Understanding greek government bond spreads

Understanding Greek Government Bond Spreads:
A different perspective
Ilias Lekkos lekkosi@piraeusbank.gr
Irini Staggel staggelir@piraeusbank.gr
Haris Giannakidis giannakidisch@piraeusbank.gr
Economic Research & Investment Strategy
June 2017

3
Introduction & Motivation
Methodology: Quantile Regression (QR) Analysis
GGB Spreads QR Model
Greek Bond Market Misalignment Index
Value at Risk & Balance of Risks
GGB Spreads Scenario

4
Since the outbreak of the Great Financial Crisis in 2008 and especially after the escalation of the Greek Crisis in
2010, both academics and investment analysts have devoted a lot of effort in exploring the behavior of Greek as
well as other periphery economies bond rates vs their corresponding German yields. In what follows we revisit
the issue of the behavior, pricing and risk management of Greek Government Bonds (GGBs) spreads and try to
address a number of unresolved issues.
The additional contribution we make to the already available body of research is twofold:
First, we estimate our models using the quantile regression (QR) methodology instead of the usual OLS
method. QR offers a number of advantages vs more traditional methods, the most important of which is
that it allows different factors with varying degree of sensitivity on the likely outcomes of Greek Bond
spreads. This is of particular significance in the Greek case as spreads vary from a low of 9 bps to a high of
3313 bps.
Second, in defining the explanatory variables we follow what we call the “FX Analogy”, which simply means
that all explanatory variables enter our model as “spreads” or “ratios” to their corresponding German ones.
Source: Piraeus Bank Research

Aim of the Study
5
The aim of our research is to enhance our understanding on the fundamental behavior of the Greek
Government Bond Spreads both before and after the Greek economic crisis. We do that by trying to
address the following issues:
 Which are the fundamental drivers of GGB spreads?
 Is this set of driving factors constant or it varies relative to the situation in the Greek bond
market and the size of the spreads?
 Even for factors that are significant across the spreads distribution do they maintain a
constant influence (constant betas) on the spreads or this varies as well?
 Are Greek Government Bonds fairly valued given the levels of their fundamentals?
 Are the risks around their fair value always symmetric or can we identify periods of positive
(i.e. increased probability for narrower spreads) and negative (i.e. increased probability for
wider spreads) risks?
 Can we use the enhanced flexibility of the model for risk management purposes? That is, can
we produce more accurate Value-at-Risk analysis for GGB spreads?
 Finally, can we employ our model to explore the behavior of GGB spreads under various
macroeconomic scenarios?

Summary of Key Findings
• According to our analysis, the main drivers of GGB spreads are the gap in economic
performance and competitiveness between the Greek and German economies as well as the
widening differences in the debt-to-GDP ratios. In addition, contagion from movements in
other Peripheral bond markets is evident, especially in periods of widespread market stress.
• Our model also reveals substantial variation on the magnitude and direction (sign) of the
influence of the above mentioned factors. Economic Activity and Competitiveness have a
significant impact in a regime of medium and low levels of conditional spreads while Fiscal
Sustainability and especially Periphery Risk are of increased importance in a regime of high
conditional spreads.
• At the current juncture, Greek Bonds are, by and large, aligned to their fundamentals but our
Misalignment Index is able to identify periods of substantial divergences between spreads and
their fundamentals while the Balance of Risk Indicator highlights periods of highly skewed risks.
• Finally, we demonstrate how our methodology can be utilized to produce more accurate value-
at-risk estimates as well as forecasts of the distribution of GGB spreads under various
economic scenarios.
6Source: Piraeus Bank Research

