Contributions to Economics [630142]

Contributions to Economics
Anastasios Karasavvoglou
Zoran Aranđelović
Srđan Marinković
Persefoni Polychronidou Editors
The First
Decade of
Living with the
Global Crisis
Economic and Social Developments in
the Balkans and Eastern Europe

Contributions to Economics
[anonimizat]

More information about this series at http://www.springer.com/series/1262
[anonimizat]

Anastasios Karasavvoglou Zoran Aran đelovic ´
Sr đan Marinkovic ´Persefoni Polychronidou
Editors
The First Decade of Living
with the Global Crisis
Economic and Social Developments in the
Balkans and Eastern Europe
[anonimizat]

Editors
Anastasios Karasavvoglou
Eastern Macedonia and Thrace
Institute of Technology
Kavala
GreeceZoran Aran đelovic ´
Faculty of Economics
University of Nis ˇ
Nisˇ
Serbia
Sr đan Marinkovic ´
Faculty of Economics
University of Nis ˇ
Nisˇ
SerbiaPersefoni Polychronidou
Eastern Macedonia and Thrace
Institute of Technology
Kavala
Greece
ISSN 1431-1933 ISSN 2197-7178 (electronic)
Contributions to Economics
ISBN 978-3-319-24266-8 ISBN 978-3-319-24267-5 (eBook)
DOI 10.1007/978-3-319-24267-5
Library of Congress Control Number: 2015960847
Springer Cham Heidelberg New York Dordrecht London
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The publisher, the authors and the editors are safe to assume that the advice and information in this
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[anonimizat]

Contents
Part I Structural Changes, Sustainable Growth and Sectoral Policy
Sectoral Analysis of Structural Changes of the Republic
of Serbia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Vladislav Marjanovic and Zoran Arandjelovic
Advances and Difficulties in Serbia ’s Reindustrialization . . . . . . . . . . . . 19
Sofija Adz ˇic ´and Dragan Stojic ´
Investigating Farmer ’s Perceptions of Adopting Alternative Farming
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Sotirios Papadopoulos, Eleni Zafeiriou, Christos Karelakis,
and Theodoros Koutroumanidis
The Impact of Migration on Albanian Agriculture: A Snapshot . . . . . . 47
Matteo Belletti and Elvira Leksinaj
Part II Social Capital in Balkan Societies
Crisis and Social Capital in Greece: A Comparative Study Between
Rural and Urban Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Anna Tokalaki, Anastasios Michailidis, Maria Partalidou,
and Georgios Theodossiou
Social Dialogue in the Era of Memoranda: The Consequences
of Austerity and Deregulation Measures on the Greek Social Partnership
Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Theodore Koutroukis and Spyros Roukanas
Social Capital and Corruption: Evidence from Western Balkan
Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Marija Dz ˇunic´and Natas ˇa Golubovic ´
v
alexandra.horobet@gmail.com

Tax Morale and Compliance in Greece: An Approach
for the Construction of a Questionnaire Survey . . . . . . . . . . . . . . . . . . . 103
Panagiotis Mitrakos, Aristidis Bitzenis, Ioannis Makedos,
and Panagiotis Kontakos
Economic Crisis in Greece and the Consequential “Brain Drain” . . . . . 113
Sofia Anastasiadou
Part III The External Sector, National State and Development
in the Balkans and Eastern Europe
The Legal Framework of European Union: Western Balkans Trade
Liberalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Odysseas G. Spiliopoulos and Dimitrios P. Petropoulos
Exchange Rate Volatility in the Balkans and Eastern Europe:
Implications for International Investments . . . . . . . . . . . . . . . . . . . . . . 137
Alexandra Horobet, Lucian Belascu, and Ana-Maria Barsan
Market Volatility and Foreign Exchange Intervention . . . . . . . . . . . . . . 165
Sr đan Marinkovic ´and Ognjen Radovic ´
Do Remittances Reduce Poverty in Developing Countries? . . . . . . . . . . 185
Costin-Alexandru Ciupureanu and Mihai Daniel Roman vi Contents
alexandra.horobet@gmail.com

Exchange Rate Volatility in the Balkans
and Eastern Europe: Implications
for International Investments
Alexandra Horobet, Lucian Belascu, and Ana-Maria Barsan
Abstract Our paper ’s objective is to study the volatility of exchange rates from the
region that have not yet adopted the Euro and are not members of the Exchange
Rate Mechanism II by considering the exchange rate regime and the implications of
currency volatility for foreign capital flows. We model exchange rate volatility by
using standard deviations of daily logarithmic changes in the exchange rates, rolling
standard deviations, Hodrick-Prescott filters to detect the trends in volatility and
ARIMA models. We find that currency volatility remains a strong issue for these
countries and that central banks have attempted to manage it, particularly after the
global financial crisis. Spikes in monthly volatility are identified for all currencies,
although with some variation in time. Over the long-run, some exchange rates
experienced sudden increases in volatility over the entire period, but rather quickly
corrected, while others have shown an episode of high volatility at the beginning of
the period and recorded a reasonable level of volatility throughout the remaining
period. Exchange rate volatility “has memory”, but some exchange rates are more
prone to the persistent effects of shocks in volatility.
Keywords Exchange rates • Volatility • Eastern Europe • Balkans
JEL Classification Codes F31 • F37 • G17
A. Horobet ( *)
Bucharest University of Economic Studies, 6 Piata Romana, Bucharest, Romania
e-mail: alexandra.horobet@gmail.com
L. Belascu
Lucian Blaga University of Sibiu, 17 Calea Dumbravii, Sibiu, Romania
e-mail: lucian_belascu@yahoo.com
A.-M. Barsan
University of Bucharest, 24 Sfantul Stefan, Bucharest, Romania
e-mail: amy_barsan@yahoo.com
©Springer International Publishing Switzerland 2016
A. Karasavvoglou et al. (eds.), The First Decade of Living with the Global Crisis ,
Contributions to Economics, DOI 10.1007/978-3-319-24267-5_11 137
alexandra.horobet@gmail.com

