Credit Spreads on Sovereign Debt
An Explanatory Regression Model &
Trading Strategy
Submitted by
Vaswar Mitra, Vinaya Jain
Independent Study Project under Prof. Campbell Harvey, Term 4 2006
May 5th, 2006
Agenda












Summary Findings
The Origins & Evolution of Sovereign Debt
Factors that influence sovereign credit spreads
Overview of Methodologies
Overview of Regression Analysis
Model 1: WB Annual
 Regression Model & Trading Strategy
Model 2: WB Monthly
 Regression Model & Trading Strategies
Model 3: ICRG Monthly
 Regression Model & Trading Strategies
Model 4: Lagged Spreads
 Regression Model & Trading Strategy
Momentum Trading Strategy
Issues to be Explored Further
Bibliography
2
Summary Findings





WB Data annual and monthly (Annual independent variables
replicated to give monthly data) data gave us high R-Squares in
the 85% range
The ICRG monthly data gave us R-Squares in the 63% range,
lower than what we got using WB data.
An explanatory model using lagged spreads gave us extremely
high R-Squares over 96% even though the residuals were not
distributed normally
Long-Only trading strategies using 1-year lags gave us good
results, both with WB data and ICRG data. The Long-Short
strategy did not work similarly well
Given the high R-Squares using lagged spreads, we felt that a
momentum strategy should work well. Our strategy yielded a
percent accuracy of only 49% and cannot be used in its current
form
3
The Origins of Sovereign Debt

The first public bonds originated in 17th century European city-states –
lifetime/redeemable annuities that paid interest to bondholders.

The UK “Consol” (1751) is the earliest example of a government bond used to
supplement revenues from taxation




Alexander Hamilton (1789) created “Hamilton 6s” to refinance existing US debt –
the first US federal bonds based on the Consols



Liquid, perpetual (redeemable at par) and highly credible. Backed by sinking funds,
Consol’s formed 96% of Britain’s total debt from 1801-1914
The Bank of England (1694) was mandated to manage government debt, issue
currency and provide liquidity between bonds and cash
Consols quickly became a “byword for financial security” and a benchmark for other
risky assets
Created the first US central bank, modeled on the Bank of England
The British system of public debt was widely adopted worldwide – Holland (1814),
Italy (1893), Japan (1880’s)
Post World War 1 – the British Treasury developed more maturities of debt to
supplement Consols

The first “gilt edged government securities” were introduced
4
The Evolution of Sovereign Debt
The magnitude of government budget deficits and consequently, the outstanding
volume of sovereign debt has fluctuated significantly over the years


France, Russia and Italy averaged high deficits in the 18th and 19th century. The UK
had the best record for balanced budget
Between the years of 1816 – 1899:





UK – Only 4 years with a deficit >1% of GNP. Average budget surplus of 4.6%
France – Only 7 years with a budget surplus during this period.
Italy – Ran a deficit every year from 1862 – 1899
USA – Average deficit of 1% of GNP
Deficits were vastly larger during the World Wars particularly in combatant nations





World War 1 Average Deficit : UK - 30% of GNP, Germany 40%, Italy 22%
World War 2: Soviet Union – 19% of GNP, Germany 36%.
Axis countries relied heavily on short term borrowings, while the US and UK had
more balanced maturity structures of sovereign debt
“Wars of credit” - between 1776 and 1783, bonds financed 40% of the UK’s war
expenditure
Short term debt (Bills) gained in popularity, as the attractiveness of long dated
war bonds declined
5
The Evolution of Sovereign Debt Contd...
Early European kingdoms were notoriously prone to defaulting on sovereign debt.
Defaults could be declared in many ways – the most common types were:

Outright Default – Suspension of principal or interest payments

Moratoria/ Rescheduling - Institutionalized processes to make creditors
agree to new terms on the debt

Conversions – the exchange of one class of bond for another, usually with a
lower coupon or higher maturity. In the UK, conversions were always
negotiated, while in other countries they could be imposed upon creditors.
Some well known examples of sovereign defaults:

Spain & France defaulted on all or part of their debt, over 10 times in the 16th
and 17th centuries

The UK declared a moratorium on interest payments in the 1680’s and
converted some debt

Turkey defaulted in 1875, and after World War I

Latin American countries like Brazil, Mexico and Colombia were “perennial
defaulters” in the late 19th and 20th centuries
6
Factors Influencing Sovereign Debt Spreads

The credit spread or ”yield” is the main determinant of the borrowing cost for a
country. According to traditional economic theory, spreads are determined by:





Expectations of real growth prospects, nominal interest rates and inflation for the
country in question (Fisher Effect, Gibson’s Paradox)
They are thought to be linked to measures of “monetary growth” , “fiscal stability”,
and the overall term structure of interest rates
The credit spread is viewed as a premium for default risk and other risks specific
to the issuing country
Political and other types of idiosyncratic risks
The “Feel good” factor – psychology and economics



Economic policy and its effect on the morale of the public
“It’s the economy stupid” – Bill Clinton
The eternal Fiscal Policy question - do higher debts lead to higher interest rates?


