Working Paper Series E-2009-02

Financial Crisis and the Choice of Currency Regime
in NEW EU Member Countries

Junko Shimizu (Senshu University)©2009 Junko Shimizu. All rights reserved. Short sections of text, notto exceed two paragraphs, may be quoted without explicit permission
provided that full credit is given to the source.

Financial Crisis and the Choice of Currency Regime
in NEW EU Member Countries†
Junko Shimizu∗
Senshu University
April, 2010

Abstract
This paper investigates the relationship between currency regimes and long-term
government bond spreads in new EU member (NEM) countries for the period between
2001 and 2008. To specify their currency regimes, we use two different methodologies,
estimating implicit basket weight on euro and measuring the z-scores. By using them, we
conduct the empirical analyses to find out how their choice of currency regime affect on
their bond spreads. As a result, we confirm that floating exchange rate regime reduces thebond spreads in NEM countries. Furthermore, more flexible exchange rate regime might
reduce the bond spreads when the financial market is volatile. Our findings provide anessential caveat of currency regime choice for emerging countries during crisis time.
Keywords: currency regime, hard-peg, floating regime, bond spread
JEL Classification: F15, F31, G15† We are grateful to Eiji Ogawa of Hitotsubashi University, Sahoko Kaji of Keio University, and allparticipants of EUSI Seminar in September 2009 for their helpful comments.
∗

Associate Professor, School of Commerce, Senshu University
2-1-1 Higashimita, Tama-ku, Kawasaki, Japan, 214-8580, Teljshimizu@isc.senshu-u.ac.jp：81-44-911-1266.
11. Introduction

The global economy was facing a world-wide financial crisis in 2008. This picture waseven worse in some emerging markets. Especially hard hit in Eastern Europe were
Bulgaria, Romania, Ukraine and Hungary. In New EU member (hereafter, NEM)
countries, there are many countries preparing to accessing euro in near future. However,
the impact of financial crisis seemed to be different in each NEM country. What causessuch differences among them?
It is pointed out that the exchange rate strategies in new EU member countries differ
considerably. An appropriate exchange rate regime, floating, managed or pegged, basicallydepends on individual countries. Their choice, however, might bring some importantchanges that have taken place in recent years. These changes include the general increasein capital mobility or the abrupt reversals of capital flows to developing and transition
economies. After EU enlargement in 2004, there were several researches about therelationship between the exchange rate regimes and the macroeconomic performances of
NEM countries. These studies found that the exchange rate regimes or exchange ratestability vis-à-vis the euro basically brought some sort of effects on their macroeconomic
performances. In this way, if we focus on credit risk in financial market, it is important toinvestigate whether their choice of exchange rate regimes affects on their financial market
in crisis time.
The purpose of this paper is to investigate the relationship between exchange rate
regimes and long-term government bond spreads in NEM countries for the period between
January 2001 and December 2008. Bond spreads of long-term government bonds areusually considered to reflect their risk premium. There are a lot of related works toinvestigate the determinants of bond spreads in emerging countries, however, our2research is characterized by analyzing them from the view point of exchange rate regime.
To analyze their exchange rate regimes, we use two different methodologies, estimating
implicit basket weights on euro by Frankel and Wei (1994) and measuring the z-scores
proposed by Ghoshm, Gulde, and Wolf (2003). By using above original variables, which
represent the exchange rate regimes in each NEM country, we conduct the empiricalanalyses to find out how their choice of exchange rate regime affect on their bond spreads.
Because we includes the latest world-wide financial crisis in the sample period, ourfindings provide an essential caveat of currency regime choice for emerging countries
during crisis time.
The paper is organized as follows. The next section presents the previous studies
concerning about the choice of exchange rate regimes in NEM countries. Section 3 figuresout the exchange rate regimes estimated by two different methods. Section 4 discusses thedeterminants of bond spreads in NEM countries. Section 5 investigates the relationshipbetween the choice of exchange rate regimes and bond spreads empirically. Section 6summarizes the main findings and concludes.

2. The choice of exchange rate regime in the NEM countries

There is considerable diversity in the exchange rate regimes of developing andtransition countries, from very hard currency pegs to relatively free floats and with many
variations between them. This is not surprising in view of the wide differences among
these countries in economic and financial circumstances. However, the choice of exchange
rate regime in new EU countries is something special. It seems that they are racing
toward the final goal, qualifying to enter the euro zone. There are a lot of researches about
the exchange rate regimes adopted by the NEM countries after EU enlargement in 2004.
3

In the process of EU enlargement, the new accession countries are required toparticipate in the ERM-ll framework. Krawczyk (2004) discussed the dangerous ofadopting ERM-ll, which may result in a serious financial instability in the region. Due tohis opinion, they should be allowed for varying monetary integration strategies as they
have been adopting a variety of exchange rate regimes.
De Grauwe and Schnable (2004) explored the impact of the exchange rate regime on
inflation and output in the Central and Eastern European (hereafter, CEE) countries. By
using the panel estimation for the period between 1994 and 2002, they tested the impactof the exchange rate regime on economic growth. Their analytical results indicated thatthe pegged exchange rate did not reduce these countries’ growth rates and brought thebenefits of more trade and lower interest rates.
Arratibel, Furceri, and Martin (2008) focused on the difference of macroeconomic
performance of different exchange rate regimes, “hard peg” and “floating” CEE countrygroups. They empirically analyzed the relation between CEE countries’ exchange rate
volatility and several macroeconomic variables. By using panel estimations for the period
between 1995 and 2006, they found that lower exchange rate volatilities were associatedwith higher growth for relatively less financially developed economies, higher stocks of
FDI for relatively more open economies, higher current account deficits, and a morevolatile credit cycle.
Darvas and Szapary (2008) argued that main risks for CEE countries with hard-peg
were no room to let the nominal exchange rate appreciate to accommodate the price level
convergence. For the countries adopting floating regimes with inflation targeting, thepossibility of letting the nominal exchange rate appreciate provides more flexibility tocontrol inflation and accommodate price level convergence. From above results, we cansummarize that both regimes, hard-peg and floating, have own benefit and loss to NEM
4countries.

3. The strategies to change exchange rate regime in NEM countries

NEM countries’ exchange rate regimes are officially reported in IMF annually report
and we can easily find out what kind of exchange rate they adopt now. However, in thispaper, we focus their strategies to change exchange rate regime from joining to EU to euro
adoption in future. In this section, we evaluate their path to change exchange rate regimesby two empirical methods and try to figure out how and when they have been changing
their regimes exactly. We apply two different methods to evaluate their regimes as follows:
One is the methodology introduced by Frankel and Wei (1994), to estimate implicit basket
weights on euro. Another is the z-scores measure proposed by Ghoshm, Gulde, and Wolf
(2003), which calculate de facto exchange rate volatility against the euro. Before reporting
our calculation results, we briefly present the current exchange rate regimes andmovements of nominal exchange rate in NEM countries.

