, and 10%, indicated by ***,**,* respectively
4.2. Empirical Results
4.2.1. Estimating Coefficients
To perform the model estimations, first, and according to the theoretical
model presented earlier, five potential variables were defined--log spread, log
GDPG, log CTOT and log RAI and two HFVs that include a commodity price
index and 10y U.S. Treasury yield. Second, multiple specifications for the
cointegrating vector for each country removing one variable at a time and with
and without the proposed fast fundamentals were estimated. Third, the best
specification model was selected10.
This is the model that provides the highest predictive performance based
modifications to the general model. The criteria for selection are positive hit ratios.
Estimated error correction term coefficients are presented in Table 4.4.
Coefficients of real GDP growth, except for Russia, and RA have the right predicted sign,
while CToT has a mixed sign depending whether the country is a heavy commodity
10
Granger causality test results confirm the order of explanatory variables.
32
33
producer or not. For commodity producers, the sign of CToT was as predicted for six out
of nine countries.
Table 4.4. Estimated error correction term
log GDPG
log CToT
Coeff.
Coeff.
log RA
Coeff.
Argentina
-4.51***
(0.15)
(0.05)
(0.14)
Brazil
-5.00E-04
-3.45***
0.91***
0.05
(0.10)
(0.16)
Colombia
Indonesia
Philippines
Russia
South Africa
Peru
2.51
-4.78***
1.43*
2.59***
(0.09)
(0.19)
(0.13)
-6.75E-04
-1.88***
1.46***
0.04
(0.17)
(0.23)
-1.03**
-2.79***
0.73***
(0.12)
(0.11)
(0.10)
7.80E-4***
-2.89***
2.29***
(0.10)
(0.27)
(0.29)
-1.53**
0.91**
2.22***
(0.21)
(0.26)
(0.22)
- 3.71E-4**
-3.99***
0.41***
(0.27)
South Korea
1.71***
(0.09)
-1.29**
-0.77*
(0.18)
(0.30)
(0.13)
2.13**
(0.27)
The time it takes deviations from equilibrium to correct, coefficient b in equation
8 are obtained from ECM specification. Seven out of nine countries end up having the
right sign in predicting the direction of correction when they are deviated from the
equilibrium (i.e., tighter spreads than the equilibrium should widen and vice versa).
Argentina and Peru are the two countries with the wrong sign, but they are not
statistically significant. The average time for correcting the spread deviation, aside from
Argentina and Peru, is twelve months with the largest in Russia (29 months) and the
shortest in the Philippines (only 4 months).
33
34
4.3.
Aggregated Results
The results as described below for Market vs. Model were aggregated, based on
their weight in the EMBI.
EM model results shown in Table 4.5. suggest that in
aggregate, dollar-denominated sovereign bonds spreads, measured by CDS spreads,
overshot the model by 288, which implies that the market is currently undervalued. This
is in contrast to the beginning of the credit crisis in late 2007 and early 2008, as risk
aversion and de-leveraging dominated almost all credit markets except EM, which at that
time, was thought to have decoupled.
Table 4.5. EM bond index spread over the last 12
months – market and estimated
Market Estimated
Difference
spread
spread
Nov., 2008
912
624
288
Oct., 2008
849
431
418
Sept., 2008
596
287
309
Aug., 2008
298
261
37
Jul., 2008
329
251
78
Jun., 2008
318
295
23
May, 2008
253
241
12
Apr., 2008
293
397
-104
Mar., 2008
351
517
-166
Feb., 2008
321
493
-172
Jan., 2008
221
453
-232
Dec., 2007
246
394
-148
Nov., 2007
238
302
-64
As of November 2008 month-end.
Weighted average using weights in the EMBI index,
scaled up to 100%.
4.3.1. Country Results
Market vs. Estimated Spreads are listed in Table 4.6. and Figure 4.5. below. The
34
35
estimated results suggest that the market (or actual) aggregate dollar-denominated
sovereign bond spread is 191 bp wider (or cheaper) than the estimated. Moreover, and
based on actual spreads, the results show that bonds are overvalued in Colombia, Russia
and South Korea while undervalued in the rest of the sample.