7

Methodology I
Quantile Regressions (QR): Looking Beyond Average Estimates
8
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
-3.84
-3.38
-2.92
-2.46
-2.00
-1.54
-1.08
-0.62
-0.16
0.30
0.76
1.22
1.68
2.14
2.60
3.06
3.52
3.98
4.44
4.90
right tail
center
left shoulder
left tail
right shoulder
Q25Q5 Q75 Q95Q50
Conditional Probability Density
Quantile Regression is a statistical methodology that models the distribution of the response variable conditional on observed
underlying factors. Specifically, we relate each specific part of the distribution of responses (using quantile functions) to a
number of explanatory variables. Consequently, we are able to produce estimates or projections for the location, dispersion
and shape of the variable’s distribution.
As in standard regression models, quantile regression enable us to analyze the impact of a number of economic variables on
the response variable, the difference being that their influence is not restricted only on the center of the spread distribution but
also on other parts such as its tails and shoulders.
𝑸 𝝉 𝒚 𝒕 𝒙 𝒕 = 𝒂 𝝉 + 𝜷 𝝉 ∗ 𝒙 𝒕
𝒇𝒐𝒓 𝒆𝒂𝒄𝒉 𝒒𝒖𝒂𝒏𝒕𝒊𝒍𝒆 𝝉 = 𝟓𝒕𝒉 , 𝟏𝟎𝒕𝒉, … , 𝟗𝟓𝒕𝒉

Methodology II: QR Advantages over the OLS framework
9
Even though quantile regression does not differ from standard methods in the fact that they use past
information to capture a relation that is assumed to hold in the future, the method does offer a number of
advantages relative to the more standard least squares regression:
 The most important is robustness with respect to extreme outcomes (outliers) observed in the past
that are unlikely to occur in the future at least in a medium term horizon.
 Quantile regression may result to a more rational asymmetric estimate for the spread distribution
compared to the standard normal distribution that is assumed by least squares which is restricted by
the fact that under OLS, “adverse” outcomes are equally likely to “good” outcomes.
 We are able to categorize underlying factors with respect to their influence on each individual
quantile of the spread distribution. For example, we can specify those factors that are associated
strongly with the dispersion of the distribution and those that drive its center or tails. In that way we
can isolate the information that is relevant to the indented use of the model e.g. risk management or
sensitivity under stress scenarios.
 By construction, quantile regression provides a natural ranking of the fitted outcomes for the variable
of interest. For example, the median corresponds to the value for bond spread where there is a 50%
probability to observe a higher or a lower outcome. Similarly, the 25th quartile indicates the value for
bond spread where there is a 25% probability to observe lower and 75% probability to observe
higher values.
 QR allows us to construct indices that benefit from improved information extracted from the whole
conditional distribution of bond spreads.

Methodology III: QR Model on GGB Spreads
10
Therefore, by estimating the conditional quantile functions of Greek Bond Spreads we effectively model their
whole distribution as a function of several economic state variables. Consequently, by using the QR method we
are able to determine location shifts, changes in uncertainty or changes in market stress for all possible spread
outcomes.
Quantile regression provides a more complete picture for Greek spreads dynamics than would be offered by a
simple average estimate. For example, if we want to investigate what is the impact of an increase in the debt to
GDP differential on the probability of a large surge in spreads, then standard regression methodologies are of
little use.

Modelling GGB Spreads: “The FX Analogy”
11
Most studies try to analyze sovereign spreads as a function of a number of variables (fiscal, growth, etc) that characterize
the economy under examination.
Here we follow a slightly more nuanced approach following what we call the “FX Analogy”. Following a line of reasoning
similar the one used when modelling FX rates, we view all explanatory variables as relative to the benchmark i.e. all
variables are viewed relative to their German counterparts.
Our final model is based on 4 factors, chosen according to qualitative, statistical fit and model parsimony criteria.
The explanatory variables cover the four main areas of: Economic Activity, Competitiveness, Periphery Risk and Fiscal
Sustainability.
Greek Bond
Spreads
Economic Activity
Greece Vs Germany
Competitiveness
Greece Vs Germany
EA Periphery Risk
EA Periphery Vs Germany
Fiscal Sustainability
Greece Vs Germany