3-1. The exchange rate regimes in NEM countries

Before introducing the euro in 1999, most NEM currencies pegged to a highly stablecurrency, such as the Deutsche Mark, the US dollar or SDR. However, their exchange rate
regimes have drastically changed ahead of joining EU in 2004. According to Table 1 whichsummarizes the exchange rate regime of NEM countries, we classify their choices ofcurrency regime into three categories; floating, hard-peg to euro, and euro adoption.
Bulgaria, Estonia, Latvia and Lithuania adopts hard-peg regimes with exchange rate
target as their monetary policies, while Czech Republic, Hungary, Poland, and Romania
5adopts managed floating or free floating with inflation targeting. Slovenia was the first
country to introduce euro among NEM countries since January 2007, followed by Cyprus
and Malta since January 2008 and Slovakia has just started to introduce euro since
January 2009.
[Insert Table 1 here]

Looking at the movement of nominal exchange rates vis-à-vis the euro since 1999, thepictures of each group are different and mixed. Figure 1 to 3 show the movement of
nominal exchange rate index, which is based on 100 at benchmark period of January 1999.
A rise in the index means nominal appreciation against the euro.
[Insert Figure 1 here]
Figure 1 shows the movement of hard peg currencies, which are Bulgaria, Estonia,
Latvia, and Lithuania. Among them, three currencies, Estonia kroon, and Bulgarian lev
have not moved since the euro was introduced into the foreign exchange market in
January 1999. Lithuanian lita appreciated almost 40 % from Jan 1999 by middle of 2002,
then fixed at this level. On the other hand, Latvian lat appreciated 20 % from 1999 to end
of 2001, and then declined and stayed at slightly below the level of January 1999. At the
moment, Estonia, Lithuania and Latvia are members of ERM ll.1
[Insert Figure 2 here]
Figure 2 shows the movement of floating currencies, which are Czech koruna,
Hungarian forint, Polish zloty, and New Romanian leu. New Romanian leu depreciatedsharply until 2004 and has stayed around at 40 % lower level than January 1999 sincethen. Romania adopted inflation targeting in 2005. On the other hand, Czech koruna
Estonia, Lithuania and Slovenia joined ERM II on 27 June 2004, Cyprus, Latvia and Malta joined on
29 April 2005 and Slovakia on 25 November 2005. Estonia and Lithuania adopts ERM ll with a fixed
rate and Latvia with +/- 1% fluctuation band as a unilateral commitment.

1

6

appreciated continuously. It went up above at 40 % higher level than January 1999 inmiddle of 2008, and has been declining since then. Polish zloty also was on uptrend after
2004 and has been sharply depreciating since middle of 2008. Hungarian forint slightly
depreciated until it adopted inflation targeting in May 2001, and has fluctuated within +/10 % range. It has also declined since middle of 2008. These floating currencies’
depreciation vis-à-vis the euro in 2008 were affected by escalation of global financial crisis
in 2008.
[Insert Figure 3 here]
Figure 3 shows the movement of euro introducing currencies, which are Slovenian tolar,
Cyprus pound, Maltese lira and Slovak koruna. Slovenian tolar started to depreciate until
2004, then stayed at 20 % lower level than January 1999 and joined the euro in January
2007. Cyprus pound and Maltese lira also stayed in a narrow range or fixed before joining
the euro in January 2008. On the other hand, Slovak koruna continued to appreciate justbefore joining the euro in January 2009. It is because Slovakia adopted ERM ll with thestandard fluctuation band of +/- 15 %.
Above observations indicate that there are various choices for NEM countries toapproach the euro adoption. It is a controversial issue which is the most appropriate
regime to manage the catching up process with euro-area countries.

3-2. The estimated exchange rate regime by Frankel and Wei (1994)

According to the methodology of Frankel and Wei (1994) and Bénassy-Quéré andCoeuré (2000), we identify exchange rate regimes of Central and East European
currencies through the estimation of an equation which relates the fluctuations of each
East European currency to the fluctuation of three anchor currencies which include the7euro, the US dollar and the Sterling pound (UK) as follows.

e& i / kt = a 0 + a 1 e& Euro / kt + a 2 e&US / kt + a 3 e&UK / kt + ε t(1)&

where eik is rates of change in the exchange rate of currency i vis-à-vis currency k, which
is a numeraire currency.
In the above equation, the constant term a0 is positive (negative) if the exchange rate of
the relevant currency regularly depreciates (or is devalued) against the numeraire
currency k. The coefficients a1, a2, a3 are interpreted as weights of the three anchor
currencies in an implicit basket peg system, which include the euro peg system if thefollowing coefficient is estimated;
a1 = 1
According to the estimated coefficients, we can classify the relevant currencies into thethree types of actual exchange rate regime as follows:1.

If none of coefficients is significant, then the exchange rate system is identified as afloating system.
2.

If one coefficient a1 (or a2) is significant and closes to unity whereas all others are notsignificant, then it is identified that the currency is pegged to the euro (or the US dollar).
3.

If various coefficients a1, a2, a3 are significant and bound by zero and one, then thecurrency is pegged to a basket composed of these currencies.
While this method to identify the exchange rate regime is very simple to understand, it
causes the serious problem how to decide a numeraire currency k. Basically, thenumeraire currency k should be traded actively in the international currency market andshould not be linked strongly to each of anchor currencies. In the previous works, theSwiss francs and the SDR were often used as a numeraire currency.2 In this paper, we
2Takagi (1996) and others who analyzes the coefficients of Asian currencies use the Swiss francs,

8

focus on the relationship between the euro and new EU currencies, so we change the roleof the Japanese yen from one of explanatory currencies to one of the numeraire currency
candidates. In order to decide the most desirable numeraire currency, we compare thecorrelation between each of the numeraire currency candidates and the anchor currencies,
which are the euro, the US dollar and the Sterling pound, plus, all of the sampled
European currencies.3[Insert Table 2 here]
Table 2-a shows a correlation matrix between each of the numeraire currency
candidates and three anchor currencies in equation (1). Among the three numeraire
currency candidates, the Japanese yen has the smallest correlation coefficients with threeanchor currencies. Table 2-b shows a correlation matrix between each of anchor currencies,
the numeraire currency candidates and NEM currencies in equation (1). Most of thesampled NEM currencies have the smallest correlation with the Japanese yen among thethree candidates. Accordingly, we choose the Japanese yen as a numeraire currency in the
regression equation (1).
Then we estimate the following equation for NEM countries (Poland, Czech, Slovakia,
Hungary, Slovenia, Estonia, Latvia, Lithuania, Malta, Cyprus, Rumania, and Bulgaria).
Our regression equation (1) can be re-write as follows:∆ log e HOME / JPY = a0 + a1∆ log e EURO / JPY + a2 ∆ log eUSD / JPY + a3 ∆ log eUK / JPY + ε t(2)All foreign exchange data are downloaded from Datastream. The whole sample period
is 1/1/1999 to 12/24/2008.4 We divide the whole sample period into some sub-sample
while Frankel and Wei (1995) used the SDR.
3

We use the Canadian dollar as a numeraire in order to calculate the correlation matrixes among

all of the sampled currencies.
4

All exchange rate data are from Datastream. We use daily NY closing exchange rates.

9

quarterly periods and estimate the coefficients on the three anchor currencies for each of
the sub-sample quarterly periods. The movements in the coefficients show how linkage ofthe currency to the three anchor currencies has been changing in the whole sample
period.5 It implies how and when the monetary authorities have been changing theirexchange rate policy over time. In this paper, we focus on the movement of the euro
coefficient on NEM currencies. Basically most euro coefficients are estimated significantly.
[Insert Table 3 here]
Above results indicate that their changing ways of currency regime are different eachother even in a same grouping currencies. In the next section, we confirm these differencesby measuring another calculation.

