WPS3626
Global Monetary Conditions versus Country-Specific Factors in the Determination
of Emerging Market Debt Spreads
Mansoor Dailami1
Paul R. Masson2
Jean Jose Padou3
Abstract
We offer evidence in this paper that US interest rate policy has an important influence in
the determination of credit spreads on emerging market bonds over US benchmark
treasuries, and therefore on their cost of capital. Our analysis improves upon the existing
literature and understanding by addressing the dynamics of market expectations in
shaping views on interest rate and monetary policy changes and by recognizing
nonlinearities in the link between US interest rates and emerging market bond spreads, as
the level of interest rates affects the market's perceived probability of default and the
solvency of emerging market borrowers. For a country with a moderate level of debt,
repayment prospects would remain good in the face of an increase in US interest rates, so
there would be little increase in spreads. A country close to the borderline of solvency
would face a steeper increase in spreads. Simulations of a 200 basis points (bps) increase
in US short-term interest rates (ignoring any change in the US 10 year Treasury rate)
show an increase in emerging market spreads ranging from 6 bps to 65 bps, depending on
debt/GDP ratios.
World Bank Policy Research Working Paper 3626, June 2005
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
1 World Bank, Development and Prospects Group.
2Rotman School of Management, University of Toronto
3University of Toronto
I. Introduction
How interest rate policies in major industrial countries affect the pricing of
emerging market debt remains an unresolved issue. Despite its very important policy and
practical implications, our understanding of this link is shaped more by episodic evidence
--in 1991, 1994, and 2003 when sharp swings in emerging market spreads coincided with
a cyclical shift in the stance of US monetary policy --than by rigorous research and robust
empirical findings. One point of view, popularized by the financial press, emphasizes the
role of investors' risk tolerance or risk appetite, even though such factors are likely
driven by a host of global macroeconomic conditions and uncertainties, including
potentially the pace of changes in US interest rates, and are more directly relevant to the
equity market than fixed income bond markets. And, while considerable literature exists
on the determinants of emerging market debt spreads over US Treasury securities, that
literature is disappointingly inconclusive concerning the effects of the global interest rate
environment. For instance, Arora and Cerisola (2000), Min et al. (2003) and Ferrucci et
al. (2004) find that the level of US interest rates plays a considerable role in the
determination of emerging market bond spreads--spreads widen as US rates go up--but
Kamin and von Kleist (1999) argue that there is little explanatory power of industrial
country short-term interest rates, once one controls for credit quality. Eichengreen and
Mody (2000), in contrast with all of the above, find that syndicated bank loans to
emerging Market countries tend to respond positively to increases in US interest rates,
and the spread on those loans responds negatively--though this surprising result is very
sensitive to regional differences. Furthermore, studies focusing on the US corporate bond
market have also found a negative relationship between credit spreads and US Treasury
1
yields (Longstaff and Schwartz, 1995; Duffee, 1996, and 1998; Colin-Dufresne,
Goldstein, and Martin, 2001)4, as predicted by structural models of credit risk following
Merton (1974).
Existing studies of the link between US interest rates and emerging market bond
market spreads have several weaknesses. One major shortcoming is the scant attention
paid to the dynamics of market expectations in shaping views on interest rate and
monetary policy changes, and how such policy changes are factored into the
determination of bond market prices and yields5. The hypothesis advanced here is that
the market anticipates the future behavior of the Fed by observing the evolution of
relevant leading short-term macroeconomic indicators and factors in such expectations
well in advance of actual changes in interest rates. This dynamic induces an important
correlation between emerging market bond spreads and key indicators of the US
economic outlook such as the US non-farm payrolls report and retail sales, as is clearly
demonstrated by recent experience--much of the market reaction regarding the bond
spreads occurred in April/May 2004, when the reported payroll figures indicated stronger
growth momentum than anticipated by the market6. The reason for this dynamic is the
particular institutional setting of interest rates in the US, where under the prevailing
regime (which in many ways is equivalent to inflation targeting, without an explicit
target), the market comes to form views about Fed behavior, based on the evolution of
4Also studies by Leake (2003), and Boss and Scheicher (2002) focusing respectively on the UK and
Euro-corporate bond markets, find a small negative relationship between credit spreads on sterling
investment-grade corporate bonds and the level and slope of the term structure of UK interest rates.
5Arora and Cerisola (2000) however look at the predictability of US monetary policy, and find that
heightened uncertainty about that policy leads to a widening of spreads.
6 As Uribe and Yue (2003) note, US interest rates also help to drive business cycles in EM economies, and
spreads respond to EM activity. However, their empirical results show that US interest rate shocks affect
domestic EM variables mostly through their effects on country spreads.
2
certain leading short-term economic indicators, such as payroll figures, retail sales, and
core inflation data7.
A second weakness of the existing literature is the lack of attention paid to the
non-linearity in the relationship between US interest rates and emerging market spreads.