Table 4.6. EM Bonds--Actual vs. Estimated
spread and the Deviation
Argentina
Brazil
Colombia
Indonesia
Peru
Philippines
Russia
South Africa
South Korea
Weighted
average
Market
spread
Estimated
Spread
Deviation
1821
434
576
1059
592
668
663
852
195
979
299
729
569
391
417
710
623
231
842
135
-153
490
201
251
-47
229
-36
737
546
191
4.3.2. Model Coefficients
Table 4.7. shows the sensitivity of spreads to each of the variables that are
included in the model. Results are discussed in details in the following section.
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A contraction of GDPG level by 100 bp results in spreads widening, on average,
by 29 bp--Argentina and South Korea being the most sensitive.
The large jump in spreads in the last month by several hundred bp is not due
solely to expectations of a slowdown in growth. The onset of the financial crisis marked
by the meltdown of the CDS market along with heightened level of risk aversion and
liquidity and solvency issues in the light of a possible counterparty risk may have all
contributed to higher spreads.
In general, one would expect a negative relationship between CToT and sovereign
bonds spreads for EMEs because that implies a country’s exports are getting pricier
compared to its imports, which should lead to tighter spreads. The results show that a one
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percent increase in Peru’s CToT results in a 45 bp sovereign bond spread tightening and a
six bp widening in South Africa’s sovereign bonds spreads.
Table 4.7: A change in the variable leads to a X-amount of bp
change in spreads
Commodity
Index
US 10-year
GDPG
CToT*
RA
Change
1%
1%
1st. Dev.
10%
10bp
Argentina
-79
61
779
-13
-3
Brazil
-10
-9
114
-74
-4
Colombia
-23
9
389
-23
-2
Indonesia
-9
-17
351
-251
-7
Philippines
-13
-10
107
-18
-14
Russia
-17
-21
1281
-145
-8
South Africa
-13
6
451
-24
-4
Peru
-28
-45
77
-103
-8
South Korea
-37
-36
343
-54
21
Average
-26
-7
432
-78
-3
Overall, an improvement in ToT leads to a spread tightening in six out of nine countries.
This confirms the predicted relationship between CToT and sovereign bonds spreads.
However, the magnitude of this relationship varies depending on the openness of the
economy and the exports volume.
Risk aversion received special attention during the recent financial crisis--the
period between the second half of 2007 and December of 2008. In fact, risk aversion
reached new levels and was caused by reasons that were not factored in before like
counterparty risk. Naturally, RA is higher for high beta countries (countries with high
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debt to GDP, negative CToT, default history, etc.). Risk Aversion impact is higher for
countries like Argentina and Russia, with the widest spreads. Therefore, it’s fair to find
that the previous three countries would be the ones experiencing negative cash flows. In
general, as the dedicated EMEs money managers’ RA increases, sovereign bonds spreads
widen indiscriminately—granted there will be some differentiation amongst EM
countries depending on their macroeconomic indicators (e.g., amount of their foreign
exchange reserve coverage of imports, total debt service, etc.) also their GDPG, CTOT,
etc. In other words, EM debt will be impacted as an asset class. A one standard deviation
move away from the mean of RA leads to an aggregate spread widening of 432 bp.
The last 10 years is a period that’s easily can be characterized by two modes of
interaction between EM sovereign bonds spreads and U.S. Treasury yields (the risk free
yield). One took place after the real estate bubble bursting in and around late 2007 and
early 2008 along with the liquidity crunch and CDS meltdown, dedicated EM investors
abandoned EM sovereign bonds in favor of U.S. treasuries in a trade that was marked by
a flight to quality. Investors wanted a safe haven for their money in an economy and a
political system that is known to be the most trustworthy in the world. The other one,
three years prior to onset of the financial crisis, was marked by lower U.S. Treasury
yields, and that sent money managers in search for a higher yield. EM sovereign bond
spreads tightened significantly in a trade that was known as a carry trade—a trade where
the international portfolio managers invest in risky assets as appose to a riskless asset
because the risky asset offers a more attractive risk-adjusted yield. The coefficients of
this model blend the two but more effectively than a simple two factor model because
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Risk Aversion is included. A 10 bp contraction (or tightening) in U.S. 10 year Treasury
bond yield leads to a three bp widening in CDS.