3-3. The Z-scores of NEM currencies

In order to measure the observed exchange rate volatility, De Grauwe and Schnable(2004) and Arratibel, Furceri and Martin (2008) calculate the z-scores. The z-scores
proposed by Ghosh, Gulde and Wolf (2003) incorporate both exchange rate fluctuations
aroundaconstant

level

and

exchangerate

fluctuations

arounda

gradualdepreciation/appreciation path. The z-score is calculated as follows:

Z t = µ t2 + σ t2

(3)

In above formula (3), µt corresponds to the arithmetic average of month-to-monthchange in the nominal exchange rate vis-à-vis the euro in year t, and σt is the standard

5

If one of the estimated coefficients is significantly negative, we re-estimate the equation without

them.

10

deviation of the month-to-month changes of the nominal exchange rate vis-à-vis the euroin year t. If z is very low, it means that the currency is pegged to the euro (hard peg). If z is
becoming lower, it means that the currency moves from floating regime to hard peg regime
with the euro.
[Insert Table 4 here]
Yearly index of z-score by using monthly exchange rate data between January 1999
and December 2008 are reported in Table 4. 6 For hard peg countries, their z-scoredeclined to zero except for Latvia, whose z-score was slightly up in 2007 and 2008. For
floating countries, on the other hand, their z-score went up in 2008. It means that theircurrencies became volatile vis-à-vis the euro due to financial turmoil. For euro adoption
countries, their z-score stayed completely zero after their joining in euro-zone except for
Slovakia, who has just joined in euro since January 2009. These results indicated that theexchange rates of floating countries and Latvia became volatile vis-à-vis the euro affectedby global financial crisis in 2008, and others were stable except for Slovakia, who
continued to appreciate vis-à-vis the euro by middle of 2008.

4. The determinants of bond spreads and exchange rate regime

4-1. Financial market integration in NEM countries

In the case of NEM countries, which are committed to adopting the euro at some point,
it is important to analyze the alignment of their financial market integration with those of
the euro area countries. Joining the euro area without a sufficient degree of financial
6

All exchange rate data are from Datastream. For calculating z-score, we use month-end NY

closing rates.

11

market integration might bring serious problems in terms of transmission of a common
monetary policy and common shocks. A lot of empirical researches confirmed that thefinancial integration of new EU countries have been promoted especially after EU
enlargement in 2004. For example, Cappiello et al (2006) used daily return of theGovernment bonds from 2000 to 2005 and found that the largest new EU member states,
the Czech Republic, Hungary and Poland, exhibit strong co-movements both between
themselves and with the euro area. Darvas and Szapary (2008) also confirmed that there
has been a substantial convergence of the nominal interest rates in NEM countries
towards the lower levels prevailing in the euro area by 2007. Since 2007, however, theworld-wide credit crisis has changed above trend of convergences. The nominal money
market convergences, which seemed to be established in most NEM countries, have been
destroyed by recent financial distress caused by the subprime mortgage loan problems.
[Insert Figure 4 here]
Figure 4 and 5 show the monthly movements of money market interest rate (overnightand 3 month, respectively) of NEM countries and euro area. Just after the introducing the
euro in January 1999, the interest rate of euro area was the lowest and all NEM countries’interest rates were much higher than it. Since then, substantial convergences of thenominal interest rate between NEM countries and euro were confirmed in both terms.
Lower figures, which focus on the period between 2006 and 2008, can indicate their
movements more precisely. In 2006, o/n interest rates of Romania and Hungary were 3 to
4 % higher than euro area, while those of Czech, Bulgaria, Slovakia and Latvia were evenlower than euro area. For 3 month interest rates, the differences between NEM countries
and euro area were within 1.5 percent except for Romania and Hungary, which operate

12floating to the euro.7 The interest rate differentials were particularly low in Estonia,
Lithuania and Bulgaria which operate hard peg to the euro and in the new euro area
member, Slovenia and Slovakia. In the Czech, their interest rate differences were negative,
consistent with the appreciating exchange rate. Since middle of 2007, however, their trendof narrowing interest differential with euro area has reversed. At first, the interest
differential in Latvia, unlike in the other countries with fixed exchange rate regime has
sharply increased since 2007. Except for Czech and Slovakia, their differentials were
becoming bigger and even accelerated in middle in 2008. These phenomenons suggest us
that the recent financial distress might give a serious and unusual impact on market
integration in each NEM country. In the next section, we focus on the long-term
government bond spreads between NEM countries and euro area.
[Insert Figure 5 here]

4-2. The long term bond spreads in NEM countries

The introduction of the single currency meant that the euro-zone countries introduced
the same monetary policy by the ECB. Ahead of the introduction of the single currency
euro in 1999, noticeable phenomena in the European government bond markets wererecognized, especially in the new-euro zone countries. By necessity, the front ends of theeach country’s government bonds yields curves was forced to converge the same level. In
addition, the long term government bond yields also were converged to the euro-zone
benchmark yields’ level.8 The degree of convergence was so big that we could explain it by
7

It was indicated that high interest rate differentials in Hungary was caused by their large fiscal

and current account deficit and high inflation.
8

The benchmark government bond in the euro-zone is 10 year German government bond.

13

the introduction of the single currency, the disappearance of the exchange rate risk andthe EU’s strict budget discipline. Although each country’s government bonds have been
dealt in reference to the spreads against the benchmark German government bond withthe same maturities, market participants became gradually unaware of the difference ofthe each country’s economic and budget condition.
Since 2007, however, the world-wide credit crisis has changed this picture drastically.
Except for the German government bonds, not only in the euro-zone countries, but in theneighboring new EU countries that aimed to be the euro member, the yield spreadwidened enormously reflecting the degree of the influence of the credit risks, the eachcountry’s budged condition and liquidity. In this paper, bond spread is defined as follows.
Bond spread i, t = Government bond yeild i ,t − German gevernment bond yield t

It is the difference between the yield to maturity (= YTM) of a NEM 10 year
government bond denominated in terms of their local currencies and the YTM of therespective 10 year German government bond.9[Insert Figure 6 here]
Figure 6 shows the monthly movements of long term government bond spreads of NEM
countries. Due to Figure 6 (upper), many differences in both their levels of bond spreads
and their degrees of convergence among NEM countries are recognized. Of course, there
are certain differences of credit rating, which definitely affect on their bond spreads.10 For
example, bond spreads of Slovenia, Czech, Estonia, Lithuania, and Slovakia, whose credit

9

Yield data are mainly from ECB, and Datastream. For Malta, Slovenia, we use the yield data of

10 year government bond in terms of the euro due to the data constraint.
10

As of April 2008, Ratings of CEE countries by Fitch were as follows; Bulgaria (BBB), Czech (A+),

Estonia(A),Hungary(BBB+),

Latvia(BBB+),

Lithuania(A),Poland(A-),

Romania(BBB),

Slovakia(A), Slovenia(AA). Some of them were downgrading recently.

14

rating were above Single A were mostly lower than other countries, such as Romania andHungary, who’s rating were below A. However, there were still differences among samerating countries. Especially when the worldwide financial crisis started to spread in 2007,
their bond spreads also started to go up sharply. At the end of 2008, five sampled NEM
countries’ bond spreads were above 500 basis points (hereafter, bp) and the difference
between the highest (Latvia, 598 bp) and the lowest among non-euro countries (Czech, 125bp) was much larger than normal time. What kinds of factors are affecting thesedifferences? Because these government bonds are issued not in the euro, but in their own
local currencies, their choice of exchange rate regimes might be one of them.

