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The Academy of Economic Studies DOFIN – Doctoral School of Finance and Banking. DETERMINANTS OF SPREADS OF ROMANIAN SOVEREIGN BONDS - an application on the EMBIG spreads –. Student: BERBECARU CLAUDIA-FLORIANA Supervisor: Professor MOISĂ ALTĂR. Bucharest, July 2008. - PowerPoint PPT Presentation
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DETERMINANTS OF SPREADS OF ROMANIAN SOVEREIGN BONDS
- an application on the EMBIG spreads –
Student: BERBECARU CLAUDIA-FLORIANA
Supervisor: Professor MOISĂ ALTĂR
The Academy of Economic Studies DOFIN – Doctoral School of Finance and Banking
Bucharest, July 2008
DETERMINANTS OF SPREADS OF ROMANIAN SOVEREIGN BONDS
In last years there was a rapid compression in the spreads of sovereign bonds issued by Emerging Economies
The compression in spreads was driven both by domestic fundamentals but also by the developments in the risk appetite of investors on the international markets
For instance, decrease in the EMBIG spreads for Romania came hand in hand with important progresses in the economy as reflected by the improved sovereign ratings from S&P
EMBIG Composite, EMBIG Romania and Credit Rating Outlook for Romania
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EMBIG spreads for Romania
EMBIG spreads - Composite index
Credit rating outlook index (CROI) - Romania (RHS)
DETERMINANTS OF SPREADS OF ROMANIAN SOVEREIGN BONDS
Developments in the risk appetite of investors on the international markets were also important for the sovereign spreads
EU accession had also an impact on the spreads for CEE countries
Spreads in June 2007 S&P Rating
1 Bulgaria 18.3 8 BBB+, Stable2 Croatia 21.5 9 BBB, Stable3 Lithuania 21.7 7.7 A, Negative4 Slovakia 22.2 6 A, Stable5 Czech Republic 22.7 6 A-, Pozitive6 Romania 28 10 BBB-, Stable7 Hungary 30.1 8 BBB+, Stable8 Poland 33 7 A-, Stable
Country CROI
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Apr-06
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Oct-06
Jan-07
Apr-07
Jul-07
Oct-07
Jan-08
Apr-08
BulgariaCzech RepublicHungaryPolandRomania
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Apr-02 Apr-03 Apr-04 Apr-05 Apr-06 Apr-07 Apr-08
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18EMBI spreads for Hungary
Credit rating outlook index
THE MODEL
The spreads of sovereign bonds for Emerging Economies are thought to reflect the default risk of these countries
They should be modeled as a function of the probability of default and of the loss given the default (or the expected recovery)
Most empirical analises use the following reduced-form equation:
tititiiiti ZXs ,,, )log(
where tis , is the spread for country i at t ,
tiX , is a set of domestic fundamentals for country i at t
tZ is a set of external factors reflecting the degree of risk
appetite of international investors
Many times tiX , is based on the sovereign ratings of rating agencies
DATA USED IN ANALYSIS
Number of countries: 11 Emerging Economies
Period of analysis: May 2002 – April 2008
EMBIG spreads They are computed by JP Morgan on daily basis as the difference between the
yields of sovereign bonds issued by Emerging Economies and the yield for a bond issued by a a developed benchmark economy
The spreads of sovereign bonds for Emerging Economies are thought to reflect the default risk of these countries