Regression analysis of UK data from 1727 shows a very low relationship between
yields and the debt/GNP and deficit/GNP Ratio
However in the late 1970’s and 1980’s there is evidence of strong positive
correlation between debt/GNP ratios and rising real interest rates.
7
Overview of Methodologies
To develop an explanatory model for sovereign debt spreads we
created three methodologies:
1.
Using World Bank (WB) annual data after correcting for
missing data
2.
Using World Bank (WB) annual data but transforming it to
monthly by replicating the annual data (independent variables)
12 times but using actual monthly spreads (dependent
variable)
3.
Using ICRG monthly data
Next we tested for viable trading strategies:
1.
For each of the explanatory models developed above, we
developed a predictive model using in sample data and tested
the model on out of sample data
2.
Tested for a momentum trading strategy
8
Overview of Regression Analysis






Stata used for regression analysis
Assumed that there would be fixed time effects.
Eliminated these effects by using time dummies for
each year
Used cluster standard errors
Ensured that independent variable correlations and
multicollinearity were within reasonable levels
Generated partial regression plots and checked for
any unusual or influential points
Finally, tested that the regression residuals for
normality
9
Model 1: WB Annual
World Bank Annual Data
10
WB Annual: Regression Variables

Dependent Variable:


Independent Variable:




Ln(Spread)
GDP Per Capita,
Reserves Per Capita,
External Debt as a Percent of GDP
We expect spreads to be:



Lower for higher GDP Per Capita
Lower for higher Reserves per Capita numbers
Higher for higher External Debt as a Percent of GDP
This was consistent with our regression results
11
WB Annual: Modifications to Data



Lack of data for certain years for certain variables presented a
problem
In general we excluded the years or countries for which data was
missing
In one case we modified the data. Data for the independent
variable “External Debt as a percent of GDP” was sometimes
missing for certain years or for a specific country altogether. To
handle this situation, we made the following modifications:
 Where the data was missing for a specific year but the country
had data for previous years, we simply used the country data
from previous years
 Where data was completely missing for the country, we used the
average across all countries for the particular year
12
WB Annual: Variables Graph
Scatter plot of spread against independent variables shows that points with
Spreads = 1 ( Ln(Spread) =0 ) are outliers
Whether this is a problem will be confirmed if the Partial Regression Plots
shows similar influential points
13
WB Annual: Regression Results
Fixed-effects (within) regression
Group variable (i): year
Number of obs
Number of groups
=
=
267
11
R-sq:
Obs per group: min =
avg =
max =
4
24.3
33
within = 0.8580
between = 0.9513
overall = 0.8485
corr(u_i, Xb)
= 0.0807
F(3,264)
Prob > F
=
=
1494.43
0.0000
(Std. Err. adjusted for 11 clusters in year)
-----------------------------------------------------------------------------|
Robust
lnspread |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gdppercapita | -.0002804
9.58e-06
-29.28
0.000
-.0003017
-.0002591
reservespe~a | -.0007464
.00014
-5.33
0.000
-.0010583
-.0004344
externalde~p |
1.554783
.6288551
2.47
0.033
.1536062
2.955959
_cons |
7.382482
.4902899
15.06
0.000
6.290048
8.474916
-------------+---------------------------------------------------------------sigma_u | .53247918
sigma_e | 1.0329095
rho | .20995742
(fraction of variance due to u_i)
------------------------------------------------------------------------------
Regression gives high (within) R-Square of 85.80% and overall
R-Square of 84.45%
The t-stats are all significant
14
WB Annual: Correlation & Multicollinearity
| lnspread gdpper~a reserv~a extern~p
-------------+-----------------------------------lnspread |
1.0000
gdppercapita | -0.9009
1.0000
reservespe~a | -0.5422
0.4458
1.0000
externalde~p |
0.3701 -0.3038 -0.0834
1.0000
Variable Correlation:
Variable |
VIF
1/VIF
-------------+---------------------gdppercapita |
1.37
0.729692
reservespe~a |
1.25
0.798312
externalde~p |
1.11
0.904308
-------------+---------------------Mean VIF |
1.24
Multicollinearity:
Low correlation between
variables
A VIF (variance inflation
factor) lower than 10 denotes
low collinearity
15
WB Annual: Partial Regression Plots
Partial Regression Plot does not reveal any unusual or influential observations.
16
WB Annual: Residual Plots
Test for Normality: There is a slight skew but overall residuals are normally
distributed
17
WB Annual: Trading Strategy Methodology