10-Year Bond Yields
Greece vs Germany (%)
Greek 10-Year Spread vs Germany (bps)
GGB Spreads: Historical Evolution
12
-5
0
5
10
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25
30
35
40
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Oct-00
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Germany 10Y Yield Greece 10Y Yield
0
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2000
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3500
Jan-00
Oct-00
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German 10-Year yield has gradually decreased from 5.54% in January 2000 to 0.32% in April 2017.
In contrast Greek 10-Year bond yields surged after 2009 recording remarkably high levels in February 2012, due to PSI.
Afterwards yields declined gradually reaching 5.82% in August 2014. In 2014, Greece returned to the bond markets after a
4-year period, in April with a 5-year bond and in July with a 3-year bond.
However, political risk during 2015 that culminated to the June referendum, drove 10-Year Greek bond yields to levels
higher than 30%. After a period of delays in the first and second review of the 3rd Economic Adjustment Programme, the
Greek economic environment showed signs of an apparent stabilization that led Greek 10-Year bond yield to reach 6.34% in
April 2017.

The 4 factors that drive GGB spreads
13
(1) Our analysis was performed on a wider range of relative variables (such as HICP in constant tax, economic sentiment indicator, the proportion of the
banking sector capitalization relative to the aggregate market capitalization etc).
Based on our econometric model (1), the evolution of GGB Spreads (GGB_SPRDS) depends on:
 The Relative Economic Activity Indicator calculated as the difference between the logarithms of Real GDP
volume index (2010 =100) of Greece versus Germany (REL_ECACT).
 The Relative Cost Competitiveness Indicator, calculated by comparing the real trade weighted exchange
rates between Greece and Germany and defined as the difference between the logarithms of Real
Effective Exchange Rate of Greece versus Germany (REL_CCOMP)
 The EA Periphery Risk Indicator, calculated as the average 10-Year bond spreads for Italy, Spain, Portugal
and Ireland versus German 10-Year bond Yield. (EAPER_RISK)
 The Relative Fiscal Sustainability Indicator, calculated as the ratio of Debt to GDP of Greece versus Debt to
GDP of Germany (REL_FSCI)
The data are collected from key databases such as Bloomberg, Oxford Economics, BIS and Eurostat.
Wherever is appropriate, the variables are adjusted for seasonality.
Data are monthly and cover the period January 2000 – April 2017.
In the case that only quarterly data are available (i.e. Real GDP, Debt to GDP), whereas the prerequisite is monthly data,
then temporal disaggregation under a cubic spline is used as an interpolation technique.

Real GDP Volume Index (2010=100)
Greece vs Germany
Relative Economic Activity Indicator
(REL_ECACT)
1st Factor: Relative Economic Activity Indicator
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78
83
88
93
98
103
108
113
118
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Germany RGDP Index Greece RGDP Index
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Clearly, the Global Financial Crisis had a large impact in the economic growth of Germany, as in mid-2009 real GDP
declined by 7% on a YoY basis. However, the German economy returned to a sustainable growth path in late 2009.
On the other hand, Greek real GDP has deteriorated since 2009. During 2009 -2016 real GDP declined by 23%.
Consequently, the Relative Economic Activity Indicator plunged almost 4 times relative to its historical high recorded
in August 2009.

Real Effective Exchange Rates
Greece vs Germany
Relative Cost Competitiveness Indicator
(REL_CCOMP)
78
83
88
93
98
103
108
113
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Greece REER SA Germany REER SA
-0.20
-0.15
-0.10
-0.05
0.00
0.05
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Deterioration of
Greek
competitiveness vs
Germany
Improvement of
Greek
competitiveness vs
Germany
2nd Factor: Relative Cost Competitiveness Indicator
An increase in the real effective exchange rate implies that the economy is losing in terms of competitive advantage relative
to its external trade counterparts.
The relative competitiveness indicator deteriorated from 2000 to mid -2012. Since then the competitiveness of the Greek
economy relative to the German one has been gradually improving. In April 2017, the real effective exchange rate in Greece
decreased by 11.7% (improvement in competitiveness) relative to its highest levels (May 2011), when at the same period
the respective index in German declined by 6% (improvement in competitiveness). As a result, in April 2017 the Relative
Cost Competitiveness Indicator returned to levels observed at end - 2009.