4-3. The determinants of sovereign bond spreads in NEM countries

The bond spreads that exist among EMU member countries are primarily explained bytheir differences in liquidity and credit risk. Regarding credit risk, the disciplinary effectof the financial markets on policy makers has been focused. For example, the relationshipbetween the development of national debt-to-GDP ratios and sovereign yield spreads
relative to German government bond indicates that the improved creditworthiness of the
EMU member countries since 1999 can explain some of the decline in the spreads.
However, credit risk is only one of the factors that determine the size of the bond spreads.
The determinants of sovereign bond spreads have been extensively investigatedespecially for emerging markets and EMU countries. Recently some researches cover the
euro denominated bonds in NEM countries, too. For example, Strahilov (2006) examined
the long run relationship between the yield spreads of Eastern European national bonds
denominated in US dollars over a US Treasury bond and the country’s fundamentals as
15

well as an US interest rate. They applied the cointegrated VAR model by using themonthly data from June 1997 and September 2002 and confirmed the importance ofnational factors when studying default risk in emerging market. Ebner (2008) studied thedeterminants of NEM government bond spreads empirically with monthly data in the
period of January 1998 and May 2007. As the explanatory variables of bond spreads, they
applied the ECB reference rate, the market liquidity, the market volatility and theconsumer price index in the euro zone, which can be regarded as exogenous to NEM
countries, in addition to macroeconomic fundamentals of each NEM country. As a result,they found that ECB reference rate and market volatility were the main driving factors toincrease bond spreads.11
Above researches indicate that the bond spreads of NEM countries are affected byboth their macroeconomic fundamentals and outside market conditions. Moreover, there
are no common patterns regarding macroeconomic fundamentals. In previous sections, we
confirm strong heterogeneity with respect to exchange rate regime within NEM countries,
which might bring some different impact on their bond spreads. In this paper, we regards
that their exchange rate regimes also is one of the important determinants of bondspreads. Then we try to do some empirical analysis in the next section.

5. Empirical analysis

5-1. Model Specification

To identify the effect of exchange rate regimes on the level of bond spreads, we execute
11

Hartelius, et.al (2008) also confirmed that expectations of future US interest rates and volatility

in those expectations are also a key determinant of emerging market spreads.

16

a cross-country panel model for the period 1st quarter 2001 and 3rd quarter 2009. In thismodel, we apply our quarterly data set, which include the indicators for exchange rate
regime and we investigate the effect of exchange rate regime on bond spreads (BS). The
number of sampled NEM countries is twelve. Due to the data constraint of bond spread,the sampled period is not equal for each NEM country. Bond spreads for each country areplotted in Figure 7.12
[Insert Figure 7 here]
The basic framework for the analysis is a bond spread determined by country specificmacroeconomic fundamentals, which are the growth rate of government gross debt
(DEBT), external balance to GDP (EXBAL), and industrial production (IP)13. In addition
to the macroeconomic factors, we include the trend of exchange rate vis-à-vis the euro
(EXTR) as well. As the external control variables, we employ European market condition
factors into our model. As mentioned in previous section, Strahilov (2006) and Ebner (2008)
confirmed that financial market condition, which represents the market volatilities or investor’s
awareness of risk are one of the important factors to determine bond spreads of emerging countries. We
use VDAX-NEW, which presents an expected 30 days market volatility of the German stockindex DAX, as a market volatility index (VDAXNEW) and the difference of 3 month
EURIBOR and O/N market rate (ECB) as a market liquidity (LIQUIDITY) in accordance

12

The data of bond spreads are start from January 2001 to December 2008 except for Bulgaria(from 2003), Romania (from April 2005), and Slovenia (from 2003).
13

We use the quarterly percentage change of general government gross debt as the growth rate ofgovernment gross debt. The external balance to GDP is calculated as the percentage rate ofcurrent account balance to GDP. We use the quarterly percentage change of Industrial Production
Index, which are calculated from calendar and seasonally adjusted figures. All data aredownloaded from Eurostat.

17

with Ebner (2008).1415
Starting from this baseline model, we add our original variables to represent theexchange rate regime, which are calculated in section 3. We apply the series of euro
coefficients (EUROCOEF), Z-score (ZSCORE)16, and a dummy variable for exchange rate
regime (REGIME) by using the value of euro coefficients, which takes 1 if they are very
close to unity (a country adopts a fixed regime) and 0 if they are depart from unity (acountry adopts a floating regime).
All data except VDAXNEW are downloaded from Eurostat, EC. Before specifying theregression model, both the explained variable, bond spread, and all explanatory variables
have to be tested their time series stationarity. Due to the unit-root tests for all variables,
BS (bond spread), VDAXNEW and LIQUIDITY are confirmed to be non stationary in level.
They turn to be stationary in first difference. The results from unit-root tests suggest thatthe model is estimated by employing BS, VDAXNEW and LIQUIDITY in first differences,which delivers consistent estimates. Our regression model is as follows;∆BS i ,t = α 0 + α1 ⋅ ∆VDAXNEWt + α 2 ⋅ ∆LIQUIDIYt+ α 3 ⋅ DEBTi ,t + +α 4 ⋅ EXBALi ,t + α 5 ⋅ ∆IPi ,t + α 6 ⋅ EXTRi ,t(4)+ α 7 ⋅ Exchange rate regime ( EUROCOEF , ZSCORE , REGIME ) i ,t + ε t

In equation (4), explanatory variables in the first line are common among all sampledcountries and investigate how European market conditions affect on bond spreads of NEM

14

VDAX-NEW, which offered by Deutsche Börse, presents an expected 30 days market volatilityof the German stock index DAX. We got the daily data from website of Deutsche Börse andBloomberg. As quarterly data, we adopt the average of monthly closing rates.
15

Our liquidity index does not express the liquidity condition of bond markets in NEM countries,

but the liquidity condition of European financial market itself. .
16

We calculate Z-score data set in quarterly basis by using the same methodology (monthly

average and monthly volatilities are used).

18

countries. On the other hand, those in the second line are country specific data andinvestigate how each of local macroeconomic fundamental factors affects on bond spreads.
We put our main variables in third line, which can investigate the effect of exchange rate
regime on bond spreads in NEM countries. Since they are strongly correlating each other,
we adopt these variables separately.

5-2. The predicted impact of explanatory variables

Our model specification is basically decided in accordance with the related previous
literatures and market customs, which are usually reported by bond market watchers.
Although there are no concrete rules with respect to the variables the model should
include, we summarize the predicted impact of explanatory variables according toeconomic and market conventions in Table 5.
[Insert Table 5 here]

As mentioned in Table 5, the variables are classified into four groups. The first group
describes the common market factors. VDAX-NEW is a proxy of European financial
market volatility. Liquidity is European money market liquidity measured by thedifference between 3month EURIBOR and O/N (ECB reference rate). Usually thisdifference becomes small when the market is liquid. It becomes larger when the market
participants are so nervous or more risk averse that they eager to borrow money.17[Insert Figure 8 here]
Figure 8 shows the both series. We recognize that VDAX-NEW sharply went up in
17

One of the most active trading terms in money market is usually in 3 month. On the other hand,

O/N rate are basically controlled by ECB.

19

middle of 2008, which represents a sudden escalation of world-wide financial crisis. As for
Liquidity, it climbs up in middle of 2007, which means the expanding of subprime loan
problems in Europe. If market is volatile and illiquid, the bond spreads of NEM countries
are supposed to be higher. Accordingly, the predicted sign of both variables are positive.
A higher government debt to GDP increases bond spread, on the other hand, growing
external balance and industrial production reduce bond spread. As for exchange rate trendvis-à-vis the euro (EXTR), the appreciation of local currency leads more appetite for theirgovernment bond by international investors, which reduce bond spread.18
In this paper, attention is particularly focused on the coefficient of exchange rate
regime. Regarding the exchange rate regime, both signs are possible. A high euro
coefficient means that a local currency is nearly pegged to the euro. On the other hand,rising z-score means more floating exchange rate regime. Accordingly, if fixed exchange
rate regime is considered to be good for bonds of NEM countries, the sign of EUROCOEF(Z-score) is positive (negative), and if more flexible regime is considered to be good forthem, the sign EUROCOEF (Z-score) is negative (positive). REGIME dummy’s sign ispositive (negative) if fixed exchange rate regime is considered to be good (not good) forthem.