In Sample Period: 1994 – 2001
Out of Sample Period: 2002 – 2003
Predictive Equation:



Sorted on the % Change expected during the next year






Ln(Spread) (1-year forward) =
8.64547 – 0.00031 * GDP Per Capita – 0.00108 * Reserves Per Capita
% Change = (Next year forecasted spread - Current Spread) / Current Spread
If % Expected Change is negative, the decision is to buy the country’s debt
Conversely if % Expected Change is positive, the decision is to sell the country’s debt
Eliminated developed countries which had spreads = 1
Long Short Strategy: After sorting, selected the top quintile (five most negative %
Expected Change) to go long and bottom five quintile (most positive % Expected
Change) to go short
Long Only Strategy: After sorting, selected the top two quintiles (ten most negative
% Expected Change) to go long only
18
WB Annual: Trading Strategy Results
Trading Strategies Comparison
100%
% Accuracy
80%
60%
Long Short
Long Only
Overall
40%
Directional Accuracy
Year Long Short Long Only Overall
2002
50%
100%
73%
2003
60%
90%
56%
20%
0%
2002
2003
Year
Overall Accuracy: The overall accuracy was 73% and 56% in the two years.
Long Short Result: The % Accuracy during the two years of OOS data is over 50%
in both the OOS years.
Long Only Result: The % Accuracy is 100% and 90% in the two years. These
results are very promising.
19
WB Annual: Conclusions

Strong explanatory power of World Bank
annual data. Gives us R-Sq in the 85% range

Long only Trading Strategy shows a lot of
promise

Need to explore reasons why the short side of the
strategy does not yield as good results
20
Model 2: WB Monthly
World Bank Annual Data
converted to Monthly
21
WB Monthly: Regression Variables

Dependent Variable:


Independent Variable:




Ln(Spread)
GDP Per Capita,
Reserves Per Capita,
External Debt as a Percent of GDP
We expect spreads to be:



Lower for higher GDP Per Capita
Lower for higher Reserves per Capita numbers
Higher for higher External Debt as a Percent of GDP
This was consistent with our regression results
22
WB Monthly: Modifications to Data

Starting with the WB annual data used in Model 1, we
made the following modifications:
1.
We replicated the annual data (independent variables) 12
times, once for each month of the year
2.
used the actual monthly spreads (dependent variable)
3.
Ran our regressions and our trading strategy as if we had
monthly data
23
WB Monthly: Variables Graph
Scatter plot of spread against independent variables shows that points with
Spreads = 1 ( Ln(Spread) =0 ) are outliers
Whether this is a problem will be confirmed if the Partial Regression Plots
shows similar influential points
24
WB Monthly: Regression Results
Fixed-effects (within) regression
Group variable (i): year
Number of obs
Number of groups
=
=
2957
11
R-sq:
Obs per group: min =
avg =
max =
33
268.8
369
within = 0.8522
between = 0.9577
overall = 0.8448
corr(u_i, Xb)
= 0.0714
F(3,2954)
Prob > F
=
=
2931.56
0.0000
(Std. Err. adjusted for 11 clusters in year)
-----------------------------------------------------------------------------|
Robust
lnspread |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gdppercapita | -.0002817
9.45e-06
-29.80
0.000
-.0003028
-.0002607
reservespe~a | -.0006339
.0001369
-4.63
0.001
-.0009388
-.0003289
externalde~p |
1.482868
.6366234
2.33
0.042
.0643827
2.901353
_cons |
7.333897
.4741553
15.47
0.000
6.277413
8.390381
-------------+---------------------------------------------------------------sigma_u | .56187781
sigma_e | 1.0392333
rho | .22619749
(fraction of variance due to u_i)
------------------------------------------------------------------------------
Regression gives high (within) R-Square of 85.22% and overall
R-Square of 84.48%
The t-stats are all very significant
25
WB Monthly: Correlation & Multicollinearity
| lnspread gdpper~a reserv~a extern~p
-------------+-----------------------------------lnspread |
1.0000
gdppercapita | -0.9044
1.0000
reservespe~a | -0.5648
0.5027
1.0000
externalde~p |
0.4019 -0.3509 -0.0904
1.0000
Variable Correlation:
Variable |
VIF
1/VIF
-------------+---------------------gdppercapita |
1.53
0.653220
reservespe~a |
1.35
0.738856
externalde~p |
1.15
0.866977
-------------+---------------------Mean VIF |
1.35
Multicollinearity:
Low correlation between
variables, the highest being
50.27%
A VIF (variance inflation
factor) lower than 10 denotes
low collinearity
26
WB Monthly: Partial Regression Plots
Partial Regression Plot does not reveal any unusual or influential observations.
The horizontal bars are because we have replicated the annual independent
variable data 12 times.
27
WB Monthly: Residual Plots
Test for Normality: There is a slight skew but overall residuals are normally
distributed
28
WB Monthly: Trading Strategy Methodology- 1
Methodology – 1 uses 1-month predictive model



