Sound Policies or Good Fortune:
What Drives Emerging Market Spreads?
Philipp Maier and Garima Vasishtha∗
Draft
Do not cite without permission
February 2008
Abstract
Since 2002, spreads on emerging market sovereign debt have fallen to historical
lows. Given the close links between sovereign spreads, capital flows to emerging markets, and economic growth, understanding the factors driving sovereign
spreads is very important. This paper uses factor analysis to study the extent to
which emerging market bond spreads – measured by JP Morgan’s EMBI Global index – are driven by global factors, such as global liquidity or commodity prices, as
opposed to country-specific macroeconomic fundamentals. Our results show that
a common factor explains a substantial portion of the co-movements in emerging
market spreads in the last decade. This factor is closely linked to global financial
conditions, as well as to energy- and non-energy commodity prices. This factor,
however, is not responsible for the reduction in EMBI spreads. Instead, emerging
markets have benefited considerably from better macroeconomic policies. Therefore, a reversal of the benign global economic environment need not have a substantial negative impact on financing conditions for emerging markets.
Bank index: Development economics, financial stability, international topics
Keywords: Sovereign spreads, emerging markets, factor analysis
JEL codes: E43, F34, F47, G12, G15
∗ Corresponding
author, Bank of Canada, International Department, 234 Wellington, Ottawa, ON,
K1A0G9, Canada, email: gvasishtha@bankofcanada.ca. The views expressed are the authors’ and need
not reflect those of the Bank of Canada. Helpful input from Carlos de Resende is acknowledged. Comments
on earlier versions have also been given by Larry Schembri and Robert Lavigne. Research assistance has
been provided by Brian DePratto, Andre Poudrier, and Christine Tse. All remaining errors are ours.
1
1
Introduction
The role and importance of emerging markets have changed substantially over the
recent years. Emerging markets are no longer recipients of capital inflows, as they
were in the 1970s and 1980s. In the face of rising energy and non-energy commodity prices, emerging markets have accumulated large current account surpluses and,
in many cases, substantial holdings of foreign reserves. This has brought important
benefits: Many of the past problems of these economies were caused by their dependence on volatile capital flows from the developed world to finance current-account
deficits, as exemplified by the Asian and the Russian crises. Today, however, the large
importers of capital are industrialized countries, and capital is supplied by emerging
markets. To understand this shift in the global economy, a thorough understanding of
emerging markets is required.
Perhaps the biggest sign of change has been in the emerging debt markets. The traditional gauge of risk of emerging market debt is JP Morgan’s ‘Emerging Market Bond
Index Global’ (EMBI Global), which tracks the price of dollar-denominated emerging market debt since the early 1990s. In 2007, the EMBI Global yielded the thinnest
spread ever recorded over riskless U.S. Treasury bonds. Two, not mutually exclusive,
explanations for this are: first, many emerging markets have made improvements in
macroeconomic fundamentals and have undertaken structural reforms. This has reduced the risk of default, and because investors demand a lower risk premium, spreads
on emerging market debt have fallen. The second explanation acknowledges the improvements in macroeconomic frameworks, but also points to the fact that risk spreads
have fallen for virtually all asset classes, not just for emerging market debt. This could
indicate that other factors besides country fundamentals are responsible for the sharp
fall in risk premia.
Against this backdrop, we examine factors influencing movements in emerging
market yield spreads. We use principal factor analysis to examine the degree to which
spreads for different countries exhibit similar patterns. Our study is not the first to
apply principal factor analysis on bond spreads. Slok and Kennedy (2004) use principal
components analysis – a technique related to principal factor analysis – to identify a
common trend in risk premia on stock and bond markets in developed and emerging
market countries since the beginning of 1988. They find that their principal components
are strongly correlated with the OECDs leading indicator of industrial production, and
a measure of global liquidity. McGuire and Schrijvers (2003) use principal components
2
to study common developments in risk premia in 15 emerging market countries over
the period 1997 to 2003. The first factor, which the authors call ‘investor risk aversion’,
explains the bulk of the common variation.1
Hund and Lesmond (2007) show that liquidity is an important component of emerging market yield spreads. These authors look at both corporate and sovereign emerging
market bonds, and use various regression techniques – although no principal factor
analysis – to test for liquidity risk. Lastly, the study closest to ours is Ciarlone et al.
(2007). These authors use factor analysis to examine emerging markets spreads, and
find that the common factor is able to explain a substantial share of the reduction in
emerging market spreads over the past few years. We improve upon their approach in a
number of ways. First, our data set includes more countries. Second, we employ richer
empirical specifications, using panel data, as well as country-specific regressions with
instrumental variables and Seemingly Unrelated Regressions. Third, we also estimate
principal factors and empirical models for different geographical regions.
To preview the conclusions, we find evidence that emerging markets spreads can
be explained by an exogenous common factor, which we can link to global financial
developments and the evolution of energy- and non-energy commodity prices. These
developments are typically outside the direct control of emerging markets. However,
while the common factor has contributed to the fall in spreads, its contribution is relatively small. Instead, our results suggest that the most important elements driving
the compression in spreads are better macroeconomic policies (primarily lower inflation and lower debt). These findings provide an explanation why emerging markets
remained largely unaffected by the recent turmoil in global financial markets.
The remainder of this paper is organized as follows. Section 2 presents stylized
facts on emerging markets’ sovereign bond spreads during 1994-2007, and outlines
possible explanations for the compression observed during this period. Section 3 outlines the empirical methodology. Section 4 presents the estimation results, and section
5 discusses the key findings.
1 We discuss ‘risk aversion’ in section 2.2. The Deutsche Bundesbank (2004) calculates a risk aversion
indicator, employing principal component analysis using risk premia on investment and speculative grade
corporate bonds in developed countries, and sovereign risk premia for some Asian and Latin American
countries.
3
EMBI Global Composite
EMBI Global Asia
EMBI Global Europe
EMBI Global Latin America
0
500
Basispoints
1000
1500
2000
2500
EMBI spreads
1997m7
2000m1
2002m7
2005m1
2007m7
Figure 1: EMBI spreads
2
Spreads on Emerging Market Bonds
Spreads for emerging market countries have fallen considerably over the last 5 years.
The most comprehensive measure of sovereign debt issued by emerging markets is
JP Morgan’s EMBI Global (henceforth: EMBI).2 According to the EMBI, sovereign
spreads have fallen for emerging market economies in virtually all regions of the world,
be it Latin America, Europe or Asia (see figure 1). In 2007, the EMBI fell to the lowest
level ever recorded.
The compression in sovereign spreads has occurred in an environment characterized by structural changes in emerging market financing. A shift to longer maturities,
lower external debt levels, and better debt management policies has led many EMs
to reduce their debt servicing burdens. The consequences of these developments are
impressive. Emerging markets were thought of suffering from ‘original sin’ less then
10 years ago, i.e. they were unable of borrowing in their own currency, because they
had defaulted on their debt previously (Eichengreen et al., 2003b). Now, they are increasingly issuing debt in local currency (The Economist, February 24th, 2007). The
ability to issue debt in local currency is a welcome shift, because if countries are only
2 The EMBI Global index, produced by JP Morgan-Chase, tracks total returns for US-dollar denominated
debt instruments issued by EM sovereign and quasi-sovereign entities, like Brady bonds, loans, Eurobonds.
The use of secondary market bond spreads is in line with the growing literature on sovereign spreads. It
avoids the critique of Eichengreen and Mody (1998) that studies based on primary spreads suffer from
selectivity bias, as the creditworthiness of primary issuers will vary with financing conditions.