Periphery 10Y Bond Yields
vs Germany Benchmark
EA Periphery Risk Indicator
(EAPER_RISK)
3rd Factor: EA Periphery Risk Indicator
16
We constructed the EA Periphery Risk Indicator, in order to map the average 10-Year bond spread of selected Euroarea periphery
countries (Portugal, Italy, Spain and Ireland) versus Germany and consequently the impact of peer countries on the Greek bond market.
Due to similar structural features underlying the economies of Euroarea periphery as well as the common sensitivity of these countries
to fiscal shocks, such as the EU debt crisis, there is a strong positive cross-correlation. The average periphery bond spreads were
materially low during 2000-2007, but increased dramatically after the global financial crisis and reached a peak in early 2012.
Bond spreads especially for Portugal and Ireland skyrocketed during the period 2011- 2012 when they were forced to agree to their
Economic Adjustment Programmes, signed in May 2011 and December 2010 respectively. However, the successful implementation of
the MoU’s in Portugal and Ireland and the ECB accommodative monetary policy, reduced the pressure and led to a gradual decline in
periphery bond spreads. Still, it is clear that this specific risk factor remains elevated compared to the pre-financial crisis period and has
recorded an upward trend in 2016 and the beginning of 2017.
0
100
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Oct-00
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0
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18
Jan-00
Oct-00
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Apr-02
Jan-03
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Apr-05
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Oct-09
Jul-10
Apr-11
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Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Germany 10Y Yield Portugal 10Y Yield Italy 10Y Yield
Spain 10Y Yield Ireland 10Y Yield

Debt to GDP ratio (%)
Greece vs Germany
Relative Fiscal Sustainability Indicator
(REL_FSCI)
4th Factor: Relative Fiscal Sustainability Indicator
40
60
80
100
120
140
160
180
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
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Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Greece Debt to GDP Germany Debt to GDP
1.00
1.20
1.40
1.60
1.80
2.00
2.20
2.40
2.60
2.80
3.00
3.20
3.40
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
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Oct-09
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Jul-16
Apr-17
Fiscal sustainability usually plays an important role on the medium term fluctuations of financial variables since it is
related with the aggregate expectations for the country’s financing prospects.
In Greece, during the public debt crisis, the debt to GDP ratio surged, surpassing 170% in 2011. In sharp contrast, the
conservative fiscal policy of the German government and the low debt refinancing rates contributed to the downward
trend of the respective debt to GDP ratio after 2009. Consequently, the debt to GDP ratio in Germany decreased by more
than 10 ppts since 2009, while the respective Greek ratio increased by more than 50 ppts.
Following this trend, the Relative Fiscal Sustainability Indicator increased exponentially since 2007, reaching its highest
level in April 2017.

18

Econometric Specification of the GGB spreads QR Model
19
Optimal Specification of the GGB Spread Model
𝑄 𝜏(𝐺𝐺𝐵𝑠𝑝𝑟𝑑𝑠 𝑡
) = 𝛽0,𝜏 + 𝛽1,𝜏 𝑅𝐸𝐿_𝐸𝐶𝐴𝐶𝑇𝑡 + 𝛽2,𝜏 𝑅𝐸𝐿_C𝐶𝑂𝑀𝑃𝑡 + 𝛽3,𝜏 𝐸𝐴𝑃𝐸𝑅_𝑅𝐼𝑆𝐾𝑡 + 𝛽4,𝜏 𝑅𝐸𝐿_𝐹𝑆𝐶𝐿 𝑡
𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑞𝑢𝑎𝑛𝑡𝑖𝑙𝑒 𝜏 = 5𝑡ℎ , 10𝑡ℎ, … , 95𝑡ℎ
Where, REL_ECACT: Relative Economic Activity Indicator
REL_CCOMP: Relative Cost Competitiveness Indicator
EAPER_RISK: EA Periphery Risk Indicator
REL_FSCI: Relative Fiscal Sustainability Indicator