In Sample Period: 1994 Jan – 2002 Dec
Out of Sample Period: 2003 Jan – 2004 Nov
Predictive Equation:



Sorted on the % Change expected during the next month





Ln(Spread) (1-month forward) =
8.090379 – 0.00029 * GDP Per Capita – 0.001111 * Reserves Per Capita
+ 0.580377 * External Debt as a % of GDP
% Change = (Next month forecasted spread - Current Spread) / Current Spread
If % Expected Change is negative, the decision is to buy the country’s debt
Conversely if % Expected Change is positive, the decision is to sell the country’s debt
Long Short Strategy: After sorting, selected the top quintile (five most negative %
Expected Change) to go long and bottom five quintile (most positive % Expected
Change) to go short
Long Only Strategy: After sorting, selected the top two quintiles (ten most negative
% Expected Change) to go long only
29
WB Monthly: Trading Strategy Results - 1
Trading Strategies Comparison
100%
90%
80%
% Accuracy
70%
60%
Long short
50%
Long Only
40%
Overall
30%
20%
10%
Month
Ju
l-0
4
Se
p04
No
v04
ay
-0
4
M
ar
-0
4
M
Ja
n04
Ju
l-0
3
Se
p03
No
v03
ay
-0
3
M
ar
-0
3
M
Ja
n03
0%
Month
Long short Long Only
Jan-03
30%
40%
Feb-03
70%
70%
Mar-03
50%
80%
Apr-03
60%
70%
May-03
50%
40%
Jun-03
60%
80%
Jul-03
20%
60%
Aug-03
60%
70%
Sep-03
60%
60%
Oct-03
50%
60%
Nov-03
40%
60%
Dec-03
60%
30%
Jan-04
60%
40%
Feb-04
40%
70%
Mar-04
50%
40%
Apr-04
50%
10%
May-04
40%
50%
Jun-04
60%
90%
Jul-04
50%
70%
Aug-04
50%
70%
Sep-04
70%
70%
Oct-04
50%
90%
Nov-04
70%
70%
Average
52%
60%
Overall
43%
65%
57%
64%
45%
65%
39%
65%
52%
57%
52%
43%
61%
52%
48%
35%
52%
65%
61%
39%
52%
61%
57%
54%
Overall Accuracy: The overall accuracy ranged from 39% to 66%, and the average was
54%
Long Short Result: The % Accuracy ranged from 20% to 70%, and the average was 52%.
In its current form, this strategy doesn’t show much promise
Long Only Result: The % Accuracy ranged from 30% to 90% and the average was 60%.
In 17 of the 23 projected months, the % Accuracy was 50% or higher. This is a potentially
viable trading strategy
30
WB Monthly: Trading Strategy Methodology- 2
Methodology – 2 uses 1-year predictive model



In Sample Period: 1994 Jan – 2001 Dec
Out of Sample Period: 2002 Jan – 2003 Dec
Predictive Equation:



Sorted on the % Change expected during the next year





Ln(Spread) (1-year forward) =
8.491300 – 0.000295 * GDP Per Capita – 0.001160 * Reserves Per Capita
% Change = (Next year forecasted spread - Current Spread) / Current Spread
If % Expected Change is negative, the decision is to buy the country’s debt
Conversely if % Expected Change is positive, the decision is to sell the country’s debt
Long Short Strategy: After sorting, selected the top quintile (five most negative %
Expected Change) to go long and bottom five quintile (most positive % Expected
Change) to go short
Long Only Strategy: After sorting, selected the top two quintiles (ten most negative
% Expected Change) to go long only
31
WB Monthly: Trading Strategy Results - 2
Trading Strategies Comparison
120%
% Accuracy
100%
80%
Long short
60%
Long Only
Overall
40%
20%
Month
Ju
l-0
4
Se
p04
No
v04
ay
-0
4
M
ar
-0
4
M
Ja
n04
Ju
l-0
3
Se
p03
No
v03
ay
-0
3
M
ar
-0
3
M
Ja
n03
0%
Month
Long short Long Only
Jan-02
70%
50%
Feb-02
60%
50%
Mar-02
70%
30%
Apr-02
50%
60%
May-02
80%
90%
Jun-02
70%
100%
Jul-02
70%
100%
Aug-02
60%
100%
Sep-02
60%
100%
Oct-02
50%
100%
Nov-02
50%
100%
Dec-02
50%
100%
Jan-03
50%
100%
Feb-03
50%
100%
Mar-03
50%
90%
Apr-03
60%
100%
May-03
70%
80%
Jun-03
40%
70%
Jul-03
60%
90%
Aug-03
60%
90%
Sep-03
60%
90%
Oct-03
70%
100%
Nov-03
70%
90%
Dec-03
60%
90%
Average
60%
86%
Overall
55%
55%
59%
64%
71%
77%
82%
77%
82%
68%
64%
64%
68%
64%
59%
64%
67%
55%
55%
64%
59%
77%
59%
50%
65%
Overall Accuracy: The overall accuracy ranged from 50% to 82%, and the average was 65%. It
was over 50% for all the months
Long Short Result: The % Accuracy ranged from 40% to 80%, and the average was 60%. In 23
of the 24 projected months, the % Accuracy was 50% or higher. This strategy can be explored
further
Long Only Result: The % Accuracy ranged from 30% to 100% and the average was 86%. In 23 of
the 24 projected months, the % Accuracy was 50% or higher. In 17 of the 24 months, the %
Accuracy was 90% - 100%. This strategy represents a viable trading strategy and needs to
be explored further
32
WB Monthly: Conclusions


