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CYPRUS UNIVERSITY OF TECHNOLOGY
FACULTY OF MANAGEMENT AND ECONOMICS
Bachelor thesis
THE MACROECONOMIC FACTORS THAT
AFFECT IN THE SHORT-TERM THE CYPRUS
SOVEREIGN BOND YIELDS
XENIA EFTHYMIOU
Limassol 2016
i
CYPRUS UNIVERSITY OF TECHNOLOGY
FACULTY OF MANAGEMENT AND ECONOMICS
DEPARTMENT OF COMMERCE, FINANCE AND SHIPPING
Bachelor thesis
THE MACROECONOMIC FACTORS THAT
AFFECT IN THE SHORT-TERM THE CYPRUS
SOVEREIGN BOND YIELDS
XENIA EFTHYMIOU
Professor Supervisor
Dr. Christos Savva
Limassol 2016
ii
Copyrights
Copyright © Xenia Efthymiou, 2016
All rights reserved.
The thesis approval by the Department of Commerce, Finance and Shipping of the Cyprus
University of Technology does not necessarily implies acceptance of the author's views on
behalf of the Department.
iii
It is with sincerest gratitude that I would like to acknowledge the support and help of my
Supervisor, Professor of Econometrics Dr. Christos Savva for supporting me throughout my
thesis with patience and encouragement. I deeply appreciate his continuous guidance,
constructive criticisms and valuable time. In addition, I would like to thank Nektarios
Michael, Doctoral Student of Econometrics for his eagerness and assistance. This thesis is
dedicated to my family. It is with heart-felt gratitude that I would like to thank them for their
love, support and strength all of these years.
iv
ABSTRACT
This report analyses the macroeconomic determinants that affect Cyprus Sovereign Bond
Yields short-term changes over the first quarter of 2001 to the third quarter of 2015 using an
Autoregressive Model of one latent factor with Cochrane–Orcutt procedure. Remarkable that
when Cyprus Economic Profile is considered different factors appeared to be influential. The
main findings support that changes in the conventional 3-Month Money Market Rate and
Unemployment Rate yield to an increase in Cyprus Sovereign Bond Yields. Additionally,
liquidity risk premium is supported as an additional determinant. In a nutshell, if Cyprus is
rated by Credit Rating Agencies as Moderate risk of high or low level (investment grade) or
substantial risk of low level (speculative grade) additional liquidity risk premium is required.
Whilst, a percentage change in Financial Soundness of Cyprus from 2012 onwards
deteriorates Cyprus Sovereign Bond Yields revealing liquidity risk premiums adjustments.
Finally, Policy Implications are discussed by primarily pointing the importance of minimising
unemployment rate and illiquid assets, especially non-performing loans.
Keywords: Sovereign Bond Yields, Cyprus Sovereign Bond Yields, Unemployment, Money
Market Rate, Credit Ratings, Financial Soundness Indicators, Cyprus Economy, Dynamic
Model, Cyprus, Macroeconomic Analysis
v
ΠΕΡΙΛΗΨΗ
Η μελέτη αυτή αναλύει τους μακροοικονομικούς παράγοντες που επηρεάζουν βραχυχρόνια
την απόδοση των Κυπριακών Κρατικών Ομολόγων για την περίοδο του πρώτου τρίμηνου το
2001 μέχρι και το τρίτο τρίμηνο του 2015 με την χρήση ενός Μοντέλου Αυτοπαλινδρόμησης
μίας χρονικής υστέρησης με την διαδικασία του Cochrane–Orcutt. Πρέπει να σημειωθεί ότι
όταν λαμβάνεται υπόψιν το Οικονομικό Προφίλ της Κύπρου, διαφορετικοί παράγοντες
παρουσιάζονται να επηρεάζουν την απόδοση των ομολόγων. Τα κύρια αποτελέσματα
υποδεικνύουν ότι ποσοστιαίες αλλαγές στα Τριμηνιαία Διατραπεζικά Επιτόκια και στα
Ποσοστά Ανεργίας οδηγούν σε βραχυχρόνια αύξηση στην απόδοση των Κυπριακών
Κρατικών Ομολόγων. Επιπρόσθετα ο παράγοντας ασφάλιστρο κινδύνου λόγω ρευστότητάς
παρουσιάζεται εξίσου να επηρεάζει την απόδοση των ομολόγων. Συνοπτικά εάν η Κύπρος
αξιολογείτε από τους Οίκους Αξιολόγησης ως μεσαίου ρίσκου επένδυση υψηλού ή χαμηλού
επιπέδου (επενδυτική βαθμίδα) ή ως σημαντικού ρίσκου χαμηλής βαθμίδας (κερδοσκοπική
βαθμίδα) οι επενδυτές απαιτούν ασφάλιστρο κινδύνου λόγω ρευστότητας. Αυτό το επιπλέον
ασφάλιστρο από το 2012 και μετέπειτα αναπροσαρμόζεται αρνητικά αναλόγως της
Οικονομικής Ευρωστίας της Κύπρου. Τέλος, γίνεται συζήτηση για τις Πολιτικές Επιπτώσεις
που έχουν τα αποτελέσματα της έρευνας με κυριότερο σημείο την ανάγκη για περιορισμό
των Ποσοστών Ανεργίας και των Μη-Ρευστοποιήσιμων Περιουσιακών Στοιχείων, ιδίως των
μη εξυπηρετούμενων δανείων.
Λέξεις κλειδιά: Αποδώσεις Κρατικών Ομολόγων, Αποδώσεις Κυπριακών Κρατικών
Ομολόγων, Ανεργία, Διατραπεζικό Επιτόκιο, Αξιολογήσεις Πιστοληπτικής Ικανότητας,
Δείκτες Οικονομικής Ευρωστίας, Κυπριακή Οικονομία, Δυναμικό Μοντέλο, Κύπρος,
Μακροοικονομική Ανάλυση
vi
TABLE OF CONTENTS
ABSTRACT ............................................................................................................................. iv
ΠΕΡΙΛΗΨΗ ............................................................................................................................... v
TABLE OF CONTENTS ......................................................................................................... vi
LIST OF TABLES ................................................................................................................. viii
LIST OF FIGURES .................................................................................................................. ix
ABBREVIATIONS ................................................................................................................... x
INTRODUCTION ..................................................................................................................... 1
1 Literature review ................................................................................................................ 3
2 Cyprus Profile .................................................................................................................. 11
2.1 Cyprus Economic Profile ........................................................................................ 11
3 Methodology .................................................................................................................... 14
3.1 Potential Determinants ............................................................................................ 14
3.1.1 GDP Growth Rate .............................................................................................. 15
3.1.2 Fiscal Conditions Class ..................................................................................... 17
3.1.3 Inflation Class .................................................................................................... 19
3.1.4 Competitiveness Class ....................................................................................... 20
3.2 Data description ....................................................................................................... 21
3.2.1 Cyprus Special Indicators .................................................................................. 22
4 Empirical analysis ............................................................................................................ 24
4.1 Core empirical sound macroeconomic variables ..................................................... 24
4.1.1 Baseline Specification ....................................................................................... 24
4.2 Adjusted Regression Model to Cyprus Economic Profile ....................................... 26
CONCLUSION ....................................................................................................................... 31
POLICY IMPLICATIONS ..................................................................................................... 33
vii
REFERENCES ........................................................................................................................ 35
APPENDICES ......................................................................................................................... 39
Appendix 1: Cyprus Sovereign Credit Ratings .................................................................... 39
Appendix 2: Moody’s Credit Ratings Guide ....................................................................... 40
Appendix 3: Data Sources ................................................................................................... 41
Appendix 4: Descriptive Statistics ....................................................................................... 42
Appendix 5: Correlation Matrix .......................................................................................... 42
Appendix 6: Foundation Baseline ........................................................................................ 43
Appendix 7: Foundation Model Residuals Cyclicality ........................................................ 44
Appendix 8: Final Model Baseline ...................................................................................... 45
viii
LIST OF TABLES
Table 1: Moody’s Historical Sovereign Credit Ratings .......................................................... 39
Table 2: Moody’s Sovereign Credit Ratings Guide ................................................................ 40
Table 3: Data Sources .............................................................................................................. 41
Table 4: Foundation Macroeconomic Variables Descriptive Statistics .................................. 42
Table 5: Core Macroeconomic Variables Bivariate Correlations ........................................... 42
Table 6: Initial Model Baseline ............................................................................................... 43
Table 7: Final Model Baseline ................................................................................................ 45
ix
LIST OF FIGURES
Figure 1: Foundation Model Residuals Cyclicality (Autocorrelation Graph) ......................... 44
Figure 2: Foundation Regression Equation ............................................................................ 25
Figure 3: Suggested Regression Equation .............................................................................. 27
x
ABBREVIATIONS
CPI: Consumer Price Index
ECB: European Central Bank
GDP: Gross Domestic Product
1
INTRODUCTION
What are the macroeconomic determinants that affect the Cyprus government borrowing
costs is the short term? Overall, Cyprus it is a small country with openness to the global
financial markets which faced severe economic problems during the last years. Even though
Cyprus is a small country it is highly exposed to its banking system. The resent financial
crisis Cyprus faced was associated with high levels of unemployment, leverage and non-
performing loans as well as gradual downgrades by the credit rating agencies. Taking into
consideration the above as well that Cyprus left the assistance program earlier and enter the
capital markets, raised remarkable concerns to which extent these macroeconomic
determinants affected the Cyprus Sovereign Bond Yields.
Although, there is a considerable number of existing studies that explore the relationship
among macroeconomic determinants and sovereign bond yields in the long-term and some of
them pointed out the different impact of them in the short-term, however, there are no
existing studies that revealed solely the short-term determinants of nominal sovereign bond
yields. Therefore, there is a need to address a model which reveals the short-term
determinants of Sovereign Bond Yields; especially, a model that captures the special Cyprus
Economic Profile.