4
CA/GDPa
Debtb /GDP
Reserves/ST debt
Fiscal balance/GDP
GDP growth
Inflation
Average
-1.80
32.20
145.90
-3.10
7.50
23.50
Year 1996
Min
-26.80
14.20
10.00
-20.20
-8.00
0.20
Max
12.70
133.20
763.20
7.20
12.20
123.00
Average
1.70
28.80
400.10
-2.40
5.20
5.90
Year 2005
Min
-16.90
15.90
23.40
-8.10
1.80
Max
15.90
176.70
1353.50
10.00
9.00
16.60
Source: International Monetary Fund (2007)
a Current account-to-GDP ratio; b Total debt (i.e. public + private debt)
Table 1: Macroeconomic fundamentals in Emerging Markets
able to borrow in foreign currency, a currency crisis almost automatically becomes a
dollar-denominated debt crisis (Eichengreen et al., 2003a).3
Does this mean that emerging markets have entered a virtuous circle, in which
lower credit spreads lead to lower debt servicing costs, which lower the probability of
sovereign default, hence improving country ratings, leading to lower spreads and, consequently, lowering debt servicing costs etc.? To answer this question, an understanding of the factors driving the reduction in emerging market spreads is required. The
literature has identified two main, not mutually exclusive, explanations for the decline
in emerging market spreads: improvements in country fundamentals, and beneficial
global (financing) conditions. Each of these is briefly discussed below.
2.1
Improvements in macroeconomic fundamentals
Since the Asian crisis, many emerging markets have made improvements in macroeconomic fundamentals and undertaken growth-enhancing structural reforms. Key economic policy changes include the switch to more flexible exchange rate regimes, lower
inflation through the adoption of inflation targeting (and the associated increased antiinflationary credibility), strong current account performance, stronger fiscal performance, and – in many cases – accumulation of foreign exchange reserves (see table
1). Also, some countries have benefited from better managing the fiscal impact of
volatile commodity prices.
3 Issuing debt in local currency has the additional advantage that it increases liquidity and resilience
of local debt markets. Government securities are often viewed as benchmarks for corporate bonds, and
by issuing local currency debt and developing local financial markets, governments can help allow local
companies to borrow for longer periods and at better terms.
5
In addition, these improvements of macroeconomic fundamentals are often accompanied by better and more timely data provision (International Monetary Fund, 2007).
This allows investors to better evaluate a country’s riskiness. Taken together, improved
country ratings reflect better economic policies, and have thus contributed to the compression in sovereign spreads.
2.2
Favourable global (financial) conditions
The second explanation argues that emerging markets benefit from low spreads not
(primarily) because of better economic policies, but because global and cyclical factors have been favourable. More specifically, it has been argued that high prices for
energy- and non-energy commodities (figure 2), and favourable global financial conditions – characterized by low interest rates and low stock market volatility in advanced
economies (figure 3), as well as an abundant supply of liquidity – have fuelled the compression of spreads. Hence, it has been argued that the compression in emerging market
spreads is primarily driven by exogenous factors, such as changes in risk appetite of
international investors.4
Global financial conditions can effect emerging market spreads, because they capture two elements: first, the risk stemming from the risk of default; second, the degree
of willingness or ability of investors to accept a risky asset. The latter may be unrelated to the actual default risk of that country, and may reflect factors like the financial
position of investors or liquidity risk in financial markets at that time. Put differently,
even if investors’ degree of risk aversion and their expectations about countries’ default
risks are constant, the share of risky emerging market assets in an investor’s portfolio
is not likely to be constant, due e.g. to portfolio constraints.5
4 See e.g. Herrera and Perry (2002); Grandes (2003); Garcia Herrero and Ortiz (2004); Calvo and Talvi
(2004); and Rozada and Yeyati (2006).
5 In our view, there is a distinction between investors’ ability or willingness to hold risk and ‘risk aversion’.
Risk aversion is a coefficient in the consumers utility function. This parameter is part of the intrinsic profile
of economic agents, and we assume that it is unchanged over time. For a given risk aversion, the willingness
or the ability to hold a risky asset can change, reflecting – for instance – changes in the composition of an
investor’s balance sheet, which make holding risky assets more or less attractive at different points in time
(see also Gai and Vause, 2004; Coudert and Gex, 2006). As an aside, note that improvements in country
fundamentals and external developments, such as prices for commodities, are easy to measure. Measuring
changes in investors’ ability and willingness to hold risk is not straightforward. Ciarlone et al. (2007)
propose to use the VIX as a proxy for uncertainty on financial markets. In our empirical analysis we follow
their approach. Alternatively, various third-party sources have developed indicators, notably: the Deutsche
Bank Currency Risk Appetite Index, the Lehman Brothers’ Risk Aversion Index, and JP Morgan’s Liquidity
and Risk Premia Index. There is also JP Morgan’s Liquidity, Credit and Volatility Index, which aggregates
two series capturing liquidity, two risk premia indicators, and three measures of market volatility (Coudert
and Gex, 2006). This last index has also been used to study changes in risk aversion during financial crises
6
1997q1
7
35
6
5
4
15
1999q3
2002q1
2004q3
1997q1
2007q1
1999q3
10Y US Treasury Bond(right axis)
2002q1
2004q3
2007q1
Figure 3: Financial conditions (US
long-term interest rates, VIX)
Figure 2: Oil and commodity price
index
2.3
VIX (left axis)
3
10
0
50
20
100
20
40
150
25
60
200
30
Commodity price index (right axis)
250
80
Oil price (left axis)
Implications
These two explanations have very different implications for policymakers. Firstly, if
global financial developments caused the compression in emerging markets spreads,
they could fall below levels that would adequately cover risk.6 Secondly, a high sensitivity of emerging market spreads to developments in advanced economies could imply
that in the event of an upturn in these rates, the cost of financing for emerging markets
can rise substantially.
To distinguish between those two explanations we employ the largest, most diverse data set for emerging countries for which consistent data on internationally traded
sovereign bonds is available (the EMBI Global). By covering different geographical
regions, as well as a mix of commodity-exporting and commodity-importing countries,
we can isolate global factors from country-specific developments. The key tool we use
is factor analysis.
3
Methodology: Factor analysis
Factor analysis is a statistical technique used to reduce the dimensionality of data, and
to detect structure in relationships between variables. Its aim is to identify factors
that account for most of the variations in the covariance or correlation matrix of the
data. Underlying this technique is the premise that unobservable internal characteristics
in emerging markets (see Dungey et al, 2003).
6 A similar development may have occurred prior to the Asian financial crisis of the 1990s, see Kamin
and Kleist (1999).
7
(or attributes) exist, in which the sample elements differ. These characteristics are
commonly referred to as ‘latent factors’ or ‘internal variables’, and are assumed to
account for the variation and co-variation (or correlation) across a range of observed
phenomena.
A reason why emerging markets spreads could contain a common factor is found
in the immunization theory (Macauley, 1938; Fisher and Weil, 1971), which claims
that there exists only one factor of uncertainty in the economy (and that this factor
has the same impact on all rates in the economy, irrespective of their maturity). This
concept should hold for different interest rates within one economy, but also between
economies, provided that we can sufficiently quantify factors such as currency risk.7
Formally, factor analysis stipulates that p observed random variables can be expressed as linear functions of m hypothetical common factors (m < p), plus an error
term. Consider x1 , x2 , .....x p random variables and f1 , f2 , ..... fm factors, then
x1 = λ11 f1 + λ12 f2 + ....... + λ1m fm + e1
x2 = λ21 f1 + λ22 f2 + ....... + λ2m fm + e2
.