Strong explanatory power of World Bank monthly
data. Gives us R-Sq in the 85% range
Trading strategy using 1-Year lag holds the most
promise


Long Short Trading Strategy shows some promise
Long only Trading Strategy represents a viable trading
strategy and needs to be explored further
 Need to explore reasons why the short side of the strategy
does not yield good results
33
Model 3: ICRG Monthly
ICRG Monthly Data
34
ICRG Country Ratings Overview

The International Country Risk Guide (ICRG) methodology

Developed in 1980, by the editors of the newsletter International Reports

Considers 22 total variables under 3 categories of risks – Economic,
Financial and Political with separate indices for each category
 Economic (50 Points)
 GDP/ Head, Real GDP growth, Inflation, Fiscal & Current Account
Deficit
 Financial (50 Points)
 % of Foreign Debt, Exchange Rate stability, Debt Service, Current
Account % of Exports
 Political (100 Points – Scale from 1-12)
 Government Stability, Socioeconomic Conditions, Corruption, Law &
Order

These 3 indices are used to produce a composite score out of 100
 80 – 100: Very Low Risk Countries
 0 – 49.5: Very High Risk Countries

The ICRG team generates current, 1 year and 5 year forecasts for each
risk category as well as for the 3 separate indices
35
ICRG Monthly: Regression Variables

Dependent Variable:


Independent Variable:




Ln(Spread)
GDP Per Head
Annual Inflation Rate
Foreign Debt as a Percent of GDP
We expect spreads to be:



Lower for higher GDP Per Head
Higher for higher Annual Inflation rates
Higher for higher Foreign Debt as a Percent of GDP
This was consistent with our regression results
36
ICRG Monthly: Variables Graph
Scatter plot of spread against independent variables shows that points with
Spreads = 1 ( Ln(Spread) =0 ) are outliers
Whether this is a problem will be confirmed if the Partial Regression Plots
shows similar influential points
37
ICRG Monthly: Regression Results
Fixed-effects (within) regression
Group variable (i): year
Number of obs
Number of groups
=
=
3910
13
R-sq:
Obs per group: min =
avg =
max =
4
300.8
436
within = 0.6467
between = 0.1186
overall = 0.6326
corr(u_i, Xb)
= -0.0196
F(3,3907)
Prob > F
=
=
748.37
0.0000
(Std. Err. adjusted for 13 clusters in year)
-----------------------------------------------------------------------------|
Robust
lnspread |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gdpheade | -.9568297
.0356213
-26.86
0.000
-1.034442
-.8792177
inflatione | -.3191931
.0336124
-9.50
0.000
-.3924281
-.245958
fordebtf | -.3175541
.0171611
-18.50
0.000
-.3549449
-.2801632
_cons |
11.32238
.2604515
43.47
0.000
10.75491
11.88986
-------------+---------------------------------------------------------------sigma_u | .61515417
sigma_e | 1.4789064
rho | .14749679
(fraction of variance due to u_i)
------------------------------------------------------------------------------
Regression gives a (within) R-Square of 64.67% and overall RSquare of 63.26%
The t-stats are all very significant
38
ICRG Monthly: Correlation & Multicollinearity
| lnspread gdpheade inflat~e fordebtf
-------------+-----------------------------------lnspread |
1.0000
gdpheade | -0.6985
1.0000
inflatione | -0.4944
0.2705
1.0000
fordebtf | -0.5241
0.3898
0.2962
1.0000
Variable Correlation:
Variable |
VIF
1/VIF
-------------+---------------------fordebtf |
1.24
0.808789
gdpheade |
1.22
0.821705
inflatione |
1.13
0.883881
-------------+---------------------Mean VIF |
1.19
Multicollinearity:
Low correlation between
variables
A VIF (variance inflation
factor) lower than 10 denotes
low collinearity
39
ICRG Monthly: Partial Regression Plots
Partial Regression Plot does not reveal any unusual or influential observations.
40
ICRG Monthly: Residual Plots
Test for Normality: The curve is doesn’t follow the normal curve too closely
especially in the first half. But broadly the residuals are normally distributed.
41
ICRG Monthly: Trading Strategy Methodology- 1
Methodology – 1 uses 1-month predictive model