The main scope of this study is to examine the macroeconomic determinants that affect in the
short-term the Cyprus Sovereign Bond Yields in order to investigate how policy makers can
influence Cyprus borrowing costs. In order to shed light on this issue a Dynamic
Autoregressive Model (AR(1)) with Cochrane–Orcutt procedure for the yearly changes of the
Bond Yields from the first quarter of 2001 and the third quarter of 2015 was used. By using
this Dynamic model, the autocorrelation indications that appeared were taken into account.
Consequently, this study enhances the existing literature since it focuses non only in short
term drivers but explores the Country Specific Drivers according to the Cyprus Profile.
A foundation model as a first stage was conducted with Ordinary Least Squares with
Heteroscedasticity Consistent Standard Errors. Then this model was used as a foundation
model where other macroeconomic determinants that capture Cyprus Economic Profile were
employed to a Dynamic Autoregressive Model with one latent factor. The conventional
macroeconomics determinants that appear to impact the short-term changes of the Cyprus
2
Sovereign Bond Yields were differently than those when Cyprus Special Economic Profile
was taken into consideration.
The suggested model that considers Cyprus Economic Profile supports the following
findings. A percentage change in 3-Month Money Market Rate will lead to a 0.345 increase
in Cyprus Sovereign Bond Yields. A percentage change in Unemployment Rate would
increase Cyprus Sovereign Bond Yields by 0.399. The percentage change in the Financial
Soundness of Cyprus lowers bond yields by about 0.099. Whilst, if Cyprus is rated as Baa1
then Short Term Yields would increase. If Cyprus is rated as Baa3 then it the rates would
surge to a higher extent. Quite puzzling, if Cyprus is rated as Ba1 then it will lead to a
positive increase in yields but to a lesser extent than Baa3. However, taking into account that
Financial Soundness of Cyprus during the same period also adjust the liquidity risk premium
that Investors required it is reasonable. Precisely, when Cyprus is Rated as Baa1 then
Sovereign Bond Yields change by 1.179 percentage points. When Cyprus is Rated as Baa3
then Sovereign Bond Yields change by 1.348 percentage points and when is rated as Ba1
Sovereign Bond Yields change by 1.331 percentage points.
The rest of the study is structured as follows. Section 1 reviews the literature of the last
couple of decades. Section 2 illustrates Cyprus Profile and more precisely the Economic
Profile of Cyprus. Section 3 reveals the empirical analysis technique that would be employed,
the potential determinants that may affect Sovereign Bond Yields as well as the Cyprus
unconventional special determinants. Section 4 discuss the empirical analysis and points the
empirical findings. Then the next section concludes with the reveal for the Policy
Implications of the Empirical Analysis.
3
1 Literature review
The literature review of the macroeconomic determinants of sovereign bond yields it is
primarily concentrated in investigating the long term factors of sovereign bond yields on
panel data of advanced economies rather than single country studies. Remarkable that there is
a big part of the literature that concentrates on explaining the yields spreads1 among different
economies but this is not the scope of this particular research. Furthermore, the empirical
literature on investigating the macroeconomic factors that influence sovereign bond yields
can be divided into real-term analysis2 and nominal-term analysis. The current study will
focus on presenting the nominal-term empirical analyses for the last couple of decades and
will highlight couple key real-term studies.
To begin with, Cantor and Packer (1996) among others, revealed that Sovereign Credit
Ratings implying risks are in line with macroeconomic fundamentals. Overall, they depicted
six determinants that seem to play crucial role in determining Sovereign Ratings as follows:
per capita income; GDP growth; inflation; external debt; level of economic development and
default history. All in all, sovereign credit ratings strongly correlated with market-determined
spreads because they effectively summarize the information contained in macroeconomic
determinants. Despite the fact that most of the correlation seems to reflect comparable
1 For example, Attinasi, Checherita and Nickel (2009) used a dynamic panel model in order to explain the
factors that expand sovereign bond yield spreads vis-à-vis Germany in selected euro area countries between
the end of July 2007 and the end of March 2009. They concluded that higher expected budget deficits and/or
greater government debt ratios relative to Germany contributed to greater extent to government bond yield
spreads in the euro area. Additionally, announcements of bank rescue packages led some investors to
reexamine sovereign credit risk by transferring risk from the private financial sector to the government.
Another example, Bernoth and Erdogan (2012) investigated the determinants of sovereign bond yield spreads
among 10 countries of the Economic and Monetary Union of the European Union from the first quarter of
1999 and first quarter of 2010. Generally, the point out the need of time-varying coefficient models.
2 For instance, Engen and Hubbard (2004) by using predicted values of the fiscal position from the
Congressional Budget Office for the United States they depicted that a percentage point increase in Federal
Government debt to GDP, all else being equal, is expected to increase by three basis points the long run real
interest rate.
4
interpretations of publicly available information by the rating agencies and by market
participants; the above mentioned author discovered that opinions of rating agencies affect
independently the market spreads. Remarkable that, sovereign bond yields changed in the
expected direction with a statistical significant manner in the event of Rating Agencies
announcements of modifications in their sovereign risk opinion. Nevertheless, the authors
noted puzzling results regarding that rating announcements influence spreads in a greater
manner for below-investment grade sovereigns and rating announcements are expected to
have a higher impact than those that are less expected.
Later, Balduzzi, Elton and Green (2001) examined the effects of arranged macroeconomic
announcements on prices, trading volume, and bid-ask spreads by using intraday data from
the interdealer government bond market from the first of July 1991 to 29th of September
1995. They concluded that 17 announcements as measured by the surprise in the announced
quantity, had a significant impact on the price of at least one of the following instruments: a
three-month bill, a two-year note, a 10-year note, and a 30-year bond. Not to mention that the
effects vary significantly according to maturity. Remarkable that the prices of all instruments
react considerably to eight different types of announcements: Durable Goods Orders,
Housing Starts, Initial Jobless Claims, Nonfarm Payrolls, Producers Price Index, Consumer
Confidence, The National Association Of Purchasing Managers Index, and New Home Sales.
Moving on Gale and Orszag (2002) synopsise the results of the major existed literature back
then in order to examine the effects of long-term discipline and interest rates effects. They
point out that it is important to consider future deficits when examine the linkage among
deficits and interest rates. Out of 17 papers that consider future deficits, 12 of them resulted
in a statistical significance association between deficits and interest rates; whereas, four
found diverse effects. Only one paper did not support any effect from projected deficits on
interest rates unsuccessfully. Briefly, macro-econometric models recommend that one percent
increase in the budget to GDP would lead to an increase in long term interest rates by about
50 basis points after a year and about 100 basis point after 10 years.
As regards to Ang and Piazzesi (2003), they worked on a Gaussian model of the yield curve
with observable macroeconomic variables and typical latent yields variables. The scope of
their paper was the identification of how macroeconomic variables affect bond prices and
dynamics of the yields curve. The authors used various zero coupon bond yields maturities.
They limited their study in the period of June 1952 to December 2000. They differentiated
5
their work from previous Vector Autoregressive Models studies of yields and
macroeconomic variables since they imposed no-arbitrage assumptions. The term structure
model used consisted factors of inflation (CPI, Producer Price index of finished goods and
spot market commodity prices) and economic growth - real activity (unemployment,
employment growth rate, industrial production growth rate and index of Help Wanted
Advertising in Newspapers) alongside with latent variables. In order to reduce the system
dimensions, they used Principal Components Analysis and obtain the first Principal
Components generated from inflation factors and the same for real activity factors. They
demonstrate that macroeconomic indicators explain a substantial part of short term and
middle term yields of approximately 85%. However, macroeconomic factors explanation
power shrinkages on explaining long-term yields with an explanation of about 40%.
On the contrary, Goldberg and Leonard (2003) analysed the United States and German
sovereign bond markets in order to define the economic announcements that impact
sovereign bond yields. This examination has taken place on the hourly changes of the
sovereign bond yields during the third of January 2000 to the 28th of June 2002. The outcome
of the above mentioned examination was: economic announcements influence significantly
bond yields. Main findings consist of that European Markets were independent on United
States financial markets. Sovereign bond yields are expected to increase on announcements
of better economic conditions or greater inflation than expected. The greater effects that were
found to impact both markets consisted of labour market announcements on payrolls,
unemployment and initial unemployment claims as well as advance readings of real GDP.
Then Ardagna, Caselli and Lane (2004) explored the effects of fiscal policy, precisely:
government debt and deficit on long-term interest rates. They used panel data from 1960 to
2002 of 16 OECD countries. The model included the variables of primary deficit and Public
Debt to GDP as well as other relevant control variables such as 3-month the Treasury bills
rate, the inflation rate, the GDP growth rate and the global indicators of world fiscal
imbalances. The study revealed that one percentage point increase on the Primary Deficit to
GDP ratio leads to a rise of 10 basis points in 10-year government bonds nominal interest
rates. In addition, in a Dynamic Vector Autoregressive Models the same event leads to a
cumulative increase of approximately 150 basis points after 10 years. The lag length of the
model was two. Briefly, the effect of such event is greater when considering that a positive
shock to the primary deficit has on expected future fiscal policy and macroeconomic
6
variables in the long term. Debt influence interest rates on non-linear way; whereby,
exclusively countries with above average levels of debt can influence interest rates by an
increase in debt. Furthermore, the study investigated that World fiscal policy is statistically
important on influencing each country’s interest rates.
Meanwhile, Izák (2004) explored the relationship among debt, deficit, inflation, growth and
interest rates by using panel analysis with fixed effects of four transition countries: Czech
Republic, Hungary, Poland and Slovakia between 1994 and 2002. It was assumed that the
variables influencing debt dynamics would also impact the interest cost of the debt. The study
suggested that one percentage point increase in inflation will lead to a 17 basis point increase
in interest rates. As per growth, the results showed that a percentage point increase in growth
will lead to 37 basis points increase in interest rates. Furthermore, one percentage point
increase in the primary balance results will lead to 12 basis points decline in interest rates.