.
x p = λ p1 f1 + λ p2 f2 + ....... + λ pm fm + e p
(1)
where λ jk are called factor loadings, and e j are error terms, also referred to as specific
factors ( j = 1, 2, ....p; k = 1, 2, .....m). In this paper, the x0 s represent quarterly series
of the levels of EMBI Global spreads, and the sample elements are given by twenty
emerging markets. Our sample runs from 1998Q1 to 2007Q2. 8
In matrix form, equation (1) is given by:
X = ΛF + E.
7 Perignon
(2)
et al. (2007) outline the limitations of analyzing the common factor structure in variables
such as bond returns. Their argument is that a multi-country dataset of bond returns captures both local and
common influences, and therefore tends to pick ‘too many’ factors. In our case, however, this concerns does
not seem to hold, since we only identify two common factors.
8 Our sample comprises: Argentina, Brazil, Bulgaria, Chile, China, Columbia, Ecuador, Hungary,
Malaysia, Mexico, Morocco, Panama, Peru, the Philippines, Poland, Russia, South Africa, Thailand, Turkey,
and Venezuela.
8
The following assumptions are underlying the factor model:
E[e] = 0,
E[ f ] = 0,
E[x] = 0
(3)
The first assumption is standard for error terms in most statistical models. The second
assumption is generally made for convenience, without loss of generality. The third
may or may not be true, and if it is not, then equation (2) can be modified to x =
µ + Λ f + e, where E[x] = µ. Additional assumptions underlying the model are:
E[ee0 ] = Ψ
(diagonal)
(4)
0
E[ f e ] = 0
(a matrix of zeros)
(5)
E[ f f 0 ] = Im
(an identity matrix)
(6)
The first assumption states that the error terms e j , ek , j 6= k are uncorrelated. Thus,
all of x which is due to common factors is contained in Λ f . The second assumption
states that the common factors are uncorrelated with the specific factors, and the third
assumption postulates that common factors are orthogonal.
4
Results
We proceed as follows: The first step is to estimate a principal factor model. Having identified common factors, we employ regression analysis to identify their main
drivers, and we compare the principal factor in emerging market spreads to principal
factors found in other bond classes and to spreads for OECD countries to examine
whether emerging market spreads follows similar or different patterns. In a second
step, we use the principal factors in a panel setting. We start by pooling all countries
together, then disaggregate the data into different geographical regions. Lastly, we
check the robustness of our findings with country-specific regression analysis.
9
4.1
1
Principal factor analysis
Estimating a principal factor model
The first step of our analysis is to estimate a principal factor model. To determine how
many principal factors should be retained, a common methodology is to plot the eigenvalues of the principal factors, starting with the largest ones (a so-called ‘screeplot’).
This shows the relative importance of the principal factors. Typically, principal factors
with an eigenvalue < 1 are discarded, as they contain little common information (the
Kaiser-Guttman criterion).
We start by estimating the principal factor model for all countries in our sample.
The corresponding screeplot is given in the top left panel of figure 4. As can be seen, the
Kaiser-Guttman criterion suggests that we should retain the first two principal factors.
While our results indicate two principal factors, most previous studies suggest that one
principal factor is enough to describe the co-movement in spreads series for emerging
markets (see e.g. Ciarlone et al., 2007). To examine why our findings differ, we first
estimate principal factor models for different regions separately.
We split our sample into Latin American, European, and Rest-of-the-World countries.9 The remaining panels of figure 4 show screeplots for each region individually.
We see that comovements in EMBI spreads within each of the geographical regions
seem to be sufficiently captured by the first principal factor. Figure 5 shows the first
and second principal factors for all countries, and each region estimated separately.
While the first principal factor indicates a clear downward movement in all EMBI series, the second principal factor does not show a clear trend during the period under
consideration.
Before we proceed, note that data availability constrains the estimation of the principal factor model. For a number of countries, we have limited observations, as the
EMBI data is not available for all countries during the period 1997-2006. Principal factor models are, however, restricted by the shortest sample available. This means that the
principal factor for all countries can only be estimated for the period 1999Q2-2006Q1.
By leaving out three countries (Chile, Hungary, and Thailand), we can increase the
sample period to 1998Q1-2006Q4. As figure 6 reveals, the differences between the
9 The group ‘Rest of the World’ comprises the countries China, Malaysia, Morocco, the Philippines,
Russia, South Africa, and Thailand. This means that we analyze all countries consistently covered by the
EMBI Global, for which data is available for 1998-2007.
10
Screeplots
Latin America
Eigenvalues
2
4
0
0
Eigenvalues
5
10
6
15
All regions
0
5
10
Number
15
20
0
2
8
10
Eigenvalues
1
2
3
0
0
Eigenvalues
1
2
3
4
Rest of the world
4
Europe
4
6
Number
1
2
3
Number
4
5
1
2
3
Number
4
5
Figure 4: Screeplots by geographic region
Principal factors by region
Second factor
1997q1
All
LA
All
LA
Europe
ROW
Europe
ROW
1999q3
2002q1
2004q3
-2
-2
-1
-1
0
Scores for factor 1
0
1
1
2
2
3
First factor
2007q1
1997q1
1999q3
2002q1
Figure 5: The first two principal factors per region
11
2004q3
2007q1
Principal factors
Second principal factor
1997q1
Full sample
1999q3
Restricted sample
2002q1
2004q3
-2
-2
-1
-1
Scores for factor 1
0
1
Scores for factor 2
0
1
2
2
First principal factor
2007q1
1997q1
Full sample
1999q3
Restricted sample
2002q1
2004q3
2007q1
Restricted sample excludes Chile, Hungary, Thailand
Figure 6: Full sample vs. restricted sample
long and the short series are relatively small (correlation between the series is 0.995
for the first principal factor and 0.983 for the second principal factor). In light of the
relatively small size of our sample, we therefore use the longer principal factor series
for the estimations below.
2
What drives principal factors?
Figure 4 indicates that EMBI spreads contain one, possibly two principal factors. Is it
useful to retain the second principal factor? Although it is not always straightforward
to interpret principal factors economically, it would be desirable if we had an intuition
of what drives them. Table 2 provides correlations of the principal factors with key
economic indicators. These correlations suggest that the first and second principal
factor are driven by relatively similar variables.
For a more formal analysis we employ a statistical model, considering each principal factor individually. Given that – by construction – the common factor reflects
covariation in all emerging market economies in our sample, they should be related to
global economic conditions, not regional variables. Key global economic indicators
include prices for energy and non-energy commodities, global monetary conditions,
12
First principal factor
Global
LA
E
ROW
Equity indices
Nasdaq
FTSE
SP500
Interest rates
3M bond
10Y bond
Yield curve
Others
Oil prices
Commodity prices
VIX
Second principal factor
Global
LA
E
ROW
-0.86
-0.64
-0.82
-0.88
-0.71
-0.86
0.42
0.78
0.58
-0.52
-0.09
-0.36
-0.90
-0.92
-0.95
-0.76
-0.91
-0.83
-0.81
-0.62
-0.77
-0.84
-0.81
-0.88
-0.68
-0.16
0.71
-0.72
-0.26
0.71
0.65
0.85
-0.45
-0.22
0.38
0.37
-0.87
-0.56
0.79
-0.86
-0.62
0.76
-0.68
-0.16
0.71
-0.83
-0.40
0.80
-0.89
-0.89
0.95
-0.87
-0.87
0.96
0.36
0.37
-0.22
-0.61
-0.59
0.72
-0.82
-0.83
0.83
-0.67
-0.69
0.56
-0.90
-0.89
0.91
-0.88
-0.89
0.84
Note: “Global” is the principal factor estimated over all countries (restricted sample); LA, E, and
ROW denote principal factors for the regions Latin America, Europe, and the rest of the world,
respectively.