The Relative Economic Activity Indicator drives Upside Risk
20
The Relative Economic Activity Indicator (REL_ECACT) is
statistically significant (at a 5% confidence level) only in the
left tail of the spread distribution.
The regression coefficients (beta) have the expected sign,
stating a negative correlation between the Greek-German
economic activity gap and spreads. However, it tends to vary
across the different parts of spreads distribution.
In contrast to the OLS method, where the coefficient (beta) is
constant, under the QR method we observe a stronger
impact as we move towards the left tail of the spread
distribution.
In other words, the Relative Economic Activity Indicator is
more strongly associated with the probability of an
unexpected decline in Greek spreads (upside risk).
For example, an increase in the real GDP index in Greece
compared to the real GDP index in Germany, not only
implies a downward location shift for the spread
distribution but also a higher probability in observing lower
spreads.
0.2 0.4 0.6 0.8
3005007009
0.2 0.4 0.6 0.8
200400600800
0.2 0.4 0.6 0.8
-50050100150
reer
0.2 0.4 0.6 0.8
-600-2000200
rgdp
-4000200400
debt_gdp
Spread Distribution Quantiles
BetaCoefficient
The solid red line indicates the beta estimate under OLS while the dash-dot black line
denotes the QR estimate. The area between the dashed red lines denote 95% confidence
intervals for OLS while the grey shaded area indicates the respective confidence interval for
QR beta estimates.
Relative Economic Activity Indicator
(beta coefficient in each Spread Distribution Quantiles)

Competitiveness determines Upside Potential
21
Similarly, the Relative Cost Competitiveness Indicator
(REL_CCOST) is also statistically significant (at a 5%
confidence level) at the left tail of the spread distribution.
As expected, the sign of the coefficient (beta) indicates a
positive correlation between cost competitiveness and
bond spreads, i.e a decrease in the indicator
(improvement of Greece’s relative competitiveness)
increases the probability for a decline in Greek bond
spreads.
Notably, the OLS and QR estimation methods produce
quite different beta estimates as the two methods
produce betas with opposite signs.
0.2 0.4 0.6 0.8
300500700
200400600
0.2 0.4 0.6 0.8
-50050100150
reer
-600-2000200
0.2 0.4 0.6 0.8
-4000200400
debt_gdp
BetaCoefficient
QR beta estimates.
Relative Cost Competitiveness Indicator

EA Periphery Risk is strongly associated with a Downside Risk
22
0.2 0.4 0.6 0.8
300500700900
(Intercept)
0.2 0.4 0.6 0.8
200400600800
periphery
050100150
reer
-2000200
rgdp
BetaCoefficient
intervals for Ols while the grey shaded area indicates the respective confidence interval for
QR beta estimates.
The Relative EA Periphery Risk Indicator (EAPER_RISK) is
statistically significant (at a 5% confidence level) throughout
the whole spread distribution.
The regression coefficient (beta) states a positive correlation
between EA periphery risk and spreads. However, in
contrast, with the OLS method, where the coefficient (beta)
is constant, under the QR method we observe a gradually
increasing impact as we move from the left to the right tail of
the distribution.
In other words, the Relative EA Periphery Risk is likely more
strongly associated with the probability of an unexpected
increase in Greek spreads (downside risk).
Consequently, an increase in the periphery risk indicator not
only implies an upward location shift for the spread
distribution but also a higher probability in observing higher
GGB spreads.
Relative EA Periphery Risk Indicator