EMBIG price indexes are used in the last part of the paper to estimate volatility, co-movements and spillover effects
DATA USED IN ANALYSIS
Credit Rating Outlook Index (CROI)
Converts the sovereign ratings of S&P on a numerical (cardinal) scale
The CROI is used as proxy for the domestic fundamentals
We use the CROI computed by Hartelius and others (2008) which takes into account both the ratings and the oulooks for the ratings
The HP filtered series (ʌ=15) is used the estimations
Stable Positive Negative
Investment grade
AAA 1 0 2.7
AA+ 2 1 3.7
AA 3 2 4.7
AA- 4 3 5.7
A+ 5 4 6.7
A 6 5 7.7
A- 7 6 8.7
BBB+ 8 7 9.7
BBB 9 8 10.7
BBB- 10 9 11.7
Sub-investment grade, categoria I
BB+ 11 10.1 12.7
BB 12 11.1 13.7
BB- 13 12.1 14.7
B+ 14 13.1 15.7
B 15 14.1 16.7
B- 16 15.1 17.7
CCC+ 17 16.1 18.7
Sub-investment grade, categoria II
CCC 18 18 18
CCC- 19 19 19
CC 20 20 20
C 21 21 21
SD 22 22 22
Category S&P sovereign ratings
Outlook
DATA USED IN ANALYSIS
Volatility index of S&P 500 (VIX)
The Chicago Board Options Exchange Volatility Index (VIX) is a key measure of market expectations of near-term volatility (30 days) conveyed by S&P 500 stock index option prices
Proxy for risk appetite in the global markets
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VIX index
DATA USED IN ANALYSIS
Other series taken into account, but not used in the final model
3-months futures on the FED funds rate - used volatility of the diference between the 3-months futures on the FED funds rate and the FED funds rate
Unit root tests
Unit root test on the individual series showed that the series are I(1) Panel unit root tests showed also that series are I(1)
EMPIRICAL MODEL
Testing for cointegration
No cointegration was found between the data when using only data for Romania Panel cointegration tests (Pedroni and Kao) showed that there is a cointegration relationship between the log of EMBIG spreads,
the log of CROI index, and the log of VIX
Estimation methods
To estimate the lon-run relationship between the variables, we use two models: A panel model with fixed effects The pool mean group (PMG) estimator of Pesaran, Shin and Smith (1997)
PANEL MODEL WITH FIXED EFFECTS – ESTIMATION RESULTS
We estimate the following equation with pooled data:
tiiititi vixcroiembig ,, log_log_log_
where 11,...,2,1i identifies the countries and 72,...,2,1t identifies the
period of time.
is the same across the countries
varies across the countries
we allow for fixed effects
we take into account that there might be autocorrelation in the
residuals across countries
PANEL MODEL WITH FIXED EFFECTS – ESTIMATION RESULTS
Variable Coefficient Std. Error t-Statistic Prob.
C -4.150236 0.196415 -21.12989 0.0000
LOG(HP_CROI_?) 2.544679 0.063320 40.18774 0.0000
BG--LOG(VIX) 1.245724 0.102703 12.12937 0.0000
BR--LOG(VIX) 1.561762 0.166648 9.371598 0.0000
CR--LOG(VIX) 0.953539 0.087435 10.90571 0.0000
HU--LOG(VIX) 0.880507 0.056080 15.70092 0.0000
MX--LOG(VIX) 0.823619 0.064358 12.79742 0.0000
PO--LOG(VIX) 1.176625 0.101113 11.63678 0.0000
RO--LOG(VIX) 1.159120 0.134922 8.591051 0.0000
SA--LOG(VIX) 0.785796 0.078891 9.960475 0.0000
SL--LOG(VIX) 0.637691 0.173224 3.681314 0.0002
TU--LOG(VIX) 0.877326 0.097576 8.991228 0.0000
VN--LOG(VIX) 1.318097 0.083019 15.87706 0.0000
Fixed Effects (Cross)
BG--C -0.729494
BR--C -0.995616
CR--C -0.032228
HU--C -0.017427
MX--C 1.080493
PO--C -0.617098
RO--C -0.636171
SA--C 1.101355
SL--C 0.681114
TU--C 0.523515