Table 2: Correlations of principal factors with key economic variables
and stock market variables. We estimate:
PFi,t = β1 + ∑ βk Xk,t + εi,t ,
i=1,2
(7)
k
whereby PFi,t denotes the first and second principal factor. Xk,t are a set of k exogenous
variables which – according to other studies – have predictive power in explaining
common factors. We use the following variables:
• Financial indicators: the S&P 500, short- and long-term U.S. bonds (the 3 monthand 10 year-yield, respectively), and the VIX;
• Natural resources: oil prices (in USD), and the IMF commodity price index.
We check the stationarity of the principal factors, as well as the explanatory variables,
using unit root tests (for all unit root tests, the alternative is a covariance-stationary autoregression with both a constant and a deterministic trend). The results of Augmented
Dickey-Fuller tests are reported in table 3. As can be seen, presence of a unit root can
be rejected for the first, but not for the second factor (and not for most of the regional
principal factors). Also, presence of a unit root cannot be rejected for the explanatory
13
PF1
PF2
PF1LA
PF1E
PF1ROW
PF2LA
PF2E
PF2ROW
Oil
SP500
10Y US
3M US
VIX
ADF
-4.24
-0.88
-3.21
-1.28
-2.80
-0.78
-2.19
-2.53
-1.91
-1.67
-2.03
-1.74
-3.53
p-value
0.01
0.95
0.10
0.87
0.21
0.96
0.48
0.31
0.63
0.75
0.57
0.72
0.05
Lags
0
0
0
2
0
0
1
1
0
0
0
1
0
Obs.
35
35
31
30
35
31
31
34
40
40
40
39
40
PF1LA , PF1E , PF1ROW denote the first principal factor for Latin America, Europe, and the rest of
the world, respectively; PF2LA , PF2E , PF2ROW are the second principal factors.
Table 3: Unit root tests: principal factors and explanatory variables
variables (possibly with the exception of the VIX). In order to ensure stationarity, we
therefore carry out all estimates in first differences (year-over-year).10
We estimate equation (7) by OLS for the global and the regional principal factors separately.11 Consider the left part of table 4 first. The second and third column
report regression results using the exogenous variables outlined above. We see that
commodity prices, as well as financial variables,12 affect the principal factor. Qualitatively similar results are obtained for the oil price, instead of commodity prices. The
fourth column reports the effects of a one-standard deviation shock of the explanatory
variables on the first principal factor. As can be seen, financial variables dominate the
effect of strong commodity prices.
The interpretation of the second common factor is more complex. Factors related to
10 An alternative way is to test for cointegration between the principal factors and the explanatory variables
(Engle and Granger, 1987). Following the procedure outlined in Johansen (1995), we find that the first
principal factor is cointegrated with oil prices, the VIX, and short- and long-term U.S. interest rates. Hence,
we could, in priciple, estimate e.g. an error-correction model (ECM). However, one of the assumptions
underlying ECM’s is that a variable is fluctuating around a long-run trend. In our view, there is no sound
economic basis why emerging market spreads should follow such a long-run trend.
11 We also estimated equation (7) using GMM, and obtained qualitatively similar results. LM tests for
serial correlation of the residuals and White heteroskedasticity tests were passed.
12 Note that the literature does not find consistent results for the effects of U.S. interest rates or the slope
of the U.S. yield curve on emerging market spreads. For example, Dooley at al. (1996) find that higher U.S.
interest rates lead to higher emerging market spreads, while Eichengreen and Mody (1998) and McGuire and
Schrijvers (2003) find a negative effect.
14
Constant
Commodities
SP500
10Y US
VIX
Obs.
R2
PF1
-0.03
-0.01***
0.00***
-0.52**
0.10***
32
0.72
PF2
0.38**
-0.01**
0.00
-0.10
0.08***
32
0.62
Effect of 1
StDev shock on PF1
0.26
0.58
0.40
0.56
PF1 and PF2 denote the first and second global principal factor, respectively.
Table 4: Regressions of the global principal factors
uncertainty on the stock market do not seem to have large explanatory power. Overall,
it is not clear that we can easily interpret the second factor, nor that including the
second factor adds to our understanding. Given that the second factor barely passed
the Kaiser-Guttman criterion, we decided to omit the second principal factor from most
estimations. Note that this does not change the estimation results qualitatively, as we
show in appendix A.
3
Comparing emerging markets spreads to other countries or assets classes
To compare the degree of covariation in emerging markets spreads to movements in
sovereign bonds of other countries or asset classes, we estimate principal factor models
for (i) different U.S. asset classes, and (ii) sovereign bond interest rates for OECD
countries.
First, consider the principal factor in EMBI spreads to principal factors found in
other asset classes. Figure 7 plots the two factors we found in EMBI spreads, and a
factor found in different U.S. asset classes. To compute the latter, we included indices
for the following asset classes:
• Investment Grade bonds,
• High Yield bonds,
• and bonds rated AAA, AA, A, and BBB (all bond-equivalent yields-to-maturity).
Given that these indices all cover U.S. indices – and, hence, U.S. denominated
assets – we can compare the principal factors directly. We see that while spreads in
15
all asset classes have fallen, differences exist between emerging market bonds and
U.S. asset classes, in particular in timing: the first principal factor in emerging market
spreads seems to lag the development in U.S. asset classes by about a year.
Second, we compare the principal factor for emerging market spreads to interest
rates for sovereign bonds for OECD countries. We consider principal factors in shortand long-term interest rates (the 3-month and 10-year yields, respectively). If the compression in spreads is driven by benign global factors – such as high global liquidity –
the fall should also be visible in interest rates of OECD economies.13 Data constraints
– and the restriction that we do not want to include emerging market countries in our
OECD sample to avoid ‘double counting’14 – leaves us with the following group of
OECD countries: Australia, Austria, Belgium, Canada, Denmark, Finland, Germany,
Ireland, Italy, Japan, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and
the United Kingdom. We approximate the spread between OECD interest rates and
U.S. rates by taking the difference of the 3-month and 10-year yields for each OECD
countries, relative to the corresponding U.S. interest rate.
As can be seen in figure 8, neither short-term, nor long-term interest rates in OECD
countries follow the pattern found in emerging market economies, as the compression
of interest rates did not coincide with the fall in interest rates of emerging markets.
Taken together, the comparison of the emerging market factor with developments in
U.S. asset markets, as well as for sovereign bond spreads in OECD countries, suggests
that there are ‘distinct developments’ found in spreads for emerging markets. This
provides a first indication that emerging markets’ spreads may not (only) be driven by
global factors, but that improvements in macroeconomic fundamentals may matter.
4
Distinguishing between different classes of risk
Additional insights can be gained from comparing principal factors in EMBI spreads
across different degrees of risk.15 We divide the sample into countries that have – on
average over the entire sample – a rating of BBB or better, and countries whose ratings
13 Ideally, all bonds would be denominated in U.S. dollars. However, most OECD countries issue debt
in their own currency. This means that factors related to the stability of their currency could potentially
affect these interest rates. However, we believe that this potential bias is small, because the principal factor
methodology identifies covariation in all series, i.e. movements that prevail in all countries.
14 Poland, for instance, would be part of both the OECD sample and the emerging market group.
15 We thank Patrick McGuire for this suggestion.
16
2
2
1
0
Basispoints
-1
1
Basispoints
0
-1
2
1
0
-1
2
1
0
-1
Factor 1 Emerging Markets (left axis)
Factor 1 OECD 3M interest rates (left axis)
1997q1
1999q3
2002q1
2004q3
1997q1
2007q1
Figure 7: Principal factors for
emerging markets and U.S. asset
classes
Constant
Commodities
SP500
10Y US
VIX
Obs.