The Relative Fiscal Sustainability is related with spread volatility
0.2 0.4 0.6 0.8
-50050100150
reer
-600-2000200
0.2 0.4 0.6 0.8
-4000200400
debt_gdp
BetaCoefficient
QR beta estimates.
The Relative Fiscal Sustainability Indicator (REL_FSCI) is
statistically significant (at a 10% confidence level) only at the
left tail of the spread distribution.
In the case of the relative Fiscal Sustainability, the sign of
the coefficient (beta) varies across quantiles, as it declares a
positive correlation between debt ratio and bond spreads
in the right tail of the spread distribution and a negative
correlation in the left tail.
This reversal of the impact across quantiles is related to the
Greek bond spread volatility. Specifically, an increase in
Relative Fiscal Sustainability (worsening in Greek debt ratio
compared to Germany) leads to a wider spread distribution
and therefore higher uncertainty about the actual bond
spread outcome.
Relative Fiscal Sustainability Indicator

Impact on Government Spreads from a 10% increase in each indicator
24
1. An improvement of the Greek GDP growth and competitiveness relative to Germany compresses GGB spreads almost at a uniform
manner across the distribution of spreads but the coefficients are statistically significant only at the low-end of the conditional spreads
distribution.
2. The high impact of the Periphery Risk provides concrete evidence of contagion or systemic risk between EA periphery bond markets
and economies especially on the high-end of the spread distribution.
3. Increases in borrowing designate as more likely both the event of lower and higher bond spreads. This somewhat counterintuitive
outcome indicates an increase in uncertainty about spreads possibly due to the fact that at different periods an increase in debt can
either be perceived as a sign of unexpected growth or as an episode associated with the difficulty of providing sustainable
disbursement of a funding tranche. In any case our model indicates that debt increases are associated with a more volatile path for
Greek bond spreads.
Impact on Greek Government Spread from a 10% increase in …
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Q45
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Q55
Q60
Q65
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Q75
Q80
Q85
Q90
Q95
OLS
impact (in basis points)
Relative Cost Competitiveness
(Real Effective Exchange Rate)
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-10 0 10 20 30 40 50 60 70
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Q95
Least…
EA Periphery Risk
(Periphery 10Y Spreads)
-5
-6
-6
-7
-7
-7
-7
-6
-5
-5
-5
-5
-3
-3
-3
-2
-1
-4
-11
-1
-15 -10 -5 0 5 10
Q5
Q10
Q15
Q20
Q25
Q30
Q35
Q40
Q45
Q50
Q55
Q60
Q65
Q70
Q75
Q80
Q85
Q90
Q95
Least Squares
ConditionalQuantiles
Relative Economic Activity
(Real GDP)
-10
-17
-22
-32
-31
-27
-27
-19
-13
-4
4
19
29
32
32
37
52
50
-25
39
-50 -30 -10 10 30 50 70
Q5
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Q95
Least Squares
Relative Fiscal Sustainability
(Debt to GDP)

The added flexibility of our model allows us not only to assess its ability (solid blue line) to track the actual evolution of GGB
spreads (solid red line) over the period 2000-2017 but also to capture periods of substantial misalignments in the GGB market.
By plotting the area in which there is a 50% chance (90% chance) of occurrence of GGB spreads based on their fundamentals, we
can visually identify periods when actual spreads deviated substantially from their fundamentals.
Apparently in the run-up to the Greek crisis in 2008-2009 Greek bonds were substantially overpriced (spreads too low vs model
estimate) as well as in the period around 2013-2014.
On the contrary, extreme uncertainty led to record undervaluation of GGBs (spreads too high) in 2011-2012 as well as around
mid-2015. In all cases, prices corrected by returning into the middle of their theoretical range after a while.
25
GGB Spreads: A Fundamental based “Fair” Value
-500
0
500
1000
1500
2000
2500
3000
3500
4000
Jan-06
May-06
Sep-06
Jan-07
May-07
Sep-07
Jan-08
May-08
Sep-08
Jan-09
May-09
Sep-09
Jan-10
May-10
Sep-10
Jan-11
May-11
Sep-11
Jan-12
May-12
Sep-12
Jan-13
May-13
Sep-13
Jan-14
May-14
Sep-14
Jan-15
May-15
Sep-15
Jan-16
May-16
Sep-16
Jan-17
90% Confidence
Bounds
50% Confidence
Bounds
Median
Estimate
Actual vs Fitted Spread Distribution