1 Introduction
The evolution of exchange rates represents a major source of concern from both a
micro- and a macroeconomic perspective, given the cvasi-generalised adoption of
floating rates since 1973. The choice of an optimal exchange rate regime is still an
unresolved question of international macroeconomics, but recent financial history
has generated a growing support for “clear-cut” exchange rate regimes—such as
hard pegged rates or free floating rates—, considered more appropriated in the
current framework of higher financial integration fuelled by unprecedented capital
mobility at the global level. From the perspective of international investments,
currency movements are highly relevant, as they influence the risk of an interna-
tional investment not only directly, through their own volatility, but also through
the link between foreign asset returns and exchange rate changes. In a framework of
increasing international portfolio investments and of business opportunities diver-
sification at the global level, but also of higher financial market integration,
investors critically evaluate the exchange rate risk, particularly when investments
are made in emerging markets, as is the case with the Balkan and Eastern European
countries. A number of studies have shown that a lack of control over currency risk
might put investors in the difficult position of not being able to overcome the costs
of holding foreign assets with the gains obtained from foreign investments (Jorion
1985 ; Eun and Resnick 1994 ; Bugar and Maurer 2002 ).
The recent financial crisis had a noteworthy impact on global financial markets
and under these circumstances it is critical to understand the exposure of interna-
tional investors to the various risk factors abroad and, in the framework of our
research, to currency risk. Various authors study the impact of global financial
turmoil on exchange rate policies in 21 emerging countries between 1994 and 2009
and found that currency volatility increases more than proportionally with the
global financial stress for most countries in their sample; also, the authors evidence
regional contagion effects between neighbouring emerging countries (Coudert
et al. 2011 ). Other authors investigate the group of BRIC countries and argue that
high currency volatility was one of the consequences of the years of uncertainty
about sustainable recovery and governments ’trouble to manage their enormous
fiscal deficits after 2008 (Mellet 2011 ).
At present, the exchange rate regimes of countries from the Balkans and Eastern
Europe are rather varied, but this may be explained by the structural diversity of
these countries and by their needs and past efforts to actively control inflation and
exchange rate volatility. Table 1summarizes the exchange rate regimes and
monetary policy frameworks for the countries in the Balkans and Eastern Europe
at the end of 2013, according to the latest report issued by the International
Monetary Fund (IMF 2013 ).
The adoption of a specific exchange rate regime has a demonstrated impact on
currency volatility. A paper that studies changes in exchange rate regimes in
Visegrad countries finds that path-dependent volatility had a limited effect on
exchange rate developments and that the introduction of floating regimes tends to 138 A. Horobet et al.
alexandra.horobet@gmail.com

increase exchange rate volatility (Kocenda and Valachy 2006 ). In the past two
decades, some of these countries became members of the European Union—Czech
Republic, Hungary and Poland in 2004, Romania and Bulgaria in 2007, and Croatia
in 2013—, with direct effects on their monetary and exchange rate policies. As a
fact, five of them changed their monetary policy rule by the adoption of inflation
targeting regime: the Czech Republic in 1998, Poland in 1999, Hungary in 2001 and
Romania in 2005. Eventually, these countries will have their currencies replaced by
the Euro, but not before at least 2 years spent in the Exchange Rate Mechanism II
(ERM II). Joining ERM II assumes the establishment of a fixed exchange rate of the
respective currency against the Euro with a variation margin of /C615 % around the
parity. Currently, only two countries are members of the ERM II (Denmark and
Lithuania), while the prospects of the others to join the system remain uncertain. An
important point is worth mentioning here, though: even if ERM II allowed for a
rather relaxed band for the exchange rates against the Euro, in reality the effective
margins for the ERM II currencies were much smaller: the Danish krone operated at
a margin lower than 1 %, the Latvian lats at a 1 % margin, while the Estonian kroon
and the Lithuanian litas had 0 % margins before Euro adoption. This indicates, on
one hand, a serious commitment of these countries ’central banks to ensure the
highest possible level of stability of exchange rates against the Euro, and, on the
other hand, a considerable pressure on the future members of ERM II to smooth out
exchange rates fluctuations before joining the system, as moving from a highly
volatile exchange rate to a rather stable one is a not on overnight process.
For what concerns the other countries in the region—Croatia, Serbia, Russia and
Turkey, their characteristics in terms of monetary and exchange rate policies are Table 1 De facto exchange regimes for Balkan and Eastern Europe countries, end 2013
Country CurrencyExchange rate
regime Monetary policy framework
Croatia Kuna
(HRK)Crawl-like
arrangementExchange rate anchor—Euro
Czech
RepublicKoruna
(CZK)Free floating Inflation targeting
Hungary Forint
(HUF)Floating Inflation targeting
Poland Zloty
(PLN)Free floating Inflation targeting
Romania Leu
(RON)Floating Inflation targeting
Russia Rouble
(RUB)Managed exchange
rate arrangementVarious indicators are monitored for the monetary
policy. The central bank has taken preliminary
steps toward inflation targeting
Serbia Dinar
(RSD)Floating Inflation targeting
Turkey Lira
(TRY)Floating Inflation targeting
Source : IMF ( 2013 )Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 139
alexandra.horobet@gmail.com