As regards debt variable, the author points out puzzling results.
Next, Dai and Philippon (2005) develop an empirical macro-finance model that combines a
no-arbitrage affine term structure model with a set of structural limitations that let to classify
fiscal policy shocks and identify the effects of these shocks on the prices of bonds of different
maturities. The pricing equation included: federal funds rate; government deficit; inflation;
real activity and one latent factor. The findings of the study depicted that government deficits
influence long-run interest rate. In brief, one percentage point increase of Deficit to GDP
ratio rises by 35 basis points after three years the 10-year rate. This showcase greater
expected spot rates as well as risk premium on long rum bonds. Overall, fiscal policy shocks
represent up to 13 percent of the variance of predicted errors in bond yields.
Afterwards, Aisen and Hauner (2008) analysied a large sample of 60 advanced and emerging
economies from 1970 to 2006 they re-examined the relationship between budget deficits and
interest rates. To address the association, they applied a Generalised Method of Moments
system. In short, the model consisted lagged dependent variable in order to account for
delayed adjustment and other variables to control depending on the country profile. For
perfect capital mobility, the nominal interest rate is determined exclusively by external
factors such as foreign nominal interest rate, expected depreciation and country specific risk
spread. Whilst, closed economies are determined merely by domestic factors such as
expected inflation, real money supply which affect interest rates temporarily through liquidity
and real interest rates. The first two dynamics are captured through real GDP growth while
7
the savings rate is captured by the constant and the budget deficit. The findings showed that
budget deficits are highly significant and positive affecting interest rates. In detail, one
percentage point increase in budget deficits to GDP leads to an increase of interest rates of
about 26 basis points. Moreover, they remarked that this effect depends primarily on
interaction term and is only significant under certain conditions. More precisely, the
conditions are the following: if deficits are high; major part of them is financed domestically;
or interact with domestic debt; financial openness is low; interest rates are liberalised or
financial sector is less developed.
In the meantime, Tam and Yu (2008) modelled the sovereign bond yield curves of the United
States, Japan and Germany using data from January 1992 to March 2006 by using a new
macro-finance framework. More precisely, the yield curve model used was a combination of
latent factors and macroeconomic variables: capacity utilisation; policy rate; and inflation.
The authors supported the dynamic interaction among the yield curve latent factors and
macroeconomic variables for the United States and Germany. However, the failed to proove
the same for the Japanese economy. Moreover, this finding may be biased due to years of
ineffectiveness monetary policy in Japan during persistent depression. With the help of a
DCC-GARCH model first proposed by Engle (2002) the authors supported the existence of
cross-country correlations of the bond markets of the examined countries.
Later, Ardagna (2009) studied the changes in interest rates (government and corporate bonds)
and stock market prices before and after the periods of large changes in fiscal policy. To
address this, she used panel data of OECD countries between 1960 and 2002. The paper
revealed that interest rates of 10-year government bonds plunge approximately by 124 basis
points during events of fiscal consolidations. In contrast, during years of loose fiscal policy,
interest rates surge by 162 basis points. Noteworthy that these events affect 3-month Treasury
Bills too. These findings depend on countries’ initial fiscal conditions and on the type of
fiscal consolidations. Specifically, fiscal adjustments that occur in years with high levels of
government deficit, which are applied by mitigation in government spending and generate a
permanent and remarkable decline in government debt are associated with greater reductions
in interest rates. In contrast, during years of fiscal expansions, the interest rates of 10-year
government bonds go up no matter the fiscal conditions of the country.
Whilst, Laubach (2009) examined the relationship among long-horizon forward rates and
future federal government deficits and debt in the United States between 1975 and 2005. The
8
author used three different interest rates series as dependent variables; 5-years-ahead 10-year
forward rate, 5-year-ahead 10-year forward rate and 10-year constant maturity Treasury
Yield. Projections published by the Congressional Budget Office were used as expectations
of future fiscal policy. The study points out that a percentage point increase in the projected
deficit to GDP ratio leaded to an increase of the forward rates of 5 and more years ahead by
20 to 30 basis points. Likewise, a percentage point increase in the projected debt to GDP
leaded to an increase of 3 to 4 basis points.
Regarding to Baldacci and Kumar (2010), they reviewed the influence of fiscal deficits and
public debt on long-term interest rates between 1980 and 2008 from a panel data of 31
Advance and Emerging economies. The study concluded that greater deficits and public debt
lead to substantial increase in long-run interest rates. This study differentiates from all others
due to the fact that this paper addressed that the above magnitude depends on the country
specific factors: the initial fiscal, institutional, other structural conditions, and spill-overs
from worldwide financial markets. All in all, high levels of fiscal deficits and public debts are
likely to put substantial upward pressures on sovereign bond yields in many advanced
economies over the medium term. Moreover, the Regression Analysis constituted short-term
interest rates to control for monetary policy effect of the term structure, CPI for inflation
purposes and output growth to control for the country’s cyclical conditions. The authors run
an auxiliary regression without taking into consideration countries special characteristics with
lagged dependent variable to separate short term and long-term effects with a Generalised
Method of Moments of the above discussed model. The results showed that the overall effect
were comparable to that found in the static model (fixed effects Least Squares estimates) with
the size of the short-run impact of government deficits being less than one third of the overall
effect.
At the same time, Gruber and Kamin (2010) research the relationship among long-term
sovereign bonds yields, fiscal balance and government debt in the OECD from 1988 to 2007
with a dynamic panel approach. Due to the endogeneity of fiscal positions to the business
cycle the authors used forwards projections of the fiscal positions. The control variables that
were included through the model consist of: short-term interest rates; lagged dependent
variable; two-year ahead projected rate of real GDP growth and CPI inflation; constant term;
fixed period and effects. The coefficients of short-term interest rates, inflation and GDP
growth are positive and statistical significant. On the first hand, the findings revealed that as
9
per G7 panel regression one percentage point increase in structural deficit to GDP ratio
increases by 15 basis points government bond yields in the long-term; whereas one
percentage point increase in net debt to GDP ratio increase bond yields by 2 basis points. On
the other hand, OECD panel fiscal effects are approximately the half of these estimated for
G7.
Noteworthy, Poghosyan (2012) developed cointegration techniques on explaining long-term
and short-term determinants of Sovereign Bond Yields in a panel data of 22 advanced
economies from the 1980 to 2010. Despite the fact that this paper investigated the
determinants of the real bond yields this paper is worth mentioning since it is one of the few
papers that examined short-term factors of sovereign bond yields. The results showed that, in
the long-term the yields rise approximately to two basis points in reaction to one percentage
point of the ratio of Government Debt to GDP; in reaction to one percentage point increase in
potential growth rate the yields go up to 45 basis points. In contrast, in the short-term there is
discrepancy of the sovereign bond yields from the level determined by the long-term
fundamentals. There is a positive effect on the sovereign bond yields in reaction to changes in
Debt to GDP ratio, Real Money Market Rate and negative effect on Inflation changes.
Inversely, the negative influence of growth rate and the Primary Balance Ratio is weaker.
However, approximately half of the discrepancy from the long-term equilibrium adjust in one
year. The paper concluded that sovereign borrowing costs of some euro area countries deviate
from the equilibrium level defined by macroeconomic fundamentals after the crisis.
Remarkable, Ichiue and Shimizu (2012) investigated the determinants of real long-term bond
yields. This paper was discussed through the Literature Review despite the fact that the
authors examined the factors affecting real bond yields since throughout this research a lot of
techniques were adopted from the authors. The authors developed cross-country panel data of
10 developed countries during 1990 and 2010. In order to address endogeneity, they have
used forward interest rates and numerous forecasts. The independent variables employed
through the model consist of: fiscal conditions; foreign borrowings; labour productivity;
demographics and inflation uncertainty. The findings suggest that when an increase in
government debt is financed entirely by foreign borrowing the increase in the forward real
interest rate is around three times when it is finance domestically. Furthermore, aging
expectations tends to lower yields. In contrast, primary balance effects are insignificant and
current account balance provides no further information beyond net foreign debt.
10
Finally, Malešević Perović (2015), examined government debt and primary balance
influences on long-term government bond yields in 10 CEE countries from 2000 to 2013. To
address any influences, the author generated a static panel model where despite government
debt to GDP and Net Government Borrowing/Lending to GDP ratio included through the
model other control variables such as GDP growth, Inflation and Money Market Rate.
Noteworthy that those control variables were statistical significant except of Inflation at a 5
percent significance level. Short term interest rates appear to have negative impact, whereas
the other two control variables have positive. However, the scope of this study was not to
address the impact of these variables and they were not discussed further throughout the
study. The findings of this papers are tested throughout an extensive variety of specifications.
The study lead to the finding that one percentage point increase in stock of government debt
leads to an increase in government bond yields of approximately 2.7 to 4 basis points;
whereas, one percentage point increase in primary deficit to GDP ratio leads to an increase in
government bond yields of 12.9 to 24.3 basis points. The author investigates and finds
significant non-linearities on the debt and interest rate relationship. Precisely a threshold of
30% is remarked as the point that if it exceeded then it turns the relationship into positive.
11
2 Cyprus Profile
Statistical Service of Cyprus (2012) provided a short country profile revealing key aspects of
Cyprus Profile. More precisely, Cyprus is a small island on the crossroads of Africa, Asia and
Europe. The independence of Cyprus was gained in 1960 by a Britain rule; however, in
nowadays an approximately area of 40 percent is occupied from 1974. In May 2004 the
Republic of Cyprus became a full member of the European Union and in January 2008 the
country joined the Eurozone.