Indeed, the spread incorporates a default probability in a non-linear way, and the effect of
higher world interest rates itself affects the default probability non-linearly. For instance,
at low rates of interest and in periods of favorable economic activity and low debt in
developing countries, a rise in US interest rates may have little effect on investors'
estimates of the probability of repaying--and indeed, on the objective likelihood of that
repayment. In contrast, when the emerging market borrower is at the borderline of its
ability to repay, a given increase in US rates may push the borrower over the edge,
sharply increasing the probability of default. Such a scenario may have occurred, for
instance, in 1982 and 1994.
A further aspect of that non-linearity is that sharp shifts of expectations of default
probabilities may be self-fulfilling, and correspond to jumps between multiple equilibria.
Indeed, those expectations can be rational because higher interest rates will increase the
likelihood that countries cannot meet their debt service obligations. While models with
sunspot equilibria are sometimes criticized as just adding an extra indeterminacy because
what triggers the jumps between equilibria is not explained, in international capital
markets, that role may be assumed by global liquidity conditions and the "appetite for
risk." In our estimation, we divide the sample into crisis and non-crisis periods. We also
7This accords with the view and assessment of key market practitioners. A credit market strategist was
quoted by Credit (2004) as saying, "it is not the rate hikes that matter, but what is happening to the
economy".
3
include proxies for international liquidity and for contagion in financial markets. Indeed,
given that there are investors in emerging market bonds that are common across
countries, it is natural to expect that a crisis in one country should be associated with
higher spreads in other markets, if they both are the result of a changed attitude to risk or
liquidity.
A third improvement relative to the current literature is our use of more recent
data (until June, 2004)--and longer time series; this may help to distinguish between
hypotheses. In particular, we use monthly data for individual country emerging market
Bond Index Plus (EMBI+) spreads, available from JP Morgan which is a major dealer in
emerging bond markets, and extending back for some countries to 1991. The bonds are
issued in US dollars, so that spreads reflect credit risk--the probability that the borrower
will not repay. The set of countries includes all the major sovereign borrowers, and the
data are based on trading in secondary markets of Brady bonds and Eurodollar issues.
Our sample includes the following 17 countries: Argentina, Brazil, Bulgaria, Colombia,
Ecuador, Mexico, Morocco, Nigeria, Panama, Peru, the Philippines, Poland, Russia,
South Africa, Turkey, Ukraine, and Venezuela. We estimate an unbalanced panel, with
data availability varying from country to country. While the data on spreads are based on
secondary market data, they provide many more data points and allow a finer
appreciation of the effects of interest rate increases than primary market data. Moreover
with transaction volumes in secondary markets surpassing those in primary markets by
several fold, spreads based on secondary market prices are more informative and less
contaminated by supply effects than the spreads for new issues.
4
II. A Framework for Analysis
Our approach to understanding the link between US interest rates and emerging
market debt spreads focuses on the impact of interest rates on the market's perceived
probability of default. The starting point here is the simple relationship between the
probability of default p on emerging market bonds and the rate of interest on riskless
securities, say US, paying rate r (see, for instance, Arora and Cerisola, 2000). If the
default is complete (with no repayment of either principal or interest8), investors are risk
neutral, and assuming away the possibility of default correlation across borrowers, then
the interest rate i on emerging market bonds should yield the same expected return as US
Treasuries, so
1+ r = (1+ i)(1- p) + p 0 (1)
Therefore, the spread S over US Treasuries can be written
S = i - r = (1+ r)1 p
(2)
- p
or, in log form,
log S = log(1+ r) + log1 p
- p
(2.a)
8In studies where default is taken to be less than complete, the rate of recovery upon default is generally
assumed to be constant (see for instance, Elton et al. 2001).A constant recovery rate does not change our
main results.
5
We go behind this simple relationship to look at the determinants of the ability to
service the debt9, while leaving aside issues of voluntary default. Suppose that the EM
sovereign borrower has a stochastic income stream Y, and that it defaults when that
income is less than the debt service. Then the probability of default will be given by
p = Pr[Y < iD] = Pr[Y < (r + (1+ r)1- )D]
p
(3)
p
We will further assume for concreteness that Y is determined by a first order
autoregression Y = Y-1 + with innovations that are i.i.d. with distribution function F().
Let Z = rD - Y-1 be the interest burden at a zero default probability minus expected
income. Then the probability of default can be written
p = Pr[ < Z + (1+ r)1- D] = F[Z + (1+ r)1- D]
p p
(4)
p p
It is important to note three things about the above equation: i) the probability
depends on the stock of debt, as well as lagged income; ii) equation (4) is highly non-
linear in the default probability; and iii) it can have multiple solutions, since, in general,
the right hand side is increasing in the default probability10. Figure 1 shows the case
where there are 3 intersections of the 45 degree line (the LHS of eq. 4) and the RHS of
the equation.
9A seminal article is Eaton and Gersovitz (1981).
10See Jeanne (1997).
6
Figure 1. Multiple Solutions for Default Probabilities
p
F[Z+(1+r)pD/(1-p)]
p
The intuition is clear: by expecting a default, investors can make a default more
likely. However, this is only true in certain ranges for the variables and the parameters.