diverse. Of particular concern for Croatia is the high level of dollarization of the
economy, which distinguishes it from other advanced transition countries and
affects its choice of exchange rate regime. The fragilities created by large quantities
of foreign currency liabilities in Croatian banks ’balance sheets were the main
justification for making exchange rate stability the key player of monetary policy in
Croatia ’s highly dollarized economy (Sosic and Kraft 2004 ). Other authors explore
a number of transition economies—Poland, Czech Republic, Slovakia and the
Republic of Serbia, with regard to their abandonment of the exchange rate targeting
and fixed exchange rate regimes and movement toward explicit/implicit inflation
targeting and flexible exchange rate regimes (Josifidis et al. 2009 ). In the case of
Serbia, the authors find a series of obstacles for a successful inflation targeting
monetary policy rule, such as a strong and persistent exchange rate pass-through
and a low interest rate pass-through. Turkey is a special case among the countries in
the region: since 1990s, Turkey has experienced economic declines after three
major crises in 1994, 1999 and 2001, having as common denominators macroeco-
nomic imbalances and external shocks. The 2001 currency crisis was produced by
capital market liberalization and speculative attacks under the fixed exchange rate
regime, which triggered the change in exchange rate regime to floating accompa-
nied by inflation targeting in 2006. For what concerns Russia, the government debt
crisis of 1998 generated a shift to a managed floating exchange rate. The exchange
rate continued to be tightly managed through 2002–2005, but in 2004 less restric-
tive capital control regulations were adopted and, in 2005, the Bank of Russia
introduced a dual-currency basket as the operational indicator for its exchange rate
policy, aiming to smooth the volatility of the Rouble exchange rate vis- /C18a-vis other
major currencies. After the global financial crisis, the Bank of Russia increased the
flexibility of its exchange rate policy and more flexibility is envisaged for the period
to come.
Our paper aims at investigating the volatility of the exchange rates against the
Euro and the US dollar for eight currencies from the Balkans an Eastern Europe that
have not yet adopted the Euro and are not members of ERM II—Czech Republic,
Hungary, Poland, Romania, Serbia, Croatia, Russia and Turkey. We address the
trends in volatilities by taking into account the exchange rate regimes used in each
of these eight countries and using daily exchange rates between 1999 and 2013.
Exchange rate volatility is modelled using monthly standard deviations of daily
logarithmic changes in the exchange rates, as well as rolling standard deviations
with different windows, which allows us to understand short-term versus long-term
changes in volatility. We apply Hodrick-Prescott filters to detect trends in monthly
standard deviations and ARIMA models to investigate the exchange rates volatility
response to past levels of volatility and to potential shocks in volatility. We extend
here the previous works on exchange rate volatility in Central and Eastern Europe,
by investigating more currencies in the region and by using other relevant instru-
ments for understanding currency volatility (Horobet and Tusa 2007 ; Horobet
et al. 2011 ).
We contribute to the research in the field with a thorough investigation of
currency volatility patterns in the region, which represents, to our knowledge, the 140 A. Horobet et al.
alexandra.horobet@gmail.com

first attempt of this kind in the literature. In order to properly understand the
evolution of currency volatility after 1999, we use a set of instruments that provide
information on short-run versus long-run volatility patterns, as well as on volatility
time-dependency and currency volatility sensitivity to potential shocks.
2 Data and Research Methodology
We use in our research exchange rates of the domestic currencies against the Euro
and the US dollar of eight countries from the Balkans and Eastern Europe that have
not yet adopted the Euro and are not members of the ERM II—more specifically
Czech Republic (Czech Koruna—CZK), Hungary (Hungarian Forint—HUF),
Poland (Polish Zloty—PLN), Romania (Romanian Leu—RON), Serbia (Serbian
Dinar—RSD), Croatia (Croatian Kuna—HRK), Russia (Russian Rouble—RUB)
and Turkey (Turkish Lira—TYR). Data on exchange rates was collected from the
Pacific Exchange Rate Service, for the period between 1999 and 2013. The first
observation dates from January 4th, 1999 for Czech Republic, Hungary, Poland,
Romania, Russia and Turkey, from March 1st, 2002 for Croatia, and from
September 4th, 2007 for Serbia.
Based on daily exchange rates, we calculate (1) the daily logarithmic returns
with EUR and the USD, respectively, as base currencies; (2) the monthly standard
deviation of the daily logarithmic returns against the EUR and the USD; and (3) the
30 days, 90 days and 360 days rolling standard deviations of daily logarithmic
returns.
We apply the Hodrick-Prescott (HP) filter, which offers a smooth estimate of the
long-term trend component of a series of data, to have a better view on the monthly
standard deviations of daily logarithmic returns. The method was proposed by
Hodrick and Prescott in 1997 to model post-war U.S. business cycles, and it uses
a two-sided linear filter that calculates the smooth series S of a series Y by
minimising the variance of Y around S, by taking into account a penalty parameter
λthat constrains the second difference of S (Hodrick and Prescott 1997 ). Specifi-
cally, the HP filter minimizes:
XT
t¼1yt/C0st ð Ț 2țλXT
t¼2stț1/C0st ð Ț /C0 st/C0st/C01 ð Ț ½ /C138 2ð1Ț
The parameter λcontrols for the degree of smoothness of the series variance: the
larger its value, the smoother the variance. We have used 14,400 as the value of λ
for smoothing the series of monthly standard deviations, which is appropriate for
the work on monthly data.
Autoregressive integrated moving average (ARIMA) models, popularly known
as the Box–Jenkins methodology, offer an analysis of the stochastic properties of
economic time series, based on the “let data speak for themselves” philosophy (Box Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 141
alexandra.horobet@gmail.com

and Jenkins 1978 ). An ARIMA (p, d, q) is an autoregressive integrated moving
average time series, where p denotes the number of autoregressive terms, d the
number of times the series has to be differenced before the series becomes station-
ary, and q the number of moving average terms. The ARIMA (p,d,q) model of the
time series {x 1,x 2,. . . } may be defined as:
ΦpBð Ț Δdxt¼ΘqBð Ț εr ð2Ț
where Bis the backward shift operator, Bx y¼xy/C01,Δ¼1/C0B is the backward
difference, and ΦpandΘqare polynomials of order p and q, respectively. ARIMA
(p,d,q) models are the product of an autoregressive part AR(p) of the form:
Φp¼1/C0φ1B/C0φ2B2/C0. . . /C0φpBpð3Ț
an integrating part of the form:
I d ð Ț ¼ Δ/C0dð4Ț
and a moving average MA(q) part of the form:
Θq¼1/C0θ1B/C0θ2B2/C0. . . /C0θpBpð5Ț
While finding d in ARIMA(p,d,q) is typically implemented with the help of
stationarity tests such as Augmented Dickey-Fuller or Phillips-Perron, the method
of choosing values for p and q requires a careful analysis of the autocorrelations and
partial autocorrelations for the times series. Still, finding the good model is usually
an iterative technique where different values for p and q are given and the model
diagnostic is carried out. We verify the ARIMA properties of the series of monthly
standard deviations, in order to identify the time-dependence of monthly volatil-
ity—AR terms—and the influence of possible shocks in volatility—the MA terms.
3 Results
3.1 Brief Analysis of Daily Exchange Rates
Figure 1shows the series of daily exchange rates of the eight currencies from the
Balkans and Eastern Europe against the EUR and the USD, between 1999 and 2013,
while Fig. 2presents the daily logarithmic changes (or returns) of the same
exchange rates. Descriptive statistics for the daily logarithmic changes are
presented in Table 2. A quick look at the graphs in Fig. 1indicates different patterns
for these countries ’exchange rates against both the EUR and the USD. Overall, the
CZK is the only currency with an appreciating trend between 1999 and 2013 against 142 A. Horobet et al.
alexandra.horobet@gmail.com