2.1 Cyprus Economic Profile
In brief, Statistical Service of Cyprus (2014) outlines the crucial points need to be framed in
this section in order to adapt a clear view of the Cyprus Economic Profile. Initially, Cyprus
prior to the global economic crisis experienced substantial economic growth alongside with
low levels of unemployment and generally steady macroeconomic conditions. These positive
economic conditions proved to worked as a boomerang for the forthcoming economic issues
since excessive credit and consumption was taking place. Subsequently, the accumulated
credit lead to the well-known severe macroeconomic imbalances. As economic crisis
emerged, these imbalances deteriorate and let Cyprus with an egregious economic crisis
which was worsen during spring of 2013. As characteristically Statistical Service of Cyprus
(2014) points out “Indicatively, the economy exhibited a contractionary path since 2011,
while unemployment followed a rapidly increasing trend since 2008.”
Further to the excessive debt levels Cyprus had, other catalytically events lead Cyprus outside
of the international capital markets in spring of 2011 as follows: Firstly, Insufficient
regulatory and supervisory framework. Secondly, Excessive credit expansion of the financial
sector. Not to mention, Panayi and Zenios (2015) findings that foreign depositors held the
primary part of Cyprus Banking Sector Assets. Bank assets were 8 times bigger than Cyprus
GDP. Thirdly, Cypriot banks’ exposure to Greek economy and Greek sovereign bonds.
Astonishing, Stephanou (2011) warned for the forthcomings and presented adjustments
needed to be taken in order the banking system in Cyprus to be under control. Last but not
least, extensively loose fiscal policy that led to rapid deterioration in public finances.
12
In addition, the bad economic position Cyprus held deteriorated even more and led to a
plunge in March of 2013. At that time, ex Laiki Bank collapsed and Eurogroup decided the
recapitalisation of the Bank of Cyprus through creditors participation (bailed-in of
depositors). The main factors that influence these events were the Eurogroup decision for
Private Sector Involvement including among others “haircut” on Greek Government debt
holdings in autumn of 2011, along with the substantial delay in the submission of a request
for financial support by the European Stability Mechanism and the International Monetary
Fund. Subsequently, Cyprus Banking Sector deteriorated rapidly and substantially, while real
economy faced difficulties with the disposable income of Cypriot citizens and their affluence
being decreasing.
In short, the agreement that was established with Programme Partners on a macroeconomic
adjustment Programme, the well-known Memorandum of Understanding had as a main scope
to address the challenges in key sectors of the economy. It was a decision taken under caution
since it was important at the time to regained consumer’s as well as investor’s confidence,
and among others to bounced back the economy to steadiness. Overall, the Memorandum
addresses challenges in three major extents: fiscal; financial and structural issues.
On the whole, Cyprus held investment grade levels of credit ratings with low risk before the
third quarter of 2011. However, from then onwards Cyprus credit rating incrementally
downgraded and reached the lowest point during the first quarter of 2013 to the third quarter
of 2014 with speculative rating of very high credit risk. Afterward the rating bounce back to
high credit risk. In fact, Cyprus was exposed to Credit Rating agencies announcements the
last couple of years3.
Panayi and Zenios (2015) sum-up economic crisis impact to the macro-economy between
2011 and 2014: The economic shrinkage was approximately a cumulative percentage point of
11% and the the unemployment rate peaked during this period at 17%. In addition, they
3 The Credit Ratings were obtained from the historical data of Moody's Investors Service, Inc. The particular
rating agency is one of the biggest and well-respected credit rating agencies around the world. There is a
debate for the timeliness and accuracy of the Credit Rating Agencies but this analysis would not explore and
identify any issues according to these debates. For example, see Bae, Kang and Wang (2015), Cantor and
Packer, (1996) and Bae, Kang and Wang, (2015).
13
remarked real estate bubble that emerge years before and the financial crisis and the crucial
role it had to the economy collapse.
Currently, the Memorandum was concluded on 31st of March 2016 and Cyprus no longer
receives financial assistance as European Stability Mechanism (2016) points out. The Cyprus
economy bounce back quickly and recovered before than expected. Therefore, from the 10
billion euros projected at the beginning only 7.3 billion euros were used. During the
Memorandum Period Cyprus regain economic growth and repair public finances rapidly. The
main problem of the crisis which was the financial sector was restructured, recapitalised and
downsized. In addition, the legal framework and the supervisory was updated. Also, Cyprus
returned to the bond market and regained back the investors trust. However, remaining puzzle
to the economy are the high levels of Non-Performing Loans.
14
3 Methodology
3.1 Potential Determinants
To put in a nutshell, during the last couple of years the majority of the literature focused on
investigating the long-term relationship among fiscal positions and sovereign bond yields. A
major part of these studies pointed out that there is a threshold where if it is reached the fiscal
deficits impact sovereign bond yields and that budget deficits in some cases appeared to
impact the yields too. However, there is not a consensus. Others, revealed the
macroeconomic announcements that immediately impact sovereign bond yields; whereas
some others pointed out the impact of world conditions.
In a synopsis of the previous literature and a consideration of the Cyprus Profile an analogous
model would be employed. In this study we followed the results of Ang and Piazzesi (2003)
who they find out that macroeconomic determinants can explain a substantial part of short
term and middle term sovereign bond yields. More precisely, macroeconomic factors have an
explanatory power approximately of 85%. With this in mind as well as Tam and Yu (2008)
finding that a macro-finance framework had dynamic interaction among the yield curve latent
factors and macroeconomic variables for the two examined countries among three; the model
baseline will be constructed into two different phases: The first one consists of identification
of the core empirical sound macroeconomic variables that affect most of the countries; The
second step would try to shed light in the determinants that affect short-term changes in
sovereign bond yields according to Cyprus Economic Profile since it is expected that a large
part of the dependent variable explanation would be undefined. All in all, in order to capture
the best possible macroeconomic determinants of Cyprus sovereign bond yields a dynamic
model that captures Cyprus Economic Profile along with the classic variables would be
generated.
Therefore, this study will differentiate from all previous studies to the extent that it will
concentrate on revealing the macroeconomic determinants that affect a single country,
Cyprus. In short, a Dynamic Model will be employed in order to capture special country
characteristics. In spite of this research, short-term factors would be examined rather than
long-term vastly tested determinants.
15
Before moving on the econometric analysis in this section, potential determinants of short-
term changes in Cyprus Sovereign bond yields would be discussed. The empirical analysis
would be based on key determinants that Poghosyan (2012) tested among others. More
precisely, Debt to GDP ratio, Money Market Rate, Inflation (CPI changes). GDP growth rate
and Primary Balance Ratio. In accordance to Ichiue and Shimizu (2012) who separated the
potential factors in five different classes: Fiscal Conditions, Foreign Borrowing, Labor
Productivity, Demographics, and Inflation Uncertainties (Inflation), the potential
determinants would be divided into analogous type of classes. More precisely the classes
would be the following: Fiscal Conditions, Labour Productivity, Demographics and
Competiveness.
Despite the fact that these papers based their investigation on the identification of the
determinants of real yields we assumed that the factors that may affect real yields may
perhaps affect nominal yields too. Recalling Fisher’s equation that states that nominal rates
are equal to the natural rate of interest (real interest rate) and the inflation. We assumed that
nominal sovereign bond yields would be impacted by events in real activity and inflation.
3.1.1 GDP Growth Rate
Sovereign bonds are substantially impacted in the long-term from the real GDP growth rate.
With regards to Claeys, Moreno and Suriñach (2012) economic growth influence private
investment demand; which consequently, impacts positively the supply of corporate bonds.
Moreover, government bonds supply decreases since GDP growth increases the tax revenues
in the meantime and especially in extremely indebted countries doubts of unsustainable debt
positions increases. Hence, this means that government bond yields are lower.
Specifically, as Ichiue and Shimizu (2012) stated that GDP growth rate should be
decomposed into two variables, the growth rate of GDP per working age population (Labor
Productivity Class) and the growth rate of working age population ratio (Demographics
Class). These variables will need to be discussed separately. The reasons are: the complexity
of the economy and the effects of other channels rather than the physical rate of interest rates
which does not allow any investigation and consideration of their implications as a unified
variable. As a consequence, the Labor Productivity Class as well as the Demographics Class
would be employed in order to proxy the variables discussed above. The main scope for this
induction would be the examination whether these variables impact significantly Cyprus
Sovereign Bond Yields in the Short-Term.
16
3.1.1.1 Labour Productivity Class
Considering labor productivity, Ichiue and Shimizu (2012) claim that in respect to the natural
theory an improvement in labor productivity growth rate leads to an uptrend in real interest
rates and thus impacts nominal interest rates too. The extent to which it will impact nominal
yields depends on the inflation.
3.1.1.2 Demographic Class
In this section the variable of growth rate of working age population would be employed.4 As
suggested by Ichiue and Shimizu (2012), demographics are believed to have the ability to
affect real interest rates through multiple channels:
On the first channel, aging population may put descending pressure on interest rates. This is
supported as follows: aging population drops the marginal productivity of capital through a
plunge in labour supply and declines investment demand.
On the other channel, they assert that aging population is reputed to have upward pressure on
long-term interest rates. This was based on the life cycle theory which as the authors states
people tend to spend their savings after their retirement. Hence, long-term interest rates will
be impacted upside through the decrease of the savings rate. Not to mention that, aging
population has upward pressure on long-term interest rates through prospects for “fiscal
deterioration” triggered by collapse of tax revenues and rising social security spending.
Either way, in practice this is not always supported. For example, Ichiue and Shimizu (2012)
tested this in the US and Japan but they found out that elderly own a higher amount of
financial assets than other age population group. All in all, even if the life cycle theory holds,
the decrease in financial assets may be supported by the fact that the lifespan of elderly is
shorter; thus, their duration on investment is lower they tend to have lower risky assets as
they will not be benefit from them due to the high volatility of those assets in the short-run
(Poterba, 2001).
4 For the period of 2000 to 2003, the only available data for the Working Age Population were the average
annual rate. In spite of these circumstances, the Working Age Population for the missing quarters were
obtained by using an average growth model to estimate the working age population during the missing
quarters.