5-3. Empirical Results

In this section, the main findings of our cross-country panel analyses are presented. It
is an unbalanced panel due to two reasons: one is the data constraint of bond spread data
and the other is the existence of euro-adoption countries. Accordingly, we execute full18

Exchange rate trend is calculated as a monthly (quarterly) return vis-à-vis the euro. Positive

value means local currency’s appreciating vis-à-vis the euro.

20

sample analysis and selected 8 countries analysis, which exclude three euro-adoption
countries (Malta, Cyprus and Slovenia) and Romania. In both analyses, we add threekinds of variables to represent the exchange rate regime, separately. Both results are
summarized in Table 6 and Table 7, respectively. It is remarkable that all coefficients of
DEBT and IP are significant and consistent with the expected sign, which confirm thateach country’s government debt and industrial production affect its bond spread. All
coefficients of EXTR are significant and consistent with the expected sign, which confirm
that the appreciation of local currencies reduce their bond spreads.19 Furthermore, thecoefficients of market volatility index, VDAXNEW are significant and consistent with the
expected sign in 9 cases out of 16 cases. It suggests that bond spreads of NEW countries
tend to be affected by the financial market condition in Europe. Regarding to the variablesof currency regime, the coefficients of EUROCOEF and REGIME are significant andpositive in the analysis of selected 8 countries. It suggests that the hard-peg to euro
increase their bond spreads.
Since the volatility index turns to be one of the important factors to decide bondspreads of NEM countries, we conduct the additional model to confirm the impact of
exchange rate regime on volatility index as follows;∆BSi ,t = β 0 + β1 ⋅ ∆VDAXNEWt + β 2 ⋅ ( ∆VDAXNEWt ⊗ Exchange rate regime i ,t )+ β 3 ⋅ ∆LIQUIDIYt + β 4 ⋅ DEBTi ,t + β 5 ⋅ EXBALi ,t + β 6 ⋅ IPi ,t(5)+ β 7 ⋅ EXTRi ,t + β 8 ⋅ Exchange rate regime i ,t + ε t

To apply the variables of exchange rate regime as an intercept of the volatility index,
we try to analyze how the exchange rate regimes influence the effect of volatility on bondspread. The lower row in Table 6 and Table 7 show the results. In the analysis of 8

It might be considered that the strong demand of NEM government bonds by international
investors leads the appreciation of NEM currencies.

19

21

countries, the coefficient of interaction between VDAXNEW and Regime are positive andsignificant, and the coefficient of interaction between VDAXNEW and ZSCORE isnegative and significant. These results indicate that hard-peg regime could increase theirbond spreads and floating regime could reduce their bond spreads when the European
financial market is volatile. 20[Insert Table 6 and Table7 here]

6. Conclusion

In this paper, we investigate the relationship between exchange rate regimes andlong-term government bond spreads in NEM countries. To analyze their exchange rate
regimes, we use two different methodologies, estimating implicit basket weights on euro
and measuring the z-scores. By using above original variables, which represent theexchange rate regimes in each NEM country, we conduct the empirical analyses to find out
whether their choice of exchange rate regime affect on their bond spreads.
Our main findings are follows. First, by evaluating the exchange rate regimes in NEM
countries, we confirm that there are several unique features among their strategies tochange exchange rate regime in their accession process toward the euro. Second,
European financial market factors, especially the volatility of financial market, have an
impact on bond spreads in NEM countries, while each country’s government debt andindustrial production also have a significant impact on them. Third, hard peg regime
might increase their bond spread. In another word, more flexible exchange rate regime
can reduces the bond spreads in NEM countries. Forth, floating regime could reduce thebond spread when the market is volatile. The last finding is interpreted as follows. For
20

In panel analysis, we use the fixed effect model with cross section weights. This result is robust

even when we use random effect model.

22emerging countries, pegging their currency to a main neighboring currency seems to be agood strategy to stabilize their economy. In normal time, it is not so hard to make theirinterest rates converge to those in pegged country. The disappearance of the foreignexchange risk makes market participants unaware of the difference of the each country’s
economic condition. The foreign capital tends to flow more into hard-peg NEM countries,
which makes their bond spreads indifferent. As the global financial crisis happen, however,bond spreads becomes strongly affected by market volatility especially for hard pegging
countries. It is because a lack of the flexibility of exchange rate, which could makecountry’s economic management more difficult in near future, might be regarded as a badnews for market participants.
Our findings provide an essential caveat of currency regime choice not only for New EU
member countries but also other emerging countries especially during crisis time. There
are other important determinants, such as liquidity condition of each local bond market,
movement of capital flow, or credit rating which should be included in explanatory
variables. News concerning about governmental credit supply and bail-out plan, or thedecision of IMF assistance also have strong impact on bond spreads. We leave them for
future researches.

23

References:

Arratibel, O., Furceri D., Martin R., 2008. “Real convergence in Central and Eastern European EU
member states; Which role for exchange rate volatility?” European Central Bank, Working Paper
Series, No. 929.

Bénassy-Quéré, A., Coeuré, B., 2000. “Big and Small Currencies: the Regional Connection,” CEPII,
Document de travail, No.2000-10.

Cappiello, L., Gérard, B., Kadareja, A., Manganelli, S., 2006. “Financial integration of new EU
Member States,” European Central Bank, Working Paper Series, No. 683.

Darvas, Z., Szapary, G., 2008. “Euro area enlargement and euro adoption strategies,” European
Commission, European Economy, Economic Papers 304.

De Grauwe, P., Schnabl, G., 2004. “Exchange Rate Regimes and Macroeconomic Stability inCentral and Eastern Europe,” CESifo Working Paper No. 1182.

Ebner, A., 2008. “An Empirical Analysis on the Determinants of CEE Government Bond Spreads,”
Unpublished Working Paper. 7th Annual Meeting of the EEFS International Conference, May
2008.

Frankel, J., A., Wei, S., J., 1994. “Yen bloc or dollar bloc? Exchange rate policies of the East Asian
economies,” in T. Ito and A. O. Krueger, eds., Macroeconomic Linkage: Savings, Exchange Rates,
and Capital Flows, Chicago, University of Chicago Press, pp.295-355.
24

Frankel, J., A., Wei, S., J., 1995. “Emerging Currency Blocs,” in H. Genberg ed., Springer, Berlin,
pp.111-143.

Ghosh, A., Gulde, A-M., Wolf, H., 2003. “Exchange rate regimes: Choices and Consequences,” MIT
Press, Cambridge, MA.

Hartelius, K., Kawasaki, K., Kodres, L., E., 2008. “Emerging Market Spread Compression: Is it
Real or is it Liquidity?” IMF Working Paper, WP/08/10.

Krawczyk, M., K., 2004. “A Currency Crisis in Europe,” HWWA Discussion Paper 278.

Strahilov, K., 2006. “The Determinants of Country Risk in Eastern European Countries; Evidence
from Sovereign Bond Spreads,” Bruges European Economic Research Papers, No.8.