VN--C -0.358444
Wald Test:
Test Statistic Value df Probability
F-statistic 32.25910 (10, 769) 0.0000 Chi-square 322.5910 10 0.0000
The Wald test rejects the null hypothesis that the coefficient of the VIX is the same across the countries
The tests rejects the null hypotesis that the fixed effects coefficents are thesame across countries
Redundant Fixed Effects Tests Test cross-section fixed effects
Effects Test Statistic d.f. Prob. Cross-section F 45.238281 (10,769) 0.0000
PANEL MODEL WITH FIXED EFFECTS – ESTIMATION RESULTS
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Bulgaria
Romania
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-50
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Apr-02 Apr-03 Apr-04 Apr-05 Apr-06 Apr-07 Apr-08
Croatia
Romania
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-50
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Apr-02 Apr-03 Apr-04 Apr-05 Apr-06 Apr-07 Apr-08
Hungary
Romania
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-50
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Apr-02 Apr-03 Apr-04 Apr-05 Apr-06 Apr-07 Apr-08
Poland
Romania
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-50
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Apr-02 Apr-03 Apr-04 Apr-05 Apr-06 Apr-07 Apr-08
Slovakia
Romania
Given that the variables are I(1) and cointegrated, the residuals can be considered as deviations from the long-run equilibrium
PANEL MODEL WITH FIXED EFFECTS – ESTIMATION RESULTS
There is a high similarity between Romania and Bulgaria which might be explained by the EU accession process:
Spreads increased above the equilibrium level in 2003 when the EU decided not to include Romania and Bulgaria in the 2004 enlargement wave
Spreads decreased towards the equilibrium level in 2004 when the 2007 accession target was announced
Spreads felt below the equilibrium level in 2007 after the EU accesion
In April 2008 Romanian embig spreads were below the equilibrium level despite the fact they increased starting mid-2007
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Bulgaria
Croatia
Romania
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CROI_BG CROI_CR CROI_RO
POOL MEAN GROUP ESTIMATOR OF PESARAN, SHIN AND SMITH (1997)
The following equation in the error correction form is estimated:
itijt
r
jjijti
q
jji
p
jjtijitititiiti
cvixcroihp
embigvixcroihpembigembig
ii
i
log__log_
log_log__log_log_log_
1
0,2,
1
0,1
1
1,,,1,,
where 11,...,2,1i denotes the countries and 72,...,2,1t denotes the time
periods.
In the long-run relationship:
- the coefficient of the CROI is the same across the countries
- the coefficient i of the VIX is different across the countries
speed of adjustment coefficient i is different across the countries
number of lags differ across the variables and across the countries
POOL MEAN GROUP ESTIMATOR OF PESARAN, SHIN AND SMITH (1997)
Schwarz Criteria selected an ARDL (2,0,1) model for Romania
log_log_
log__log_log_log_
tRO,1,21,1,
,1,,
ROtROtRORO
tROtROtROROtRO
cvixembig
vixcroihpembigembig
0.6844-log_0.3451log_0.2479
log_1.4888_log_2.5270log_-0.1185log_
tRO,1,
,1,,
ttRO
ttROtROtRO
vixembig
vixcroihpembigembig
Variable Coefficient Standard error T-statistic
RO -0.1185 0.0327 -3.6262
2.5270 0.2249 11.2336
RO 1.4888 0.3325 4.4782
1,RO 0.2479 0.0949 2.6118
1,2RO 0.3451 0.1063 3.2467
ROc -0.6844 0.2005 -3.4141
POOL MEAN GROUP ESTIMATOR OF PESARAN, SHIN AND SMITH (1997)