both the EUR and the USD; the RON, RSD, RUB and TRY depreciated against the
EUR and USD, as a general trend, but swings in the exchange rates over these years
were important, particularly in the case of the RUB and even RSD. Over the entire
frame of exchange rates observations for each currency pair, the CZK appreciated
by 29.2 % and the PLN by a tiny 0.06 %, while all the other currencies depreciated
against the EUR, with the notable case of the TRY—a depreciation of 87.32 %.
When the exchange rates against the USD are considered, three currencies recorded
overall appreciations against the American currency—the HRK (54.7 %), the CZK
(49.9 %) and the PLN (16.11 %). As in the EUR case, the TRY depreciated heavily,
by 85.3 % overall, followed by RON, with a depreciation rate of 69.3 %.
It is worthwhile mentioning the higher stability in the RONEUR exchange rate
after 2008 compared to the previous years, but which is not found in the case of
RONUSD exchange rate—this is explained by the fact that on the Romanian
foreign exchange market the RONEUR exchange rate is the reference rate and
observed by the Romanian central bank, while the RONUSD exchange rate is
determined as a cross-rate, taking into account the USDEUR exchange rate in the
international foreign exchange market. By far, the HUFEUR, HUFUSD, PLNEUR, 20 24 28 32 36 40
500 1000 1500 2000 2500 3000 3500 CZKEUR
10 15 20 25 30 35 40 45
500 1000 1500 2000 2500 3000 3500 CZKUSD
7.0 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8
500 1000 1500 2000 2500 3000 3500 HRKEUR
456789
500 1000 1500 2000 2500 3000 3500 HRKUSD
220 240 260 280 300 320 340
500 1000 1500 2000 2500 3000 3500 HUFEUR
120 160 200 240 280 320 360
500 1000 1500 2000 2500 3000 3500 HUFUSD
3.0 3.5 4.0 4.5 5.0
500 1000 1500 2000 2500 3000 3500 PLNEUR
2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8
500 1000 1500 2000 2500 3000 3500 PLNUSD
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
500 1000 1500 2000 2500 3000 3500 RONEUR
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
500 1000 1500 2000 2500 3000 3500 RONUSD
70 80 90 100 110 120
500 1000 1500 2000 2500 3000 3500 RSDEUR
40 50 60 70 80 90 100
500 1000 1500 2000 2500 3000 3500 RSDUSD
20 25 30 35 40 45 50
500 1000 1500 2000 2500 3000 3500 RUBEUR
20 24 28 32 36 40
500 1000 1500 2000 2500 3000 3500 RUBUSD
0.0 0.5 1.0 1.5 2.0 2.5 3.0
500 1000 1500 2000 2500 3000 3500 TRYEUR
0.0 0.4 0.8 1.2 1.6 2.0 2.4
500 1000 1500 2000 2500 3000 3500 TRYUSD
Fig. 1 Daily exchange rates against EUR and USD, 1999–2013. Note: The first observation is
January 4, 1999 and the last observation is December 31, 2013; exchange rates are quoted with
EUR and USD as base currencies Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 143
alexandra.horobet@gmail.com

PLNUSD and HRKEUR exchange rates display the highest volatility over the
period, especially after the end of 2008. An interesting observation regards the
high correlations between the exchange rates of each of the eight currencies against
the EUR and the USD, presented in Table 2.
When daily exchange returns are considered, the different evolutions of
exchange rates are reflected in the diverse patterns shown in Fig. 2. Again, there
are exchange rates with a rather high volatility over the entire period—HRKEUR,
HRKUSD, HUFUSD, PLNUSD, RSDEUR, RSDUSD, exchange rates with spikes
in volatility—CZKEUR, CSKUSD, RUBEUR, RUBEUR, TRYEUR and
TRYUSD, and also rather stable exchange rates—RONEUR and RONUSD. At
the same time, the well documented phenomenon of volatility clustering is easily
observable (Engle 1982 ; Bollerslev 1986 ; Cont 2005 ). -.04 -.03 -.02 -.01 .00 .01 .02 .03 .04 .05
500 1000 1500 2000 2500 3000 3500 CZKEUR
-.06 -.04 -.02 .00 .02 .04 .06
500 1000 1500 2000 2500 3000 3500 CZKUSD
-.020 -.015 -.010 -.005 .000 .005 .010 .015 .020
500 1000 1500 2000 2500 3000 3500 HRKEUR
-.05 -.04 -.03 -.02 -.01 .00 .01 .02 .03
500 1000 1500 2000 2500 3000 3500 HRKUSD
-.04 -.02 .00 .02 .04 .06 .08
500 1000 1500 2000 2500 3000 3500 HUFEUR
-.06 -.04 -.02 .00 .02 .04 .06 .08
500 1000 1500 2000 2500 3000 3500 HUFUSD
-.08 -.06 -.04 -.02 .00 .02 .04 .06 .08
500 1000 1500 2000 2500 3000 3500 PLNEUR
-.08 -.06 -.04 -.02 .00 .02 .04 .06 .08
500 1000 1500 2000 2500 3000 3500 PLNUSD
-.10 -.05 .00 .05 .10 .15 .20
500 1000 1500 2000 2500 3000 3500 RONEUR
-.10 -.05 .00 .05 .10 .15 .20
500 1000 1500 2000 2500 3000 3500 RONUSD
-.04 -.03 -.02 -.01 .00 .01 .02 .03 .04
500 1000 1500 2000 2500 3000 3500 RSDEUR
-.08 -.06 -.04 -.02 .00 .02 .04 .06
500 1000 1500 2000 2500 3000 3500 RSDUSD
-.12 -.08 -.04 .00 .04 .08 .12
500 1000 1500 2000 2500 3000 3500 RUBEUR
-.12 -.08 -.04 .00 .04 .08 .12
500 1000 1500 2000 2500 3000 3500 RUBUSD
-.10 -.05 .00 .05 .10 .15 .20 .25
500 1000 1500 2000 2500 3000 3500 TRYEUR
-.15 -.10 -.05 .00 .05 .10 .15 .20 .25
500 1000 1500 2000 2500 3000 3500 TRYUSD
Fig. 2 Daily returns of exchange rates against the EUR and USD, 1999–2013. Note: The first
observation is January 5, 1999 and the last observation is December 31, 2013
Table 2 Correlations between the exchange rates against EUR and USD, 1999–2013
Currency CZK HRK HUF PLN RON RSD RUB TRY
Correlation
coefficient0.6199 0.2907 0.7504 0.7261 0.7133 0.6464 0.7614 0.8459144 A. Horobet et al.
alexandra.horobet@gmail.com