17
3.1.2 Fiscal Conditions Class5
3.1.2.1 Sovereign Debt and Deficit Ratio
It is reputed that fiscal debt and deficit may influence sovereign bond yields through multiple
perspectives. As per Malešević Perović (2012), in the short-term a rise in government deficit
enhances aggregate demand, which subsequently increases interest rates. Likewise, an
increase in government debt affects aggregate demand over wealth effects on aggregate
consumption. Gruber and Kamin (2010) reveal that a compensation is needed in bond yields
when an increase in debt causes doubts that the government may default. As Baldacci and
Kumar (2010) and Ardagna, Caselli and Lane (2004) reveal, uncertainties regarding the
economic activity in combination with enormous deficits and debt may well lead to credit
risk premium; therefore, higher fiscal bond yields. All in all, taking into account the above
perspectives, sovereign bond yields are expected to rise when there is an upward trend in debt
and deficit6.
Overall, in theory the relationship among fiscal conditions and interest rates may be
expressed by two variables: Debt to GDP and Deficit to GDP. Precisely, Ardagna, Caselli
and Lane (2004) mentioned that even if you believe that one of them is important it is
valuable to include the other variable for controlling reasons as well as interactions between
them.
5 Among this class, two critical variables are omitted due to data availability. Firstly, Budget Deficits are
reputed to affect sovereign bond yields. A countless part of the empirical literature was based on exploring
and proving the relationship among Budget Deficits and Sovereign Bond Yields. However, due to the lack of
available information this will not be tested throughout this research. Secondly, foreign borrowing was not
tested. Funding sources are suggested as explanatory influences on government debt effects on sovereign
bond yields. However, due to the lack of available information this determinant will not be tested.
6 Noteworthy, Baldacci and Kumar (2010) revealed that over the medium-term massive amounts of fiscal
deficits and public debts are expected to have upward pressures on sovereign bond yields of advanced
economies. Other studies explored the fiscal positions relationships with sovereign bond yields stressed out
that there is a non-linear effect. If a threshold is reached then the sovereign bond yields are affected. For
example, Ardagna, Caselli and Lane (2004). Throughout the examine period Cyprus Held high levels of Deficit.
Thus, it would not be examined any existence of non-linearities. Not to mentioned that those papers tested
long-term relationships.
18
3.1.2.2 Primary Balance and Fiscal Balance Ratio
Not to mention that, Ichiue and Shimizu (2012) explained that fiscal balance contains finance
expenses (interest payments). An increase in interest rates is expected to cause deterioration
in fiscal balance. As a consequence, using fiscal balance in the regression will end up
including a “reverse causation”; hence, fiscal balance may well be overestimated. To get rid
of this endogeneity bias Primary Balance should be used as a flow variable. Primary deficit
rather than total deficit was used by Ardagna, Caselli and Lane (2004) in order to capture in a
better way the independent changes in fiscal policy.
The model will employ General Government Deficit to GDP and Primary Balance to GDP as
independent variables.
3.1.2.3 Gross Government Deficit and Net Government Deficit Ratio
There is a controversial discussion on whether Gross Government Debt or Net Government
Debt influence substantially sovereign bond yields. The theoretical side suggest that net debt
is the variable of importance. In respect to Ichiue and Shimizu (2012) they support of the
above argument as follows; if debts can be repaid by the financial assets held by the
government, it is proper to consider that the default risk effect can be determined by net debt.
On the other side, others dispute that due to the illiquidity of financial assets held by the
government; gross government debt believe to be a better indicator.
Taking into consideration the above, Gross Government Deficit would be adjusted to Net
Deficit after deducting the most liquid and sound assets7.
3.1.2.4 Unemployment Rate
Recalling basic economics, an increase in unemployment rate leads to higher government
spending. In order to cover the unemployment needs with different unemployment benefits,
government increases its spending, while government income tax is facing a downfall. All in
7 The distinction between the appropriate assets to be deducted from Consolidated General Government Debt
was adopted from Eurostat’s (2014) suggested asset classes. In short, Cyprus Maastricht Debt was adjusted
from currency and deposits, securities other than shares (excluding financial derivatives) and loans. It should
be noted that Loans comprise of Non-Performing Loans. This part of Loans could not be excluded due to
limited data availability.
19
all, in order to bounce back the economy, the government is most likely to choose to borrow
instead of imposing reductions upon other government spending. Under these circumstances,
private spending drops, as well as private investment and GDP diminishes. Overall, an
increase in unemployment rate increases bond yields through the channel that high levels of
unemployment rates are an indication of financial distress.
Throughout the Literature Review of the last couple of decades the relationship between this
variable and the sovereign bond yields was not empirically tested. Instead, existing literature
have tested United States and Germany’s economic announcements and their influence on the
bond yields as a whole (For example se Balduzzi, Elton and Green (2001) and Goldberg and
Leonard (2003)).
3.1.3 Inflation Class
Taking into account Gruber and Kamin (2010), inflation expectations play a significant role
too on fiscal debt relationship with sovereign bond yields. A climb in sovereign bond yields
is mandatory when possible concerns that the central banks will “monetize” sovereign debt
and deficits exists as they may perhaps lift inflation expectations.
3.1.3.1 Inflation Uncertainty
Even more, inflation uncertainty is represented within the risk premium in interest rates.
Given that Wright (2011), found a high and positive relationship among long-term inflation
uncertainty and the term premiums on nominal bonds, hereby confirm the above statement. In
a nutshell, Piazzesi and Schneider (2006) asserted that investors require compensation for the
risk of unexpected inflation. Therefore, bond risk premium may be subject to unexpected
inflation risk. In this case, it is easy to interpret that countries with high levels of inflation
uncertainty may choose to issue short-term debt (Wright, 2011). As the author reveals,
inflation uncertainty may collapse in the case that the government maintains a specific
inflation target. In addition, business cycle state it is expected to impact inflation
uncertainties. 8
8 Numerous empirical studies focused on exploring the relationship between inflation uncertainty and
inflation. Among others, Conrad and Karanasos (2005) and Caporale and Kontonikas (2009) investigate the
aforementioned relationship and support the existence of this relationship.
20
In this class, inflation rate would be employed throughout CPI. Considering Jiranyakul and
Opiela (2010) findings that rising inflation rises inflation uncertainty and that rising inflation
uncertainty increases inflation, inflation rate is a good proxy to use.
3.1.4 Competitiveness Class
3.1.4.1 Exchange Rates
As Aisen and Hauner (2008) depicts\ed an economy that is described as open it is expected to
be majorly impacted by external factors. Such factors include exchange rates. Recalling basic
economics in the short-term exchange rates may do not reflect the Purchasing Power Parity,
hence, providing information of the countries competiveness among other countries. In order
to capture such effects, Net Effective Exchange Rate would be employed through the model.9
3.1.4.2 Short-Term Interest Rates Class
One of the shortest interest rate is the Interbank Rate10. The level of this short-term rate it is
widely expected to influence the economy. Recalling basic economics and the Term Structure
of Interest Rates11 it is widely accepted that lower interest rates yield to higher consumption
and investments. This is taking place since banks would be able to borrow one from another
with lower costs and hence these lower borrowing costs will pass through the interest rates of
individuals and corporations. All in all, lower interest rates are giving a boost to the economy
and inflation to increase. Otherwise, higher interest rates are translated to higher savings rates
and borrowing rates. Thus, the consumption deteriorates as well as the investments since
individuals and business save rather spend. By the same token, the economy slowdown and
the inflation decreases. Subsequently, Central Banks by influencing the Interbank rate
through Open Market Operations, Discount Window and Reserve Requirements are able to
influence the economy as a whole.
9 More Precisely, the Nominal Effective Exchange Rate would be adopted from Eurostat’s historical data and
represents the weighted average of bilateral nominal exchange rate against the currencies of 42 selected
trading partners.
10 The Interbank Rate it is the interest rate within the interbank market. Otherwise, it is the interest rate that
the banks within a market borrow from each other.
11 (Mishkin, 2007)
21
As regards bond yields, the Interbank Rate plays a significant role in coordinating the yields
through the expectations of future interest rates. According to the level of the Short-Term
Interest rates the Inflation expectations are adjusted as already aforementioned. Hence, the
time value of money is adjusted accordingly leading to higher or lower yields. For example,
if they are high inflation expectations then bond yields will decline. In order to compensate
for the shortfall in the purchasing power of future cash flows higher yields are given.
Accordingly, different bonds would be impacted differently. As per this study, we will focus
on examining the left side of the yields curve (short-term) of the 10-year Cyprus Sovereign
Bond Yields.
Therefore, the 3-Month Money Market Rate12 would be employed as a proxy for the short-
term interest rates.
3.2 Data description
In order to address the macroeconomic determinants that affect the short-term changes of the
Cyprus Sovereign Bond Yields a Regression Analysis would be conducted with data from the
first quarter of 2000 and the third quarter of 2015. Generally, the data were generated from
the database of Statistical Service of Cyprus, Eurostat, Statistical Data Warehouse of the
European Central Bank and Datastream13. The dependent variable of Cyprus Sovereign Bond
Yields is represented by the 10-year Cyprus Sovereign Bond Yields. The changes of the
variables were obtained by taking the differences of the variables from the values that they
had the year before.
Looking at the different range of their standard deviation, all the variables were standardised.
In this way, the contribution of the independent variables to the dependent during the
Regression Analysis would be the correct one. This sacrifice is important in order to gain the
best possible estimations.
12 The interbank rate that would be employed consist of the Cyprus interbank rate before Cyprus entered the
Eurozone in 2008; frrom then onwards, Euribor rate would be used.