R2
1999q3
2002q1
-2
Factor 1 OECD 10Y interest rates (left axis)
-2
Factor 1 U.S. bond classes (right axis)
-2
-2
Factor 1 Emerging Markets (left axis)
2004q3
2007q1
Figure 8: Principal factor for
emerging markets and OECD interest rates
PF1 All countries
-0.03
-0.01***
0.00***
-0.52**
0.10***
32
0.72
PF1 High risk
0.01
-0.01***
-0.51***
-0.51***
0.11***
32
0.74
PF 1 Low risk
-0.23
0.00
-0.09
-0.09
0.07**
24
0.50
Table 5: Regression of the first principal factor by risk class
is below BBB. We call them ‘Low risk’ and ‘High risk’ countries, respectively.16 Figure 9 shows the first principal factor estimated over all countries in our sample, and the
low- and high-risk countries. As can be seen, the differences are small.
Table 5 reports regressions of equation (7) by emerging market risk class. The
second column shows the results for the principal factor estimated over all countries
in our sample, and columns three and four show regressions for the principal factors
for high- and low-risk countries, respectively. Differentiating between high- and lowrisk countries reveals differences: the principal factor for low-risk countries is only
affected by volatility in U.S. equity markets (as measured by the VIX), whereas highrisk countries are also affected by commodity prices or the stance of U.S. monetary
policy. This suggests that high-risk countries are more susceptible to foreign shocks
than low-risk countries.
16 The low-risk group comprises Chile, Hungary, Malaysia, Poland, and Thailand; the high-risk group
comprises Argentina, Brazil, Bulgaria, China, Colombia, Ecuador, Mexico, Morocco, Panama, Peru, the
Philippines, Russia, South Africa, Turkey, and Venezuela.
17
-2
-1
0
1
2
Principal factors by riskiness
First principal factor All countries
-3
First principal factor Low-risk countries
First principal factor High-risk countries
1997q1
1999q3
2002q1
2004q3
2007q1
Figure 9: Principal factors for all countries, and low- and high-risk countries
5
Interpretation
Taken together, the evidence from the principal factor model suggests that EMBI spreads
contain a common factor, affected by financial market variables, as well as commodity prices. We found differences among emerging economies of different risk classes,
and differences seem to exist between the common factor in emerging markets and
other (U.S.) asset classes or OECD economies. This suggests that the principal factor
in emerging markets reflects a common development in emerging markets, which is
likely to be different than developments in other asset classes.
Having estimated the principal factor model, table 6 reports factor loadings – the
partial correlation of the EMBI series for each country with the principal component –
and a measure of the uniqueness, i.e. the percentage of the variance in EMBI series in
a country that can not be explained by the common factor (the flipside of uniqueness is
communality, i.e. the share of variation that can be explained by the common factor)
for the first factor. We see that the factor loadings are > 0.8 for most countries. This is
an indication that the first factor could be a statistically important element in explaining
EMBI spreads. In what follows, we test for this notion in a statistical model.
4.2
Panel regressions
Having examined the factors driving the principal factor, we now turn to the question
how important the principal factor is in explaining the compression of emerging market
spreads. We first examine all countries in our sample jointly using panel estimation
18
Argentina
Brazil
Bulgaria
Chile
China
Columbia
Ecuador
Hungary
Malaysia
Mexico
Morocco
Panama
Peru
Philippines
Poland
Russia
S. Africa
Thailand
Turkey
Venezuela
Global principal factor
Factor loadinga Uniquenessa
-0.16
0.98
0.82
0.32
0.96
0.07
0.96
0.08
0.85
0.28
0.90
0.19
0.83
0.30
0.58
0.66
0.98
0.03
0.96
0.07
0.98
0.04
0.94
0.12
0.94
0.12
0.44
0.81
0.96
0.07
0.90
0.19
0.99
0.03
0.95
0.10
0.84
0.29
0.87
0.25
Regional principal factor
Factor loadingb Uniquenessb
0.40
0.84
0.95
0.09
0.99
0.02
0.87
0.24
0.92
0.16
0.97
0.06
0.81
0.34
0.54
0.71
0.98
0.04
0.93
0.14
0.96
0.09
0.98
0.03
0.97
0.05
0.45
0.79
0.97
0.05
0.93
0.13
0.98
0.05
0.93
0.13
0.79
0.37
0.96
0.08
Table 6: Factor loadings and uniqueness, using global and regional principal factors
techniques. Panel estimation has the advantage of pooling the data, which increases
the size of the sample, and allows more precise estimation of common coefficients.
1
Unit root tests
An assumption underlying conventional panel estimation techniques is that the data
series are stationary. We evaluate stationarity based on the following four types of
panel unit root tests: Levin, Lin and Chu (2002), Breitung (2000), Im, Pesaran and Shin
(2003), and the two Fisher-type tests using ADF and PP tests presented in Maddala and
Wu (1999) and Choi (2001). All tests have as null hypothesis the presence of a unit
root. The alternative hypothesis for the Levin, Lin and Chu (2002) and Breitung (2000)
tests is that the series does not contain a unit root; the alternative hypothesis for Im,
Pesaran and Shin (2003) and Fisher-ADF and Fisher-PP is that some cross-sections are
without unit root. We run all tests on the levels, and allow for intercept and trend.
The results from the panel unit root tests are reported in table 7. As can be seen,
19
Common unit root
Levin, Lin, Chu (2002)
p-value
Breitung (2000)
p-value
Levin, Lin, Chu (2002)
p-value
Breitung (2000)
p-value
Individual unit root
Im, Pesaran, Shin (2003)
p-value
ADF - Fisher χ 2
p-value
PP - Fisher χ 2
p-value
Im, Pesaran, Shin (2003)
p-value
ADF - Fisher χ 2
p-value
PP - Fisher χ 2
p-value
a
b
EMBI
1.88
0.97
-0.01
0.50
GDP
-1.65
0.05
2.00
0.98
EMBI
-3.53
0.00
76.58
0.00
49.34
0.15
GDP
-7.49
0.00
154.67
0.00
79.43
0.00
Budgeta
0.58
0.80
-1.32
0.09
ST debt
-1.81
0.04
-4.13
0.00
Budgeta
-6.29
0.00
215.32
0.00
835.70
0.00
ST debt
-1.59
0.06
65.67
0.01
33.21
0.77
CPI
-0.62
0.27
0.81
0.79
LT debt
-2.80
0.00
-0.99
0.16
CPI
-4.11
0.00
85.29
0.00
68.62
0.00
LT debt
-2.51
0.01
200.32
0.00
67.97
0.00
Exports
-3.01
0.00
-2.50
0.01
Reserves
-1.08
0.14
0.22
0.59
Exports
-2.78
0.00
293.20
0.00
261.48
0.00
Reserves
-0.66
0.25
50.28
0.13
54.31
0.07
Fin. Dev.b
-0.18
0.43
-1.29
0.10
XR
-6.07
0.00
-5.28
0.00
Fin. Dev.b
-1.12
0.13
58.46
0.03
75.45
0.00
XR
-0.67
0.25
257.26
0.00
28.45
0.87
Budget deficit
Financial development (bank credit-over-GDP
Table 7: Panel unit root tests tests on levels, allowing for trends and intercepts
for most series the results are not conclusive. Consider, for instance, the GDP growth
series: while Levin, Lin and Chu (2002) is able to reject presence of a unit root at the 5
percent level, the Breitung (2000) test strongly indicates that the panel series contain a
unit root. Given these mixed results, we decide to take first differences (year-over-year)
of all series to ensure that all series are stationary.
2
Panel estimation: All countries
Since our focus is on a specific set of countries, rather than drawing the countries
randomly from a large population, econometric theory suggests that a fixed effects
model is the appropriate specification.17 In the most general form, we estimate the
17 See
Baltagi (1995). We also estimated a random effects model, but it was rejected by the Hausman test.