Takagi, S., 1996. “The yen and its East Asian neighbors, 1980-95: cooperation or competition?”
NBER Working Paper, No.5720.

25

Figure 1. Hard peg currencies vis-à-vis the euro (100=Jan 1999)
ESTONIAN KROONLATVIAN LATLITHUANIAN LITA

BULGARIAN LEV

100=Jan 1999
160
140
120
100
80
60
40

Ja
n10

Ja
n09

Ja
n08

Ja
n07

Ja
n06

Ja
n05

Ja
n04

Ja
n03

Ja
n02

Ja
n01

Ja
n00

Ja
n99

20

Figure 2. Floating currencies vis-à-vis the euro (100=Jan 1999)
POLISH ZLOTYCZECH KORUNAHUNGARIAN FORINT
NEW ROMANIAN LEU

100=Jan 1999
160
140
120
100
80
60
40

Ja
n10

Ja
n09

Ja
n08

Ja
n07

Ja
n06

Ja
n05

Ja
n04

Ja
n03

Ja
n02

Ja
n01

Ja
n00

Ja
n99

20

Figure 3. Euro adoption currencies vis-à-vis the euro (100=Jan 1999)
SLOVAK KORUNACYPRUS POUNDSLOVENIAN TOLAR
MALTESE LIRA

100=Jan 1999
150
140
130
120
110
100
90
80
70
60
9
-9
n
Ja

00
nJa

01
nJa

02
nJa

03
nJa

04
nJa

05
nJa

06
nJa

07
nJa

08
nJa

09
nJa

10
nJa

(Source: Datastream)

26

Figure 4. The movement of Money Market Rates (short-term, O/N)(Jan 1999- Dec 2009)(%)

Euro area
Estonia
Lithuania
PolandSlovakia

140
120

Bulgaria
CyprusHungary
Romania

Czech Republic
LatviaMalta
Slovenia

100
80
60
40
20

-9
Ja 9
n00
Ju
l-0
Ja 0
n01
Ju
l-0
Ja 1
n02
Ju
l-0
Ja 2
n03
Ju
l-0
Ja 3
n04
Ju
l-0
Ja 4
n05
Ju
l-0
Ja 5
n06
Ju
l-0
Ja 6
n07
Ju
l-0
Ja 7
n08
Ju
l-0
Ja 8
n09
Ju
l-0
9

Ju
l

Ja
n

-9
9

0

(Jan 2006- Dec 2009)(%)

Euro area
Czech Republic
CyprusLithuania
Malta
Romania
Slovakia

25

20

Bulgaria
Estonia
LatviaHungary
PolandSlovenia

15

10

5

9

-0
9

ct
-0
O

Ju
l

Ap
r-0
9

8

-0
9
Ja
n

ct
-0
O

-0
8
Ju
l

Ap
r-0
8

7

-0
8
Ja
n

ct
-0
O

-0
7
Ju
l

6

-0
7
Ap
r-0
7

Ja
n

ct
-0

-0
6

O

Ju
l

Ap
r-0
6

Ja
n

-0
6

0

(Source:Eurostat)27

Figure 5. The movement of Money Market Rates (3 Month)(Jan 1999- Dec 2009)(%)

Euro area
Estonia
Lithuania
PolandSlovakia

120

100

Bulgaria
CyprusHungary
Romania

Czech Republic
LatviaMalta
Slovenia

80

60

40

20

-9
Ja 9
n00
Ju
l-0
Ja 0
n01
Ju
l-0
Ja 1
n02
Ju
l-0
Ja 2
n03
Ju
l-0
Ja 3
n04
Ju
l-0
Ja 4
n05
Ju
l-0
Ja 5
n06
Ju
l-0
Ja 6
n07
Ju
l-0
Ja 7
n08
Ju
l-0
Ja 8
n09
Ju
l-0
9

Ju
l

Ja
n

-9
9

0

(Jan 2006- Dec 2009)(%)

Euro area
Estonia
Lithuania
PolandSlovakia

25

20

Bulgaria
CyprusHungary
Romania

Czech Republic
LatviaMalta
Slovenia

15

10

5

9

-0
9

ct
-0
O

Ju
l

Ap
r-0
9

8

-0
9
Ja
n

ct
-0
O

-0
8
Ju
l

Ap
r-0
8

7

-0
8
Ja
n

ct
-0
O

-0
7
Ju
l

6

-0
7
Ap
r-0
7

Ja
n

ct
-0

-0
6

O

Ju
l

Ap
r-0
6

Ja
n

-0
6

0

(Source: Eurostat)28

Figure 6. The movement of Long-term Government Bond Yield Spread(Jan 2001- Dec 2009)
CyprusCzech Republic
Poland

(%)
14.0

Malta
LatviaRomania

Slovenia
Lithuania
Slovakia

Bulgaria
Hungary
Estonia

12.0

10.0

8.0

6.0

4.0

2.0

0.0

Ju
l-0
9
Ja
n10

Ju
l-0
8
Ja
n09

Ju
l-0
7
Ja
n08

Ju
l-0
6
Ja
n07

Ju
l-0
5
Ja
n06

Ju
l-0
4
Ja
n05

Ju
l-0
3
Ja
n04

Ju
l-0
2
Ja
n03

Ju
l-0
1
Ja
n02

Ja
n01

-2.0

(Source: Eurostat)(Jan 2006- Dec 2009)(%)

CyprusSlovenia
Czech Republic
Lithuania
PolandSlovakia

14.0

12.0

10.0

Malta
Bulgaria
LatviaHungary
Romania
Estonia▲: Hard peg countries△: Soft peg countries€: Euro-adoption countries▲Latvia▲Lithuania

8.0△Romania△Hungary

6.0

4.0△Poland△Czech2.0▲Estonia▲Bulgaria€ Cyprus€ Malta

0.0€ Slovenia△€ Slovakia

M

Ja
n06
ar
M 06
ay
-0
Ju 6
l-0
Se 6
p0
N 6
ov
-0
Ja 6
n0
M 7
ar
M 07
ay
-0
Ju 7
lSe 07
p0
N 7
ov
-0
Ja 7
n0
M 8
ar
-0
M 8
ay
-0
Ju 8
l-0
Se 8
p0
N 8
ov
-0
Ja 8
n0
M 9
ar
-0
M 9
ay
-0
Ju 9
l-0
Se 9
pN 09
ov
-0
Ja 9
n10

-2.0

(Source: Eurostat)29

Figure 7. The Movement of Bond Spread in NEM Countries
Bulgaria

Cyplus

5

4

Czech

Estonia

3.0

2.0

6

2.5

1.6

5

2.0

1.2

4

1.5

0.8

3

1.0

0.4

2

0.5

0.0

1

0.0

-0.4

3

2

1

0
01

02

03

04

05

06

07

08

09

01

02

03

04

Hungary

05

06

07

08

09

0
01

02

03

Latvia

04

05

06

07

08

09

01

12

12

7

10

10

6

8

5

6
4
2

2

0

2

1

-2

0

05

06

07

08

09

01

02

03

04

05

06

07

08

09

02

03

04

05

06

07

08

09

01

2.5

3.0

7

2.0

2.5

4

6

1.5

2.0

3

5

1.0

1.5

2

4

0.5

1.0

1

3

0.0

0.5

0

2

-0.5
01

02

03

04

05

06

03

04

07

08

09

05

07

08

09

Slovakia

8

09

02

Slovenia

5

08

09

0.2
01

6

07

08

0.4

Romania

06

07

0.6

3.5

05

06

0.8

3.0

04

09

1.0

9

03

08

1.2

4

Poland

02

07

1.6

7

01

06

1.4

3

04

05

Malta

8

4

03

04

1.8

6

02

03

Lithuania

8

01

02

0.0
01

02

03

04

05

06

07

08

09

01

02

03

04

05

06

(Source: Eurostat)

Figure 8. The Movement of VDAX-NEW and Liquidity
60

120
100

50

80

40

60
40

30

20

20
0

10

-20

01

02

03

04

05

06

07

VDAXNEW

08

09

01

02

03

04

05

06

07

08

09

Liquidity(Source: Eurostat, Deutsche Börse)Note : Liquidity is calculated as the difference of euro 3month money market rate and O/N
ECB reference rate.