13 See Appendix 3
22
3.2.1 Cyprus Special Indicators
Overall, Cyprus is a small country with openness to the global financial markets. The country
faced severe economic problems during the last years. Generally, the country is high
leveraged and exposed to the banking system. However, the exposure shrinkage in a crucial
extent during the Memorandum Period. All in all, Cyprus is expected to be impacted
significantly from credit ratings announcements (Cantor and Packer, 1996) since it was
considered as a high risk investment. Additionally, as Cyprus may be considered as an
economy with remarkable openness to the global financial markets as per Aisen and Hauner
(2008) Cyprus sovereign bond yields are expected to be determine largely by external factors
and crucially from credit risk spreads. Again, supporting that Credit Ratings may contribute
to the explanation of sovereign bond yields. Generally, the economic profile of Cyprus
revealed that Cyprus sovereign bond yields may well be impacted by risk indicators among
other macroeconomic variables.
The second Regression Analysis that would be conducted includes independent variables
based on Cyprus Economic Characteristics:
A latent factor would be generated in order to capture any delayed adjustment as Cyprus is a
small economy; whilst, they are couple of studies supporting the existence of latent factors
even in bigger economies such as Tam and Yu (2008). In short, the model would be a
Dynamic Autoregressive one latent factor (AR(1)) model with the help of Cohrane-Orcutt.
Furthermore, Financial Soundness Indicators would be obtained for the period of 2012 to the
last quarter examined from the IMF database. As Financial Soundness Indicators (2006)
complication guide states Financial Soundness Indicators represent indicators of the current
financial health and soundness of the financial institutions in a country, and of their corporate
and household counterparts. They comprise both accumulated individual institution data and
indicators that are representative of the markets in which the financial institutions operate. As
the only type of information available for Cyprus was the Core Financial Soundness
Indicators for Depositors and the Encouraged it was decided to used only the Core to defend
any Small Sample Issues that may arise from adding too many variables in relation to the
Sample size.
23
Last but not least, Duymmy Variables would be generated in order to represent the credit
ratings of Cyprus as it was mentioned before Cyprus faced serious economic issues and high
risk. The data was obtained from Moody’s Investors Service Inc. Historical Database.
24
4 Empirical analysis
4.1 Core empirical sound macroeconomic variables
4.1.1 Baseline Specification
According to the literature review as well as the potential factors discussed above, the key
factors that evidently impact bond yields are seven macroeconomic variables. Firstly, as a
decomposition of the real GDP, Labour Productivity Per Person and the Working Age
Population as mentioned above, will perhaps influence Cyprus sovereign Bond Yields.
Secondly, other major macroeconomic variables that were tested widely are also believed to
have an influence on fiscal bond yields. Specifically, these six variables consist of 3-Month
Money Market Interest Rate, Unemployment Rate, Inflation Rate, Net Deficit to GDP ratio,
Primary Balance to GDP ratio and Net Effective Exchange Rate.
Noteworthy, the Ordinary Least Squares Regression Analysis was conducted with robust
standard errors in order to cure if any heteroscedasticity exists.
A Regression Analysis was generated with the core empirical sound macroeconomic
independent variables. The analysis portrayed few variables as statistical insignificant. As a
result, the most statistical insignificant variable at a confidence interval of 95% was excluded
at a time. The results showed that Inflation Rate, Net Effective Exchange Rate, Net Deficit to
GDP and Labour Productivity per Person are insignificant to explain the short term changes
of Cyprus Sovereign Bond Yields. Thus, these variables do not contribute to the explanation
of the model.
Before moving on and presenting the generated model it is important to adapt a clearer view
of the variable omissions from the model with the help of bivariate correlation14 of the
variables:
Firstly, removing Inflation Rate as the most insignificant variable it is worth stating that it is
highly, positive and significantly correlated with the variable of Working Age Population.
Additionally, the variable is also negative and significantly correlated with the variable Net
Deficit to GDP. Thereby, the variable exclusion did not affect the model explanation since
14 See Appendix 5
25
the information that the variable contain can also be achieved by a high extent from the
independent variable of Working Age Population as well as form the Net Deficit to GDP
independent variable.
Taking into account the rationale behind this finding, it is no surprise that these variables are
correlated, as an increasing amount of working age population leads to higher generated
revenues boosting the economy, hence creating inflation. In a nutshell, in short-term
increasing inflation is a sign of economic upward trend which is translated into higher GDP,
consequently net deficit to GDP would diminished. Despite that it is appropriate to omit this
variable in order to avoid any multicollinearity issues that may arise due to the correlation of
the variable with other independent variables that would violate Linear Regression
Assumptions.
Secondly, the omission of Net Effective Exchange Rate it was supported by the non-
statistical significance of the variable. This was not a surprise since the generated model
investigates the macroeconomic indicators affecting the yearly changes of the Cyprus
Sovereign Bond Yields. This was not a surprise since most of the currencies are pegged to
other currencies.
Thirdly, excluding Net Deficit to GDP independent variable from the model it is remarkable
to mention that is highly, positive and significantly correlated with Unemployment Rate
Variable. A high unemployment rate means that the economy is on a recession and a sluggish
trend. Therefore, that’s why these variables are correlated since high unemployment and high
Net Deficit to GDP both are indications that the economy is facing a recession. The omission
of Net Deficit to GDP independent variable from the model did not caused dramatic changes
since the variable is highly correlated with Unemployment Rate. Again, it is appropriate to
remove such variables with high autocorrelation in order to avoid multiocolliniearity
problems.
Finally, Labour Productivity per Person variable dropped from the model as an insignificant
independent variable.15 The zero hypothesis of this variable cannot be rejected at a statistical
level of 5%. As consequence this variable would be excluded from the model. This is not a
surprise since this variable mostly indicates long-term influences on sovereign bond yields.
15 Remember that was a one proxy for the real GDP growth determinant.
26
Additionally, the constant factor of the regression was statistically insignificant. However, the
constant was not excluded from the model as removing the constant from the model would
have impacted the model inversely. The effect would be a violation of linear regression
assumption of zero mean of the disturbances.
All in all, the model suggests that short-term changes of Cyprus Sovereign Bond Yields can
be explained through: Working Age Population; 3-Month Money Market Interest Rate;
Unemployment Rate and Primary Balance to GDP ratio. Each of the variable is statistical
significant at a 1% significant level as well as the comprehensive significance of all the
variables. The suggested model baseline is the below:
Δ z10-year Cyprus Sovereign Bond Yields= 0.000 + 0.319*LN zWorking Age Population
+0.436*Δ z3-Month Money Market Rate
+0.457*Δ zUnemployment Rate
+0.130*Δ zPrimary Balance to GDP
Figure 2: Foundation Regression Equation
According to the foundation model, a percentage change in Working Age Population will
yield to 0.319 increase in Cyprus Sovereign Bond Yields. Likewise, a percentage increase in
Unemployment Rate will lead to 0.457 increase in Cyprus Sovereign Bond Yields. Whereas,
a percentage increase in 3-Month Money Market Interest Rate would lead to a 0.436 increase
in Cyprus Sovereign Bond Yields. To a lesser extent, a percentage increase in Primary
Balance Ratio will yield to an increase of 0.130 in the Cyprus Sovereign Bond
Yields.Overall, the independent variables can explain Cyprus Sovereign Bond Yields short-
term changes at 42% level (Adjusted R square). It is generally considered as a model with
some explanatory power. However, there is room of improvement to the model.
4.2 Adjusted Regression Model to Cyprus Economic Profile
Taking into consideration Cyprus Economic Profile it is important to adjust the suggested
model to Cyprus special characteristics. Subsequently, the above model was kept as a
foundation and upon it new macroeconomic and dummy variables were tested. This time the
27
Regression Analysis was conducted with a Dynamic model of Cochrane-Orcutt
Autoregressive model of one latent factor (AR(1))16.
In addition, Principal Components Analysis was conducted for the standardised Financial
Soundness Indicators during the period of 2012 were Cyprus Recession started to the last
quarter examine. All in all, as the sample size of the data is small and there was no room for
excess variables into the model that is why a Principal Components Analysis was conducted.
Overall, four Principal Components were chosen under the rule of “Greater than One”
eigenvalue17.
The aforementioned variables were added to the basic model and afterwards other Dummy
Variables were added to test the importance of Cyprus Sovereign Credit Ratings.18 Not to
mention that A2 credit Rating was omitted from the model in order to avoid multicollinearity
issues. Anyway, the dummy variable importance would be captured through the model’s
coefficient.
The Regression Analysis depicted couple of variables as statistical insignificant. As a
consequence, the most statistical insignificant variable at a confidence interval of 95% was
excluded at a time. The results showed that three Principal Components out of four, the
16 The decision-making on this was crucially based on the fact that when plotting the residuals of the basic
model a Positive Autocorrelation is obvious (see Appendix 7). The residuals are correlated violating the
assumption of the OLS assumptions that residuals are independent. Residuals are moving with a cyclical
motion throughout time. That is why a Cochrane-Orcutt model was employed.
17 Additionally, two Dummy Variables were generated in order to adjust the coefficient value to the Cyprus
Crisis Period. One of the two was a general verification of any additional liquidity premium bondholders may
require during the second quarter of 2011 were Cyprus lost access to the international markets until the last
quarter examine. The second dummy was generated in order to specify if any reduction of investors liquidity
premium taken place due to Cyprus entrance into a Memorandum Assistance Program in the second quarter
of 2013. The generated dummy variables failed to reveal in a statistical significant manner any differences in
liquidity premium investors required during the Cyprus crisis other than Financial Soundness Indicators depict
or Credit Ratings.
18 Remarkable that the Dummy Variables of the Credit Ratings tested at a second phase. Dummy Variables
were not tested with the other variables because the sample of the model was really small and the degrees of
freedom would have been affected significantly.
28
Working Age Population, the Primary Balance and the ratings of Ba3, A2, B3, Aa3, Caa319
are insignificant to explain the short term changes of Cyprus Sovereign Bond Yields. Thus,
these variables do not contribute to the explanation of the model.