20
following specification:
EMBIi,t = β1 + β2,i PFt + ∑ βk Xk,i,t + ∑ βl Yl,t + εi,t ,
k
(8)
l
where EMBIi,t denotes the EMBI series for country i, and the first global principal
factor is given by PFt . Xk,i,t are k country-specific exogenous variables, and Yl,t denotes
l global variables – i.e. variables that are identical for all countries, such as the price of
oil. εi,t is a normally-distributed error term. Note that equation (8) allows the intercept
to vary between countries. This captures time-invariant country-specific effects, such as
institutional features of the political system or the rule of law, which have not changed
during our sample period.
We ran regressions with the following country-specific and global variables (note
that not all of them are included in all regressions).
• Country-specific variables: Country ratings, GDP growth, inflation, the exchange
rate against the U.S. dollar, and the ratios of short- and long-term debt-to-GDP,
exports-to-GDP, reserves-to-GDP, the fiscal balance-to-GDP, and the ratio of
bank credit-to-GDP as a proxy for financial development; in addition, we add
two variables proxying structural policies: a dummy variable IT taking the value
1 when a country introduces inflation targeting, and to proxy the overall economic structure, we use the 2008 regulation index from the Heritage Foundation;18
• Global variables: Prices for natural resources (oil and non-energy commodity
prices), the S & P500, the VIX, and world GDP growth.19
Lastly, we add a variable indicating when a country experienced a currency or banking crisis. This variable is defined as one during currency or banking or sovereign debt
crises, and zero otherwise.20 Note that estimating the panel by ordinary least squares
yields inconsistent estimates, if EMBI spreads and some of the macroeconomic variables are simultaneously determined. To test whether instrumental variables estimation
18 We also experimented with local stock market indices and variables proxying financial integration with
the United States, but these did not have a significant impact.
19 U.S. short-and long-term interest rates were not significant at conventional levels.
20 Our dating of crisis periods is taken from Kaminsky (2003), Table 4. For countries that are not included
in her sample, we created our own crises dummies based on information contained in IMF country reports.
21
Constant
PF (global)
Rating
GDP
CPI
LT debt
ST debt
Exports
Reserves
XR
Deficit
Fin. Dev.
Crisis
Regulation
IT
SP500
VIX
Oil
World GDP
Obs.
Countries
R2
j-test (p-value)
Exogeneity (p-value)a
Model 1
0.69***
2.21***
3.56***
Model 2
1.39***
2.05***
0.06***
0.11***
19.28***
12.59*
-165.69***
0.77
0.00
1.00
6.08**
8.00***
Model 3
3.43
2.05***
0.06***
0.13***
17.81***
13.68**
-162.31***
0.24
0.00
1.00
6.14**
7.20***
-0.03
-2.18**
599
20
0.45
0.00
0.21
448
19
0.43
0.01
0.61
448
19
0.44
0.00
0.84
Model 4
-0.82
1.42*
0.07***
0.13***
17.44***
11.25
-170.31***
0.67
0.00
1.31**
5.08*
7.65***
0.04
-2.19**
-0.01*
-0.22
0.07
6.87
448
19
0.46
0.00
0.85
*/**/*** denotes significance at the 10/5/1 percent level
a Davidson-MacKinnon test of exogeneity
Table 8: Panel regressions using a global principal factor, dependent variable: EMBI
spreads
was necessary, we ran a Davidson-MacKinnon (1993) test. The results indicate that instrumental variables techniques are required. We therefore use two-stage least-squares,
and instrument GDP growth, inflation, the exchange rate, ratings, the ratios of the budget deficit, bank credit, long- and short-term debt, exports- and reserves to GDP by
their lagged values (2 lags).
The results of the instrumental variable estimations are given in table 8. We estimate models with different sets of country-specific variables. Model 1 in the second
column uses the principal factor and country ratings, which proxy country fundamentals.21 We see that a better rating – which translates into a lower value of the rating
21 The
ratings are from Standard and Poor’s. A lower value indicates a better rating, i.e. an AAA-rating
22
variable – is associated with a larger fall in EMBI spreads. Model 2 uses more detailed country-specific exogenous variables. Changes in emerging market spreads are
positively related to GDP, inflation, short- and long-term debt, the degree of financial developments, and macroeconomic crises – loosely speaking, higher volatility in
macroeconomic fundamentals translates into higher spreads. The size of the budget
deficit and reserve holdings – both as ratios to GDP – are insigificant. Lastly, the negative coefficient on the export-to-GDP ratio suggests that large falls in exports can drive
up EMBI spreads.
Model 3 adds two variables that signal structural macroeconomic policy changes:
a variable indicating the introduction of inflation targeting (IT ), and the regulation index from the Heritage Foundation (in our instrumental variables estimation, we use the
property rights and corruption indices from the Heritage Foundation as instruments for
the regulation index). A higher value of the regulation index indicates less government
interference, which is traditionally believed to be beneficial for economic development. According to model 3, introduction of inflation targeting lowers EMBI spreads,
whereas the proxy for regulation is insignificant.
Lastly, model 4 adds global variables, such as the VIX, the S&P500, oil prices, and
world GDP growth. These variables are added as a robustness check, but we would
not expect them to be significant at conventional levels, given that the effects of global
developments should be captured by our principal factor (at the same time, significance
for the principal factor can be expected to weaken, because of multicollinearity). As
expected, they are largely insignificant.
3
Panel estimation: Data disaggregated by region
An alternative way to estimate equation (8) is to use the regional principal factors
derived above, i.e. to estimate:
EMBIi,t = β1 + β2,i PFj,t + ∑ βk Xk,i,t + ∑ βl Yl,t + εi,t ,
k
(9)
l
whereby the principal factor for region j is given by PFj,t . We report estimates for variations of models 3 and 4 in columns 2-3 (table 9). Comparing these results to table 8
we see that by and large, both give relatively similar results qualitatively. Next, we escorresponds to a value of 1; a D-rating is given by a value of 23.
23
Constant
PF (regional)
GDP
CPI
LT debt
ST debt
Exports
Reserves
XR
Deficit
Fin. Dev.
Crisis
Regulation
IT
SP500
VIX
Oil
World GDP
Obs.
Countries
R2
j-test (p-value)
Exogeneity (p-value)a
All regions pooled
Model 3a Model 4a
0.34
1.86
1.03*
1.00
0.08***
0.08***
0.17***
0.16***
18.39*** 18.88***
10.73
9.63
-104.38* -141.81**
-0.42
-1.07
-0.00**
0.00
0.61
0.96
5.82*
4.82
7.57***
7.88***
0.05
0.01
-3.49***
-3.24***
-0.01
-0.11
0.03
-12.10
422
418
19
19
0.43
0.44
0.00
0.00
0.59
0.67
Estimation by region (Model 3b)
LA
Europe
ROW
9.95*
-9.66***
2.80
0.73
-0.93
0.88***
0.08**
0.05
-0.09**
0.03
0.20***
0.03
46.90***
3.18
-0.69
96.23*
11.03
1.51
-896.57** 133.72*
-44.05
-47.23**
-0.85
0.27
0.00
-0.13
0.36***
3.09
0.59
6.66
3.26
15.85***
1.63
10.98**
13.74***
0.94
-0.13
0.17**
-0.06
0.10
-3.76
-0.55
189
8
0.56
0.00
0.58
96
5
0.73
0.02
0.00
137
6
0.56
0.00
0.38
*/**/*** denotes significance at the 10/5/1 percent level
a Davidson-MacKinnon test of exogeneity
Table 9: Panel regressions using regional principal factors, dependent variable: EMBI
spreads
timate equation (8) for each region separately. These estimates are reported in columns
5-7 of table 9. Keeping in mind that the sample size drops considerably when using
regional principal factors, which increases the standard errors and makes it harder to
find statistically significant estimates, our results are reasonably robust across specifications.