The "fundamental" variable Z needs to be in a certain range, and, in particular, debt D
has to be large enough that increases in interest costs can make debt service painful for
the emerging market borrower. A necessary condition for three solutions is that the
cumulative distribution function should have a slope greater than unity at some point--in
particular, at the middle intersection. Since the slope of this curve is simply the
probability density function f(x)=F'(x), it is straightforward to show that this condition
requires that
f [Z + (1+ r)1 p D]· 1+ r D > 1 (5)
- p (1- p)2
at the middle intersection point.
7
What is the effect of increasing the US interest rate on the probability of default?
Increasing the US rate shifts up the curve that corresponds to the RHS of (4): it increases
the value of p corresponding to the leftmost and the rightmost intersections--that is,
increases the default probabilities. It has the opposite effect on the middle intersection,
but, as in most models, that intersection is unstable, so it can be ignored. In addition to
increasing the value of p at the intersection points, it may also eliminate the two leftmost
intersections, leaving only the third one, with the highest probability of default. Thus, if
it shifts the curve up enough it may have a dramatic effect on the (rationally expected)
occurrence of default, since the upper equilibrium makes a default very likely.
Let us then examine how increases in the US interest rate would affect the spread.
From (2.a),
d log S = 1 + 1 dp
(6)
dr 1+ r p(1- p) dr
We see that, since dp/dr>0, both terms on the right hand side of (6) are positive, so that
increases in US rates increase the spread. In addition to the non-linearity embodied in
dp/dr, the derivative dlogS/dr is also highly non-linear in p. A given change in the
probability of default dp/dr will have a larger effect on the spread when probabilities of
default are either very low or high than when they are close to one-half.
The implications of the above for developing a more rigorous estimation
methodology are two-fold. First, it is not sufficient just to include the US interest rate in
a linear regression explaining the spread. The effect on the spread is non-linear, and
depends on other variables. The time series of correlations of EMBI spreads with US
interest rates (measured over 36 month rolling periods between December 1992 and June
8
2004 ) show a great deal of fluctuation, with a clear break between crisis and non-crisis
periods (Figures 2 and 3).Thus interacting US interest rate variables with variables which
capture the severity of the debt problem may be essential to capturing the effect of global
monetary conditions on emerging market spreads.
Figure 2. Correlation of EMBI and US interest rates rolling three-year
periods. Dec 1995-Aug 2004
1
0.8
fedrate
0.6
gs10
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
Dec-95 Jun-96 Dec-96 Jun-97 Dec-97 Jun-98 Dec-98 Jun-99 Dec-99 Jun-00 Dec-00 Jun-01 Dec-01 Jun-02 Dec-02 Jun-03 Dec-03 Jun-04
Figure 3 Correlation of EMBI and US interest rates rolling
three-year periods Dec 1995-Aug 2004
1
0.8 fedrate
0.6
0.4 tb3m
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
Dec-95Apr-96Aug-96Dec-96Apr-97Aug-97Dec-97Apr-98Aug-9Dec-98Apr-99Aug-99Dec-99Apr-00Aug-00Dec-00Apr-01Aug-01Dec-01Apr-02Aug-02Dec-02Apr-03Aug-03Dec-03Apr-04Aug-04
8
Second, the possibility of multiple equilibria should be taken into account. We
see this possibility as being related to changes in global liquidity conditions and the
9
appetite for risk, and hence we introduce variables that attempt to capture those features
in regressions explaining the spreads. In addition, it may well be the case that the effect
of explanatory variables on the spreads is different, depending on which equilibrium is
chosen. Thus, it may make sense to divide the sample into two sub-samples: "normal"
times, and "crisis" periods when a particular country faces sharply higher spreads as a
result of a debt default or currency attack. For instance, it seems plausible that if a
country is in crisis, then the effect of world monetary conditions on the spread will be
less significant. It is likely that the country can emerge from crisis and reduce the very
high interest rates that it faces mainly by its own actions--unless the crisis was provoked
by a shift in global risk appetite (see next paragraph). In normal times, however,
countries' spreads may be very sensitive to the conditions in global capital markets,
especially if they are in a middle region of high but not overwhelming debt. Dividing the
sample would prevent outliers from distorting estimates of the influence of "push"
factors. The occurrences of very high spreads in our sample are mostly associated with
defaults or currency crises on the part of the borrowing country: Mexico in December
1994, Russia in August 1998, and Argentina in February 2002, for instance.
Moreover, the existence of multiple equilibria gives a natural role to contagion
effects in international capital markets11. Again, this contagion may operate through a
change in global risk appetite, and in our model, be signaled by changes in the spread
between US corporate borrowers with high and low risk. It may also be evidenced by a
11For a discussion of the relationship between multiple equilibria and contagion, see Masson (1999; 2001).
A contrary view of the relevance of multiple equilibria in financial markets is presented by Morris and Shin
(1998; 2002). However, their argument relies on the iterated elimination of all dominated strategies (with
infinite iteration), which experimental evidence suggests does not apply in real world situations (Camerer,
1997).