30

Table 1. Official Monetary Policy Strategies of NEM countries

Exchange rate regime Monetary policy strategy

Features

Bulgaria

peg to euro

Exchange rate target

Czech

Managed floating

Inflation target (1998)

CyprusFixed (euro since 2008)

Estonia

ERM ll with +/-15%*

Malta

Fixed (euro since 2008)

LatviaERM ll with +/-15%**

Exchange rate target

Lithuania

ERM ll with +/-15%*

Exchange rate target(with Currency Board)

Hungary

Floating

Inflation target (2001)(crawling peg)

PolandFloating

Inflation target (1998)(crawling peg)

Romania

Managed floating

Inflation target (2005)

Slovakia

Fixed (euro since 2009)(Inflation target) ***Slovenia

Fixed (euro since 2007)

Exchange rate target(with Currency Board)(with Currency Board)

Source: ECB, Arratibel, Furceri and Martin (2008), Darvas and Szapary (2008)*: In Estonia and Lithuania, their nominal band of ERM ll is +/- 15%, however their actual
band is 0%.
**: In Latvia, their nominal band of ERM ll is +/- 15%, however their actual band is +/- 1%.
*** : In Slovakia, the inflation target was set below 2% at end-2007 and at end-2008.

Table 2. Correlation Matrix between sampled currencies and the candidates of numerairea. Correlation Matrix with explanatory variables
US dollar

euroSterlingpound

US dollar
Euro

1.000
0.411

0.411
1.000

0.524
0.740

Sterling pound
Japanese yen
Swiss francs

0.524
0.450
0.339

0.740
0.403
0.864

1.000
0.428
0.670

SDR

0.802

0.826

0.812

b. Correlation Matrix with explained variable (NEM currencies)
Polishzloty

Hungarian Czech
forintkorunaSlovakkorunaSlovenian Cyprus
tolar
pound

Maltese Estonian Latvian Lithuania Bulgarian Rumanian
lira
kroon
lat
n litalev
leu

US dollar
Euro
Sterling pound

0.384
0.574
0.516

0.339
0.820
0.622

0.364
0.867
0.662

0.383
0.688
0.548

0.411
0.964
0.726

0.416
0.996
0.741

0.544
0.955
0.844

0.409
0.994
0.738

0.693
0.823
0.799

0.554
0.762
0.712

0.418
0.997
0.741

0.416
0.434
0.453

Japanese yen
Swiss francs
SDR

0.284
0.477
0.573

0.323
0.694
0.683

0.353
0.747
0.729

0.362
0.660
0.632

0.377
0.826
0.804

0.409
0.864
0.829

0.458
0.837
0.900

0.403
0.856
0.822

0.590
0.734
0.939

0.477
0.701
0.823

0.407
0.861
0.829

0.297
0.382
0.529

Author's calculation
Data: All foreign exchange data are from Datastream. Sample period is 1/1/1999 to 12/24/2008.
To calculate each correlation, we use the Canadian dollar as a numeraire.

32

Table 3. Estimated euro coefficients of NEM currencies
Bulgaria

Estonia

LatviaLithuania

Czech

Hungary

PolandRomania*

Slovenia

1Q 1999
0.989
0.959
0.241
0.000
1.052
0.393
0.420
0.587
2Q 1999
1.001
0.972
0.241
0.000
0.938
0.623
0.536
0.597
3Q 1999
0.991
0.987
0.288
0.000
0.919
0.716
0.106
0.941
4Q 1999
1.008
0.957
0.232
0.000
0.759
0.712
0.647
0.850
1Q 2000
0.975
0.986
0.237
0.000
0.898
0.965
0.546
0.995
2Q 2000
0.989
1.022
0.232
0.002
0.935
0.995
0.361
0.036
0.992
3Q 2000
0.994
1.004
0.210
0.003
0.916
0.987
0.325
0.975
4Q 2000
0.972
0.991
0.219
0.000
0.843
0.970
0.442
0.998
1Q 2001
0.989
1.004
0.247
0.000
0.908
1.001
0.638
0.994
2Q 2001
0.983
0.954
0.250
0.000
0.925
0.982
0.342
0.994
3Q 2001
0.991
0.994
0.260
0.000
0.835
0.892
1.000
4Q 2001
0.995
1.009
0.284
0.013
0.925
0.653
0.354
0.988
1Q 2002
0.917
0.990
0.290
0.330
0.939
0.885
0.237
0.946
2Q 2002
0.951
1.012
0.315
0.999
0.883
0.937
0.588
0.206
1.040
3Q 2002
0.964
0.949
0.292
0.995
0.581
1.036
0.341
1.014
4Q 2002
0.974
0.995
0.273
0.995
0.708
0.953
0.526
0.861
1Q 2003
0.945
0.994
0.326
0.991
0.892
0.831
0.141
0.929
2Q 2003
0.990
0.996
0.337
1.008
0.999
1.066
0.808
0.835
1.001
3Q 2003
1.000
0.997
0.308
1.000
0.781
0.911
0.812
0.717
1.021
4Q 2003
0.924
1.000
0.366
0.999
0.989
0.890
0.372
1.080
0.990
1Q 2004
0.997
1.000
0.344
1.000
0.962
0.981
0.366
0.757
0.991
2Q 2004
1.002
1.000
0.334
0.999
1.018
0.879
0.674
0.891
1.000
3Q 2004
0.975
1.000
0.354
1.001
0.939
1.003
0.820
0.903
0.982
4Q 2004
0.994
1.000
0.356
1.001
0.832
0.848
0.678
0.300
0.992
1Q 2005
1.004
1.000
0.953
1.002
1.055
1.074
1.022
0.500
0.969
2Q 2005
0.995
1.000
0.999
1.001
0.928
0.947
0.672
1.031
0.911
3Q 2005
0.989
1.000
0.999
0.998
0.861
1.001
1.060
0.969
4Q 2005
1.000
1.000
0.969
1.002
0.958
0.945
1.000
0.698
1.004
1Q 2006
1.002
1.000
1.001
0.999
0.949
0.927
0.759
1.023
1.008
2Q 2006
0.997
1.000
1.000
0.999
0.956
0.888
0.864
0.872
0.917
3Q 2006
1.044
1.000
0.992
0.999
0.983
0.797
0.874
0.870
1.005
4Q 2006
1.009
1.000
0.966
1.000
0.852
1.282
1.032
1.335
1.003
1Q 2007
0.986
1.000
0.820
0.999
0.966
1.150
1.102
1.029
2Q 2007
0.952
1.000
1.089
1.000
0.997
1.369
1.001
1.161
3Q 2007
0.996
1.000
0.898
0.999
0.711
1.071
1.125
0.684
4Q 2007
0.999
1.000
1.009
1.000
1.044
1.008
1.073
0.535
1Q 2008
0.995
1.000
0.956
1.001
1.313
1.315
1.256
1.227
2Q 2008
0.995
1.000
1.006
0.999
0.925
0.977
0.980
0.979
3Q 2008
0.970
1.000
1.000
1.001
1.204
1.203
1.049
1.317
4Q 2008
0.982
1.000
0.996
1.000
1.051
0.950
0.919
1.210
1Q 2009
1.000
1.000
0.988
1.002
1.230
1.480
1.390
1.100
2Q 2009
3Q 2009-0.250-0.250-0.260-0.2500.000-0.530-0.500
4Q 2009
Author's calculation. All coefficients are statistically significant within 10 % significance level. (-) means the coefficient is not significant.
*: In Rumania, their euro coefficients by 2003 were not statistically significant due to their adopting US$ peg regime.