Considering the exchange rates against the EUR, an analysis of data in Table 3
shows that only one currency—CZK—appreciated, on average, against the EUR, at
a rate of 0.14 % per month, while all the other currencies depreciated against the
common currency—the highest average depreciation was recorded for TRY
(1.11 % per month) and the lowest for HRK (0.20 % per month). The average
monthly change in the value against the EUR for PLN was a surprising 0 %. At the
same time, the volatility of daily returns was considerable for many currencies,
either on an absolute basis (minimum and maximum values) or by taking into
account their standard deviations. The most volatile exchange rates over the entire
period under analysis were the TRY (a standard deviation on 5.25 % per month), the
RUB (standard deviation of 4.32 % per month) and the RON (a standard deviation
of 3.71 % per month), while the most stable currencies were the HRK (standard
deviation of 1.09 %) and the CZK (standard deviation of 2.15 %). All exchange rate
changes are non-normally distributed, with negative skewness for CZK, HRK,
HUF, PLN, RON and TRY, positive skewness for RSD and RUB, and excess
kurtosis—the same leptokurtic distributions are also indicated by the Jarque-
Berra test of normality.
When we investigate the exchange rates against the USD (see Table 4), we
observe that three currencies (CZK, HRK and PLN) recorded, on average over the
period, appreciations against the USD—the highest average appreciation belongs to
HRK, while all the other currencies depreciated on average against the USD—the
highest depreciation was recorded for the TRY (thus confirming the results for the
TRY exchange rate against the EUR). As in the EUR case, the exchange rates
volatility was high, reaching 4.01 % on a monthly basis for TRY and 3.36 % per
month for HUF. Daily jumps in the series of exchange rates changes are also
observable in the relation to the USD—the highest were present for TRY
(an appreciation of the USD of 482.8 % per month, or 24.13 % per day, on
February 22, 2001) and RON (an appreciation of 335.6 % per month, or 16.78 %
per day, on March 11, 2009). On average, the highest appreciations of the USD
were higher compared to the appreciations against the EUR (196.3 % compared to
176.93 % on a monthly basis), as is the case with the largest depreciations of the
USD (on average, the highest depreciations of the USD were 142.4 % per month,
while the largest depreciations of the EUR generated an average of 121.83 % per
month). The Jarque-Berra test of normality indicates leptokurtic distributions, with
negative skewness for HUF, PLN, RON, RUB and TRY, positive skewness for
CZK, HRK and RSD, and excess kurtosis for all exchange rate return series.
A quick look at Tables 5and 6, which show the correlations between the
logarithmic returns in the exchange rates against the EUR and the USD, indicates
stronger links between the exchange rates against the USD, compared to the
exchange rates against the EUR. In the case of correlations against the EUR, the
highest correlation coefficient is 0.5542 between HUFEUR and PLNEUR, while the
lowest is negative, with a value of /C00.0118, between RSDEUR and CZKEUR. For
correlations against the USD, the highest coefficient has a value of 0.8190 between
HRKUSD and CZKUSD, and the lowest has a value of 0.1303 between RUBUSD
and TRYUSD. The average correlation coefficient for the exchange rates against the
EUR was 0.1579 and for the exchange rates against the USD was 0.5038. Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 145
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Table 3 Descriptive statistics of daily returns of exchange rates against the EUR (monthly basis)
CZKEUR HRKEUR HUFEUR PLNEUR RONEUR RSDEUR RUBEUR TRYEUR
Mean /C00.0014 0.0002 0.0009 0.0000 0.0071 0.0046 0.0032 0.0111
Median /C00.0020 0.0000 /C00.0040 /C00.0040 0.0000 0.0040 0.0000 0.0020
Maximum 0.8980 0.3680 1.2120 1.3280 3.2760 0.6300 1.6160 4.8260
Minimum /C00.6800 /C00.3300 /C00.7720 /C01.4820 /C01.4900 /C00.7440 /C02.3540 /C01.8940
Std. dev. 0.0215 0.0109 0.0284 0.0317 0.0371 0.0262 0.0432 0.0525
Skewness 0.2228 0.0561 0.5303 0.0805 3.8363 /C00.2295 /C00.2041 3.9157
Kurtosis 9.7902 10.1969 10.2694 12.5719 70.9176 9.9530 22.6156 76.5521
Jarque-Bera 7188.92 6367.97 8378.62 14,228.32 725,277.50 3192.52 59,761.47 849,410.50
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Observations 3726 2950 3726 3726 3726 1578 3726 3726 146 A. Horobet et al.
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Table 4 Descriptive statistics of daily returns of exchange rates against the USD (monthly basis)