10
positive effect of the spread in non-crisis countries of a dummy variable that counts the
number of other countries in the crisis state. This could be expected to operate if the
country itself is not in crisis; for the same reason as above, once a country is in the crisis
state it is mainly affected by its own variables--or, if the crisis was caused by a loss of
risk appetite, by an abatement of contagion and a return to more normal conditions.
III. Empirical Methodology
As in much of the empirical literature, we use panel regressions of the emerging
market interest rate spread over US treasuries on domestic determinants of a country's
credit-worthiness as well as global variables that explain the supply and cost of credit to
emerging market. Ferrucci et al. (2004) call the first set of variables "pull factors", and
the second, "push factors"; their research concludes that both sets are important in
explaining EM spreads. However, as the discussion above suggests, it is difficult to
separate the two sets of variables conceptually. For instance, the level of US interest
rates influences a country's creditworthiness since borrowing that is sustainable in a low
world interest rate environment (whatever the country's economic fundamentals) may not
be so when interest rates are high.
We follow Ferrucci et al. (2004) in distinguishing between long run influences on
spreads, which are constrained to have the same coefficients for all countries, with the
short run dynamics that can vary from country to country and may include other
explanatory variables. The short-run dynamics are specified to take the form of an error
correction model, where the errors are deviations from the long-run equilibrium
relationship.
11
The econometrics behind this model in a panel context are described in Pesaran,
Shin, and Smith (1999). The model is estimated using their Pooled Mean Group
Estimator. In particular, we posit a long run relationship linking the log of spreads of EM
interest rates over comparable US Treasuries, to US interest rates, the spread between
high and low risk US corporate bonds, and various variables reflecting the borrower's
creditworthiness (trade openness, debt/income, the ratio of short-term debt in the total,
and reserves/debt). If we call all these right hand side variables the vector X, then the
long-run relationship is
log Sit = i + Xijt
j
j
(7)
where the intercept term may vary across countries, allowing for fixed effects, while the
slope coefficients are constrained to be the same. The dynamic equations take the form
error-correction models for which the short-run adjustment toward the same long-run
relationship can vary across countries:
logSit =i[i + Xijt -logSit ]+iYit
j -1 -1
j
(8)
The vector Y may include first differences of all or a subset of the X variables, plus
other variables that influence the short run dynamics but not the long-term equilibrium
level of spreads. In the latter category we include current changes in US monetary policy
variables and forecasts of their future evolution (to be described below).
12
Note that the estimation of (8) is not straightforward using panel estimation
programs, because of the non-linear constraints on the long run coefficients. In particular,
if we estimate
logSit =i + bijXijt + ci logSit + diYit +ui
-1
j
(9),
then we need to impose the following constraints
bij = i, j,k
ci
bkj ck
(10).
Estimation can be done in two stages, that is, first estimate the long term
regression, (7), and then use the lagged residuals to replace the term in square brackets in
(8). We use the two-stage approach for our exploratory regressions that aim to narrow
down the set of explanatory variables. However, this method is inefficient since it
ignores the effect of short-term dynamics when estimating the long-term coefficients .
Therefore, the PMG estimates impose long-run coefficients in the context of a dynamic
panel regression.
When considering variables that may influence short-run dynamics, we include
changes in the above long-run determinants and also forecasts of the change in stance of
US monetary policy. Thus we first estimate forecasting equations for US interest rates
that include as explanatory variables lagged changes in US capacity utilization, retail
sales, the producer price index, and M2 data. Then we test whether these forecasts have
an additional effect, over and above the long run relationship between US interest rates
and emerging market spreads and the contemporaneous change in US rates.
13
In order to maximize the number of observations, and focus on the
macroeconomic determinants of US monetary policy, we estimate the model at a monthly
frequency, even if this limits the availability of country-specific determinants of
emerging market credit-worthiness. For many of the countries in our sample, data on
consumer prices, reserves and the money supply are available monthly, while quarterly or
annual data are available for GDP and foreign debt. We interpolate the latter variables
where necessary.
In specifying the effects of US monetary policy, we include in the long-run
relationship the levels of US short rates (the US Treasury bill rate12), the 10-year
Treasury bond rate, and the interest rate spread between high and low risk US corporate
bonds. The latter variables, as well as being influenced by US monetary policy, may also
capture global risk appetite. We interact the US T Bill rate with the country's debt to
GNI ratio, since as discussed in section II, non-linearities are likely to be important.
We have argued that it is important to divide the sample into crisis and non-crisis
periods. A country i is in a non-crisis period t if in that period it is not suffering a crisis,
even if other countries are. But it is also possible that the existence of crises at t in other
countries--and their number--may increase the interest rate for country i. A significant
coefficient on the number of other countries in crisis may be evidence of contagion
effects, since, if positive, it would come over and above the country's own fundamentals
and global monetary conditions.13
12Arora and Cerisola (2000) suggest that the federal funds rate is more appropriate, but we find the TB rate
to be a better explanatory variable of EM spreads.
