Description
Emerging markets are nations with social or business activity in the process of rapid growth and industrialization. The economies of China and India are considered to be the largest. According to The Economist many people find the term outdated, but no new term has yet to gain much traction.
Financial Study on Us Real Interest Rates and
Default Risk in Emerging Economies
Abstract
This paper empirically investigates the impact of changes in US real interest rates on
sovereign default risk in emerging economies using the method of identification through
heteroskedasticity. Policy-induced increases in US interest rates starkly raise default risk in emerging
market economies. However, the overall correlation between US real interest rates and the risk of
default is negative, demonstrating that the efects of other variables dominate the anterior relationship.
Keywords: real interest rates; default risk; sovereign debt; identification through het -
eroskedasticity.
Introduction
The theoretical economic efect of changes in US real interest rates on default risk in
emerging economies has been studied by, amongst others, Guimaraes (2011) and the channel is
often cited as a non-domestic driver of country risk premia (Neumeyer and Perri 2005). The
mechanism runs that when US real interest rates rise, the opportunity costs to those who buy
emerging economies' debt increase, which raises interest rates in emerging economies. This direct
efect increases the debt burden on emerging economies, raising the risk that they will default on
their debt and requiring emerging economies to ofer even higher interest rates in compensation.
Anecdotal evidence from the Latin American debt crisis of the 1980's and the Mexican crisis in
1994, both of which were preceded by sharp interest rate hikes in the US, suggests that this
theoretical channel might be an important empirical one.
Empirically identifying this theoretical relationship is not trivial, however, owing to the
usual problems of reverse causality and common omitted variables. The latter is espe- cially
problematic because US real interest rates and default risk in emerging economies are both
afected by variables that cannot be easily measured, such as global market factors, risk appetite,
and expectations about economic performance and the political scenario.
This paper identifies the efects of changes in US real interest rates on default risk in emerging
economies using the method of identification through heteroskedasticity as set out by Rigobon
(2003) and Rigobon and Sack (2004). As discussed in detail in Section 2, we take data on US real
interest rates from in?ation-indexed Treasury bonds, and proxy default risk using J.P. Morgan's
Emerging Markets Bond Index Plus (EMBI+) premia in emerging economies over the period
between 1998 and 2008. The idea behind the identification method is that there is a greater
variance of changes in real interest rates on dates when the Federal Open Market Committee
(FOMC) meets. The meetings of the FOMC can be seen as an extra shock to US interest rates,
which have an impact on the EMBI+ premia.
The key identifying assumption is that the timing of FOMC meetings does not afect the
EMBI+ premia through any channel other than the changes in real interest rates. Other shocks
that directly afect the EMBI+ premia are assumed to be uncorrelated with the timing of FOMC
meetings. This assumption resembles the desired characteristics of an instrument in IV
regressions. However, the timing of FOMC meetings afects the variance, not the level of shocks,
so a usual IV strategy cannot be employed. The methodology of identification through
heteroskedasticity yields a synthetic instrument based on diferences
2
in the covariance matrices of our data between dates when the FOMC does and does not
meet.
Our findings are presented in Section 3, where we show that unexpected policy- induced
increases in interest rates lead to greater EMBI+ premia and, by implication, default risk in
emerging economies. A 1 basis-point increase in 10-year US real interest rates raises EMBI+
premia by around 1 basis point, which means that the cost of bor- rowing in emerging economies
rises substantially more than in the US. This confirms the hypothesised theoretical relationship
between changes in US real interest rates and the risk of default and suggests that more attention
ought to be paid to this relationship in the literature on default risk.
A positive correlation between default risk and US real interest rates would imply that
emerging economies should issue debt contingent on US real interest rates because such a
contingency would negate the increased default risk not associated with fundamental changes in
emerging economies. Note, however, that this policy prescription depends not on the causal
relationship between US real interest rates and the EMBI+ premium, but on the correlation
between both. Omitted variables that significantly afect this correlation would also afect the
performance of debt contracts contingent on US real interest rates.
In actuality, on dates when the FOMC does not meet, we observe a significant cor- relation
with the opposite sign: changes in real interest rates are negatively related to changes in EMBI+
premia. Moreover, the overall correlation between real interest rates and the EMBI+ premium is
negative: a 2 bp increase in the 10-year US real rate is on average related to a 1 bp decrease in
the EMBI+. The results suggest that high real interest rates re?ect favourable external conditions
for emerging markets, which reduce the risk of default. This finding resonates with that of
Longstaf et al. (2011), where global risk factors (proxied by US markets) are shown to be the
major determinant of sovereign credit risk premia. Regardless of the precise reason for the
negative correlation, the policy implication is clear: emerging economies should not issue debt
contingent on US real interest rates.
Previous academic work has attempted to establish the nature of the relationship between
US real interest rates and sovereign default risk by applying diferent meth- ods to deal with the
aforementioned endogeneity problems. Some of this work has re- lied on structural assumptions
in vector autoregressions to identify the relationship (e.g., Uribe and Yue 2006). For our
purposes, high-frequency data on financial prices can pro-
vide more information and allow for a cleaner identification strategy.
1
1
Uribe
and Yue (2006) also study the efect of interest rates and the EMBI+ premium on variables like output,
3
An alternative to structural assumptions are 'traditional' instruments in IV strate-
gies, such as in Zettelmeyer (2004), where changes in the policy rate are employed as
instruments for longer-term real interest rates. This methodology also needs to assume that
changes in the instrument do not afect EMBI+ premia through alternative chan- nels. Moreover,
the instruments themselves must be exogenous, which is a stronger, and therefore less desirable,
assumption than that employed in this paper.
Additional studies investigate the direct efect of changes in the US federal funds tar- get rate
on emerging market spreads (Arora and Cerisola 2001). However, the theoretical relationship of
interest is between default risk and the longer-term real interest rate, not the short-term nominal
rate, which cannot be assumed to be endogenous. Moreover, even changes in the target rate might
not be truly exogenous (see Rigobon and Sack 2004). In a more closely related exercise,
Robitaille and Roush (2006) employ an event study approach using Brazilian data and find
similar results to those of our paper.
2 Data and empirical methodology
Our measure of the interest rate, i, is from 10-year in?ation-indexed Treasury bonds.
2
To
quantify the risk of default, e, we use J.P. Morgan's Emerging Markets Bond Index Plus (EMBI+),
which is comprised of medium-term debt of more than one year to maturity.
3
All data are
obtained from the Global Financial Database (www.globalfinancialdata.com).
We want to obtain long data series with minimal concern for events that might ob- fuscate a
potential relationship. For this reason we select emerging economies that have not defaulted, and
use daily data running from January 1998 to December 2008. We are interested in how a change
in the interest rate afects the EMBI+ premia, so our sample
consists of values of ?e
t
= e
t
+1 ÷ e
t
÷
1
and ?i
t
= i
t
+1 ÷ i
t
÷
1
and is divided in two: the
sub-sample C corresponds to the dates of monetary policy shocks, and the sub-sample N
corresponds to dates with no shocks.
4
,
5
There are two endogeneity concerns that mean a simple ordinary least squares regres- sion
will not identify the efect of changes in US real interest rates on the risk of default (EMBI+
premia). First, changes in the EMBI+ premia can cause changes in the interest
and in that case our methodology cannot be applied.
2
Our analysis is robust to the use of alternative measures of the real interest rate based on in?ation-adjusted nominal Treasury
rates of 3 months and 10 years. See Appendix A.
3
EMBI+ tracks total returns for traded US dollar- and other external currency-denominated Brady bonds, loans, Eurobonds
and local market instruments.
4
For additional justification for using data in diferences rather than levels, see Appendix B.
5
Sub-sample C contains the dates of scheduled and unscheduled FOMC meetings and the Federal Re-
serve Chairman's semi-annual monetary policy testimony to Congress. For a full list of these dates, see
http://www.federalreserve.gov/monetarypolicy/fomccalendars.htm
4
rate, for example, when default risk falls and in response investors switch demand from
safe Treasury assets to emerging market debt. Second, and more importantly, the inter- est rate
and the exchange rate are in?uenced by other common omitted variables. The
following system of equations is a simple representation of both endogeneity issues
6
:
?e
t
=o?i
t
+ z
t
+q
t
(1 )
?i
t
=|?e
t
+¸z
t
+c
t
(2 )
Where ?i
t
is the change in US real interest rate; ?e
t
the change in the EMBI+ premium;
z
t
a vector of omitted variables including, for example, external market conditions;c
t
a monetary
policy shock; andq
t
a shock to EMBI+.
The objective is to identifyo in Equation 1. Our identification strategy is borrowed
from Rigobon and Sack (2004), who show that the impact of monetary policy shocks on asset
prices can be identified because the variance of shocks is substantially larger on the days in sub-
sample C. Their paper used the identification strategy to establish a significant response of 10-
year Treasury yields to monetary policy shocks.
That monetary policy shocks can in?uence 10-year real interest rates means that the variance
of changes in these rates is significantly larger on the days in sub-sample C. This
efect is not large, but is large enough to significantly afect the variance of ?i
t
. We exploit
this efect by combining it with the assumption that the policy shock to real interest rates
neither afects EMBI+ through z
t
norq
t
, but only through its efect on ?i.
In sum, we assume that the variance of interest rate shocks (c
t
) in sub-sample C is higher than
the variance in sub-sample N; whilst the variances ofq
t
and z
t
are the same
across both sub-samples. As is usual in other identification strategies for our underlying
system of equations, we assume z
t
,c
t
andq
t
have no serial correlation and are uncorrelated
with each other. Our assumptions can be written in terms of the second moments of the
shocks in the two sub-samples C and N in the following way:
o
C
>o
N
c c
o
C
=o
N
q q
o
C
=o
N
z z
To help justify the underlying assumptions, Table 1 shows the increase in the variation
in the US real interest rate and the change in covariance between the real interest rate
6
We
show in Appendix C that allowing for a richer lag structure does not materially afect the results.