As expected, an increase in commodity prices was associated with tighter
sovereign bond spreads for all countries. A ten percent increase in prices would be
associated with a 78 bp spread tightening of the overall market. Commodity exporters
benefit from higher commodity prices, as their balance of trade will experience higher
surpluses, higher levels of foreign exchange reserves, lower external financing
requirement, and stronger ability to payback debt which would all result in a higher credit
rating and narrower spreads.
4.4. Testing of the Model
The whole aim of this model is to develop a tool for emerging markets dollardenominated sovereign bond investors that generates a market signal that result in
profitable trading. In this thesis, model success factor is defined as the number of times
the model generates a profitable trading signals. In this section, the model is put to test by
evaluating its performance in the past by calculating in-sample and out-of-sample hit
ratios (Table 4.8.).
4.4.1. In Sample Testing
The model estimates coefficients using all the data available to the present.
These coefficients are used to calculate past equilibrium spreads then compared the
actual spreads vs. modeled forecasted spreads. If the model predicted that the relative
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spread would tighten and it actually did within a one month period that this would be
considered as a hit.
Table 4.8. EM bond index spread forecast evaluation--hit
rate
In Sample
Out of Sample
Country
2000 2003 2004 2005 Argentina
65%
63%
54%
57%
Brazil
59%
66%
56%
52%
Colombia
66%
63%
65%
66%
Indonesia
53%
46%
53%
56%
Philippines
72%
73%
72%
77%
Russia
47%
49%
55%
51%
South Africa
66%
81%
88%
82%
Peru
61%
45%
52%
54%
South Korea
68%
66%
61%
50%
Average
61.89% 61.33% 61.78% 60.56%
The model coefficients were estimated using data from 2000 to present, and used
to calculate past spreads. The direction of the spreads vs. the forecast are compared—a
hit is considered if the model predicted spread tightening and market spreads actually
tighten within one month. The best specification model produced an average hit ratio of
61.89 percent, which means that the model was able to predict the right direction of
spreads movement, for deviations that are larger than one standard deviation, of the
posterior movement in spreads by close to 61 percent of the time.
4.4.2. Out-of-sample Testing
The real direction of spreads is compared to the forecasted one. The model
coefficients were estimated using the same data that were available a month prior to the
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one in which the spread ratio forecast was constructed. In order to have a reasonable
sample size to produce estimations, projections were started from January 2003 in order
to have four years of data (except for Argentina, Indonesia and South Korea) when the
model was first estimated.
Figure 4.2. Market vs. Estimated Spreads for In Sample Testing
Market vs. Estimated Spreads
Market vs. Estimated Spreads Standard Deviation of Error
4
800
Standard Deviation
Basis Points
3
600
400
200
2
1
0
-1
-2
0
Market Spread
Jul-08
Jul-08
Jan-08
Jul-07
Jan-07
Jul-06
Jan-06
Jul-05
-4
Jan-05
Jan-08
Jul-07
Jan-07
Jul-06
Jan-06
Jul-05
Jan-05
-3
Estimated Spread
Weighted average using weights in the EMBI index scaled to 100 percent
That means towards the end, the time series contained 9.5 years of data. The best
specification model produces an average hit ratio of 61.78 percent—2004 estimation.
However, the results for 2004 and 2005 have to be taken with caution due to the small
frequency of spread errors above one standard deviation.
4.4.3. Looking for Strong Signals
Only signals that are greater than one standard deviation were considered to
trigger a buy or a sell decision. Thus, anytime when spread errors are greater than one
standard deviation are observed, a buy or sell action will be triggered.