CZKUSD HRKUSD HUFUSD PLNUSD RONUSD RSDUSD RUBUSD TRYUSD
Mean /C00.0022 /C00.0030 0.0001 /C00.0008 0.0063 0.0044 0.0024 0.0103
Median 0.0000 /C00.0040 /C00.0020 /C00.0060 0.0030 0.0020 0.0000 0.0000
Maximum 1.1040 0.5540 1.3480 1.4340 3.3560 1.1340 1.9460 4.8280
Minimum /C01.0720 /C00.9420 /C01.0720 /C01.3580 /C01.3660 /C01.3100 /C02.3040 /C01.9680
Std. dev. 0.0286 0.0232 0.0336 0.0326 0.0304 0.0320 0.0311 0.0401
Skewness /C00.0867 /C00.1011 0.1054 0.1825 3.5556 /C00.1982 0.2057 3.8914
Kurtosis 6.0587 5.1561 6.5913 8.6597 61.9480 8.0236 30.6822 77.0268
Jarque-Bera 1457.14 576.42 2009.25 4993.76 547,323.40 1669.66 118,994.70 860,168.20
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Observations 3726 2950 3726 3726 3726 1578 3726 3726 Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 147
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Table 5 Correlations between daily returns of exchange rates against the EUR, 1999–2013
CZKEUR HRKEUR HUFEUR PLNEUR RONEUR RSDEUR RUBEUR TRYEUR
CZKEUR 1.0000 0.1480 0.4740 0.4366 0.1289 /C00.0118 0.0804 0.1269
HRKEUR 0.1480 1.0000 0.1344 0.1307 0.0762 0.0661 0.1015 0.0804
HUFEUR 0.4740 0.1344 1.0000 0.5542 0.1534 0.0598 0.0640 0.2194
PLNEUR 0.4366 0.1307 0.5542 1.0000 0.2457 0.0274 0.1974 0.2545
RONEUR 0.1289 0.0762 0.1534 0.2457 1.0000 0.0305 0.1926 0.2054
RSDEUR /C00.0118 0.0661 0.0598 0.0274 0.0305 1.0000 0.0110 0.0414
RUBEUR 0.0804 0.1015 0.0640 0.1974 0.1926 0.0110 1.0000 0.1918
TRYEUR 0.1269 0.0804 0.2194 0.2545 0.2054 0.0414 0.1918 1.0000
Note : The correlations for HRKEUR and RSDEUR are calculated for the period March 1, 2002–December 31, 2013, and September 4, 2007–December
31, 2013, respectively 148 A. Horobet et al.
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Table 6 Correlations between daily returns of exchange rates against the USD, 1999–2013
CZKUSD HRKUSD HUFUSD PLNUSD RONUSD RSDUSD RUBUSD TRYUSD
CZKUSD 1.0000 0.8190 0.7976 0.7273 0.4376 0.6601 0.2667 0.2905
HRKUSD 0.8190 1.0000 0.7692 0.7376 0.5969 0.7464 0.5281 0.3752
HUFUSD 0.7976 0.7692 1.0000 0.7775 0.4595 0.6484 0.2805 0.3686
PLNUSD 0.7273 0.7376 0.7775 1.0000 0.4563 0.6406 0.3106 0.3529
RONUSD 0.4376 0.5969 0.4595 0.4563 1.0000 0.6264 0.1672 0.2191
RSDUSD 0.6601 0.7464 0.6484 0.6406 0.6264 1.0000 0.4975 0.4187
RUBUSD 0.2667 0.5281 0.2805 0.3106 0.1672 0.4975 1.0000 0.1303
TRYUSD 0.2905 0.3752 0.3686 0.3529 0.2191 0.4187 0.1303 1.0000
Note : The correlations for HRKUSD and RSDUSD are calculated for the period March 1, 2002–December 31, 2013, and September 4, 2007–December
31, 2013, respectively Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 149
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3.2 Analysis of Exchange Rate Volatility
We now turn our attention to the in-depth study of exchange rate volatility. After a
brief analysis of descriptive statistics for the monthly standard deviations of
exchange rate returns against the EUR and USD, we investigate the trends in
monthly volatility using the Hodrick-Prescott filter, we observe the volatility
behaviour over short-run and long-run using rolling standard deviations with
various windows, and we model monthly volatilities with the help of ARIMA
models. Combined, the results of these three approaches offer us a more compre-
hensive view over the time-dependencies of exchange rate volatilities in the
Balkans and Eastern Europe.
Tables 7and8provide descriptive statistics for the monthly series of volatilities
for the 16 exchange rates under scrutiny. For what concerns the exchange rates
against the EUR, the means of monthly volatilities range between 0.22 % for HRK
and 3.91 % for TRY, with the highest monthly volatility recorded for TRY (27.55 %
for February 2001) and the lowest for 0.06 % for HRK (June 2008). When the
exchange rates against the USD are considered, the average monthly volatilities
range between 2.72 % for RUB and 3.98 % for HUF; the highest monthly volatility
belongs again to TRY (27.55 % in February 2001) and the lowest to CZK (1.36 % in
June 2007). The most volatile series of monthly standard deviations were the ones
for TRYEUR and TRYUSD, while the series with the lowest volatility were the
HRKUSD and HRKEUR. As indicated by skewness and kurtosis, all series of
monthly standard deviations show negative asymmetry and excess kurtosis, thus
presenting the attributes of a leptokurtic distribution.
As a possible indication of potential shock transmission between exchange rates
volatilities, we have also calculated the correlations between monthly standard
deviations both against the EUR and USD (see Tables 9and10 ). The average