13Eichengreen, Rose and Wyplosz (1995) find that the occurrence of crises elsewhere tends to increase a
country's likelihood to experience a crisis.
14
Dating of crisis periods is not straightforward, however. One approach is to let
the data on exchange market pressure (the sum of exchange rate changes and reserve
changes, appropriately weighted) identify crisis periods. A recent application is
Kaminsky (2003), and we use the crisis dates she identifies (Kaminsky, 2003, table 2),
but instead of assuming that the duration of each crisis is 24 months, we use a much
smaller window, six months following the crisis date in her list--except for Argentina.
In practice, the period of very high spreads has been limited to a few months, when a
resolution has been in sight. Argentina's 2002 default is an exception, and we make the
crisis dummy continue to the end of our sample. We also add the Russian crisis, starting
in August 1998, since Russia is not in her sample.
IV. Estimation Results
We first describe the long-run (static) regressions of the log of spreads on both
push and pull factors. Of especial interest is the relative importance of the two sets, and
evidence of non-linearity in the relationship. Table 1 gives the coefficient estimates, with
the sample divided into crisis and non-crisis periods, with the latter dominating in our
sample (1497 months versus only 54 crisis periods14).
Country specific variables seem to dominate US interest rates in influence over
emerging market spreads. In particular, trade openness has a strong negative effect on
spreads, which is plausible since more open countries are better able to adjust their
14As noted above, our choice of a small crisis window (6 months) limits the number of crisis periods. In
addition, our reliance on Kaminsky (2003) for the crisis dates no doubt misses some crises, since not all the
countries in our sample are on her list.
15
Table 1. Long-Term Influences on Emerging Market Spreads,
1991M1-2004M615
Coefficient values (absolute t-ratios)
explanatory variables full sample crisis periods non-crisis periods non-crisis
(1) (2) (3) periods
(4)
(1) US variables
long-term interest rate -.0192(0.94) .2261(1.18) -.0143(.74) ..
hi-low corporate .0739(9.08) .0601(0.57) .0794(10.0) .0796 (12.4)
spread
(2) country specific
variables
trade openness -.9260(24.2) -.0640(.08) -.918(23.6) -.913 (23.9)
debt/gni .0093(7.75) .0200(2.82) .0087(7.77) .0089 (8.09)
reserves/debt -.0212(15.9) -.0622(8.01) -.0194(16.0) -.0191 (16.0)
short-term/total debt -.0291(19.8) -.1055(4.37) -.0283(20.2) -.0280 (20.1)
(3) interaction
variables
US Tbill rate times .00066(3.21) .0011(0.69) .00084(4.31) .00081 (4.35)
country's debt/gni
contagion dummy .0115(1.20) -.2123(2.56) .0186(1.97) ..
constant 6.944(37.5) 7.213(6.36) 6.779(38.9) 6.707 (89.1)
(4) statistics
no. of obs. 1551 54 1497 1497
R2 .5785 .8243 .5976 .5960
p-value for zero coeffs .0000 .0000 .0000 .0000
15Period of estimation depends on the country's data availability. Static regression of the log of spreads on
the explanatory variables listed above, with all coefficients constrained to be the same for all countries.
16
balance of payments in order to generate earnings to service external debt; this variable
may also reflect the finding in the growth literature that more open countries tend to grow
faster. As expected, the level of debt to a country's income has a significant positive
influence on the spread it faces, while the reserves/debt ratio and the proportion of short-
term debt both have a significant negative influence. The latter effect may simply reflect
an upward-sloping term structure.
Turning to the US interest rate variables, in non-crisis periods the US short-term
rate, entered linearly, did not have a significant coefficient (not reported), while the long-
term interest rate (reported here) has a negative, but insignificant, coefficient. These
results contrast with those of Ferrucci et al. (2004). However, the interest rate spread on
high-risk versus low-risk US corporate borrowers comes in very strongly in non-crisis
periods.
As we have argued above, the effect of US rates can be expected to be non-linear,
and the US Treasury bill rate interacted with the borrowing country's debt/gni ratio does
in fact enter significantly in non-crisis periods (column 3) and produces significantly
higher explanatory power than the interest rate entered alone. Thus, the impact of rising
US rates is higher, the higher is a country's level of indebtedness. In non-crisis periods,
countries are also vulnerable to crises in other countries, as the contagion dummy (the
number of crises elsewhere) has a significant positive effect.
The relationship between global monetary conditions and the emerging market
spread is quite different during periods of crisis, as we can see from column 2 of the
table. A chi-square test rejects equality of the two sets of coefficients. In crisis periods
17
the US Hi-low spread and the US Treasury bill rate interacted with a country's debt have
no significant effect, as is also the case for the US long rate (which was already true in
non-crisis periods). In contrast, all the "pull" factors except trade openness continue to
have a significant influence on emerging market spreads in the expected direction.
Somewhat surprisingly, the contagion dummy (that is, the number of other countries in
crisis) has no longer a positive effect on spreads--it is the reverse; conditioned on a
country being in crisis, its spreads do not suffer from other countries also being in crisis.