5
and EMBI+ premia over the sub-samples. The fact that the standard deviations of
EMBI+ premia appear to decrease from sub-sample N to sub-sample C, when we expect mild
increases, suggests that we require a more accurate statistical test of whether our assumptions on
the variance of shocks over the two sub-samples are valid.
7
Applying the test set out in Levene
(1960), reported in Table 2, we established that the standard deviation of the real interest rate
increases significantly in sub-sample C, while the variance of EMBI+ does not significantly
change because the efect of the variance increase in
Equation 2 only weakly efects the variance of EMBI+ through the interest rate.
8
Table 1: Data descriptives
Standard Covariance with
deviation US real rate
Sub-sample C Sub-sample N Sub-sample C Sub-sample N
US real rate 0.093 0.063 . .
Emerging Market 24.491 29.020 0.198 -0.211
Latin America 25.017 32.317 0.278 -0.253
Brazil 30.249 48.318 0.357 -0.278
Bulgaria 24.476 27.181 0.175 -0.117
Mexico 19.221 21.876 0.066 -0.214
Panama 12.486 14.849 0.028 -0.208
Peru 20.892 20.939 0.128 -0.185
Venezuela 43.545 50.526 0.852 -0.263
Note: 131 observations in sub-sample C, 2,604 days in sub-sample N.
We are not assuming that the FOMC ignores factors that afect emerging market
default risk, nor are we supposing that FOMC decisions have no impact on emerging market
prices - that is actually the efect we are estimating. We are precisely assuming that FOMC
decisions do not directly reveal important information about emerging mar- kets that might
otherwise afect EMBI+ premia, they are only afecting EMBI+ premia through changes in US
real interest rates. The underlying view is that the Committee might have private information
about how it will react to movements in emerging markets and how it plans to conduct monetary
policies in general but does not know more than the market about emerging economies.
7
We
cannot apply standard tests of variance equality, because they require that the underlying data be normally
distributed. As is reported in Appendix D, demonstrated through plots of each variables' quantiles against those of the normal
distribution and empirical tests of skewness and kurtosis, none of our series are normally distributed.
8
Although the test results are presented using the sample mean of the data, similar results are obtained when using the 50th
percentile or 10% trimmed mean.
6
Table 2: Levene (1960) test of equal variance
US real rate
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Test statistic
based on mean
12.371
0.215
0.458
2.273
0.000
0.031
0.021
0.908
0.635
p-value
0.000
0.643
0.499
0.132
0.977
0.860
0.884
0.341
0.801
Note: Null hypothesis is equal variance
Now, consider the following variables:
?i
?
C
,
\ i
N
?
?
?
?I ÷
\
T
C
T
N
?e
?
C
, ?e
?
N ?
?E ÷
\
T
C
\T
N
?i
?
C
, ÷?i
?
N ?
w÷
\
T
C
\T
N
A major result in Rigobon and Sack (2004) is thato can be consistently estimated
by a standard instrumental variables approach with the novel instrument, w, which is
correlated with the dependent variable, ?I, but is neither correlated with z
t
norq
t
. It
is correlated with ?I because the greater variance in sub-sample C implies the positive
correlation between ?i
?
C/ T
C
and ?i
?
C/ T
C
more than outweighs the negative cor-
\ \
relation between ?i
?
N / T
N
and ÷?i
?
N / T
N
. It is neither correlated with z
t
norq
t
\ \
because the positive and negative correlation of each part of the vector cancel each other
out.
The usual assumption in IV regressions is that the instrument afects the dependent variable
only through the regressor. The key diference here is that instead of having a variable assumed to
be correlated withc and uncorrelated with any of the other variables, we assume that the variance ofc is
larger on the days in sub-sample C and the variances of other variables are the same in both sub-samples.
7
3 Results
Table 3 presents the results from implementing our identification strategy, which reveals
that policy shocks to real interest rates are positively correlated with emerging economies'
EMBI+. This coincides with our original intuition that when the US tightens monetary policy, it
is harder for emerging economies to borrow, and the risk of default proxied by EMBI+ increases.
Table 3: The response of EMBI+ premia to interest rate shocks
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
0.868
1.115
1.334
0.649
0.607
0.496
0.659
2.279
Std Err
0.179
0.195
0.269
0.170
0.138
0.094
0.140
0.318
T-stat
4.840
5.717
4.969
3.808
4.394
5.264
4.697
7.162
Note: Each estimation uses 2,735 observations.
The magnitude of the response is large: an unexpected increase in the 10-year real
interest rate of one basis point leads to an increase in the EMBI+ premium of a similar order of
magnitude.
Table 4 shows the results from analysis of the relationship between US real interest rates and
EMBI+ premia in each separate sub-sample (the results across both samples are in Table 5).
Crucially, the 'normal' correlation between ?E and ?I is actually negative (and smaller in absolute
value) in sub-sample N. Our interpretation is that increases in US real interest rates are correlated
with other things that are good for emerging markets and thus decrease their cost of borrowing.
Future research ought to investigate which aspects of international financial markets, correlated
with US real interest rates, are most important to the risk of emerging market default.
The results in Table 3 are substantially diferent from the OLS estimates using only the sub-
sample C presented in Table 4. While the former shows a strong positive re- lation, the latter
shows a mild and insignificant efect. Rosa (2011) has noted that, in some applications, the
results from employing the identification through heteroskedastic- ity methodology are not much
diferent from a simple OLS using the subsample where the FOMC meets. That is not the case
here since we are using the long-term interest rates, where endogeneity is likely to be much more
important than when the policy rate is used,
8
Table 4: Separate analysis of sub-samples
Sub-sample C Sub-sample N
Coef Std Err T-Stat Coef Std Err T-stat
Emerging Market 0.230 0.224 1.029 -0.494 0.087 -5.700
Latin America 0.317 0.228 1.390 -0.591 0.096 -6.131
Brazil 0.406 0.275 1.474 -0.649 0.145 -4.492
Bulgaria 0.217 0.226 0.960 -0.274 0.081 -3.363
Mexico 0.089 0.177 0.503 -0.500 0.065 -7.692
Panama 0.036 0.114 0.311 -0.487 0.044 -11.186
Peru 0.146 0.191 0.766 -0.430 0.062 -6.937
Venezuela 0.924 0.389 2.371 -0.617 0.151 -4.076
Note: 131 observations in sub-sample C, 2,604 days in sub-sample N.
Table 5: Full sample analysis
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.423
-0.503
-0.547
-0.226
-0.443
-0.437
-0.375
-0.467
Std Err
0.082
0.091
0.135
0.077
0.062
0.041
0.059
0.143
T-stat -
5.174
-5.535
-4.038
-2.934 -
7.194
-10.586 -
6.347 -
3.266
Note: Each estimation uses 2,735 observations.
and the correlation between variables in the N sample is diferent from the causal efect.
4 Concluding remarks
The strong and positive relation between exogenous changes in US real interest rates and
the EMBI+ premium highlights the importance of US interest rate shocks. The fact that the
overall correlation between US rates and the EMBI+ premium is negative highlights the
importance of other aspects of international financial markets, such as favourable external
conditions to emerging economy borrowing. From a policy perspective, our result has
implications for proposals to issue debt that is contingent on exogenous factors that afect the
ability to repay. One of these ideas is that a higher US real interest rate makes it more difcult for
emerging market economies to repay, so reducing emerging market debt payments when US
interest rates increase would be welfare improving. Our finding that the overall correlation is
negative implies that making emerging market sovereign debt contingent on US real interest
rates would have an opposite result from the desired
9
efect. Research on sovereign default should note that shocks afecting foreign real interest
rates might have very diferent efects on emerging market default risk.
References
Arora, V. and Cerisola, M. (2001). How Does U.S. Monetary Policy In?uence Sovereign
Spreads in Emerging Markets?, IMF Staf Papers 48: 474-498.
Guimaraes, B. (2011). Sovereign default: which shocks matter?, Review of Economic
Dynamics 14(4): 553-576.
Levene, H. (1960). Robust tests for equality of variances, in I. O. et al. (ed.), In Con-
tributions to Probability and Statistics: Essays in Honor of Harold Hotelling , Stanford University Press,
pp. 278-292.
Longstaf, F. A., Pan, J., Pedersen, L. H. and Singleton, K. J. (2011). How sovereign is
sovereign credit risk?, American Economic Journal: Macroeconomics 3(2): 75-103.
Neumeyer, P. and Perri, F. (2005). Business cycles in emerging economies: the role of
interest rates, Journal of Monetary Economics 52(2): 345-380.
Rigobon, R. (2003). Identification through heteroskedasticity, Review of Economics and
Statistics 85(4): 777-792.
Rigobon, R. and Sack, B. (2004). The impact of monetary policy on asset prices, Journal
of Monetary Economics 51(8): 1553-1575.
Robitaille, P. and Roush, J. (2006). How Do FOMC Actions and U.S. Macroeconomic
Data Announcements Move Brazilian Sovereign Yield Spreads and Stock Prices?, Board
of Governors of the Federal Reserve System, International Finance Discussion Paper
No. 868 .
Rosa, C. (2011). The validity of the Event-Study Approach: Evidence from the Impact of
the Fed's Monetary Policy on U.S. and Foreign Asset Prices, Economica (311): 429-439.
Uribe, M. and Yue, V. (2006). Country spreads and emerging countries: Who drives
whom?, Journal of International Economics 69(1): 6-36.
Zettelmeyer, J. (2004). The impact of monetary policy on exchange rates: evidence from
three small open economies, Journal of Monetary Economics 51(3): 635-652.
10
A Appendix - alternative US interest rates
In this appendix we prepare four alternative estimates of US real interest rates which are
then used in place of the real rates reported in the main text as a robustness exercise.
We obtain two nominal interest rate series and two in?ation measures from the Global
Financial Database (www.globalfinancialdata.com). Both interest rate series are constant maturity,
consistent with the data in the main text. We use a 3 month T-Bill rate consistent with existing
quantitative studies in the literature, and a 10 year Treasury Bond rate consistent with the data in
the main text because we maintain that long termrates are a more appropriate measure of the
opportunity cost to investors in emerging market sovereign debt.