Country ARDL Model Phi log_hp_croi log_vix
Bulgaria 2 0 1 Coefficient -0.17 2.53 1.43
Std. Error 0.05 0.22 0.27
t-statistic -3.43 11.23 0.05
Brasilia 1 1 1 Coefficient -0.09 2.53 1.69
Std. Error 0.03 0.22 0.51
t-statistic -2.88 11.23 3.34
Croatia 2 0 0 Coefficient -0.29 2.53 1.08
Std. Error 0.05 0.22 0.15
t-statistic -5.97 11.23 7.05
Hungary 2 0 0 Coefficient -0.38 2.53 0.92
Std. Error 0.07 0.22 0.09
t-statistic -5.70 11.23 9.92
Mexic 1 2 1 Coefficient -0.17 2.53 0.89
Std. Error 0.07 0.22 0.19
t-statistic -2.51 11.23 4.62
Poland 1 1 1 Coefficient 0.04 2.53 1.22
Std. Error 0.04 0.22 0.76
t-statistic 1.00 11.23 1.60
Country ARDL Model Phi log_hp_croi log_vix
Romania 2 0 1 Coefficient -0.12 2.53 1.49
Std. Error 0.03 0.22 0.33
t-statistic -3.63 11.23 4.48
South Africa 1 1 0 Coefficient -0.24 2.53 1.22
Std. Error 0.06 0.22 0.22
t-statistic -3.87 11.23 5.44
Slovakia 1 0 0 Coefficient 0.00 2.53 12.08
Std. Error 0.04 0.22 86.80
t-statistic 0.13 11.23 0.14
Turkey 1 2 1 Coefficient -0.13 2.53 1.67
Std. Error 0.05 0.22 0.51
t-statistic -2.47 11.23 3.29
Venezuela 2 0 0 Coefficient -0.24 2.53 1.41
Std. Error 0.04 0.22 0.16
t-statistic -5.47 11.23 8.81
Coefficients in the long-run relationship for the 11 countries in the pool model
POOL MEAN GROUP ESTIMATOR OF PESARAN, SHIN AND SMITH (1997)
Panel with fixed effects estimation:
Pool mean group estimator results:
5.7755log_4888.1_log_2.5270__log_ ,1, ttROtRO vixcroihpeqembig
6864.4log_1591.1_log_2.5447__log_ ,1, ttROtRO vixcroihpeqembig
-120
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Panel with fixed effects - Deviation fromequilibrium
PMG estimator - Deviation from equilibrium
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EMBI spreads for Romania
Panel w ith f ixed effects - Equilibrium levelPMG estimator - Equilibrium level
MAIN PRACTICAL IMPLICATIONS OF THE MODEL
The spreads for the Romanian sovereign bonds decreased by 225 bp between May 2002 and April 2008. The estimated model, based on the long-run equilibrium relationship, implies only a decrease of 51 bp.
The higher decrease in the effective spreads is due to the fact that the Romanian bonds were undervaluated in 2002 (the spreads were above their equilibrium level) and they were overvaluated in April 2008 (the spreads were below their equilibrium level).
The 51 bp decreased based on the equilibrium level is due exclusively to the fundamentals (as reflected by the decrease in the S&P sovereign rating), while the external factors had no impact during this interval. This is because following the crisis on the international markets the VIX index returned to the same level as in 2002, which means that the investors started to price appropriately the risk.
In the long run, a country cannot bet on the external factors to reduce its borrowing costs. Rather, it should implement appropriate domestic policies in order to improve domestic fundamentals.
CO-MOVEMENTS AND SPILLOVER EFFECTS IN THE DAILY RETURNS OF SOVEREIGN BONDS
We test for the existence of a co-movement in the prices of sovereign bonds and for the existence of spillover effects between the Emerging Countries from Europe
We use daily returns from May 2002 to May 2008 for 6 countries: Poland, Hungary, Slovakia, Romania, Bulgaria, Croatia
To test for co-movements we use the permanent components of the conditional variance obtained from univariate Component GARCH (1,1) models:
,2ttt cr with ),0(~/ 2
1 ttt NI
112
1312
1212
112 )()()( ttttttttt Dqaqaqaq
)()( 21
21211 tttt bqbq
where tr is the daily return, 2
t is the conditional variance and tq is the permanent component of the conditional variance
CO-MOVEMENTS AND SPILLOVER EFFECTS IN THE DAILY RETURNS OF SOVEREIGN BONDS
Estimation results:
Sample period: May 2002 – May 2008
Romania Bulgaria Croatia Poland Hungary
ARCH in Mean c 70.198*** 27.64*** 86.1432*** 36.4175*** 29.3184***
ARCH Term a1 0.1938*** 0.101*** 0.1548** 0.0709** 0.0625***
GARCH Term a2 0.4147*** 0.6193*** -0.0285 0.0671 0.2391*
Asymetric Term a3 -0.076* n.a. -0.1443** -0.1098*** -0.0357
Trend Intercept ω 0.000002*** 0.000004*** 0.000002*** 0.000005*** 0.000005***
Trend AR Term b1 0.9982*** 0.9963*** 0.9969*** 0.8987*** 0.9861***
Forecast error b2 0.0195*** 0.0091** 0.0171*** 0.0231*** 0.0354*** LM Obs*R-squared 0.1035 1.6387 0.0329 0.2591 0.8766
a1 + a2 0.6085 0.7203 0.1263 0.138 0.3016 *, ** and *** indicate significance at the 1%, 5% and 10% confidence level, respectively
CO-MOVEMENTS AND SPILLOVER EFFECTS IN THE DAILY RETURNS OF SOVEREIGN BONDS
Permanent components of the conditional standard deviations
0.0%
0.1%
0.2%
0.3%
0.4%
0.5%
0.6%
2-May-02 17-Oct-02 3-Apr-03 18-Sep-03 4-Mar-04 19-Aug-04 3-Feb-05 21-Jul-05 5-Jan-06 22-Jun-06 7-Dec-06 24-May-07 8-Nov-07 24-Apr-08
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VIX (RHS) Bulgaria Croatia Hungaria Poland Romania
Correlation coefficients
There is a high similarity especially between the permanent components for conditional volatility for Romania, Bulgaria, and Croatia
Bulgaria Croatia Hungary Poland
Romania 0.91 0.81 0.48 0.27
Bulgaria 0.77 0.56 0.14
Croatia 0.64 0.47
Hungary 0.45
Bulgaria Croatia Hungary Poland
Romania 0.53 0.16 0.2 -0.11
Bulgaria 0.07 0.16 0*
Croatia 0.31 0.22Hungary 0.24
Permanent components
Transitory components
Note: All coefficients are statistically significant
The coefficient for Poland and Bulgaria is not statisticalsignificant
CO-MOVEMENTS AND SPILLOVER EFFECTS IN THE DAILY RETURNS OF SOVEREIGN BONDS
Principal components analysis for the permanent components of conditional volatility
A. All five countries are included
Cumulative CumulativeNumber Value Difference Proportion Value Proportion
1 3.29 2.30 0.66 3.29 0.662 1.00 0.52 0.20 4.29 0.863 0.47 0.30 0.09 4.76 0.954 0.17 0.10 0.03 4.93 0.995 0.07 --- 0.01 5.00 1.00
B. Only Romania, Bulgaria and Croatia are included
Cumulative CumulativeNumber Value Difference Proportion Value Proportion
1 2.66 2.41 0.89 2.66 0.892 0.25 0.16 0.08 2.91 0.973 0.09 --- 0.03 3.00 1.00
C. Only Romania and Bulgaria are included
Cumulative CumulativeNumber Value Difference Proportion Value Proportion
1 1.91 1.81 0.95 1.91 0.952 0.09 --- 0.05 2.00 1.00
Eigenvalues: (Sum = 5, Average = 1)
Eigenvalues: (Sum = 3, Average = 1)
Eigenvalues: (Sum = 2, Average = 1)
CO-MOVEMENTS AND SPILLOVER EFFECTS IN THE DAILY RETURNS OF SOVEREIGN BONDS
In order to test for spillover effects we reestimate the CGARCH model using in the equation for the permanent component of the volatility for a country the lagged estimated permanent components for the other countries
,,2
,, tROtROtRO cr with ),0(~/ 2;1, tROttRO NI
1,1;2
1,31,2
1,21,2
1,12
, )()()( tROtROtROtROtROtROtROttRO Dqaqaqaq
1,32
1,2
1,21,1, )()( tjtROtROtROtRO qbbqbq
From country i
to Romania
Coefficient 3b Standard error z-statistics Prob.