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Figure 4.3. Market vs. Estimated Spreads for Out-of-Sample Testing
Market vs. Estimated Spreads
Market vs. Estimated Spreads Standard Deviation of Error
4
800
Standard Deviation
400
200
2
1
0
-1
-2
0
Market Spread
Jul-08
Estimated Spread
42
Jul-08
Jan-08
Jul-07
Jan-07
Jul-06
Jan-06
Jul-05
-4
Jan-05
Jan-08
Jul-07
Jan-07
Jul-06
Jan-06
Jul-05
-3
Jan-05
Basis Points
3
600
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Chapter 5
CONCLUSION
5.1.Summary of Findings
Academic literature shows three main frameworks to estimate sovereign debt
valuations in emerging markets—time series analysis, cross section econometric
techniques that use HFVs, and conintegration technique which turned out to be a good
compromise between data availability, frequency, and the robustness of the results.
A combination of macroeconomic fundamentals (real GDP growth and change in
terms of trade), Risk Aversion Indicator, and HFVs (commodity prices and U.S. 10-year
yield) were used to estimate cointegration models across twelve countries.
As predicted, the relationship between spreads and GDPG was found to be
negative. CTOT tends to have a different impact across countries. Overall, an improved
terms of trade leads to a spread tightening in six out of nine countries in this study. While
in all countries an increase in risk aversion has impacted spreads, this phenomenon is
even more profound in countries with higher spread volatility such as Argentina and
Russia. U.S. Treasury yields impact on spreads changed over time. More recently lower
U.S. treasuries yields have driven spreads wider. Commodity prices are associated with a
reduction in EMEs debt spreads, as majority of the countries in this study are heavy
commodity exporters. When tested, in-sample testing (started 2000) the best specification
resulted in a hit ratio close to 62 percent, and that implied the accuracy of the model in
predicting spread moves for spread deviations above one standard deviation from the
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mean. When out-of-sample testing was conducted starting 2003, 2004, and 2005, hit ratio
didn’t significantly improve.
Fundamental drivers (or macroeconomic indicators) are not excellent short-term
predictors of sovereign bonds spreads, as spreads may react to market dynamics due to
factors like supply, yield differential, coupon rate, liquidity, trade momentum, and timing
factor (event risk such as elections, natural disasters, etc.). For example, South Africa’s
dollar denominated sovereign bonds spreads have increased (or widened compared to
similar maturity U.S. Treasury bills) in reaction to local quasi-government U.S. dollar
quasi-government debt issuers, ESKOM and TRANSNET Figure 5.1. Those two entities
issued a higher coupon bonds compared to the sovereign coupons for similar debt
maturity—over 110 bp wider than South Africa’s sovereign, which appealed to many
emerging market investors and led many to sell out of the sovereign and buy the
government-guaranteed local bond. The Figure below shows South Africa’s 5.5 percent
maturing in March 2020 spread reaction—a widening from 105 bp over the U.S. 10-year
Treasury to over 150 bp just after the issuance of the quasi-sovereign debt.
The conintegration model in this study would be useful for tactical or a strategic
sovereign bond portfolio management, as it will offer better than 60 percent accurate
signals of the spreads directions. However, those signals couldn’t be produced on a daily
or intraday basis due to low frequency of data.
5.2.Suggestions for Future Research
Future research might get a better hit ratio if it utilizes more HFVs with daily
values, less macroeconomic variables with low release frequency, and by incorporating
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dummy variables that could factor in issues that are related to liquidity, the coupon rate,
type of bond holders, and the shape of the yield curve. Macroeconomic fundamentals
have excellent predictive power but not in the short-term, where bond traders might
totally ignore fundamentals and trade based on short-term technical variables.
Figure 5.1. South Africa’s Sovereign vs. Corporate Bonds Spread Movement
Spread went from 105 bp in January 5th , 2011 to 155 bp on 1/31 after
ESKOM and TRANSNET (both government guaranteed debt) issuance.
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