The first measure of in?ation is based on the Bureau of Labor Statistics monthly Consumer
Price Index. We obtain the annual in?ation rate in the year prior to each month, and average
over the previous three months' annual in?ation rates to obtain a monthly estimate of future
in?ation. The second measure is the University of Michigan survey of annual CPI in?ation
expectations, which are also reported monthly. Both monthly series are assigned to the last
working day of the month and subsequently cubic splined to obtain interpolated daily series of
annual expected in?ation.
Each gross interest rate is divided by both gross expected in?ation measures and netted.
Figure 1 below shows the comparison of rates over time, and Table 6 shows the cross-
correlations between the series.
Tables 7 - 16 show the results from repeating the analysis described in the main text with the
full sample, individual sub-samples (FOMC and non-FOMC meeting days) and applying the
method of identification through heteroscedasticity for the four alternative measures of real
interest rates.
When using T-Bill rates, the standard errors are generally lower but the coefcients are much
smaller. There are fewer significant coefcients and the magnitudes appear to be lower (no
statistical tests of diferences were run). Running the analysis separately on the sub-samples
shows that the coefcients on the days when the FOMC meet are again insignificant, but those
days when the FOMC do not meet appear to be of smaller magnitude although they remain
significantly negative.
When using T-Bond rates, the coefcients are generally of comparable magnitudes but the
standard errors are much larger resulting in fewer significant positive coefcients. This is probably
re?ecting the fact that our measures of expected in?ation are noisy when applied to daily data. All
coefcients that are significant are positive.
11
Figure 1: US Real Interest Rates
1998 2000 2002 2004 2006 2008
Inflation Indexed 10÷year Bond Yield
3m TBill real rate using BLS CPI
10Yr Bond real rate using BLS CPI 3m
TBill real rate using UMICH CPI
10Yr Bond real rate using UMICH CPI
Table 6: Correlation between real interest rate measures
T IPS T-Bill & T-Bill & T-Bond
&Yield BLS CPI UMICH CPI BLS CPI
T-Bill & BLS CPI 0.753 .
T-Bill & UMICH CPI 0.766 0.936 .
T-Bond & BLS CPI 0.763 0.769 0.620 .
T-Bond & UMICH CPI 0.846 0.710 0.745 0.862
12
5
0
U
S
R
e
a
l
I
n
t
e
r
e
s
t
R
a
t
e
s
÷
5
A.1 T-Bill rates and BLS CPI in?ation expectations
Table 7: Full sample analysis (T-Bill & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.301
-0.332
-0.325
-0.147
-0.211
-0.207
-0.237
-0.479
StdErr
0.060
0.067
0.100
0.058
0.045
0.031
0.044
0.106
T-stat -
4.986
-4.950
-3.248
-2.552 -
4.688 -
6.678 -
5.419 -
4.539
Table 8: The response of EMBI+ premia to interest rate changes (T-Bill & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
0.227
0.165
0.199
0.211
0.177
0.114
0.224
0.137
StdErr
0.107
0.115
0.160
0.105
0.081
0.055
0.084
0.189
T-stat
2.122
1.443
1.244
2.014
2.174
2.078
2.677
0.726
Table 9: Separate analysis of FOMC and non-FOMC meeting days (T-Bill & BLS CPI)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market -0.007 0.146 -0.045 -0.339 0.065 -5.247
Latin America -0.055 0.148 -0.369 -0.368 0.072 -5.105
Brazil -0.033 0.178 -0.184 -0.364 0.108 -3.358
Bulgaria 0.053 0.152 0.347 -0.173 0.062 -2.812
Mexico 0.005 0.115 0.047 -0.239 0.048 -4.970
Panama -0.028 0.074 -0.373 -0.230 0.033 -6.934
Peru 0.019 0.125 0.154 -0.271 0.047 -5.809
Venezuela -0.135 0.269 -0.502 -0.524 0.113 -4.639
13
A.2 T-Bill rates and Univ. of Michigan CPI in?ation expectations
Table 10: Full sample analysis (T-Bill & UMICH CPI exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.241
-0.248
-0.301
-0.146
-0.203
-0.191
-0.238
-0.476
StdErr
0.059
0.065
0.097
0.056
0.044
0.030
0.043
0.102
T-stat -
4.117
-3.810
-3.098
-2.617 -
4.661 -
6.343 -
5.595 -
4.651
Table 11: The response of EMBI+ premia to interest rate changes (T-Bill & UMICH CPI exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
0.282
0.186
0.238
0.280
0.235
0.153
0.281
0.224
StdErr
0.112
0.120
0.168
0.110
0.086
0.058
0.088
0.199
T-stat
2.513
1.553
1.416
2.541
2.747
2.646
3.203
1.126
Table 12: Separate analysis of FOMC and non-FOMC meeting days (T-Bill & UMICH CPI
exp.)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market 0.038 0.145 0.263 -0.276 0.063 -4.405
Latin America -0.016 0.147 -0.109 -0.277 0.070 -3.964
Brazil -0.013 0.177 -0.074 -0.336 0.105 -3.214
Bulgaria 0.081 0.151 0.539 -0.174 0.059 -2.929
Mexico 0.031 0.115 0.271 -0.232 0.046 -4.994
Panama -0.007 0.074 -0.094 -0.213 0.032 -6.642
Peru 0.040 0.124 0.318 -0.272 0.045 -6.033
Venezuela -0.102 0.268 -0.380 -0.522 0.109 -4.783
14
A.3 10Yr Bond rates and BLS CPI in?ation expectations
Table 13: Full sample analysis (T-Bond & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.865
-0.962
-0.975
-0.377
-0.873
-0.577
-0.576
-1.027
StdErr
0.064
0.071
0.108
0.063
0.046
0.032
0.047
0.114
T-stat -
13.484
-13.497
-8.997
-6.014
-18.825 -
17.893 -
12.267
-8.986
Table 14: The response of EMBI+ premia to interest rate changes (T-Bond & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
1.316
1.909
2.944
1.317
0.686
0.679
0.684
2.682
StdErr
0.509
0.591
0.825
0.478
0.373
0.268
0.372
0.905
T-stat
2.586
3.233
3.570
2.755
1.840
2.533
1.837
2.963
Table 15: Separate analysis of FOMC and non-FOMC meeting days (T-Bond & BLS CPI)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market -0.378 0.210 -1.801 -0.899 0.067 -13.453
Latin America -0.322 0.213 -1.511 -1.007 0.074 -13.521
Brazil -0.101 0.259 -0.389 -1.036 0.114 -9.101
Bulgaria 0.000 0.221 0.002 -0.404 0.065 -6.194
Mexico -0.526 0.161 -3.259 -0.898 0.048 -18.615
Panama -0.297 0.105 -2.828 -0.597 0.034 -17.758
Peru -0.298 0.180 -1.657 -0.595 0.049 -12.251
Venezuela -0.200 0.392 -0.510 -1.085 0.119 -9.128
15
A.4 10Yr Bond rates and Univ. of Michigan CPI in?ation expectations
Table 16: Full sample analysis (T-Bond & UMICH CPI exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.761
-0.824
-0.910
-0.364
-0.830
-0.537
-0.559
-0.994
StdErr
0.063
0.070
0.105
0.061
0.045
0.031
0.046
0.111
T-stat -
12.149
-11.813
-8.643
-5.976
-18.373 -
17.056 -
12.244
-8.960
Table 17: The response of EMBI+ premia to interest rate changes (T-Bond & UMICH CPI
exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
2.144
2.722
4.267
2.227
1.334
1.180
1.294
4.266
StdErr
0.807
0.928
1.332
0.767
0.599
0.441
0.579
1.458
T-stat
2.656
2.934
3.203
2.904
2.227
2.677
2.237
2.925
Table 18: Separate analysis of FOMC and non-FOMC meeting days (T-Bond & UMICH CPI
exp.)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market -0.282 0.213 -1.327 -0.793 0.065 -12.166
Latin America -0.240 0.216 -1.114 -0.862 0.073 -11.852
Brazil -0.056 0.261 -0.215 -0.966 0.110 -8.747
Bulgaria 0.063 0.222 0.284 -0.392 0.063 -6.206
Mexico -0.473 0.164 -2.890 -0.853 0.047 -18.217
Panama -0.254 0.107 -2.380 -0.555 0.033 -16.975
Peru -0.254 0.181 -1.399 -0.579 0.047 -12.279
Venezuela -0.127 0.395 -0.321 -1.051 0.115 -9.127
16
B Appendix - estimation in levels
The analysis presented in this appendix is intended to justify time-diferencing the data
in the paper. We show that (i) there is no significant increase in the variance of the levels of the
US real interest rate on the dates the FOMC meets, which is inconsistent with the fundamental
assumption underpinning the methodology of identification through heteroskedasticity; and (ii)
the data we use are highly persistent over time, and as a result the usual tests cannot reject a unit
root. An analysis in levels would be subject to the critique that any results were spurious.
The fundamental assumption underpinning the methodology of identification is not directly
testable because we cannot identify the shocks. But the best available evidence we have suggests
that it is appropriate to apply the methodology in diferences, but not in levels. Table 19 shows
the descriptive statistics for our variables in levels using data defined to capture the level of each
variable on the day after the FOMC meeting dates. The analysis is repeated in Table 20 using the
level of variables on the same day as the FOMC meeting. In both cases, and similar to Table 1,
there is no significant diference in the standard deviation of EMBI+ variables on the days when
the FOMC meets from the days when it does not. In Table 19 there is a (weakly) significant
reduction in the standard deviation of the US real interest rate on the days when the FOMC
meets, and in Table 20 there is no significant change. This is not consistent with the assumption
that the variance of the interest rate would significantly increase on FOMC meeting days.