correlation for the monthly standard deviations against the USD is 0.7127, higher
than in the case of monthly standard deviations against the EUR (0.2763), thus
indicating that potential shocks in the exchange rates against the USD might be
transmitted quicker than the shocks in the exchange rates against the EUR. The
explanation, in our view, resides in the controlled exchange rates against the EUR
for many of these currencies, while the exchange rates against the USD are rather
freely moving, taking into account mainly the USDEUR exchange rate in the
international foreign exchange market. The highest correlation for the volatilities
against the EUR is recorded for RONEUR and RSDEUR, while against the USD is
found for HUF and PLN (0.9507). At the other end, the lowest correlations were
/C00.0835 for the monthly standard deviations of RUBEUR and HUFEUR and
0.2358 for the monthly standard deviations of RUBUSD and TRYUSD.
Controlling for the smoothness in series variance, the application of the HP filter
shows three distinct patterns of evolution for the eight currencies under analysis
(see Fig. 3). The first pattern is observable for CZK, HRK, HUF and PLN (except
for HRKEUR): a decreasing volatility trend from January 1999 until the end of
2006, followed by increasing volatility until the end of 2009, and subsequent 150 A. Horobet et al.
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Table 7 Descriptive statistics of monthly standard deviations of exchange rates returns against the EUR, January 1999–December 2013
SD_CZKEUR SD_HRKEUR SD_HUFEUR SD_PLNEUR SD_RONEUR SD_RSDEUR SD_RUBEUR SD_TRYEUR
Mean 0.0043 0.0022 0.0056 0.0063 0.0067 0.0048 0.0074 0.0391
Median 0.0037 0.0021 0.0049 0.0056 0.0059 0.0037 0.0053 0.0305
Maximum 0.0146 0.0072 0.0238 0.0239 0.0497 0.0176 0.0429 0.2755
Minimum 0.0017 0.0006 0.0013 0.0019 0.0013 0.0008 0.0014 0.0096
Std. dev. 0.0023 0.0010 0.0030 0.0033 0.0051 0.0034 0.0066 0.0346
Skewness 1.9921 1.7834 1.8777 2.3104 4.0759 1.6353 2.6069 3.9073
Kurtosis 7.7998 8.3931 9.7045 10.7258 32.1602 5.4875 10.3601 22.0575
Observations 180 142 180 180 180 76 180 180 Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 151
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Table 8 Descriptive statistics of monthly standard deviations of exchange rates returns against the USD, January 1999–December 2013
SD_CZKUSD SD_HRKUSD SD_HUFUSD SD_PLNUSD SD_RONUSD SD_RSDUSD SD_RUBESD SD_TRYUSD
Mean 0.0345 0.0285 0.0398 0.0379 0.0333 0.0374 0.0272 0.0391
Median 0.0322 0.0276 0.0357 0.0338 0.0299 0.0334 0.0165 0.0305
Maximum 0.1045 0.0686 0.1378 0.1434 0.2256 0.1190 0.1912 0.2755
Minimum 0.0129 0.0130 0.0148 0.0130 0.0044 0.0158 0.0012 0.0096
Std. dev. 0.0136 0.0095 0.0178 0.0192 0.0212 0.0174 0.0312 0.0346
Skewness 1.9649 1.4986 1.8577 2.0673 4.9760 2.1692 2.3760 3.9073
Kurtosis 8.7826 6.4021 8.3965 9.1878 41.5775 9.2007 9.2682 22.0575
Observations 180 141 180 180 180 75 180 180 152 A. Horobet et al.
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Table 9 Correlations of monthly standard deviations of exchange rates against the EUR
SD_CZKEUR SD_HRKEUR SD_HUFEUR SD_PLNEUR SD_RONEUR SD_RSDEUR SD_RUBEUR SD_TRYEUR
SD_CZKEUR 1.0000 0.2076 0.5822 0.5465 0.2340 0.3612 0.2080 0.0698
SD_HRKEUR 0.2076 1.0000 0.1321 0.3040 0.3013 0.2091 0.3630 /C00.0179
SD_HUFEUR 0.5822 0.1321 1.0000 0.4998 0.0410 0.2740 /C00.0835 0.1189
SD_PLNEUR 0.5465 0.3040 0.4998 1.0000 0.3319 0.2304 0.4841 0.1819
SD_RONEUR 0.2340 0.3013 0.0410 0.3319 1.0000 0.6097 0.5627 0.2479
SD_RSDEUR 0.3612 0.2091 0.2740 0.2304 0.6097 1.0000 0.2725 0.4329
SD_RUBEUR 0.2080 0.3630 /C00.0835 0.4841 0.5627 0.2725 1.0000 0.0321
SD_TRYEUR 0.0698 /C00.0179 0.1189 0.1819 0.2479 0.4329 0.0321 1.0000 Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 153
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Table 10 Correlations of monthly standard deviations of exchange rates against the USD
SD_CZKUSD SD_HRKUSD SD_HUFUSD SD_PLNUSD SD_RONUSD SD_RSDUSD SD_RUBUSD SD_TRYUSD
SD_CZKUSD 1.0000 0.8799 0.8996 0.8839 0.8071 0.7143 0.5523 0.7551
SD_HRKUSD 0.8799 1.0000 0.8942 0.8473 0.8407 0.8040 0.5975 0.7039
SD_HUFUSD 0.8996 0.8942 1.0000 0.9507 0.8305 0.6722 0.5900 0.7488
SD_PLNUSD 0.8839 0.8473 0.9507 1.0000 0.8036 0.6074 0.5778 0.7386
SD_RONUSD 0.8071 0.8407 0.8305 0.8036 1.0000 0.7365 0.4314 0.8213
SD_RSDUSD 0.7143 0.8040 0.6722 0.6074 0.7365 1.0000 0.4161 0.6158
SD_RUBUSD 0.5523 0.5975 0.5900 0.5778 0.4314 0.4161 1.0000 0.2358
SD_TRYUSD 0.7551 0.7039 0.7488 0.7386 0.8213 0.6158 0.2358 1.0000 154 A. Horobet et al.
alexandra.horobet@gmail.com