26

27
-50
-40
-30
-20
-10
0
10
20
30
40
50
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Market Complacency
Market Stress
Greek bond market was under
severe stress during 2011-2012
with market valuations very close
to the upper bound
Market stress resumed
after 2014, but gradually
converged to more “fair”
market valuations.
Greek bond market was
characterized by relative
complacency in April
2017, with a tendency to
overvalue Greek bonds.
However index boundary
levels suggest that bond
markets remain anchored
near the “fair” value
range implied by the
model.
We construct the Greek Bond Market Misalignment Index which depicts the past evolution as well as the current level of
market assessment about the Greek Bond Market. By construction the index is bounded between 50 and -50 points. When the
index moves close to its 50 ceiling (i.e. market valuations start to deviate positively from the model’s “fair” value) the Greek
bond market is under stress and Greek government bonds are undervalued. In contrast, negative values suggest that the bond
market overvalues Greek government bonds probably due to greater confidence about the Greek economy future prospects.

28

Value at Risk and GGBs
29
Value at Risk (VaR) estimates are perhaps the key input in the risk management decision process. Since this measure relates to the
odds of an extreme surge in bond spreads it can be considered as a worst-case scenario for Greek bonds.
However, as implied by the differences in the VaR estimates between the least squares and QR regression, the two methods result
in economically important deviations. Specifically, the standard least squares consistently indicated higher VaR 90% estimates
relative to QR for the period starting from 2000 up to 2010. Similarly, least squares estimates were on average more than 180 bps
lower than QR in the post-crisis period.
As a result, the QR methodology dictates a considerably different stance when making risk management decision. In periods of low
market stress QR signals a more benign stance relative to more standard estimates, while in periods of high market stress it
indicates a more conservative stance towards risk.
-350
-150
50
250
450
650
850
-1100
-600
-100
400
900
1400
1900
2400
2900
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Difference (RHS) QR VaR 90% OLS VaR 90%
QR model more conservative
OLS model more conservative
90% Value at Risk Estimates

…Balance of Risks Indicator
30
From the Fair-Valuation chart on page 25, the careful reader would have realized that the 50% & 90% confidence bounds
are not symmetric around the median estimate. The direct implication of that asymmetry is that our model specification
can provide not only an indicator of the fair-valuation of GGBs but also an assessment of the directional balance of risks
around the fair-value estimate. According to the Balance of Risk Indicator positive values imply increased risks for higher
spreads (downside risk for GGBs). Conversely, negative values signal increase possibility for lower spreads creating
potential for GGBs. The index shows that the Greek bond market was under severe pressure after 2009 with the model
signaling that risk was skewed towards the left tail (downside risk) of the implied spread distribution. In April 2017, the
indicator shows that risks remain balanced towards wider Greek bond spreads.
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Risks tilted to higher spreads
Bond - negative
Risks tilted to lower spreads
Bond - positive
Balance of Risks Indicator