Table 19: Data descriptives (levels)
Standard Covariance with Levene (1960) test
deviation US real rate of equal variance
FOMC No FOMC FOMC No FOMC mean test p-value
US real rate 0.885 0.898 . . 2.717 0.066
Emerging Market 314.595 319.333 194.756 202.617 0.103 0.749
Latin America 296.342 295.865 124.524 127.392 0.037 0.847
Brazil 421.413 418.673 153.673 156.705 0.078 0.780
Bulgaria 302.345 313.245 223.860 238.411 0.449 0.503
Mexico 180.834 184.149 114.845 120.151 0.183 0.668
Panama 119.021 120.635 61.070 63.281 0.009 0.924
Peru 217.518 213.622 124.616 123.782 0.032 0.858
Venezuela 381.318 386.208 130.639 152.410 0.008 0.927
Notes: Levene (1960) test statistic based on mean; null hypothesis is equal variance
FOMC means the set of days immediately after FOMC meetings
Table 21 shows the results from tests of stationarity on the variables in levels and
17
Table 20: Data descriptives (levels)
Standard Covariance with Levene (1960) test
deviation US real rate of equal variance
FOMC No FOMC FOMC No FOMC mean test p-value
US Real Rate 0.883 0.898 . . 0.668 0.414
Emerging Market 314.952 319.314 194.621 202.629 0.070 0.792
Latin America 298.070 295.774 123.374 127.455 0.054 0.816
Brazil 426.906 418.392 151.228 156.836 0.091 0.764
Bulgaria 309.204 312.911 227.779 238.222 0.096 0.757
Mexico 182.428 184.070 115.595 120.116 0.147 0.702
Panama 120.181 120.577 61.350 63.272 0.018 0.893
Peru 216.637 213.664 123.413 123.843 0.018 0.894
Venezuela 379.584 386.308 130.098 152.424 0.003 0.953
Notes: Levene (1960) test statistic based on mean; null hypothesis is equal variance
FOMC means the set of days on which FOMC meetings are held
first diferences. Both tests include a constant but no trend term; the Phillips-Perron
specification includes seven Newey-West lags.
The variables in levels are all non-stationary. Identical specifications for the difer- enced
time-series employed in the paper show they are stationary. We conclude that it is more
appropriate to specify the model in terms of diferences than in levels.
Table 21: Stationarity test statistics
Levels First Diferences
Phillips-Perron Dickey-Fuller Phillips-Perron Dickey-Fuller
US real rate -1.32 -1.28 -24.36 -25.14
Emerging Market -1.11 -1.03 -22.70 -23.83
Latin America -1.49 -1.46 -23.32 -24.66
Brazil -2.84 -2.80 -22.36 -23.47
Bulgaria -1.60 -1.59 -25.08 -25.26
Mexico -1.51 -1.50 -23.05 -24.31
Panama -0.88 -0.59 -24.74 -25.27
Peru -1.95 -1.92 -25.21 -25.58
Venezuela -0.44 -0.29 -25.50 -26.43
Notes: Null hypothesis is stationarity in all unit root tests
Phillips-Perron specifications use seven Newey-West lags
Critical values are -3.43 (1%); -2.86 (5%); -2.57 (10%)
18
C Appendix - dynamic model
This appendix reports the results from a dynamic specification of the model, as an in-
vestigation of dynamic efects, for example overshooting, in the reaction of the EMBI+ spread to
changes in US real interest rates
9
. We maintain the definition of the variables
as in the main text, i.e. ?X
t
÷ X
t
+1 ÷ X
t
÷
1
, but re-specify the model as follows:
Table 22: ?E
t
= o
1
?I
t
+o
2
?I
t
÷
2
+o
3
?E
t
÷2
instrumented
Table 23: ?e
t
=o
1
?i
t
+o
2
?i
t
÷
2
+o
3
?e
t
÷2
The Tables below should be compared with Tables 3 and 5 in the main text. Following
the notation in the main text, the instruments employed in the 2SLS estimates of dynamic
model in Table 22 are w,?I
t
÷
2
, and ?E
t
÷
2
.
We find that in general the coefcients on the lags in both specifications were statis-
tically insignificant and conclude that there is no systematic evidence of dynamic efects present in
the data.
Table 22: Identification via heteroscedasticity dynamic analysis
Co-efcients Standard Error T-statistic
US RR L.US RR L.DV US RR L.US RR L.DV US RR L.US RR L.DV
E. Market 0.96 0.00 7.34 0.17 0.00 6.50 5.57 2.56 1.13
L Am. 1.23 0.00 4.26 0.19 0.00 7.08 6.52 0.37 0.60
Brazil 1.48 0.00 2.49 0.26 0.00 9.81 5.69 0.17 0.25
Bulgaria 0.69 0.00 8.56 0.16 0.00 6.21 4.17 0.48 1.38
Mexico 0.68 0.00 6.18 0.13 0.00 4.99 5.15 1.55 1.24
Panama 0.54 -0.00 2.57 0.09 0.00 3.46 5.91 -0.01 0.74
Peru 0.73 0.00 5.22 0.13 0.00 5.05 5.43 1.66 1.04
Venezuela 2.28 0.00 -1.24 0.31 0.00 11.76 7.33 4.82 -0.11
Notes: DV - dependent variable - EMBI+ premium.
US RR - US real interest rate.
Each estimation uses 2,611 observations.
9
We
gratefully acknowledge this follows the suggestion of an anonymous referee.
19
Table 23: Full sample dynamic analysis
Co-efcients Standard Error T-statistic
US RR L.US RR L.DV US RR L.US RR L.DV US RR L.US RR L.DV
E. Market -0.39 -0.02 0.04 0.08 0.08 0.02 -4.64 -0.25 2.00
L. Am. -0.47 -0.06 0.00 0.09 0.09 0.02 -5.00 -0.68 0.06
Brazil -0.49 -0.16 0.00 0.14 0.14 0.02 -3.53 -1.11 0.13
Bulgaria -0.20 0.17 -0.03 0.08 0.08 0.02 -2.49 2.12 -1.65
Mexico -0.41 -0.03 0.00 0.06 0.06 0.02 -6.63 -0.47 0.19
Panama -0.42 -0.06 0.01 0.04 0.04 0.02 -9.91 -1.39 0.75
Peru -0.36 0.03 0.07 0.06 0.06 0.02 -6.02 0.51 3.67
Venezuela -0.47 -0.13 0.03 0.15 0.15 0.02 -3.18 -0.88 1.27
Notes: DV - dependent variable - EMBI+ premium.
US RR - US real interest rate.
Each estimation uses 2,611 observations.
D Appendix - tests of variance
The increase in the variation in the US real interest rate and the change in covariance
between the real interest rate and EMBI+ premia over the sub-samples are apparent from Table 1
in the main text, but the fact that the standard deviations of EMBI+ premia appear to decrease
from sub-sample N to sub-sample C, when we expect mild increases, suggests we require a more
accurate statistical test of whether our assumptions on the variance of shocks over the two sub-
samples are valid.
Importantly, however, we cannot apply standard tests of variance equality, because they
require that the underlying data be normally distributed. As the plots of each variables' quantiles
against those of the normal distribution in Figure 2 demonstrate, and the empirical tests of
skewness and kurtosis confirm in Table 24, none of our series are normally distributed.
Levene (1960) provides a test where the null is equal variance when samples are drawn from
a distribution that is not Gaussian normal. The results from this test are presented in Table 25, and
show that the variance of the US real interest rate significantly increases,
but the variance of all EMBI+ premia does not change significantly.
10
On the basis of these results, we conclude that the standard deviation of the real interest rate
increases significantly on the days when the variance of interest rate move- ments is greater. We
cannot reject the null that the variance of EMBI+ is the same in both sub-samples. According to
our assumptions, the policy shocks should yield only
10The
results are presented using the sample mean of the data, similar results are obtained when using the 50th
percentile or 10% trimmed mean.
20
Figure 2: Q-Q plots of each variable quantiles against normal distribution quantiles
÷100 ÷50 0 50 100 ÷100 ÷50 0 50 100
Inverse Normal Inverse Normal
÷200 ÷100 0 100 200 ÷100 ÷50 0 50 100
Inverse Normal Inverse Normal
÷100 ÷50 0 50 100 ÷50 0 50
Inverse Normal Inverse Normal
÷100 ÷50 0 50 100 ÷200 ÷100 0 100 200
Inverse Normal Inverse Normal
21
4
0
0
4
0
0
÷
4
0
0
÷
2
0
0
0
2
0
0
E
m
e
r
g
i
n
g
M
a
r
k
e
t
÷
4
0
0
÷
2
0
0
0
2
0
0
L
a
t
i
n
A
m
e
r
i
c
a
5
0
0
0
2
0
0
4
0
0
6
0
0
B
u
l
g
a
r
i
a
0
B
r
a
z
i
l
÷
4
0
0
÷
2
0
0
÷
5
0
0
÷
2
0
0
÷
1
0
0
0
1
0
0
2
0
0
3
0
0
M
e
x
i
c
o
2
0
0
0
1
0
0
P
a
n
a
m
a
÷
1
0
0
1
0
0
0
0
5
0
1
0
0
1
5
0
P
e
r
u
0
5
0
0
V
e
n
e
z
u
e
l
a
÷
1
0
0
÷
5
0
÷
5
0
0
Table 24: Test of skewness and kurtosis
US real rate
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
skewness
p-value
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
kurtosis p-
value
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Note: Null hypothesis is normal distribution
Table 25: Levene (1960) test of equal variance
US real rate
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Test statistic
based on mean
12.371
0.215
0.458
2.273
0.000
0.031
0.021
0.908
0.635
p-value
0.000
0.643
0.499
0.132
0.977
0.860
0.884
0.341
0.801
Note: Null hypothesis is equal variance
small increases in the variance of EMBI+, as the unexpected policy shocks to US real
interest rates are only a small part of the variation of emerging market default risk, so the results
of the tests on variances in both sub-samples, albeit not conclusive, are not at odds with the
identifying assumptions.
22
doc_217525373.docx
Emerging markets are nations with social or business activity in the process of rapid growth and industrialization. The economies of China and India are considered to be the largest. According to The Economist many people find the term outdated, but no new term has yet to gain much traction.