Of course, the fact of being in a crisis situation may itself depend on the contagion
dummy. It is also true that the constant term is significantly higher in column 2 than in
column 3. In sum, it seems that interest rates charged to crisis countries are more
dependent on their own behavior than on conditions on global capital markets.
When the crisis and non-crisis periods are pooled (column 1), not surprisingly the
estimates resemble those of column 3, given the preponderance of non-crisis
observations. However, the contagion dummy is now insignificant, and the explained
variance is lower than for either sub sample. Since equality of the two sets of coefficients
in the sub-samples is rejected, the usual procedure of estimating a combined sample of
crisis and non-crisis observations on spreads is not legitimate.
We then turn to the dynamic equations, which we estimate first by using the
residual from the long-run equations, in particular a somewhat more parsimonious model
reported in Table 1, column 4. The PMG technique is later used to estimate
unconstrained short run dynamics and a common long run relationship, but our initial
estimates, reported in Table 3, also constrain the short run dynamics to be the same. The
18
common short-run dynamics then give some idea of the average effect on spreads across
the set of emerging market countries.
Of particular interest to us is to see whether the forecasted stance of US monetary
policy, and not just current interest rate variables, helps explain the evolution of emerging
market spreads. Therefore, we first attempt to relate our US interest rate variables--the T
Bill rate, the 10-year Treasury rate, and the interest rate spread between high and low risk
corporate borrowers--to indicators of inflationary pressures and the strength of US
economic activity. These are presented in Table 2, where 3 lags of each of the
explanatory variables are included in each case. Changes in the latter variables are often
cited by "Fed watchers" as leading indicators of changes in Fed policy.
The three interest rate variables are affected differently by movements in these
indicators. The T Bill rate seems to respond significantly, and positively, to upticks in
retail sales and capacity utilization, while evidence of a significant effect of producer
prices and M2 is weaker. Indeed, the latter variable is negative, so that a liquidity effect
of monetary expansion may operate. Long-term bonds also respond positively to retail
sales, while the first lag of M2 expansion is significantly positive, perhaps reflecting fears
of future inflation from an easier monetary policy. Finally, the corporate bond spread
does not seem to respond systematically to our set of indicators.
We then proceed to use the predicted values of the forecasting equations of Table
2--and other explanatory variables--in dynamic equations for the emerging market
spread. These are reported in Table 3, where all the coefficients, except for the
intercepts, are identical across countries.
19
Table 2. Forecasting Equations for First Differences in US Interest Rates
1992M5-2004M6
Coefficient values (absolute t-ratios)
Explanatory US Tbills 10-year Treasuries Corporate Hi-Lo
variables: changes Spread
in logs of:
Producer Price
Lag 1 2.400 (1.29) 4.438 (1.68) -4.359 (.76)
Lag 2 2.308 (1.23) -2.677 (1.01) 4.176 (.73)
Lag 3 -2.716 (1.48) -4.973 (1.90) .356 (.06)
Retail Sales
Lag 1 2.751 (1.95) 6.266 (3.13) -9.099 (1.62)
Lag 2 5.643 (3.84) 6.826 (3.27) 1.745 (.39)
Lag 3 2.791 (1.99) 3.612 (1.81) -2.408 (.56)
Capacity
Utilization
Lag 1 10.124 (3.54) 5.524 (1.36) 4.291 (.49)
Lag 2 3.604 (1.24) .425 (.10) -3.769 (.42)
Lag 3 7.128 (2.38) .707 (.16) -11.098 (1.21)
M2
Lag 1 -3.081 (1.37) 8.563 (2.67) -12.034 (1.74)
Lag 2 -2.082 (.96) -.547 (.18) -1.761 (.26)
Lag 3 -3.601 (1.57) 1.201 (.37) -3.812 (.54)
Constant -.0324 -.1303 .1163
No. of obs. 146 146 146
R2 .3418 .1796 .068
p-value .0000 .0000 .0000
20
Table 3. Dynamic Error Correction Models for Changes in Log Spreads: Fixed
Effects, Non-crisis periods, 1991M1-2004M6
Coefficient values16 (absolute t-ratios)
explanatory variables Actual US interest Actual and forecast Parsimonious
rate changes only US interest rate model
(1) changes (2) (3)
(1) lagged residual from -.1040 (9.33) -.1037 (9.12) -.1035 (9.13)
Column 4 of Table 1
(2) changes in US Interest
Rate Variables
US T Bills .0338 (1.66) .00092 (.04) ..
10 year Treasuries .0653 (3.41) .0674 (3.46) .0671 (3.66)
US hi-low corporate .1274 (16.9) .1280 (16.6) .1288 (17.0)
spread
Forecast US T Bills .. .0660 (1.36) .0735 (1.96)
Forecast 10yr Treasuries .. .0114 (.21) ..