Before moving on and presenting the generated model it is important to adapt a clearer view
of the variable omissions from the model:
First and foremost, Working Age Population and Primary Balance to GDP despite their
significance on the foundation model they appear as non-statistical significant indicators to
the new adjusted model. Thus, these variables omitted from the model. Subsequently, no one
of the two variables that Ichiue and Shimizu (2012) provided as key indicators of Sovereign
Bond Yields as proxies of real GDP were significant. The thing is that Cyprus Special
Characteristics give room to other type of macroeconomic variables to explained short-term
changes of the Sovereign Bond Yields that may well do not work for other Countries. In
contrast, Primary Balance to GDP ratio is not able to contribute to the explanation of the
model. This finding is not a big surprise since this variable is generally considered to be a
long-term macroeconomic factors.
The suggested final model is as follows:
Δ z10-year Cyprus Sovereign Bond Yields= -0.232 + 0.345*Δ z3-Month Money Market Rate
+0.399* Δ zUnemployment Rate
-0.099*Δ zFinancial Soundness Indicators
+1.179*Δ Credit Rating: Baa1
+1.348*Δ Credit Rating: Baa3
+1.331*Δ Credit Rating: Ba1
Figure 3: Suggested Regression Equation
Specifically, the findings were as follows. The suggested model that considers Cyprus
Economic Profile supports the following findings. A percentage change in 3-Month Money
Market Rate will lead to a 0.345 increase in Cyprus Sovereign Bond Yields. A percentage
change in Unemployment Rate would increase Cyprus Sovereign Bond Yields by 0.399. The
percentage change in the Financial Soundness of Cyprus lowers bond yields by about 0.099.
Whilst, if Cyprus is rated as Baa1 then Short Term Yields would increase. If Cyprus is rated
as Baa3 then it the rates would surge to a higher extent. Quite puzzling, if Cyprus is rated as
19 See Appendix 1 and Appendix 2 for further information
29
Ba1 then it will lead to a positive increase in yields but to a lesser extent than Baa3. However,
taking into account that Financial Soundness of Cyprus during the same period also adjust the
liquidity risk premium that Investors required it is reasonable. Precisely, when Cyprus is
Rated as Baa1 then Sovereign Bond Yields change by 1.179 percentage points. When Cyprus
is Rated as Baa3 then Sovereign Bond Yields change by 1.348 percentage points and when is
rated as Ba1 Sovereign Bond Yields change by 1.331 percentage points.
To outline the main findings between the third quarter of 2011 and the first quarter of 2012,
moderate credit rick levels that Credit Ratings (Baa1 and Baa3) illustrated that in a statistical
manner bondholders required additional liquidity risk premium during these periods of
ratings. This is reasonable as these credit ratings are a step beyond speculative investments.
Likewise, bondholders seemed to required extra liquidity risk premium when Cyprus entered
the speculative grade the next quarter. From then onwards adjustments on liquidity risk
premiums were based on Financial Soundness Indicators. Investors were appeared to
adjusting their liquidity premiums they had due to Cyprus Economic Crisis based on the
Financial Soundness Indicators of Cyprus. Specifically, the attention was focused on liquid
assets and to a lesser extent to non-performing (Principal Components Major Loadings).
More Precisely, the Principal Components Indicates that a betterment of the Financial
Soundness of Cyprus leads to lower liquidity risk premiums required by the investors.
In order to adapt a clearer view of what the Principal Component of the Financial Soundness
Indicators depict a closer look was taken into Financial Soundness Indicators. (2006).
Financial Soundness Indicators are primarily impacted by the Liquidity of the assets. The
importance of liquidity is that the level of the liquidity impacts the ability of a banking
system to bear shocks. Therefore, in spite of the baseline results that illustrate the significance
of Liquidity it is important to note that the Cyprus Crisis was among others a Banking Crisis
as I aforementioned. This finding is reasonable to impact Cyprus Sovereign Bond Yields. An
epitome of the importance of liquidity comes back to 2013 when the depositors “bail-in”
taken place. Depositors lost their confidence in the banking system. In sequence, this was
turned into a liquidity crisis that had put downward pressure to solvent banks into insolvency.
As the above Compilation Guide presents, if Banks lose access to funding they would be
forced to sell assets at discount in order to gain liquidity.
In order to address the Liquidity Position, the liquid asset ratio was employed. Specifically,
this ratio points out the amount balance sheet “shrinkage” the Banking System can absorb
30
before being forced to sell the illiquid assets. Likewise, the ratio of liquid assets to short-term
liabilities is employed. This ratio designates the short-term liabilities that would have to be
covered by asset sales if access to funding were lost. It is important to highlight
disproportionate maturity mismatches and reckless liquidity management.
It is important to mention that illiquid assets are highly impacted by the non-performing loans
which are a major part of the banks illiquid assets. All in all, the Principal Component
primarily captures the current issues Cyprus Banking System is being dealing with the last
couple of years. Under the improvement or not of the Cyprus Financial Soundness the Cyprus
Sovereign Bond Yields short-term changes were affected. Hence, revealing the importance of
treating and managing liquid assets and non-performing loans.
Generally, the generated model can explain 80% of the short-term changes of Cyprus
Sovereign Bond Yields variation. This indicates a high explanatory power of the model 20.
20 It is important to note down the quality of the suggested Dynamic Model. On the first hand, a Collinearity
Test would be generated in order to test the collinearity among the independent variables. Variance Inflation
Factors indicated that the autocorrelation of the variables is minimal. Thus, the model does not violate the
assumption of non-collinearity between the independent variables. On the other hand, it is important to test
the explanation power of the model throughout the time horizon examined. Overall there is not a remarkable
biased of the model. Generally, the model can explain the short-term changes of Cyprus Sovereign Bond
Yields. Furthermore, looking at the Q-Q plot of the model it can be interpreted that residuals follow a normal
distribution but may be an indication of long tails. Last but not least, as the model consist a constant the
assumption of zero mean of the disturbances holds.
31
CONCLUSION
This paper investigates the relationship among the macroeconomic determinants and the
short-term changes of the 10-year Sovereign Bond Yields between the first quarter of 2001
and the third quarter of 2015. In order to address the macroeconomic determinants that affect
Cyprus Sovereign Bond Yields a Dynamic Time Series Model was employed that took into
account country-specific factors.
As a first stage, an Ordinary Least Squares Model was generated to explore the foundation
factors that may affect Cyprus Sovereign Bond Yields according to past Literature and the
Economic Theory. Overall, four macroeconomic factors appear to significantly affect Cyprus
Sovereign Bond Yields short-term changes: Working Age Population, 3-Month Money
Market Rate, Unemployment Rate and Primary Balance to GDP.
As a second stage, Cyprus Special Economic Profile was taken into consideration and other
variables were tested along with the aforementioned foundation variables. The model applied
was an Autoregressive one latent factor model with Cohrane-Orcutt procedure. Allowing for
a latent factor gives the advantage of correcting the model from late adjustments. The results
indicated that the new generated model has a higher statistical significance and explanatory
power.
The suggested model indicates that Cyprus Sovereign Bond Yields short-term changes can be
explained through changes over 3-Month Money Market Rate, Unemployment Rate, liquidity
premium: Credit Ratings of the first and last level of Moderate by the Investment Grade and
the first level of the Substantial Credit Risk by the Speculative Grade and lastly by the
Financial Soundness Indicators. In addition, from 2012 onwards the liquidity premium
investors required was merely based upon the improvement or the deterioration of the Cyprus
Financial Soundness. Precisely, an improvement to the Financial Soundnees Indicators yields
to a downward adjustment of the short-term bond yields. Briefly, the liquidity of the assets as
well as the Non-Performing Loans which are a big part of the Banks illiquid assets play a
significant role.
Nevertheless, this model has an explanatory power of 80% which is in line with Ang and
Piazzesi (2003) results who demonstrated that macroeconomic indicators explain a
substantial part of the short term changes of the Cyprus Sovereign Bond Yields of
32
approximately 85%. In addition, Baldacci and Kumar (2010) who reviewed the influence of
fiscal deficits and public debt on long-term interest depicted that the size of the short-run
impact of government deficits was less than one third of the overall long-run effect. Hence,
again empirical analysis findings are in line with the above findings. It is not puzzling that the
model did not showcase any statistical significant impact of Net Deficit to GDP in the short-
term. Furthermore, the fact that GDP growth, inflation and debt were not included into the
model as significant variables with explanatory power are not puzzling findings if one
considers Cantor and Packer (1996) who examine Credit Ratings revealed that Credit Ratings
Agencies are taking into account these type of variables among others in order to rate a
Country. All in all, significant changes through these variables may be captured through
Credit Ratings.
More Precisely, the empirical analysis findings are the following. A percentage change in 3-
Month Money Market Rate will lead to a 0.345 increase in Cyprus Sovereign Bond Yields. A
percentage change in Unemployment Rate would increase Cyprus Sovereign Bond Yields by
0.399. The percentage change in the Financial Soundness of Cyprus lowers bond yields by
about 0.099. Whilst, if Cyprus is rated as Baa1 then Short Term Yields would increase. If
Cyprus is rated as Baa3 then it the rates would surge to a higher extent. Quite puzzling, if
Cyprus is rated as Ba1 then it will lead to a positive increase in yields but to a lesser extent
than Baa3. However, taking into account that Financial Soundness of Cyprus during the same
period also adjust the liquidity risk premium that Investors required it is reasonable.
Precisely, when Cyprus is Rated as Baa1 then Sovereign Bond Yields change by 1.179
percentage points. When Cyprus is Rated as Baa3 then Sovereign Bond Yields change by
1.348 percentage points and when is rated as Ba1 Sovereign Bond Yields change by 1.331
percentage points.