Financial Study on Us Real Interest Rates and
Default Risk in Emerging Economies
Abstract
This paper empirically investigates the impact of changes in US real interest rates on
sovereign default risk in emerging economies using the method of identification through
heteroskedasticity. Policy-induced increases in US interest rates starkly raise default risk in emerging
market economies. However, the overall correlation between US real interest rates and the risk of
default is negative, demonstrating that the efects of other variables dominate the anterior relationship.
Keywords: real interest rates; default risk; sovereign debt; identification through het -
eroskedasticity.
Introduction
The theoretical economic efect of changes in US real interest rates on default risk in
emerging economies has been studied by, amongst others, Guimaraes (2011) and the channel is
often cited as a non-domestic driver of country risk premia (Neumeyer and Perri 2005). The
mechanism runs that when US real interest rates rise, the opportunity costs to those who buy
emerging economies' debt increase, which raises interest rates in emerging economies. This direct
efect increases the debt burden on emerging economies, raising the risk that they will default on
their debt and requiring emerging economies to ofer even higher interest rates in compensation.
Anecdotal evidence from the Latin American debt crisis of the 1980's and the Mexican crisis in
1994, both of which were preceded by sharp interest rate hikes in the US, suggests that this
theoretical channel might be an important empirical one.
Empirically identifying this theoretical relationship is not trivial, however, owing to the
usual problems of reverse causality and common omitted variables. The latter is espe- cially
problematic because US real interest rates and default risk in emerging economies are both
afected by variables that cannot be easily measured, such as global market factors, risk appetite,
and expectations about economic performance and the political scenario.
This paper identifies the efects of changes in US real interest rates on default risk in emerging
economies using the method of identification through heteroskedasticity as set out by Rigobon
(2003) and Rigobon and Sack (2004). As discussed in detail in Section 2, we take data on US real
interest rates from in?ation-indexed Treasury bonds, and proxy default risk using J.P. Morgan's
Emerging Markets Bond Index Plus (EMBI+) premia in emerging economies over the period
between 1998 and 2008. The idea behind the identification method is that there is a greater
variance of changes in real interest rates on dates when the Federal Open Market Committee
(FOMC) meets. The meetings of the FOMC can be seen as an extra shock to US interest rates,
which have an impact on the EMBI+ premia.
The key identifying assumption is that the timing of FOMC meetings does not afect the
EMBI+ premia through any channel other than the changes in real interest rates. Other shocks
that directly afect the EMBI+ premia are assumed to be uncorrelated with the timing of FOMC
meetings. This assumption resembles the desired characteristics of an instrument in IV
regressions. However, the timing of FOMC meetings afects the variance, not the level of shocks,
so a usual IV strategy cannot be employed. The methodology of identification through
heteroskedasticity yields a synthetic instrument based on diferences
2
in the covariance matrices of our data between dates when the FOMC does and does not
meet.
Our findings are presented in Section 3, where we show that unexpected policy- induced
increases in interest rates lead to greater EMBI+ premia and, by implication, default risk in
emerging economies. A 1 basis-point increase in 10-year US real interest rates raises EMBI+
premia by around 1 basis point, which means that the cost of bor- rowing in emerging economies
rises substantially more than in the US. This confirms the hypothesised theoretical relationship
between changes in US real interest rates and the risk of default and suggests that more attention
ought to be paid to this relationship in the literature on default risk.
A positive correlation between default risk and US real interest rates would imply that
emerging economies should issue debt contingent on US real interest rates because such a
contingency would negate the increased default risk not associated with fundamental changes in
emerging economies. Note, however, that this policy prescription depends not on the causal
relationship between US real interest rates and the EMBI+ premium, but on the correlation
between both. Omitted variables that significantly afect this correlation would also afect the
performance of debt contracts contingent on US real interest rates.
In actuality, on dates when the FOMC does not meet, we observe a significant cor- relation
with the opposite sign: changes in real interest rates are negatively related to changes in EMBI+
premia. Moreover, the overall correlation between real interest rates and the EMBI+ premium is
negative: a 2 bp increase in the 10-year US real rate is on average related to a 1 bp decrease in
the EMBI+. The results suggest that high real interest rates re?ect favourable external conditions
for emerging markets, which reduce the risk of default. This finding resonates with that of
Longstaf et al. (2011), where global risk factors (proxied by US markets) are shown to be the
major determinant of sovereign credit risk premia. Regardless of the precise reason for the
negative correlation, the policy implication is clear: emerging economies should not issue debt
contingent on US real interest rates.
Previous academic work has attempted to establish the nature of the relationship between
US real interest rates and sovereign default risk by applying diferent meth- ods to deal with the
aforementioned endogeneity problems. Some of this work has re- lied on structural assumptions
in vector autoregressions to identify the relationship (e.g., Uribe and Yue 2006). For our
purposes, high-frequency data on financial prices can pro-
vide more information and allow for a cleaner identification strategy.
1
1
Uribe
and Yue (2006) also study the efect of interest rates and the EMBI+ premium on variables like output,
3
An alternative to structural assumptions are 'traditional' instruments in IV strate-
gies, such as in Zettelmeyer (2004), where changes in the policy rate are employed as
instruments for longer-term real interest rates. This methodology also needs to assume that
changes in the instrument do not afect EMBI+ premia through alternative chan- nels. Moreover,
the instruments themselves must be exogenous, which is a stronger, and therefore less desirable,
assumption than that employed in this paper.
Additional studies investigate the direct efect of changes in the US federal funds tar- get rate
on emerging market spreads (Arora and Cerisola 2001). However, the theoretical relationship of
interest is between default risk and the longer-term real interest rate, not the short-term nominal
rate, which cannot be assumed to be endogenous. Moreover, even changes in the target rate might
not be truly exogenous (see Rigobon and Sack 2004). In a more closely related exercise,
Robitaille and Roush (2006) employ an event study approach using Brazilian data and find
similar results to those of our paper.
2 Data and empirical methodology
Our measure of the interest rate, i, is from 10-year in?ation-indexed Treasury bonds.
2
To
quantify the risk of default, e, we use J.P. Morgan's Emerging Markets Bond Index Plus (EMBI+),
which is comprised of medium-term debt of more than one year to maturity.
3
All data are
obtained from the Global Financial Database (www.globalfinancialdata.com).
We want to obtain long data series with minimal concern for events that might ob- fuscate a
potential relationship. For this reason we select emerging economies that have not defaulted, and
use daily data running from January 1998 to December 2008. We are interested in how a change
in the interest rate afects the EMBI+ premia, so our sample
consists of values of ?e
t
= e
t
+1 ÷ e
t
÷
1
and ?i
t
= i
t
+1 ÷ i
t
÷
1
and is divided in two: the
sub-sample C corresponds to the dates of monetary policy shocks, and the sub-sample N
corresponds to dates with no shocks.
4
,
5
There are two endogeneity concerns that mean a simple ordinary least squares regres- sion
will not identify the efect of changes in US real interest rates on the risk of default (EMBI+
premia). First, changes in the EMBI+ premia can cause changes in the interest
and in that case our methodology cannot be applied.
2
Our analysis is robust to the use of alternative measures of the real interest rate based on in?ation-adjusted nominal Treasury
rates of 3 months and 10 years. See Appendix A.
3
EMBI+ tracks total returns for traded US dollar- and other external currency-denominated Brady bonds, loans, Eurobonds
and local market instruments.
4
For additional justification for using data in diferences rather than levels, see Appendix B.
5
Sub-sample C contains the dates of scheduled and unscheduled FOMC meetings and the Federal Re-
serve Chairman's semi-annual monetary policy testimony to Congress. For a full list of these dates, see
http://www.federalreserve.gov/monetarypolicy/fomccalendars.htm
4
rate, for example, when default risk falls and in response investors switch demand from
safe Treasury assets to emerging market debt. Second, and more importantly, the inter- est rate
and the exchange rate are in?uenced by other common omitted variables. The
following system of equations is a simple representation of both endogeneity issues
6
:
?e
t
=o?i
t
+ z
t
+q
t
(1 )
?i
t
=|?e
t
+¸z
t
+c
t
(2 )
Where ?i
t
is the change in US real interest rate; ?e
t
the change in the EMBI+ premium;
z
t
a vector of omitted variables including, for example, external market conditions;c
t
a monetary
policy shock; andq
t
a shock to EMBI+.
The objective is to identifyo in Equation 1. Our identification strategy is borrowed
from Rigobon and Sack (2004), who show that the impact of monetary policy shocks on asset
prices can be identified because the variance of shocks is substantially larger on the days in sub-
sample C. Their paper used the identification strategy to establish a significant response of 10-
year Treasury yields to monetary policy shocks.
That monetary policy shocks can in?uence 10-year real interest rates means that the variance
of changes in these rates is significantly larger on the days in sub-sample C. This
efect is not large, but is large enough to significantly afect the variance of ?i
t
. We exploit
this efect by combining it with the assumption that the policy shock to real interest rates
neither afects EMBI+ through z
t
norq
t
, but only through its efect on ?i.
In sum, we assume that the variance of interest rate shocks (c
t
) in sub-sample C is higher than
the variance in sub-sample N; whilst the variances ofq
t
and z
t
are the same
across both sub-samples. As is usual in other identification strategies for our underlying
system of equations, we assume z
t
,c
t
andq
t
have no serial correlation and are uncorrelated
with each other. Our assumptions can be written in terms of the second moments of the
shocks in the two sub-samples C and N in the following way:
o
C
>o
N
c c
o
C
=o
N
q q
o
C
=o
N
z z
To help justify the underlying assumptions, Table 1 shows the increase in the variation
in the US real interest rate and the change in covariance between the real interest rate
6
We
show in Appendix C that allowing for a richer lag structure does not materially afect the results.
5
and EMBI+ premia over the sub-samples. The fact that the standard deviations of
EMBI+ premia appear to decrease from sub-sample N to sub-sample C, when we expect mild
increases, suggests that we require a more accurate statistical test of whether our assumptions on
the variance of shocks over the two sub-samples are valid.