Forecast US hi-low .. .1619 (4.26) .1513 (4.92)
spread
(3) changes in country
specific variables
trade openness .1016 (.96) .0982 (.93) ..
debt/gni .00888 (2.49) .00894 (2.49) .00974 (2.74)
reserves/debt -.0219 (3.35) -.0220 (3.35) -.0237 (3.63)
short-term/total debt -.0281 (3.55) -.0302 (3.77) -.0304 (3.81)
(4) contagion dummy .00288 (.97) .00444 (1.47) ..
(5) statistics
no. of obs. 1471 1441 1443
R2 Within .2218 Within .2369 Within .2368
Between .3108 Between .3161 Between .3080
Overall .1960 Overall .2119 Overall .2122
p-value for zero .0000 .0000 .0000
coefficients
p-value for test all u(i)=0 .0009 .0033 .0038
16Separate country intercepts are not reported.
21
The first column of Table 3 reports estimates of a model that includes only the
residual from the first-stage regression (from Table 1) and the first differences of the
long-run determinants. In the notation of equation (8), the set of Y variables is identical
to the X variables. Notable in the results is that the lagged residual is strongly significant,
consistent with an error correction model and implying that 10 percent of the deviation
from the long-run relationship is closed each month. The short-run dynamics are
significantly affected by some of the same variables. In particular, increases in both the
US long rate and corporate spread tend to increase the Emerging market spread, as does
the country's debt/gni ratio. Conversely, increases in reserves and the proportion of debt
that is short-term tend to lower the spread. Columns 2 and 3 add forecast (one month
ahead) US interest rate variables. The forecast change in the hi-low spread and, in the
parsimonious model (dropping variables with insignificant coefficients), also the forecast
change in the US T Bill rate, tend to increase spreads. Thus, movements in US economic
activity and inflation have an indirect effect on emerging market spreads.
The Pooled Mean Group estimates tend to confirm the conclusions derived from
the two-step procedure. These results, presented in Table 4, impose the same long-run
relationship but allow the short-run dynamics to differ across countries. The common
long-run coefficients are given in the upper part of the table. As expected, they have the
same signs as those in Table 1: the T Bill rate times debt has a positive (but insignificant)
coefficient, the US 10-year rate a negative effect, greater trade openness and reserves
reduce the spread, while higher debt increases it. The error-correction term, as captured
by the coefficient on the lagged dependent variable, is almost everywhere significant with
the right sign. Its median value, around -.15, is somewhat higher in magnitude than that
22
Table 4. Pooled-Mean-Group Dynamic Error Correction Models for Changes in
Log Spreads: Fixed Effects, Non-crisis periods, 1991M1-2004M6
Coefficient values17 (absolute t-ratios)
Explanatory variables Full model Parsimonious model
(1) Long-run coefficients
10-yr Treasury rate -.2301 (4.29) -.2058 (4.41)
US hi-low corporate .00585 (.29) .00753 (.43)
spread
US Tbill rate times .000775 (1.80) .000562 (1.48)
country's debt/gni
trade openness -.4676 (1.84) -.5352 (2.27)
debt/gni .00471 (1.21) .00734 (2.08)
reserves/debt -.0221 (3.40) -.0225 (3.83)
short-term/total debt -.00628 (.77) -.00116 (.16)
(2) coefficient on lagged
EM spread, by country:
Argentina -.0720 (2.08) -.0577 (2.03)
Brazil -.1312 (3.28) -.1301 (3.39)
Bulgaria -.1372 (3.00) -.1362 (3.04)
Colombia -.3232 (1.92) -.2851 (1.87)
Ecuador -.1286 (3.28) -.1315 (3.41)
Mexico -.1699 (2.96) -.1823 (3.10)
Morocco -.1601 (3.99) -.1547 (4.14)
Nigeria -.1393 (3.93) -.1188 (3.99)
Panama -.4963 (3.50) -.5228 (3.67)
Peru -.4452 (3.81) -.1420 (5.11)
Philippines -.1405 (2.50) -.1461 (2.91)
Poland -.1545 (2.64) -.1339 (2.70)
Russia -.1205 (2.74) -.1254 (2.97)
South Africa -.0191 (.39) -.0157 (.37)
Turkey -.3084 (1.47) -.3139 (1.98)
Ukraine -.3742 (1.76) -.3420 (2.04)
Venezuela -.1321 (3.20) -.1345 (3.39)
(3) statistics
no. of obs. 1443 1443
R2 .3294 .3194
Adjusted R2 .2458 .2703
p-value for zero .0000 .0000
coefficients
17Coefficients on the explanatory variables in first-differences and separate constant terms, which are
allowed to differ across countries, are not reported.
23
estimated in Table 3. The short-run dynamic terms captured by coefficients on the Y
variables are too numerous to be reported; they are diverse but include many significant
values, including on forecasted US rate variables. The parsimonious model of the
rightmost column of the table drops insignificant Y variables to get more efficient
estimates.
V. Conclusions
Our results suggest that, in order to understand the effect of global monetary
conditions and of an emerging market country's own policies, we need to separate the
sample of emerging market spreads into two, distinguishing crisis from non-crisis
periods, and need to allow for non-linearity in the effect of US rates on emerging market
spreads. Our results confirm the differences between crisis and non-crisis periods,
including differences of the effect of US rates, and confirm the existence of non-linearity.