All in all, this empirical study showcased the need for further analysis upon Cyprus
Sovereign Bond Yields. Additional analysis should be conducted in order to explore the
medium-term and long-term determinants that affect Cyprus Sovereign Bond Yields. This is
important since Cyprus Economic Profile is expected to play a significant role rather than the
conventional determinants already examined.
33
POLICY IMPLICATIONS
According to the empirical analysis employed short-term changes of Cyprus Sovereign Bond
Yields can be determined through 3-Month Money Market Interest Rate, Unemployment
Rate and the liquidity risk premium that investors require (Credit Ratings of Moderate Credit
Risk – the lowest level of investment grade and substantial credit risk – the first level of
Speculative grade, whereas Financial Soundness Indicators that primarily account for liquid
assets adjust the level of liquidity throughout the economic crisis period from 2012 onwards).
As Cyprus is in the Eurozone and the Interbank Rate can only be influenced by the European
Central Bank Cyprus policy makers can influence merely the short-term sovereign yields
through the Unemployment Rate and the Financial Soundness Indicators. The credit ratings
cannot be impacted directly since they represent exogenous variable that is driven by other
variables not clearly known. The above findings are really important for Cyprus Policy
Makers. Cyprus just concluded the Memorandum and is expected to need credit in the near
term by issuing new government bonds. Subsequently, knowing how to effect the Country’s
borrowing costs is really important.
Firstly, the policy makers during the last years tried to level off the upward trend of
unemployment rate and to put downward pressures. However, according to the last survey of
the Statistical Service unemployment stills in high levels despite of the moderate decline
there are 36,986 unemployed out of them 12,112 are long term unemployed individuals.
Therefore, long-term unemployment is a crucial puzzle in Cyprus. It is important to provide
new tools in order to reduce Long-Term unemployment. This can be achieved by programs
that provide guidance and assistance on helping individuals to change their work industry to
others that are of higher demand. Consequently, industries with excessive supply would
bounce back near to the steady state and other Industries with Excessive demand would lead
too to the steady state. Another tool should be the induction of low cost programs that help
individuals with no technical skills to adapt new skills important for demanding industries.
Remarkable is that out of the unemployed 8,367 of them are youth under 29 years old. This
reveals the need to help the youth find employment and help them gain employment
experience that they do not have. Again, Cyprus Policy Makers may work on mentorship
assistance that will help youth employment force to find job. Also by providing tax incentives
to jobs to employ young employees despite paying for their wage for couple of months. This
34
is appropriate as giving the business a free employee for six months is not a guarantor that
they would keep the employee into the business afterwards.
Secondly, the policy makers may influence Financial Soundness Indicators by improving the
banking sector health. This can be achieved mainly by treating illiquid assets to liquid. A
major concern must be made to the Non-Performing Loans among others illiquid assets. In
order Banks to improve their Liquidity Levels they ought to maintain a substantial amount of
Capital as resembles the Basel. By doing accordingly they would be able to respond
immediately in major liquidity needs. Currently, such a policy is difficult to maintain since
European Central Bank imposes negative interest rates to Corporate Banks reserves to the
Central Bank. This unconventional policy certainly implies downward pressure on Cyprus
Banking System. Banks are facing profitability deterioration whilst trying to keep up high
liquidity levels.
35
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39
APPENDICES
Appendix 1: Cyprus Sovereign Credit Ratings
Table 1: Moody’s Historical Sovereign Credit Ratings
Date Currency Rating Rating Action
13-Nov-15 Domestic B1 Upgrade
14-Nov-14 Domestic B3 Upgrade
21-Mar-14 Domestic Caa3 Rating Affirmation
10-Jan-13 Domestic Caa3 Downgrade
16-Nov-12 Domestic B3 On Watch - Possible Downgrade
8-Oct-12 Domestic B3 Downgrade
13-Jun-12 Domestic Ba3 On Watch - Possible Downgrade
13-Jun-12 Domestic Ba3 Downgrade
13-Mar-12 Domestic Ba1 Downgrade
4-Nov-11 Domestic Baa3 On Watch - Possible Downgrade
4-Nov-11 Domestic Baa3 Downgrade
27-Jul-11 Domestic Baa1 Downgrade
16-May-11 Domestic A2 On Watch - Possible Downgrade
24-Feb-11 Domestic A2 Downgrade
13-Jan-11 Domestic Aa3 On Watch - Possible Downgrade
3-Jan-08 Domestic Aa3 Upgrade
10-Jul-07 Domestic A1 Upgrade
17-May-07 Domestic A2 On Watch - Possible Upgrade
19-Jul-99 Domestic A2 New
40
Appendix 2: Moody’s Credit Ratings Guide
Table 2: Moody’s Sovereign Credit Ratings Guide
Credit Rating Description
Aaa In
ves
tmen
t G
rad
e
Minimal Credit Risk
Aa1 Very Low Credit Risk
Aa2
Aa3
A1 Low Credit Risk
A2
A3
Baa1 Moderate Credit Risk
Baa2
Baa3
Ba1
Sp
ecu
lati
ve
Gra
de
Substantial Credit Risk
Ba2
Ba3
B1 High Credit Risk
B2
B3
Caa1 Very High Credit Risk
Caa2
Caa3
Ca In, or very near, default, with
some prospect of recovery of
principal and interest.
C In default, with little prospect
for recovery of principal or
interest
Source: (Moody's Investors Service, 2016)
41
Appendix 3: Data Sources
Table 3: Data Sources
Variable Source
10-Year Cyprus Sovereign Bond Yields Datastream/ Eurostat
3-Month Euribor Statistical Data Warehouse (ECB)
CPI Statistical Service of Cyprus
Credit Ratings Moody's
Currency and Deposits Eurostat
Cyprus 3-Month Money Market Rate Eurostat
Financial Soundness Indicators International Modetary Fund
GDP Statistical Service of Cyprus/Eurostat
Labour Productivity Per Person Darastream
Loans Eurostat
Net Effective Exchange Rate Eurostat
Primary Balance Ratio Statistical Data Warehouse (ECB)
Securities other than shares Eurostat
Unemployment Statistical Service of Cyprus
Unemployment Rate Eurostat
Working Age Population Darastream
42
Appendix 4: Descriptive Statistics
Table 4: Foundation Macroeconomic Variables Descriptive Statistics
Variable Mean Std. Dev. Minimum Maximum
Δ Unemployment Rate 0.742 1.640 -1.400 4.500
Δ Primary Balance Ratio 0.068 7.545 -35.115 36.158
LN Working Age Population 0.018 0.016 -0.012 0.045
Δ 3-Month Money Market Rate -0.435 1.192 -4.111 1.340
42
Appendix 5: Correlation Matrix
Table 5: Core Macroeconomic Variables Bivariate Correlations
Δ zLabour
Productivity
LN zWorking
Age Population
Δ zMoney
Market
LN
zCPI
Δ zUnemployment
Rate
Δ zNet
Deficit
Ratio
Δ zPrimary
Balance
Ratio
LN
zNEER
1.000 -0.106 0.293 0.184 -0.354 -0.304 0.052 -0.145 Δ zLabour Productivity
-0.106 1.000 0.025 0.583 -0.019 -0.474 -0.014 0.003 LN zWorking Age Population
0.293 0.025 1.000 0.200 -0.187 -0.179 0.239 -0.082 Δ zMoney Market Rate
0.184 0.583 0.200 1.000 -0.129 -0.478 0.007 0.312 LN zCPI
-0.354 -0.019 -0.187 -0.129 1.000 0.665 -0.070 -0.077 Δ zUnemployment Rate
-0.304 -0.474 -0.179 -0.478 0.665 1.000 -0.155 -0.116 Δ zNet Deficit Ratio
0.052 -0.014 0.239 0.007 -0.070 -0.155 1.000 -0.296 Δ zPrimary Balance Ratio
-0.145 0.003 -0.082 0.312 -0.077 -0.116 -0.296 1.000 LN zNEER
Notes: Correlation coefficients, using the observations 2001:1 - 2015:3
5% critical value (two-tailed) = 0.2564 for n = 59
43
Appendix 6: Foundation Baseline
Table 6: Initial Model Baseline
[1]
Constant 0.000
(0.141)
LN zWorking Age Population 0.319
(0.121)***
Δ zMoney Market Rate 0.436
(0.152)***
Δ zUnemployment Rate 0.457
(0.149)***
Δ zPrimary Balance Ratio 0.130
(0.050)***
Observations: 1st Quarter 2001- 3rd Quarter 2015 (59)
Dependent variable: Δ z10-year Cyprus Sovereign Bond Yields
Method: Ordinary Least Squares
HAC standard errors, bandwidth 2 (Bartlett kernel)
R-squared: 0.462
Adjusted R-squared: 0.422
Notes: The values in parentheses are the standard errors of the variables
"***" stands for 1 percent of significance levels, respectively.
44
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2002 2004 2006 2008 2010 2012 2014
uhat1
Appendix 7: Foundation Model Residuals Cyclicality
Figure 1: Foundation Model Residuals Cyclicality (Autocorrelation Graph)
45
Appendix 8: Final Model Baseline
Table 7: Final Model Baseline
[2]
Constant −0.232
(0.324)
Δ zMoney Market Rate 0.345
(0.112)***
Δ zUnemployment Rate 0.399
(0.152)**
Δ zFinancial Soundness Indicators −0.099
(0.039)**
Δ Credit Rating: Baa1 1.179
(0.424)***
Δ Credit Rating: Baa3 1.348
(0.492)***
Δ Credit Rating: Ba1 1.331
(0.433)***
Observations: 2nd Quarter 2001- 3rd Quarter 2015 (58)
Dependent variable: Δ z10-year Cyprus Sovereign Bond Yields
Method: Cochrane-Orcutt
rho = 0.817124
R-squared: 0.822
Adjusted R-squared: 0.801
Notes: The values in parentheses are the standard errors of the variables
"**" and "***" stands for 5 and 1 percent of significance levels, respectively.