7
Applying the test set out in Levene
(1960), reported in Table 2, we established that the standard deviation of the real interest rate
increases significantly in sub-sample C, while the variance of EMBI+ does not significantly
change because the efect of the variance increase in
Equation 2 only weakly efects the variance of EMBI+ through the interest rate.
8
Table 1: Data descriptives
Standard Covariance with
deviation US real rate
Sub-sample C Sub-sample N Sub-sample C Sub-sample N
US real rate 0.093 0.063 . .
Emerging Market 24.491 29.020 0.198 -0.211
Latin America 25.017 32.317 0.278 -0.253
Brazil 30.249 48.318 0.357 -0.278
Bulgaria 24.476 27.181 0.175 -0.117
Mexico 19.221 21.876 0.066 -0.214
Panama 12.486 14.849 0.028 -0.208
Peru 20.892 20.939 0.128 -0.185
Venezuela 43.545 50.526 0.852 -0.263
Note: 131 observations in sub-sample C, 2,604 days in sub-sample N.
We are not assuming that the FOMC ignores factors that afect emerging market
default risk, nor are we supposing that FOMC decisions have no impact on emerging market
prices - that is actually the efect we are estimating. We are precisely assuming that FOMC
decisions do not directly reveal important information about emerging mar- kets that might
otherwise afect EMBI+ premia, they are only afecting EMBI+ premia through changes in US
real interest rates. The underlying view is that the Committee might have private information
about how it will react to movements in emerging markets and how it plans to conduct monetary
policies in general but does not know more than the market about emerging economies.
7
We
cannot apply standard tests of variance equality, because they require that the underlying data be normally
distributed. As is reported in Appendix D, demonstrated through plots of each variables' quantiles against those of the normal
distribution and empirical tests of skewness and kurtosis, none of our series are normally distributed.
8
Although the test results are presented using the sample mean of the data, similar results are obtained when using the 50th
percentile or 10% trimmed mean.
6
Table 2: Levene (1960) test of equal variance
US real rate
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Test statistic
based on mean
12.371
0.215
0.458
2.273
0.000
0.031
0.021
0.908
0.635
p-value
0.000
0.643
0.499
0.132
0.977
0.860
0.884
0.341
0.801
Note: Null hypothesis is equal variance
Now, consider the following variables:
?i
?
C
,
\ i
N
?
?
?
?I ÷
\
T
C
T
N
?e
?
C
, ?e
?
N ?
?E ÷
\
T
C
\T
N
?i
?
C
, ÷?i
?
N ?
w÷
\
T
C
\T
N
A major result in Rigobon and Sack (2004) is thato can be consistently estimated
by a standard instrumental variables approach with the novel instrument, w, which is
correlated with the dependent variable, ?I, but is neither correlated with z
t
norq
t
. It
is correlated with ?I because the greater variance in sub-sample C implies the positive
correlation between ?i
?
C/ T
C
and ?i
?
C/ T
C
more than outweighs the negative cor-
\ \
relation between ?i
?
N / T
N
and ÷?i
?
N / T
N
. It is neither correlated with z
t
norq
t
\ \
because the positive and negative correlation of each part of the vector cancel each other
out.
The usual assumption in IV regressions is that the instrument afects the dependent variable
only through the regressor. The key diference here is that instead of having a variable assumed to
be correlated withc and uncorrelated with any of the other variables, we assume that the variance ofc is
larger on the days in sub-sample C and the variances of other variables are the same in both sub-samples.
7
3 Results
Table 3 presents the results from implementing our identification strategy, which reveals
that policy shocks to real interest rates are positively correlated with emerging economies'
EMBI+. This coincides with our original intuition that when the US tightens monetary policy, it
is harder for emerging economies to borrow, and the risk of default proxied by EMBI+ increases.
Table 3: The response of EMBI+ premia to interest rate shocks
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
0.868
1.115
1.334
0.649
0.607
0.496
0.659
2.279
Std Err
0.179
0.195
0.269
0.170
0.138
0.094
0.140
0.318
T-stat
4.840
5.717
4.969
3.808
4.394
5.264
4.697
7.162
Note: Each estimation uses 2,735 observations.
The magnitude of the response is large: an unexpected increase in the 10-year real
interest rate of one basis point leads to an increase in the EMBI+ premium of a similar order of
magnitude.
Table 4 shows the results from analysis of the relationship between US real interest rates and
EMBI+ premia in each separate sub-sample (the results across both samples are in Table 5).
Crucially, the 'normal' correlation between ?E and ?I is actually negative (and smaller in absolute
value) in sub-sample N. Our interpretation is that increases in US real interest rates are correlated
with other things that are good for emerging markets and thus decrease their cost of borrowing.
Future research ought to investigate which aspects of international financial markets, correlated
with US real interest rates, are most important to the risk of emerging market default.
The results in Table 3 are substantially diferent from the OLS estimates using only the sub-
sample C presented in Table 4. While the former shows a strong positive re- lation, the latter
shows a mild and insignificant efect. Rosa (2011) has noted that, in some applications, the
results from employing the identification through heteroskedastic- ity methodology are not much
diferent from a simple OLS using the subsample where the FOMC meets. That is not the case
here since we are using the long-term interest rates, where endogeneity is likely to be much more
important than when the policy rate is used,
8
Table 4: Separate analysis of sub-samples
Sub-sample C Sub-sample N
Coef Std Err T-Stat Coef Std Err T-stat
Emerging Market 0.230 0.224 1.029 -0.494 0.087 -5.700
Latin America 0.317 0.228 1.390 -0.591 0.096 -6.131
Brazil 0.406 0.275 1.474 -0.649 0.145 -4.492
Bulgaria 0.217 0.226 0.960 -0.274 0.081 -3.363
Mexico 0.089 0.177 0.503 -0.500 0.065 -7.692
Panama 0.036 0.114 0.311 -0.487 0.044 -11.186
Peru 0.146 0.191 0.766 -0.430 0.062 -6.937
Venezuela 0.924 0.389 2.371 -0.617 0.151 -4.076
Note: 131 observations in sub-sample C, 2,604 days in sub-sample N.
Table 5: Full sample analysis
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.423
-0.503
-0.547
-0.226
-0.443
-0.437
-0.375
-0.467
Std Err
0.082
0.091
0.135
0.077
0.062
0.041
0.059
0.143
T-stat -
5.174
-5.535
-4.038
-2.934 -
7.194
-10.586 -
6.347 -
3.266
Note: Each estimation uses 2,735 observations.
and the correlation between variables in the N sample is diferent from the causal efect.
4 Concluding remarks
The strong and positive relation between exogenous changes in US real interest rates and
the EMBI+ premium highlights the importance of US interest rate shocks. The fact that the
overall correlation between US rates and the EMBI+ premium is negative highlights the
importance of other aspects of international financial markets, such as favourable external
conditions to emerging economy borrowing. From a policy perspective, our result has
implications for proposals to issue debt that is contingent on exogenous factors that afect the
ability to repay. One of these ideas is that a higher US real interest rate makes it more difcult for
emerging market economies to repay, so reducing emerging market debt payments when US
interest rates increase would be welfare improving. Our finding that the overall correlation is
negative implies that making emerging market sovereign debt contingent on US real interest
rates would have an opposite result from the desired
9
efect. Research on sovereign default should note that shocks afecting foreign real interest
rates might have very diferent efects on emerging market default risk.
References
Arora, V. and Cerisola, M. (2001). How Does U.S. Monetary Policy In?uence Sovereign
Spreads in Emerging Markets?, IMF Staf Papers 48: 474-498.
Guimaraes, B. (2011). Sovereign default: which shocks matter?, Review of Economic
Dynamics 14(4): 553-576.
Levene, H. (1960). Robust tests for equality of variances, in I. O. et al. (ed.), In Con-
tributions to Probability and Statistics: Essays in Honor of Harold Hotelling , Stanford University Press,
pp. 278-292.
Longstaf, F. A., Pan, J., Pedersen, L. H. and Singleton, K. J. (2011). How sovereign is
sovereign credit risk?, American Economic Journal: Macroeconomics 3(2): 75-103.
Neumeyer, P. and Perri, F. (2005). Business cycles in emerging economies: the role of
interest rates, Journal of Monetary Economics 52(2): 345-380.
Rigobon, R. (2003). Identification through heteroskedasticity, Review of Economics and
Statistics 85(4): 777-792.
Rigobon, R. and Sack, B. (2004). The impact of monetary policy on asset prices, Journal
of Monetary Economics 51(8): 1553-1575.
Robitaille, P. and Roush, J. (2006). How Do FOMC Actions and U.S. Macroeconomic
Data Announcements Move Brazilian Sovereign Yield Spreads and Stock Prices?, Board
of Governors of the Federal Reserve System, International Finance Discussion Paper
No. 868 .
Rosa, C. (2011). The validity of the Event-Study Approach: Evidence from the Impact of
the Fed's Monetary Policy on U.S. and Foreign Asset Prices, Economica (311): 429-439.
Uribe, M. and Yue, V. (2006). Country spreads and emerging countries: Who drives
whom?, Journal of International Economics 69(1): 6-36.
Zettelmeyer, J. (2004). The impact of monetary policy on exchange rates: evidence from
three small open economies, Journal of Monetary Economics 51(3): 635-652.
10
A Appendix - alternative US interest rates
In this appendix we prepare four alternative estimates of US real interest rates which are
then used in place of the real rates reported in the main text as a robustness exercise.
We obtain two nominal interest rate series and two in?ation measures from the Global
Financial Database (www.globalfinancialdata.com). Both interest rate series are constant maturity,
consistent with the data in the main text. We use a 3 month T-Bill rate consistent with existing
quantitative studies in the literature, and a 10 year Treasury Bond rate consistent with the data in
the main text because we maintain that long termrates are a more appropriate measure of the
opportunity cost to investors in emerging market sovereign debt.