A “Fair” Value on Bond Spread Volatility
QR 80% Confidence Intervals
Least Squares 80% Confidence Interval
During the period before 2009, the model-implied 80% confidence
bounds for Greek bond spreads ranged between 160-345 bps.
Immediately after the first Economic Adjustment Programme signed
in May 2010 model estimates pointed to a much wider range
between 520 – 1024 bps. Greek bond volatility (and hence the width
of the confidence bound) increased gradually, peaking in early 2011.
Seven years later, in April 2017, the implied uncertainty in Greek
bond spreads lies at approximately the same levels as in June 2010.
Evidently, a “fair” value for the volatility in bond spreads is more
accurate and intuitively less prone to irrational interpretations
compared to the OLS estimates. Specifically, least squares bounds
are wider (higher volatility) than the QR bounds in the pre-crisis
period and considerably lower than QR bounds in the post-crisis
period. Consequently, standard regression methods overestimate
volatility under normal circumstances and underestimate volatility
in periods of severe market stress.
Furthermore, the estimated lower bound under OLS is negative for
Greek bond spreads, implying that Greek 10-Year yields could be
lower than the respective yields for German bonds of the same
maturity. On the contrary, the lower bound under QR is always
positive and thus more sensible in terms of economic intuition.
-500
0
500
1000
1500
2000
2500
3000
3500
4000
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Lower Upper
-800
-300
200
700
1200
1700
2200
2700
3200
Jan-00
Oct-00
Jul-01
Apr-02
Jan-03
Oct-03
Jul-04
Apr-05
Jan-06
Oct-06
Jul-07
Apr-08
Jan-09
Oct-09
Jul-10
Apr-11
Jan-12
Oct-12
Jul-13
Apr-14
Jan-15
Oct-15
Jul-16
Apr-17
Lower Upper

32

Bond Spread Projection over the next 6 –months:
Scenario Assumptions
33
The scenario is constructed upon the assumptions that:
 The Relative Economic Activity indicator is assumed to improve as the Greek economy will return to
growth and real GDP increases by 0.7%. For Germany we assume a 1.5% YoY growth rate.
 The Relative Cost Competitiveness Indicator will increase as a more hawkish ECB policy affects export
oriented countries such as Germany more compared to Greece, causing only the German REER to
increase by 1% on a monthly basis for the next 6 months.
 The Relative EA Periphery Risk Indicator will decline gradually towards 2014 levels as the German 10 -
Year yield will increase faster than the yields in the periphery. Moreover the ECB will continue the QE
programme in 2017 for the periphery in order to support economic growth prospects for periphery
countries in 2017.
 The Relative Fiscal Sustainability Indicator will remain unchanged as the debt ratios will remain stable at
current levels over the next 6 months.

Assumptions – EA Periphery Risk Indicator Assumptions - Relative Fiscal Sustainability Indicator
Assumptions - Relative Economic Activity Indicator Assumptions - Relative Cost Competitiveness Indicator
Bond Spread Projection: Scenario Assumptions
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
82
84
86
88
90
92
94
96
98
100
102
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
Jan-16
Apr-16
Jul-16
Oct-16
Jan-17
Apr-17
Jul-17
Oct-17
GR reer DE reer Relative Competitiveness (RHS)
-0.34
-0.32
-0.30
-0.28
-0.26
-0.24
-0.22
-0.20
-0.18
75
80
85
90
95
100
105
110
115
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
Jan-16
Apr-16
Jul-16
Oct-16
Jan-17
Apr-17
Jul-17
Oct-17
GR RGDP DE RGDP Relative Economic Activity (RHS)
0
50
100
150
200
250
300
350
400
450
500
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
Jan-16
Apr-16
Jul-16
Oct-16
Jan-17
Apr-17
Jul-17
Oct-17
34
2.20
2.40
2.60
2.80
3.00
3.20
3.40
40
60
80
100
120
140
160
180
200
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
Jan-16
Apr-16
Jul-16
Oct-16
Jan-17
Apr-17
Jul-17
Oct-17
GR Debt Ratio DE Debt Ratio Relative Fiscal Sustainability (RHS)

Bond Spread Projection: 6-month Confidence Regions
According to the scenario assumptions for the underlying factors of Greek bond spreads, there is a 20%
chance that spreads will lie in the range 460-720 bps until November 2017.
Nevertheless, the risk for higher spreads is still substantial and negative surprises may lead bond spreads
towards the 900-1100 bps range while based on the model’s projections there is only a 10% probability that
spreads will fall below 400 bps over the next 6 months.
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
Jan-16
Apr-16
Jul-16
Oct-16
Jan-17
Apr-17
Jul-17
Oct-17
90% Confidence 50% Confidence 20% Confidence Spread
Bond Spread Projection

36
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