Lastly, we use the distinction between ‘low risk’ and ‘high risk’ countries introduced above, and estimate model 3 and 4 for each group of countries separately (table
10). The key difference between different risk classes is that ‘high risk’ countries seem
to be more susceptible to global economic developments: whereas the principal factor
is significant for high-risk countries, emerging market spreads for low-risk countries
24
seem to be driven by economic fundamentals. Including the global variables separately
(right part of table 10) does not change this.
4.3
Country-specific regressions
A potential drawback of panel regressions is the implicit assumption that countries
behave in a sufficiently similar way. By estimating a single coefficient for each independent variable for all countries, panel estimations cannot account for heterogeneous
effects of, say, inflation or debt-to-GDP ratios between countries. By running countryspecific regressions, we can examine whether the key conclusions from the panel regressions hold for all countries alike. We start with the following specification:
EMBIt = β1 + β2 PFt + ∑ β j X j,t + ∑ βkYk,t + εt .
j
(10)
k
As before, each country’s EMBI is given by EMBIt ; X j,t denote j country-specific
variables, Yk,t indicate k global variables, and εt is a normally-distributed error term.
We estimate this regression for each country separately.
To avoid endogeneity, we estimate equation (10) with instrumental variables, whereby
we instrument all country-specific variables using lagged values (for the results we report we included two lags, but we also ran separate regressions with one lag). Results
are reported in table 11. At first glance, it is apparent that considerable heterogeneity
exists between countries. Also, due to the short sample size, it is not straightforward to
get significant estimates (this issue is shared with other studies, see e.g. Ciarlone et al.,
2007). Broadly speaking, we find that the first principal factor is statistically significant. Debt variables continue to be important, and it is also apparent that the effects of
reserve accumulation or higher export-to-GDP ratios seem to differ between countries.
5
Discussion
Since 2002, emerging market spreads have fallen to historically low levels. This
has prompted academics and policymakers to investigate the sustainability of these
favourable developments. In theory, spreads could fall because of ‘push’ or ‘pull’ factors, reflecting benign developments in global financial and commodity markets, and/or
25
Constant
PFa
GDP
CPI
LT debt
ST debt
Exports
Reserves
XR
Deficit
Fin. Dev.
Crisis
Regulation
IT
SP500
VIX
Oil
World GDP
Obs.
Countries
R2
j-test (p-value)
Exogeneity (p-value)b
Model 3
High risk Low risk
4.85**
4.57
0.82***
0.00
-0.06**
0.05*
-0.02
0.13***
-0.27
24.92***
1.04
13.72
1.27
-238.59**
-2.37*
-13.66
0.00**
0.00
0.09
3.72
-0.27
21.82***
0.40
16.65***
-0.10**
-0.06
0.62
-2.15
113
5
0.45
0.00
0.25
278
14
0.54
0.01
0.56
Model 4
High risk Low risk
2.83
4.91
0.85***
0.00
-0.11***
0.05*
0.00
0.13***
-0.44
23.86***
1.33
17.74
3.35
-259.93**
-0.88
-14.01
0.00***
0.00
-0.03
3.27
1.10
22.67**
0.08
16.09***
-0.06*
-0.08
0.10
-1.88
0.00
-0.01
0.04
-0.08
0.02
-0.01
22.25
29.93
113
278
5
14
0.60
0.55
0.00
0.01
0.16
0.47
*/**/*** denotes significance at the 10/5/1 percent level
a The principal factor reflects the group of countries, i.e. we use the principal factor derived for high-risk (low-risk) countries for the group of high-risk (low-risk) countries.
b Davidson-MacKinnon test of exogeneity
Table 10: Panel regressions using regional principal factors, dependent variable: EMBI
spreads
26
Table 11: Country-specific regressions
27
Argentina
1.44
6.03
0.06
-0.11
137.40**
-901.79***
8254.69
197.29
-45.24
30
0.81
Morocco
-0.86
1.89***
0.11
-0.05
-1.44
7.63
246.73
3.08
-1.33
30
0.84
Coefficient
Constant
PF
GDP
CPI
LT debt
ST debt
Exports
Reserves
XR
Obs.
R2
Coefficient
Constant
PF
GDP
CPI
LT debt
ST debt
Exports
Reserves
XR
Obs.
R2
Panama
0.04
1.04***
0.49**
0.55***
-46.49**
549.85*
5413.71
-24.75***
0.00
28
0.75
Brazil
0.18
4.01**
-0.07
-0.06
3.39
17.45
622.57
-10.82
-5.97
30
0.77
Peru
0.30
2.75***
-0.07
0.15*
-1.91
-19.97
-67.87
2.61
3.84**
30
0.86
Bulgaria
1.16*
0.54
-0.04
-0.20*
5.36***
15.99***
-128.75**
-11.09***
4.79*
30
0.4
Philippines
0.04
0.67
0.09
-0.11
-6.40***
-1.19
258.55**
-12.21***
-0.08
29
0.86
Chile
0.44*
0.65***
0.01
0.06*
1.21
1.60
-37.74**
-0.28
0.00
27
0.76
Poland
0.99**
0.82*
-0.03
0.05
-0.43
-6.84
-29.93
1.86
1.85
18
0.88
China
-0.20
0.26***
0.05
0.05
-8.96
36.17
-56.84
-1.41
-1.51
30
0.8
Russia
0.23
-1.29
0.05
0.12*
10.04
-55.85*
469.88
6.66
-1.83*
30
0.93
Columbia
-1.00*
3.40***
-0.13*
-0.15
-3.66
-103.15**
25.83
-9.95
0.00
30
0.87
S. Africa
0.31
1.55***
-0.28**
-0.09**
82.21***
-80.65*
78.45
61.59*
-1.08**
30
0.79
Ecuador
0.62
9.17**
0.28*
-1.16
-268.84
1369.67
19758.34
-1018.57
-0.01
12
0.99
Thailand
-0.65
0.01
-0.03
0.17**
-1.90***
2.75*
-12.90
-0.76
0.15
27
0.94
Hungary
-0.39***
0.12
0.04
-0.03
-0.01
1.14
32.81***
-1.18**
-0.01**
27
0.55
Turkey
0.91
2.79***
-0.15***
0.00
5.93
5.03
-378.64*
0.80
8.19*
28
0.77
Malaysia
-1.15***
0.60***
-0.16***
-0.22
-10.46***
7.40***
29.50***
-0.17
-1.84
30
0.92
Venezuela
1.11
2.22***
-0.01
0.08
19.60
179.06
-102.56
-47.64***
0.00
29
0.8
Mexico
-0.48
1.50***
-0.01
0.02
28.14***
-210.87**
533.49**
19.43
-0.63***
30
0.95
sound country fundamentals, driven by structural reforms and better macroeconomic
policies. Which of the two factors have been more important? While benign conditions in global financial and commodity markets can reverse very quickly, country
fundamentals typically do not deteriorate ‘over night’. Thus, it is important to understand the reasons for the compression in spreads, as this can provide information about
the sustainability of benign financing conditions for emerging markets.
We have conducted a principal factors analysis to study EMBI spreads. By extracting principal factors from EMBI spreads, we estimate the degree of co-movement between all countries in our sample. As countries differ in economic developments, the
principal factor captures global conditions, as opposed to country-specific economic
fundamentals. We find that commodity prices, as well as conditions on financial markets are key variables driving the principal factor (these factors drive the global, as well
as the regional, principal factors).