The first measure of in?ation is based on the Bureau of Labor Statistics monthly Consumer
Price Index. We obtain the annual in?ation rate in the year prior to each month, and average
over the previous three months' annual in?ation rates to obtain a monthly estimate of future
in?ation. The second measure is the University of Michigan survey of annual CPI in?ation
expectations, which are also reported monthly. Both monthly series are assigned to the last
working day of the month and subsequently cubic splined to obtain interpolated daily series of
annual expected in?ation.
Each gross interest rate is divided by both gross expected in?ation measures and netted.
Figure 1 below shows the comparison of rates over time, and Table 6 shows the cross-
correlations between the series.
Tables 7 - 16 show the results from repeating the analysis described in the main text with the
full sample, individual sub-samples (FOMC and non-FOMC meeting days) and applying the
method of identification through heteroscedasticity for the four alternative measures of real
interest rates.
When using T-Bill rates, the standard errors are generally lower but the coefcients are much
smaller. There are fewer significant coefcients and the magnitudes appear to be lower (no
statistical tests of diferences were run). Running the analysis separately on the sub-samples
shows that the coefcients on the days when the FOMC meet are again insignificant, but those
days when the FOMC do not meet appear to be of smaller magnitude although they remain
significantly negative.
When using T-Bond rates, the coefcients are generally of comparable magnitudes but the
standard errors are much larger resulting in fewer significant positive coefcients. This is probably
re?ecting the fact that our measures of expected in?ation are noisy when applied to daily data. All
coefcients that are significant are positive.
11
Figure 1: US Real Interest Rates
1998 2000 2002 2004 2006 2008
Inflation Indexed 10÷year Bond Yield
3m TBill real rate using BLS CPI
10Yr Bond real rate using BLS CPI 3m
TBill real rate using UMICH CPI
10Yr Bond real rate using UMICH CPI
Table 6: Correlation between real interest rate measures
T IPS T-Bill & T-Bill & T-Bond
&Yield BLS CPI UMICH CPI BLS CPI
T-Bill & BLS CPI 0.753 .
T-Bill & UMICH CPI 0.766 0.936 .
T-Bond & BLS CPI 0.763 0.769 0.620 .
T-Bond & UMICH CPI 0.846 0.710 0.745 0.862
12
5
0
U
S
R
e
a
l
I
n
t
e
r
e
s
t
R
a
t
e
s
÷
5
A.1 T-Bill rates and BLS CPI in?ation expectations
Table 7: Full sample analysis (T-Bill & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.301
-0.332
-0.325
-0.147
-0.211
-0.207
-0.237
-0.479
StdErr
0.060
0.067
0.100
0.058
0.045
0.031
0.044
0.106
T-stat -
4.986
-4.950
-3.248
-2.552 -
4.688 -
6.678 -
5.419 -
4.539
Table 8: The response of EMBI+ premia to interest rate changes (T-Bill & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
0.227
0.165
0.199
0.211
0.177
0.114
0.224
0.137
StdErr
0.107
0.115
0.160
0.105
0.081
0.055
0.084
0.189
T-stat
2.122
1.443
1.244
2.014
2.174
2.078
2.677
0.726
Table 9: Separate analysis of FOMC and non-FOMC meeting days (T-Bill & BLS CPI)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market -0.007 0.146 -0.045 -0.339 0.065 -5.247
Latin America -0.055 0.148 -0.369 -0.368 0.072 -5.105
Brazil -0.033 0.178 -0.184 -0.364 0.108 -3.358
Bulgaria 0.053 0.152 0.347 -0.173 0.062 -2.812
Mexico 0.005 0.115 0.047 -0.239 0.048 -4.970
Panama -0.028 0.074 -0.373 -0.230 0.033 -6.934
Peru 0.019 0.125 0.154 -0.271 0.047 -5.809
Venezuela -0.135 0.269 -0.502 -0.524 0.113 -4.639
13
A.2 T-Bill rates and Univ. of Michigan CPI in?ation expectations
Table 10: Full sample analysis (T-Bill & UMICH CPI exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.241
-0.248
-0.301
-0.146
-0.203
-0.191
-0.238
-0.476
StdErr
0.059
0.065
0.097
0.056
0.044
0.030
0.043
0.102
T-stat -
4.117
-3.810
-3.098
-2.617 -
4.661 -
6.343 -
5.595 -
4.651
Table 11: The response of EMBI+ premia to interest rate changes (T-Bill & UMICH CPI exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
0.282
0.186
0.238
0.280
0.235
0.153
0.281
0.224
StdErr
0.112
0.120
0.168
0.110
0.086
0.058
0.088
0.199
T-stat
2.513
1.553
1.416
2.541
2.747
2.646
3.203
1.126
Table 12: Separate analysis of FOMC and non-FOMC meeting days (T-Bill & UMICH CPI
exp.)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market 0.038 0.145 0.263 -0.276 0.063 -4.405
Latin America -0.016 0.147 -0.109 -0.277 0.070 -3.964
Brazil -0.013 0.177 -0.074 -0.336 0.105 -3.214
Bulgaria 0.081 0.151 0.539 -0.174 0.059 -2.929
Mexico 0.031 0.115 0.271 -0.232 0.046 -4.994
Panama -0.007 0.074 -0.094 -0.213 0.032 -6.642
Peru 0.040 0.124 0.318 -0.272 0.045 -6.033
Venezuela -0.102 0.268 -0.380 -0.522 0.109 -4.783
14
A.3 10Yr Bond rates and BLS CPI in?ation expectations
Table 13: Full sample analysis (T-Bond & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.865
-0.962
-0.975
-0.377
-0.873
-0.577
-0.576
-1.027
StdErr
0.064
0.071
0.108
0.063
0.046
0.032
0.047
0.114
T-stat -
13.484
-13.497
-8.997
-6.014
-18.825 -
17.893 -
12.267
-8.986
Table 14: The response of EMBI+ premia to interest rate changes (T-Bond & BLS CPI)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
1.316
1.909
2.944
1.317
0.686
0.679
0.684
2.682
StdErr
0.509
0.591
0.825
0.478
0.373
0.268
0.372
0.905
T-stat
2.586
3.233
3.570
2.755
1.840
2.533
1.837
2.963
Table 15: Separate analysis of FOMC and non-FOMC meeting days (T-Bond & BLS CPI)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market -0.378 0.210 -1.801 -0.899 0.067 -13.453
Latin America -0.322 0.213 -1.511 -1.007 0.074 -13.521
Brazil -0.101 0.259 -0.389 -1.036 0.114 -9.101
Bulgaria 0.000 0.221 0.002 -0.404 0.065 -6.194
Mexico -0.526 0.161 -3.259 -0.898 0.048 -18.615
Panama -0.297 0.105 -2.828 -0.597 0.034 -17.758
Peru -0.298 0.180 -1.657 -0.595 0.049 -12.251
Venezuela -0.200 0.392 -0.510 -1.085 0.119 -9.128
15
A.4 10Yr Bond rates and Univ. of Michigan CPI in?ation expectations
Table 16: Full sample analysis (T-Bond & UMICH CPI exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef -
0.761
-0.824
-0.910
-0.364
-0.830
-0.537
-0.559
-0.994
StdErr
0.063
0.070
0.105
0.061
0.045
0.031
0.046
0.111
T-stat -
12.149
-11.813
-8.643
-5.976
-18.373 -
17.056 -
12.244
-8.960
Table 17: The response of EMBI+ premia to interest rate changes (T-Bond & UMICH CPI
exp.)
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Co-ef
2.144
2.722
4.267
2.227
1.334
1.180
1.294
4.266
StdErr
0.807
0.928
1.332
0.767
0.599
0.441
0.579
1.458
T-stat
2.656
2.934
3.203
2.904
2.227
2.677
2.237
2.925
Table 18: Separate analysis of FOMC and non-FOMC meeting days (T-Bond & UMICH CPI
exp.)
Sub-sample C Sub-sample N
Co-ef StdErr T-stat Co-ef StdErr T-stat
Emerging Market -0.282 0.213 -1.327 -0.793 0.065 -12.166
Latin America -0.240 0.216 -1.114 -0.862 0.073 -11.852
Brazil -0.056 0.261 -0.215 -0.966 0.110 -8.747
Bulgaria 0.063 0.222 0.284 -0.392 0.063 -6.206
Mexico -0.473 0.164 -2.890 -0.853 0.047 -18.217
Panama -0.254 0.107 -2.380 -0.555 0.033 -16.975
Peru -0.254 0.181 -1.399 -0.579 0.047 -12.279
Venezuela -0.127 0.395 -0.321 -1.051 0.115 -9.127
16
B Appendix - estimation in levels
The analysis presented in this appendix is intended to justify time-diferencing the data
in the paper. We show that (i) there is no significant increase in the variance of the levels of the
US real interest rate on the dates the FOMC meets, which is inconsistent with the fundamental
assumption underpinning the methodology of identification through heteroskedasticity; and (ii)
the data we use are highly persistent over time, and as a result the usual tests cannot reject a unit
root. An analysis in levels would be subject to the critique that any results were spurious.
The fundamental assumption underpinning the methodology of identification is not directly
testable because we cannot identify the shocks. But the best available evidence we have suggests
that it is appropriate to apply the methodology in diferences, but not in levels. Table 19 shows
the descriptive statistics for our variables in levels using data defined to capture the level of each
variable on the day after the FOMC meeting dates. The analysis is repeated in Table 20 using the
level of variables on the same day as the FOMC meeting. In both cases, and similar to Table 1,
there is no significant diference in the standard deviation of EMBI+ variables on the days when
the FOMC meets from the days when it does not. In Table 19 there is a (weakly) significant
reduction in the standard deviation of the US real interest rate on the days when the FOMC
meets, and in Table 20 there is no significant change. This is not consistent with the assumption
that the variance of the interest rate would significantly increase on FOMC meeting days.