In Sample Period: 1993 Dec – 2003 Dec
Out of Sample Period: 2004 Jan – 2005 Nov
Predictive Equation:



Sorted on the % Change expected during the next month





Ln(Spread) (1-month forward) =
11.05438 – 0.94991 * GDP Per Head Rating – 0.33263 * Annual Inflation Rate Rating
- 0.30559 * Foreign Debt as a % of GDP Rating
% Change = (Next month forecasted spread - Current Spread) / Current Spread
If % Expected Change is negative, the decision is to buy the country’s debt
Conversely if % Expected Change is positive, the decision is to sell the country’s debt
Long Short Strategy: After sorting, selected the top quintile (five most negative %
Expected Change) to go long and bottom five quintile (most positive % Expected
Change) to go short
Long Only Strategy: After sorting, selected the top two quintiles (ten most negative
% Expected Change) to go long only
42
ICRG Monthly: Trading Strategy Results - 1
Trading Strategies Comparison
120%
% Accuracy
100%
80%
Long Short
60%
Long Only
Overall
40%
20%
Month
Ju
l-0
5
Se
p05
No
v05
ay
-0
5
M
ar
-0
5
M
Ja
n05
Ju
l-0
4
Se
p04
No
v04
ay
-0
4
M
ar
-0
4
M
Ja
n04
0%
Month
Long Short Long Only
Jan-04
70%
40%
Feb-04
70%
100%
Mar-04
40%
30%
Apr-04
40%
10%
May-04
30%
60%
Jun-04
60%
90%
Jul-04
50%
80%
Aug-04
20%
70%
Sep-04
50%
70%
Oct-04
50%
100%
Nov-04
70%
70%
Dec-04
30%
60%
Jan-05
60%
90%
Feb-05
50%
0%
Mar-05
60%
30%
Apr-05
40%
60%
May-05
40%
80%
Jun-05
50%
80%
Jul-05
50%
50%
Aug-05
60%
100%
Sep-05
40%
20%
Oct-05
40%
50%
Nov-05
70%
30%
Average
50%
60%
Overall
61%
64%
32%
36%
50%
64%
75%
54%
71%
64%
68%
50%
43%
54%
48%
56%
59%
59%
61%
57%
36%
50%
50%
55%
Overall Accuracy: The overall accuracy ranged from 32% to 75%, and the average was 55%
Long Short Result: The % Accuracy ranged from 20% to 70%, and the average was 50%. In 14
of the 23 projected months, the % Accuracy was above 50%. We cannot use this strategy in its
current form
Long Only Result: The % Accuracy ranged from 0% to 100% and the average was 60%. In 16
of the 23 projected months, the % Accuracy was 50% or higher. This is a potentially viable
trading strategy but would have to be refined further
43
ICRG Monthly: Trading Strategy Methodology- 2
Methodology – 2 uses 1-year predictive model



In Sample Period: 1993 Dec – 2002 Dec
Out of Sample Period: 2003 Jan – 2004 Dec
Predictive Equation:



Sorted on the % Change expected during the next year





Ln(Spread) (1-year forward) =
11.10612 – 0.97069 * GDP Per Head Rating – 0.28848 * Annual Inflation Rate Rating
- 0.31489 * Foreign Debt as a % of GDP Rating
% Change = (Next year forecasted spread - Current Spread) / Current Spread
If % Expected Change is negative, the decision is to buy the country’s debt
Conversely if % Expected Change is positive, the decision is to sell the country’s debt
Long Short Strategy: After sorting, selected the top quintile (five most negative %
Expected Change) to go long and bottom five quintile (most positive % Expected
Change) to go short
Long Only Strategy: After sorting, selected the top two quintiles (ten most negative
% Expected Change) to go long only
44
ICRG Monthly: Trading Strategy Results - 2
Trading Strategies Comparison
120%
% Accuracy
100%
80%
Long Short
60%
Long Only
Overall
40%
20%
Month
Ju
l-0
4
Se
p04
No
v04
ay
-0
4
M
ar
-0
4
M
Ja
n04
Ju
l-0
3
Se
p03
No
v03
ay
-0
3
M
ar
-0
3
M
Ja
n03
0%
Month
Long Short Long Only
Jan-03
50%
90%
Feb-03
50%
90%
Mar-03
60%
90%
Apr-03
40%
90%
May-03
50%
80%
Jun-03
40%
70%
Jul-03
50%
90%
Aug-03
70%
70%
Sep-03
50%
70%
Oct-03
60%
90%
Nov-03
80%
100%
Dec-03
60%
100%
Jan-04
70%
90%
Feb-04
70%
100%
Mar-04
40%
80%
Apr-04
80%
90%
May-04
40%
90%
Jun-04
50%
100%
Jul-04
60%
100%
Aug-04
50%
90%
Sep-04
60%
100%
Oct-04
40%
90%
Nov-04
50%
80%
Average
55%
89%
Overall
57%
54%
61%
61%
52%
50%
64%
57%
57%
54%
64%
57%
54%
54%
50%
67%
52%
59%
64%
57%
54%
43%
43%
56%
Overall Accuracy: The overall accuracy ranged from 43% to 67%, and the average was 56%.
Long Short Result: The % Accuracy ranged from 40% to 80%, and the average was 55%. In 19
of the 24 projected months, the % Accuracy was 50% or higher. This strategy can be explored
further
Long Only Result: The % Accuracy ranged from 70% to 100% and the average was 89%. This
result represents a viable trading strategy and needs to be explored further
45
ICRG Monthly: Conclusions


Explanatory power of ICRG data was surprising
below that of the World Bank monthly data. This
gave us R-Sq in the 63% range compared to 85%
range for WB data
Trading strategy using 1-Year lag holds the most
promise


Long Short Trading Strategy shows some promise with 19
of the 24 months having over 50% accuracy
Long only Trading Strategy represents a viable trading
strategy but needs to be explored further
 Need to explore reasons why the short side of the strategy
does not yield good results
46
Model 4: Lagged Spreads
Monthly Spreads Regressed on 1-Month
Lagged Spreads
47
Lagged Spreads: Regression Variables



Dependent Variable:
 Ln(Spread)
Independent Variable:
 Ln(Spread) 1-Month Lag
 Foreign Debt Service as a Percent of Exports of Goods and
Services
We expect spreads to be:
 Higher if the previous month spread was high due to a
momentum effect.
 Higher for higher Foreign Debt Service ratio. A high ratio shows
that a country may default in the near future.
This was consistent with our regression results
48
Lagged Spreads: Variables Graph
The Graph above shows extremely high correlation of the lagged spreads
with the current spreads
49
Lagged Spreads: Regression Results
Fixed-effects (within) regression
Group variable (i): year
Number of obs
Number of groups
=
=
3869
12
R-sq:
Obs per group: min =
avg =
max =
67
322.4
435
within = 0.9693
between = 0.9903
overall = 0.9696
corr(u_i, Xb)
= -0.0290
F(2,3867)
Prob > F
=
=
38349.32
0.0000
(Std. Err. adjusted for 12 clusters in year)
-----------------------------------------------------------------------------|
Robust
lnspread |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------lnspread1m~g |
.9672258
.0134447
71.94
0.000
.9376343
.9968173
debtservf | -.0429803
.0203903
-2.11
0.059
-.087859
.0018984
_cons |
.5121753
.2146835
2.39
0.036
.03966
.9846905
-------------+---------------------------------------------------------------sigma_u | .07105769
sigma_e | .43487784
rho | .02600427
(fraction of variance due to u_i)
------------------------------------------------------------------------------
Regression gives extremely high (within) R-Square of 96.93%
and overall R-Square of 96.96%
The t-stats are all significant
50
Lagged Spreads: Correlation & Multicollinearity
| lnspread lnspre~g debtse~f
-------------+--------------------------lnspread |
1.0000
lnspread1m~g |
0.9841
1.0000
debtservf | -0.0967 -0.0646
1.0000
Variable Correlation:
Variable |
VIF
1/VIF
-------------+---------------------debtservf |
1.00
0.995828
lnspread1m~g |
1.00
0.995828
-------------+---------------------Mean VIF |
1.00
Multicollinearity:
Low correlation between
variables
A VIF (variance inflation
factor) lower than 10 denotes
low collinearity
51
Lagged Spreads: Partial Regression Plots
Partial Regression Plot shows the extremely high correlation between lagged
and current spreads.
52
Lagged Spreads: Residual Plots
Test for Normality: The curve is doesn’t follow the normal curve throughout
the curve and this shows a potential problem with this model
53
Lagged Spreads: Trading Strategy Methodology



In Sample Period: 1993 Dec – 2003 Dec
Out of Sample Period: 2004 Jan – 2005 Nov
Predictive Equation:

Ln(Spread) (1-month forward) =

-0.40302 + 1.00144 * Ln(Spread) + 0.04623 * Debt Service Ratio Rating
(The positive sign for the Debt Service Ratio coefficient on the in sample regression came as
a surprise especially because the sign was negative in the explanatory model. This
despite the low variable correlation and high t-stats. We reasoned that if a country has a
high Debt Service ratio today, it might be making efforts to reduce its debt which would
lead to fall in future spreads)





Sorted on the % Change expected during the next month

% Change = (Next month forecasted spread - Current Spread) / Current Spread
If % Expected Change is negative, the decision is to buy the country’s debt
Conversely if % Expected Change is positive, the decision is to sell the country’s debt
Long Short Strategy: After sorting, selected the top quintile (five most negative %
Expected Change) to go long and bottom five quintile (most positive % Expected
Change) to go short
Long Only Strategy: After sorting, selected the top two quintiles (ten most negative %
Expected Change) to go long only
54
Lagged Spreads: Trading Strategy Results
Trading Strategies Comparison
120%
% Accuracy
100%
80%
Long Short
60%
Long Only
Overall
40%
20%
05
ov
-
5
N
Se
p0
l-0
5
Ju
ay
-0
5
M
ar
-0
5
5
Month
M
n0
Ja
04
4
ov
N
Se
p0
l-0
4
Ju
ay
-0
4
M
ar
-0
4
M
Ja
n0
4
0%
Month
Long Short Long Only
Jan-04
70%
40%
Feb-04
60%
90%
Mar-04
40%
10%
Apr-04
40%
10%
May-04
60%
70%
Jun-04
60%
100%
Jul-04
40%
60%
Aug-04
40%
60%
Sep-04
40%
60%
Oct-04
50%
100%
Nov-04
70%
80%
Dec-04
0%
30%
Jan-05
70%
100%
Feb-05
50%
0%
Mar-05
40%
10%
Apr-05
50%
80%
May-05
40%
80%
Jun-05
60%
100%
Jul-05
50%
40%
Aug-05
50%
90%
Sep-05
10%
0%
Oct-05
80%
60%
Nov-05
80%
40%
Average
50%
57%
Overall
39%
54%
32%
43%
61%
75%
54%
46%
39%
57%
68%
32%
61%
46%
41%
48%
48%
59%
43%
50%
32%
50%
64%
50%
Overall Accuracy: The overall accuracy ranged from 32% to 75%, and the average was 50%
Long Short Result: The % Accuracy ranged from 0% to 80%, and the average was 50%. In 14
of the 23 projected months, the % Accuracy was 50% or higher. We cannot use this strategy in
its current form
Long Only Result: The % Accuracy ranged from 0% to 100% and the average was 57%. In 14
of the 23 projected months, the % Accuracy was 50% or higher. Again we cannot use this
strategy in its current form
55
Lagged Spreads: Conclusions



Explanatory power of ICRG data was
surprisingly high with R-Sq over 96% much
higher than all other models
However the residuals did not show a normal
distribution – there might be potential
problems with the model
Trading strategy using 1-month lag did not
show much promise. The results lacked any
consistency
56
Momentum Strategy



Given the high correlation of lagged monthly spread with current
spread, we felt that a momentum strategy should work well
We calculated the monthly change in spread (CAGR) for last 6
months, 3 months and 1 month
If the current month CAGRs are higher than previous month
CAGRs in at least two of the above three calculations, then the
decision is to sell, else buy

Result: The percent accuracy of this strategy (after ignoring all
the records with spreads = 1) was 49%. This was well below
what we would have liked in a good trading strategy

Conclusion: There is promise in this strategy but it needs to be
explored further
57
Issues to be Explored Further




Despite attempting several trading strategies it was
difficult to come up with a convincing one
The short side of the Long-short strategy was
especially poor and would need to be explore
Our OOS was typically for periods in 2003 and 2004
when the economy was coming out of a recession.
The results may be skewed because of this effect.
We should test the strategy in more “normal” periods
Finally, the momentum strategy seems to hold
promise and needs be explored
58
Bibliography





Ferguson, Niall. “The Cash Nexus – Money and Power in the Modern World
1700-2000. Penguin Books. Chapters 4 – 8
Ferguson, Niall. “Paper & Iron – Hamburg business and German politics in
the era of inflation, 1897 – 1927”
The International Country Risk Guide,
http://www.icrgonline.com/page.aspx?page=icrgmethods#Background_of_t
he_ICRG_Rating_System
The University of California, Los Angeles. “Resources to help you learn and
use Stata”
http://www.ats.ucla.edu/stat/stata/
The World Bank, International Monetary Fund and World Markets Research
Center websites.
59