We use this principal factor in panel and country-specific regressions to analyze the
degree to which the fall in EMBI spreads is driven by better macroeconomic policies.
Our results indicate that the principal factor is statistically important in explaining the
compression in spreads. But how important is the principal factor economically?
The second column of table 12 summarizes the results of panel model 3b in terms
of the effects of a one-standard deviation shock to the exogenous variables. The reductions in inflation and long-term debt have the biggest effect, followed by higher
export-to-GDP ratios. All three can be thought of as better macroeconomic fundamentals, as all three reduce vulnerability to external shocks. In contrast, the benefits
from global conditions – proxied by the principal factor – seem relatively small. This
suggests that the principal factor has been a less important element in the reduction in
EMBI spreads, or put differently: our results lend support to the hypothesis that better policies were a key factor in allowing emerging markets to finance themselves at
favourable rates. If emerging market spreads are primarily driven by macroeconomic
fundamentals, these results could provide an explanation why emerging markets have
hardly been affected by recent turmoil in the financial sector.
Note, however, two caveats. Firstly, as the country-specific results have shown,
the benefits from better macroeconomic policies vary considerably between countries.
Secondly, while we find a clear link between the principal factor and benign global
conditions – i.e. high energy- and non-energy commodity prices, low volatility in
stock markets, and low (U.S.) interest rates – it is not entirely clear that the impact of
28
All regions Model 3 Model 4
Global conditions
PF (global)
1.38
0.96
SP500
2.14
VIX
1.19
Oil
0.53
World GDP
0.09
Macroeconomic fundamentals
GDP
1.15
1.22
CPI
11.82
12.46
LT debt
5.18
5.07
ST debt
0.99
0.81
Exports
2.56
2.68
Reservesa
0.02
0.06
XRa
0.36
0.25
Deficita
0.51
0.67
Fin. Dev.
1.33
1.10
Crisis
1.91
2.02
Regulation
0.34
0.56
IT
1.00
1.01
a
Effect not statistically significant
Table 12: Effects of a one-standard deviation shock to global and country-specific
factors on EMBI spreads
these benign conditions occurs only through the principal factor. To some extent, such
additional effects were captured in our specification 4b, when we added the S&P 500,
the VIX and oil prices as additional exogenous variables. The third column of table 12
shows the effects of a one-standard deviation shock to the exogenous variables using
panel model 4b. The magnitude of the (additional) effect of, say, oil seems small (and
is not even statistically significant). Still, it is probably easier for emerging markets to
reduce and restructure their debt, if fiscal revenues are high because of high oil prices.
How likely is it that these benign global conditions for emerging economies will
prevail? Should the world economy slow down in response to the recent trouble in
credit markets, commodity prices are likely to come under pressure, too. It remains to
be seen how EMBI spreads would react, if emerging economies had to deal with the
(fiscal) impact of lower commodity prices.
29
Constant
PF1
PF2
GDP
CPI
LT debt
ST debt
Exports
Reserves
XR
Deficit
Fin. Dev.
Crisis
Regulation
IT
SP500
VIX
Oil
World GDP
Obs.
Countries
R2
Model 3
3.43
2.05***
0.06***
0.13***
17.81***
13.68**
-162.31***
0.24
0.00
1.00
6.14**
7.20***
-0.03
-2.18**
Model 3c
5.80
1.92**
0.15
0.06***
0.12***
18.11***
14.13**
-169.38***
-0.89
0.00
1.07
6.01**
7.35***
-0.08
-2.10*
448
19
0.44
448
19
0.43
Model 4
-0.82
1.42*
0.07***
0.13***
17.44***
11.25
-170.31***
0.67
0.00
1.31**
5.08*
7.65***
0.04
-2.19**
-0.01*
-0.22
0.07
6.87
448
19
0.46
Model 4b
0.94
1.67**
-0.42
0.07***
0.13***
17.89***
11.33
-171.70***
0.20
0.00
1.32**
4.81*
7.62***
0.01
-2.48**
-0.01*
-0.17
0.05
14.07
448
19
0.45
Table 13: Panel regressions with one or two principal factors (estimated by IV)
A
A.1
Additional robustness checks
Adding a second principal factor
As explained in section 4.1, the results of the factor analysis are not entirely clear on
whether we should use one or two principal factors. Given that there is no straightforward interpretation for the second principal factor, the estimates reported only included
the first factor. We have also estimated equation (10) with two principal factors, i.e.
EMBIt = β1 + β2 PF1,t + β3 PF2,t + ∑ β j X j,t + ∑ βkYk,t + εt .
j
(11)
k
We estimate equation (11) without and with global variables (models 3d and 4d,
respectively). Adding the second factor does not change our results qualitatively, as
can be seen in table 13.
30
A.2
Robustness checks for country-specific regressions
For the country-specific regressions, we checked the robustness of our results by adding
a crisis variable (estimations are done using instrumental variables). The results are
qualitatively comparable. Also, during crises, EMBI series might exhibit structural
breaks. Despite adding a crisis variable, it could be that non-linear effects are inadequately captured in our specification. To test the robustness of our estimations and
to account for the fact that a few outliers might bias the results, we also estimated the
equation by quantile regression. The differences between the two methodologies are
small (detailed results available upon request), leading us to believe that our findings
are not substantially impacted by outliers.
Lastly, figure 10 shows the difference between actual and predicted values of equation (10), when estimated using instrumental values. We see that the fit of the countryspecific regressions is, by and large, very good.
B
Data sources
Our analysis covers 20 emerging market economies: Argentina, Brazil, Bulgaria, Chile,
China, Columbia, Ecuador, Hungary, Malaysia, Mexico, Morocco, Panama, Peru, the
Philippines, Poland, Russia, South Africa, Thailand, Turkey, and Venezuela. These
are all countries for which the EMBI Global provides consistent data between 1998Q1
and 2007Q2. For some tests, we group the countries into the following regions: Latin
America, Europe, and Rest of the World (ROW).
The country-specific components of JP Morgan’s EMBI Global index is our measure of sovereign spreads. We use monthly and quarterly data of the following data
sources: EMBI Global series are taken from JP Morgan, macroeconomic data on reserves, inflation, exports, GDP in local currency, and the exchange rate are from the
IMF’s IFS database, and ratings are from Standard and Poor’s. Ratings data were
transformed into numbers (an AAA rating equals 1; an AA ratings equals 2 etc.). Note
that we use annual GDP data for China, Ecuador, Panama, and Venezuela, due to data
availability, and interpolated the series using a linear conversion. U.S. bond data for
the 3 month and the 10 year Treasury are from Datastream, and the U.S. yield curve is
proxied by the difference in the two series.
31
32
Fitted values
Fitted values
Fitted values
Morocco
Russia
Fitted values
Columbia
Argentina
South Africa
Panama
Ecuador
Brasil
Thailand
Peru
Hungary
Bulgaria
Fitted values
Fitted values
Fitted values
Fitted values
Figure 10: Actual vs. fitted values
Fitted values
Fitted values
Fitted values
Fitted values
Turkey
Philippines
Malaysia
Chile
Fitted values
Fitted values
Fitted values
Fitted values
Venezuela
Poland
Mexico
China
Country-specific estimation by instrumental variables
Fitted values
Fitted values
Fitted values
Fitted values
As regards data transformation, we use nominal GDP in local currency and convert
it to real GDP using the GDP deflator (except for Brazil, Bulgaria, China, Columbia,
Panama, Russia, and Venezuela, where only CPI data was available to deflate the series). Short-and long-term debt-to-GDP ratios, reserves-to-GDP ratio, and exports-toGDP ratios are computed using nominal values in U.S. dollar.
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