Table 19: Data descriptives (levels)
Standard Covariance with Levene (1960) test
deviation US real rate of equal variance
FOMC No FOMC FOMC No FOMC mean test p-value
US real rate 0.885 0.898 . . 2.717 0.066
Emerging Market 314.595 319.333 194.756 202.617 0.103 0.749
Latin America 296.342 295.865 124.524 127.392 0.037 0.847
Brazil 421.413 418.673 153.673 156.705 0.078 0.780
Bulgaria 302.345 313.245 223.860 238.411 0.449 0.503
Mexico 180.834 184.149 114.845 120.151 0.183 0.668
Panama 119.021 120.635 61.070 63.281 0.009 0.924
Peru 217.518 213.622 124.616 123.782 0.032 0.858
Venezuela 381.318 386.208 130.639 152.410 0.008 0.927
Notes: Levene (1960) test statistic based on mean; null hypothesis is equal variance
FOMC means the set of days immediately after FOMC meetings
Table 21 shows the results from tests of stationarity on the variables in levels and
17
Table 20: Data descriptives (levels)
Standard Covariance with Levene (1960) test
deviation US real rate of equal variance
FOMC No FOMC FOMC No FOMC mean test p-value
US Real Rate 0.883 0.898 . . 0.668 0.414
Emerging Market 314.952 319.314 194.621 202.629 0.070 0.792
Latin America 298.070 295.774 123.374 127.455 0.054 0.816
Brazil 426.906 418.392 151.228 156.836 0.091 0.764
Bulgaria 309.204 312.911 227.779 238.222 0.096 0.757
Mexico 182.428 184.070 115.595 120.116 0.147 0.702
Panama 120.181 120.577 61.350 63.272 0.018 0.893
Peru 216.637 213.664 123.413 123.843 0.018 0.894
Venezuela 379.584 386.308 130.098 152.424 0.003 0.953
Notes: Levene (1960) test statistic based on mean; null hypothesis is equal variance
FOMC means the set of days on which FOMC meetings are held
first diferences. Both tests include a constant but no trend term; the Phillips-Perron
specification includes seven Newey-West lags.
The variables in levels are all non-stationary. Identical specifications for the difer- enced
time-series employed in the paper show they are stationary. We conclude that it is more
appropriate to specify the model in terms of diferences than in levels.
Table 21: Stationarity test statistics
Levels First Diferences
Phillips-Perron Dickey-Fuller Phillips-Perron Dickey-Fuller
US real rate -1.32 -1.28 -24.36 -25.14
Emerging Market -1.11 -1.03 -22.70 -23.83
Latin America -1.49 -1.46 -23.32 -24.66
Brazil -2.84 -2.80 -22.36 -23.47
Bulgaria -1.60 -1.59 -25.08 -25.26
Mexico -1.51 -1.50 -23.05 -24.31
Panama -0.88 -0.59 -24.74 -25.27
Peru -1.95 -1.92 -25.21 -25.58
Venezuela -0.44 -0.29 -25.50 -26.43
Notes: Null hypothesis is stationarity in all unit root tests
Phillips-Perron specifications use seven Newey-West lags
Critical values are -3.43 (1%); -2.86 (5%); -2.57 (10%)
18
C Appendix - dynamic model
This appendix reports the results from a dynamic specification of the model, as an in-
vestigation of dynamic efects, for example overshooting, in the reaction of the EMBI+ spread to
changes in US real interest rates
9
. We maintain the definition of the variables
as in the main text, i.e. ?X
t
÷ X
t
+1 ÷ X
t
÷
1
, but re-specify the model as follows:
Table 22: ?E
t
= o
1
?I
t
+o
2
?I
t
÷
2
+o
3
?E
t
÷2
instrumented
Table 23: ?e
t
=o
1
?i
t
+o
2
?i
t
÷
2
+o
3
?e
t
÷2
The Tables below should be compared with Tables 3 and 5 in the main text. Following
the notation in the main text, the instruments employed in the 2SLS estimates of dynamic
model in Table 22 are w,?I
t
÷
2
, and ?E
t
÷
2
.
We find that in general the coefcients on the lags in both specifications were statis-
tically insignificant and conclude that there is no systematic evidence of dynamic efects present in
the data.
Table 22: Identification via heteroscedasticity dynamic analysis
Co-efcients Standard Error T-statistic
US RR L.US RR L.DV US RR L.US RR L.DV US RR L.US RR L.DV
E. Market 0.96 0.00 7.34 0.17 0.00 6.50 5.57 2.56 1.13
L Am. 1.23 0.00 4.26 0.19 0.00 7.08 6.52 0.37 0.60
Brazil 1.48 0.00 2.49 0.26 0.00 9.81 5.69 0.17 0.25
Bulgaria 0.69 0.00 8.56 0.16 0.00 6.21 4.17 0.48 1.38
Mexico 0.68 0.00 6.18 0.13 0.00 4.99 5.15 1.55 1.24
Panama 0.54 -0.00 2.57 0.09 0.00 3.46 5.91 -0.01 0.74
Peru 0.73 0.00 5.22 0.13 0.00 5.05 5.43 1.66 1.04
Venezuela 2.28 0.00 -1.24 0.31 0.00 11.76 7.33 4.82 -0.11
Notes: DV - dependent variable - EMBI+ premium.
US RR - US real interest rate.
Each estimation uses 2,611 observations.
9
We
gratefully acknowledge this follows the suggestion of an anonymous referee.
19
Table 23: Full sample dynamic analysis
Co-efcients Standard Error T-statistic
US RR L.US RR L.DV US RR L.US RR L.DV US RR L.US RR L.DV
E. Market -0.39 -0.02 0.04 0.08 0.08 0.02 -4.64 -0.25 2.00
L. Am. -0.47 -0.06 0.00 0.09 0.09 0.02 -5.00 -0.68 0.06
Brazil -0.49 -0.16 0.00 0.14 0.14 0.02 -3.53 -1.11 0.13
Bulgaria -0.20 0.17 -0.03 0.08 0.08 0.02 -2.49 2.12 -1.65
Mexico -0.41 -0.03 0.00 0.06 0.06 0.02 -6.63 -0.47 0.19
Panama -0.42 -0.06 0.01 0.04 0.04 0.02 -9.91 -1.39 0.75
Peru -0.36 0.03 0.07 0.06 0.06 0.02 -6.02 0.51 3.67
Venezuela -0.47 -0.13 0.03 0.15 0.15 0.02 -3.18 -0.88 1.27
Notes: DV - dependent variable - EMBI+ premium.
US RR - US real interest rate.
Each estimation uses 2,611 observations.
D Appendix - tests of variance
The increase in the variation in the US real interest rate and the change in covariance
between the real interest rate and EMBI+ premia over the sub-samples are apparent from Table 1
in the main text, but the fact that the standard deviations of EMBI+ premia appear to decrease
from sub-sample N to sub-sample C, when we expect mild increases, suggests we require a more
accurate statistical test of whether our assumptions on the variance of shocks over the two sub-
samples are valid.
Importantly, however, we cannot apply standard tests of variance equality, because they
require that the underlying data be normally distributed. As the plots of each variables' quantiles
against those of the normal distribution in Figure 2 demonstrate, and the empirical tests of
skewness and kurtosis confirm in Table 24, none of our series are normally distributed.
Levene (1960) provides a test where the null is equal variance when samples are drawn from
a distribution that is not Gaussian normal. The results from this test are presented in Table 25, and
show that the variance of the US real interest rate significantly increases,
but the variance of all EMBI+ premia does not change significantly.
10
On the basis of these results, we conclude that the standard deviation of the real interest rate
increases significantly on the days when the variance of interest rate move- ments is greater. We
cannot reject the null that the variance of EMBI+ is the same in both sub-samples. According to
our assumptions, the policy shocks should yield only
10The
results are presented using the sample mean of the data, similar results are obtained when using the 50th
percentile or 10% trimmed mean.
20
Figure 2: Q-Q plots of each variable quantiles against normal distribution quantiles
÷100 ÷50 0 50 100 ÷100 ÷50 0 50 100
Inverse Normal Inverse Normal
÷200 ÷100 0 100 200 ÷100 ÷50 0 50 100
Inverse Normal Inverse Normal
÷100 ÷50 0 50 100 ÷50 0 50
Inverse Normal Inverse Normal
÷100 ÷50 0 50 100 ÷200 ÷100 0 100 200
Inverse Normal Inverse Normal
21
4
0
0
4
0
0
÷
4
0
0
÷
2
0
0
0
2
0
0
E
m
e
r
g
i
n
g
M
a
r
k
e
t
÷
4
0
0
÷
2
0
0
0
2
0
0
L
a
t
i
n
A
m
e
r
i
c
a
5
0
0
0
2
0
0
4
0
0
6
0
0
B
u
l
g
a
r
i
a
0
B
r
a
z
i
l
÷
4
0
0
÷
2
0
0
÷
5
0
0
÷
2
0
0
÷
1
0
0
0
1
0
0
2
0
0
3
0
0
M
e
x
i
c
o
2
0
0
0
1
0
0
P
a
n
a
m
a
÷
1
0
0
1
0
0
0
0
5
0
1
0
0
1
5
0
P
e
r
u
0
5
0
0
V
e
n
e
z
u
e
l
a
÷
1
0
0
÷
5
0
÷
5
0
0
Table 24: Test of skewness and kurtosis
US real rate
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
skewness
p-value
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
kurtosis p-
value
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Note: Null hypothesis is normal distribution
Table 25: Levene (1960) test of equal variance
US real rate
Emerging Market
Latin America
Brazil
Bulgaria
Mexico
Panama
Peru
Venezuela
Test statistic
based on mean
12.371
0.215
0.458
2.273
0.000
0.031
0.021
0.908
0.635
p-value
0.000
0.643
0.499
0.132
0.977
0.860
0.884
0.341
0.801
Note: Null hypothesis is equal variance
small increases in the variance of EMBI+, as the unexpected policy shocks to US real
interest rates are only a small part of the variation of emerging market default risk, so the results
of the tests on variances in both sub-samples, albeit not conclusive, are not at odds with the
identifying assumptions.
22
doc_217525373.docx