Bulgaria 0.020721 0.008247 2.512418 0.0120
Croatia 0.018687 0.005076 3.681276 0.0002
Hungary 0.004638 0.001308 3.545817 0.0004
Poland 0.043294 0.00949 4.561988 0.0000
From Romania
to country i
Coefficient 3b Standard error z-statistics Prob.
Bulgaria 0.180597 0.061303 2.945963 0.0032
Croatia 0.107563 0.057154 1.881976 0.0598
Hungary 0.006334 0.003187 1.987443 0.0469
Poland 0.001284 0.00155 0.828561 0.4074
There are spillover-effects especially both to and from Romania
CONCLUSION
The Credit Rating Outlook Index (CROI) and the volatility index VIX explain well the developments in the spreads of sovereign bonds
There is a large similitude between the deviations of spreads from the level implied by the long-run relationship in the case of Bulgaria and Romania, which we explain by the EU accession process of these two countries.
We find also a comovement in the volatility of daily returns of CEE sovereign bonds, with spillover effects especially between Bulgaria and Romania. The co -movement is located at the level of the permanent component of the conditional volatility, which mean s that it is related to fundamental factors.
In the long run, a country cannot bet on the external factors to reduce its borrowing costs. Rather, it should implement appropriate domestic policies in order to improve domestic fundamentals.
Additional research is welcomed. For instance, modeling the impact of EU accession on the spreads of CEE sovereign bonds is challenging from an econometric point of view given that this is an unobservable variable. Also, alternative estimation methods might be used in order to check the robustness of the empirical results.
REFERENCES
Alexander, C. (2001), “Market Models: A Guide to Financial Data Analysis”, John Wiley
& Sons Ltd
Chicago Board Options Exchange (2003), VIX CBOE Volatility Index, www.cboe.com
Edwards, S. (1983), “LCD’s Foreign Borrowing and Default Risk: An Empirical
Investigation 1976 – 1980”, NBER Working Paper Series No. 1172
Ferrucci, G. (2003), “Empirical Determinants of Emerging Market Economies’ Sovereign
Bond Spreads”, Bank of England, Working Paper No. 205
Fiess, N (2003), “Capital Flows, Country Risk and Contagion”, World Bank Policy
Research Working Paper 2943
Gonzalez Hermosillo, B. (2008), “Investors’ Risk Appetite and Global Financial Market
Conditions”, IMF Working Paper 08/85
Hartelius, K., K. Kashiwase and L.E. Kodres (2008), “Emerging Market Spread
Compression: Is it Real or is it Liquidity”, IMF Working Paper 08/10
Hauner, D., J. Jonas, and M.S. Kumar (2007), “Policy Credibility and Sovereign Credit:
The Case of New EU Member States”, IMF Working Paper 07/1
REFERENCES
J.P. Morgan (1999), “Introducing the J.P. Morgan Emerging Market Bond Index Global
(EMBI Global), www.morganmarkets.com
Luengnaruemitchai, P. and S. Schadler (2007), “Do Economists’ and Financial Markets’
Perspectives on the New Members of the EU Differ?” , IMF Working Paper 07/65
Maier, Philipp (2008), “Sound Policies or Good Fortune: What Drives the Compression in
Emerging Market Spreads?”, Paper presented at the Canadian Economics Assosiation
42nd Annual Meetings at University of British Columbia, Vancouver
McGuire, P. and M.A. Schrijvers (2003), “Common Factors in Emerging Market
Spreads”, BIS Quarterly Review
Ozatay, F. E. Ozmen and G. Sahinbeyoglu (2007), “Emerging Markets Sovereign
Spreads, Global Financial Conditions and US Macroeconomic News”, Economic
Research Center Working Papers in Economics 07/07
Pesaran, M.H., Y. Shin and R.P. Smith (1997), “Pool estimation of Long Run
Relationship in Dynamic Heterogeneous Panels”, Birkbeck College London
Pramor, M. and N.T. Tamirisa (2006), “Common Volatility Trends in the Central and
Eastern European Currencies and the Euro”, IMF Working Paper 06/206
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