Furthermore, variables capturing anticipation of US monetary policy changes
have significant effects on emerging EM spreads, in addition to the current values of US
interest rate variables.
What is the prospect for emerging market spreads at present, in 2005 , in the light
of expected increases in US interest rates as the very expansionary monetary conditions
are brought back to a more neutral position--and perhaps to a tightening stance should
US inflation ratchet up? Our framework for analysis suggests some tentative
conclusions.
It is useful to consider the question in two stages. First, the level of interest rates
in world capital markets--strongly influenced by US rates--will affect the solvency of
24
emerging market borrowers. However, if they have moderate levels of debt, their
repayment prospects will remain good and there will be little increase in the probability
of default, and hence little increase in spreads. For countries that are close to the
borderline of solvency, however, global interest rates can have a dramatic impact on the
ability to repay, and could lead to a much steeper increase in their spreads. So the
situation of each individual emerging market country is crucial to gauging the effect of
US monetary tightening. As an illustration, Figure 4 displays the impact a 200-basis-
point increase in the U.S. Treasury bill interest rate on emerging market spreads (using
the long-run estimates of Table 1, and assuming no change in the long-term rate or
corporate spreads): this translates into increases ranging from 6 basis points (for countries
with debt-to-GNI ratios below 40 percent) to 64 basis points (for highly indebted
countries with debt-to-GNI ratios above 90 percent).
Figure 4 Change in sovereign bond spreads from 200
basis point increase in U.S. interest rates for countries
with different indebtedness
Average change in spreads (bps)
70 64
60
50
40 33
30
20
20
10 6
0
<40 40-60 61-80 >90
Debt/GNI (%)
Second, if the tightening of monetary conditions tips a country into a position of
default, then it might provoke a more widespread shift towards reduced risk appetite by
25
provoking a significant unwillingness of investors in emerging market debt to rollover
existing debt or extend new debt--what Calvo calls a "sudden stop." This has occurred
on at least half a dozen occasions over the past two decades, and would correspond in our
model to a shift to a crisis equilibrium. While the causes of the crises are many, and there
is no consensus that US monetary policy was even an important contributing factor in
each of them, the debt crisis of August 1982 and Mexico's devaluation of 1994 both
followed a sharp tightening of US monetary policy, and attacks on Asian currencies in
1997-98 and strain on Argentina's currency board in 2000-2002 came during a period of
US dollar strength. Subsequent abandonment of US dollar pegs (de facto or de jure) has
no doubt left these emerging market countries less vulnerable to currency crisis.
For a number of reasons it seems to us that the risk that US monetary tightening
might lead to dramatic increases in emerging market spreads and in global risk appetite is
much lower than in those past periods mentioned above. First, countries' levels of
indebtedness are generally lower, as a ratio to GDP, than they were in those earlier
periods, as countries have learned the dangers of external borrowing, especially short
term, and the level of foreign exchange reserves is also considerably higher.18 However,
countries are differentially affected by the current high level of commodity prices, some
benefiting greatly through their commodity exports, while others may be mainly
impacted by the higher value of their oil imports.
Second, the fact that monetary tightening is largely anticipated (which was not the
case, for instance, in March 1994) is likely to lead to a less brutal adjustment of spreads
and to permit emerging market countries to take palliative measures in the meantime,
26
including lengthening maturities to lock in lower rates. For those countries that still limit
the fluctuations of their currencies against the US dollar, the fact that the dollar has
weakened against the euro and yen gives more room for maneuver.
Finally, there is evidence that investors are much more able to discriminate among
borrowers, and less likely to infer that problems in one country signal problems in
others.19 For instance, the default by Argentina in 2002--the largest default in history--
did not cause much disruption in world capital markets, nor did neighboring countries
suffer major increases in their spreads. Thus, should higher interest rates push a country
to the edge of default, the likelihood of generalized contagion seems much lower.
18Though aggregate figures are very much influenced by China, India, Korea, and a few other Asian
countries.
19Masson (2003) found that co-movement of EM spreads was lower in crises subsequent to the Asian
crisis, indicating greater differentiation among countries.
27
Appendix: List of Variables
variable name definition source
EM spread Emerging Market Bond Index JP Morgan
debt/gni total debt /Gross National Income GDF, World Bank
short-term debt/total debt GDF, World Bank
tradeop (imports+exports of G&S)/GDP IFS, IMF
reserves/debt for. exchange reserves/total debt GDF, World Bank
US long-term interest rate government 10-year bond yield US BEA
US Tbill rate secondary market yield, 3-mo. treas. bill US BEA
US hi-low corp. spread Moody's Baa-Aaa corp. bond yield US BEA
crisis dummy =1 if country in crisis, otherwise 0 Kaminsky (2003)
contagion dummy =n if n other countries are in crisis based on Kaminsky (2003)
US producer price index US BEA
US capacity utilization Industrial sector US BEA
US retail sales All sectors US BEA
US M2 Federal Res. Board
28
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