CyprusMalta

Slovakia

0.960
0.952
0.951
0.952
0.940
0.938
0.964
0.983
0.980
0.986
1.017
0.979
1.024
1.004
0.957
1.018
1.004
0.979
1.003
0.985
1.001
1.003
1.017
0.987
1.020
0.996
0.982
0.993
0.988
1.012
1.011
1.016
1.020
0.972
0.998
1.003

0.489
0.521
0.546
0.530
0.521
0.512
0.519
0.502
0.527
0.506
0.531
0.556
0.513
0.491
0.538
0.689
0.714
0.727
0.738
0.692
0.724
0.578
0.725
0.718
0.703
0.904
0.966
1.002
0.999
0.997
0.994
1.000
1.000
0.991
1.000
0.999

0.354
0.768
0.555
0.881
0.748
0.536
0.788
0.661
0.615
0.700
0.853
0.637
0.749
0.195
0.753
0.968
0.961
1.145
0.732
0.802
0.528
1.047
0.948
0.540
0.901
0.895
0.979
0.535
0.779
0.618
0.559
0.574
1.240
1.097
1.009
0.914
1.076
1.151
0.729
0.864

euro zone

33

Table 4. Z-score of monthly exchange rate changes vis-à-vis the euro (%)
Hard peg countries

Floating countries

Euro adoption countries

Bulgaria

Estonia

LatviaLithuania

Czech

Hungary

PolandRomania

Slovenia

CyprusMalta

Slovakia

1999

0.17

0.11

1.60

2.45

2.38

1.14

2.66

5.31

0.84

0.16

0.86

1.55

2000

0.16

0.05

2.81

3.78

1.17

0.45

2.28

4.48

0.64

0.17

1.57

1.35

2001

0.13

0.06

1.65

2.40

1.62

2.35

3.94

2.63

0.37

0.33

1.02

1.12

2002

0.29

0.06

1.57

1.00

1.98

0.83

2.80

3.25

0.69

0.33

0.94

1.86

2003

0.23

0.06

1.86

0.02

1.18

2.63

3.42

2.02

0.26

0.52

0.61

0.94

2004

0.19

0.00

1.11

0.02

1.69

1.36

2.48

2.32

0.19

0.30

0.40

0.93

2005

0.03

0.00

0.07

0.00

1.36

1.56

2.86

2.15

0.11

0.42

0.27

1.57

2006

0.05

0.00

0.08

0.00

1.00

3.39

2.27

1.80

0.03

0.17

0.01

1.72

2007

0.05

0.00

0.77

0.00

1.60

1.56

1.64

2.63

0.00

0.14

0.01

1.59

2008

0.14

0.00

0.32

0.00

3.28

3.02

4.24

3.50

0.00

0.00

0.00

2.10

2009

0.62

0.62

1.00

0.62

2.77

4.63

4.20

2.35

0.00

0.00

0.00

0.00

Author's calculation.
Source: Datastream

Table 5. Predicted sign of explanatory variables
Explanatory variable• Common market factors
VDAXNEW (Market Volatility Indicator)
LIQUIDITY (3mEURIBOR-O/N)• Individual macroeconomic factors
IP (Growth rate of Quarterly Industrial Production)
DEBT (Growth rate of Quarterly Government Debt to GDP)
External Balance (CA to GDP)
EXTR (Exchange trend, Quarterly Change of Exchange Rate, Euro/CEE)• Individual exchange rate regime factors
EUROCOEF (Euro Coefficient)ZSCORE (Z-score)
REGIME (Exchange Regime Dummy)

Predicted sign
+
+
+
-

?

34

Table 6. Estimation Results of Cross-country Panel Estimation (All sampled countries)

Euro Coefficient (EC)Z-score (Z)Exchange rate regime dummy (ER)

VariableC∆VDAXNEW∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.0890.011 **
-0.0030.019 ***
-0.034 ***0.007-0.030 **
0.155
0.222

VariableC∆VDAXNEW∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
Z_NEM
Unweighted R-squared

Coefficient
0.037
0.012 **
-0.0030.019 ***
-0.035 ***0.005-0.030 **
0.002
0.209

VariableC∆VDAXNEW∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.0500.012 **
-0.0030.017 ***
-0.036 ***0.005-0.029 **
0.179
0.222

VariableC∆VDAXNEW∆VDAXNEW×EC_NEM∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.131 ***-0.0040.017-0.0030.019 ***
-0.034 ***0.006-0.032 ***0.197 **
0.225

VariableC∆VDAXNEW∆VDAXNEW×Z_NEM∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient
0.032
0.014 ***
-0.002-0.0030.019 ***
-0.035 ***0.005-0.033 **
0.006
0.214

VariableC∆VDAXNEW∆VDAXNEW×ER_NEM∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.0510.008
0.008-0.0040.017 ***
-0.037 ***0.005-0.031 **
0.185
0.224

Author's calculation. Cross-sections included 12 NEM countries. Total pooled (unbalanced) observations are 359.
Estimation method is a Fixed Effect Model with cross section weights. *** significant at 1% level ** sigunificant at 5% level * significant at 10%35

Table 7. Estimation Results of Cross-country Panel Estimation (Selected 8 countries)

Euro Coefficient (EC)Z-score (Z)Exchange rate regime dummy (ER)

VariableC∆VDAXNEW∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.1500.014 ***
-0.0030.022 ***
-0.040 ***0.002-0.030 ***0.220 **
0.238

VariableC∆VDAXNEW∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient
0.014
0.014 ***
-0.0030.020 ***
-0.043 ***-0.002-0.029 **
0.000
0.215

VariableC∆VDAXNEW∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.0680.014 ***
-0.0030.019 ***
-0.043 ***0.003-0.029 **0.277 *
0.235

VariableC∆VDAXNEW∆VDAXNEW×EC_NEM∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.194 *-0.0010.018-0.0030.022 ***
-0.040 ***0.001-0.032 ***0.261 **
0.239

VariableC∆VDAXNEW∆VDAXNEW×Z_NEM∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient
0.011
0.023 ***
-0.005 **-0.0020.019 ***
-0.043 ***-0.001-0.035 ***
0.007
0.216

VariableC∆VDAXNEW∆VDAXNEW×ER_NEM∆LIQUIDITY
DEBT_NEM
IP_NEM
EXBAL_NEM
EXTR_NEM
EC_NEM
Unweighted R-squared

Coefficient-0.0700.009 *
0.017 *
-0.0030.017 ***
-0.043 ***0.004-0.031 ***0.312 **
0.232

Author's calculation. Cross-sections included 8 NEM countries. Total pooled (unbalanced) observations are 264.
Estimation method is a Fixed Effect Model with cross section weights. *** significant at 1% level ** sigunificant at 5% level * significant at 10%36