-.004 .000 .004 .008 .012
.000 .004 .008 .012 .016
2000 2002 2004 2006 2008 2010 2012
SD_CZKEUR Trend Cycle -.02 .00 .02 .04 .06 .08
.00 .02 .04 .06 .08 .10 .12
2000 2002 2004 2006 2008 2010 2012
SD_CZKUSD Trend Cycle
-.002 .000 .002 .004 .006
.000 .002 .004 .006 .008
02 03 04 05 06 07 08 09 10 11 12 13
SD_HRKEUR Trend Cycle -.02 -.01 .00 .01 .02 .03 .04
.01 .02 .03 .04 .05 .06 .07
02 03 04 05 06 07 08 09 10 11 12 13
SD_HRKUSD Trend Cycle
-.005 .000 .005 .010 .015 .020
.000 .005 .010 .015 .020 .025
2000 2002 2004 2006 2008 2010 2012
SD_HUFEUR Trend Cycle -.04 .00 .04 .08 .12
.00 .04 .08 .12 .16
2000 2002 2004 2006 2008 2010 2012
SD_HUFUSD Trend Cycle
-.005 .000 .005 .010 .015 .020
.000 .005 .010 .015 .020 .025
2000 2002 2004 2006 2008 2010 2012
SD_PLNEUR Trend Cycle -.04 .00 .04 .08 .12
.00 .04 .08 .12 .16
2000 2002 2004 2006 2008 2010 2012
SD_PLNUSD Trend Cycle
-.01 .00 .01 .02 .03 .04
.00 .01 .02 .03 .04 .05 .06
2000 2002 2004 2006 2008 2010 2012
SD_RONEUR Trend Cycle -.05 .00 .05 .10 .15 .20
.00 .05 .10 .15 .20 .25
2000 2002 2004 2006 2008 2010 2012
SD_RONUSD Trend Cycle
Fig. 3 (continued)Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 155
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decline in volatilities until the end of 2013. In this group, the series of monthly
volatilities were smoother for the HRKEUR, although a small increase is observ-
able in 2008 and 2009, followed by a succeeding decline until the end of 2013. The
second group of exchange rates, with a different pattern in their monthly volatility
trend, is formed of RON, RUB and TRY: all three currencies had more stable
monthly volatilities between 1999 and 2013, indicating the countries ’central banks
concern regarding the exchange rate fluctuations. The third pattern is observable in
the case of the RSD (indeed, only after 2007), showing a decreasing trend in
volatilities of the currency against both the EUR and USD.
The next step in our analysis focuses on the differences between short-run and
long-run trends in volatility, with the support of rolling standard deviations (RSD)
of daily logarithmic returns in exchange rates: the 30-days window RSD evolution -.008 -.004 .000 .004 .008 .012
.000 .004 .008 .012 .016 .020
2008 2009 2010 2011 2012 2013
SD_RSDEUR Trend Cycle -.04 -.02 .00 .02 .04 .06 .08
.00 .02 .04 .06 .08 .10 .12
2008 2009 2010 2011 2012 2013
SD_RSDUSD Trend Cycle
-.02 -.01 .00 .01 .02
.00 .01 .02 .03 .04 .05
2000 2002 2004 2006 2008 2010 2012
SD_RUBEUR Trend Cycle -.10 -.05 .00 .05 .10
.00 .05 .10 .15 .20
2000 2002 2004 2006 2008 2010 2012
SD_RUBUSD Trend Cycle
-.05 .00 .05 .10 .15 .20 .25
.0 .1 .2 .3
2000 2002 2004 2006 2008 2010 2012
SD_TRYEUR Trend Cycle -.05 .00 .05 .10 .15 .20 .25
.0 .1 .2 .3
2000 2002 2004 2006 2008 2010 2012
SD_TRYUSD Trend Cycle
Fig. 3 Monthly standard deviations of exchange rates returns against the EUR and USD, values
and HP filter, January 1999–December 2013 156 A. Horobet et al.
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shows the short-run (1 month) shocks in volatility (see Fig. 4a, b ), the 90-days
window RSD shows the medium-term (3 months) shocks in volatility (see Fig. 5a,
b), and the 360-days window RSD shows the long-term (1 year) shocks in volatility
(see Fig. 6a, b ).
A few observations are noteworthy based on our results: (1) regardless of the
window length, spikes in volatility are easily observable over the entire period,
particularly for some exchange rates—CZKEUR, HRKEUR, HRKUSD, HUFEUR,
HUFUSD, PLNEUR, PLNUSD; (2) for other exchange rates, the spikes in volatility
are present only for some months—for example, if we consider the 30-day window
RSD, the RONEUR and RONUSD exchange rates have an abrupt increase in
volatility at the beginning of 1999, followed by rather calm times and another
(smaller) spike in 2008; the same is true for RUBEUR, RUBUSD, TRYEUR and
TRYUSD exchange rates; (3) when we move from short-term to medium-term
volatility, the differences between exchange rates observable in the case of short-
term volatility are more obvious: on one hand, we observe exchange rates that .00 .01 .02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 3500 RSD30_CZKEUR
.000 .005 .010 .015 .020 .025 .030
500 1000 1500 2000 2500 3000 3500 RSD30_HRKEUR
.00 .02 .04 .06 .08 .10
500 1000 1500 2000 2500 3000 3500 RSD30_HUFEUR
.00 .02 .04 .06 .08 .10
500 1000 1500 2000 2500 3000 3500 RSD30_PLNEUR
.00 .04 .08 .12 .16 .20
500 1000 1500 2000 2500 3000 3500 RSD30_RONEUR
.00 .02 .04 .06 .08 .10 .12
500 1000 1500 2000 2500 3000 3500 RSD30_RSDEUR
.00 .04 .08 .12 .16 .20
500 1000 1500 2000 2500 3000 3500 RSD30_RUBEUR
.00 .05 .10 .15 .20 .25 .30 .35
500 1000 1500 2000 2500 3000 3500 RSD30_TRYEUR
Fig. 4 (continued)Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 157
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experienced sudden increases in volatility over the entire period, rather quickly
corrected (in 3–4 months) and, on the other hand, other exchange rates that, after
experiencing higher volatilities at the beginning of the period, have remained at
reasonable levels of volatility throughout the remaining period; (4) the long-run
RSD offers a good image of the persistency of high levels of volatility for some
currencies: as one may observe, the period between 2008 and 2010 shows increased
volatilities for all exchange rates that were persistent over some months before
being corrected; at the same time, for some exchange rates (CZKEUR, HRKEUR,
RONUSD, TRYEUR, TRYUSD) such persistency in volatility is also observable
for other periods.
Table 11 presents the result of the Augmented Dickey-Fuller (ADF) and
Phillips-Perron (PP) tests of stationarity for the monthly series of standard devia-
tions for daily returns in exchange rates. All series are non-stationary in levels and
stationary in the first difference—in the case of SD–RSDEUR, the ADF test .00 .02 .04 .06 .08 .10 .12
500 1000 1500 2000 2500 3000 3500 RSD30_CZKUSD
.01 .02 .03 .04 .05 .06 .07 .08
500 1000 1500 2000 2500 3000 3500 RSD30_HRKUSD
.00 .02 .04 .06 .08 .10 .12 .14
500 1000 1500 2000 2500 3000 3500 RSD30_HUFUSD
.00 .02 .04 .06 .08 .10 .12 .14
500 1000 1500 2000 2500 3000 3500 RSD30_PLNUSD
.00 .04 .08 .12 .16 .20
500 1000 1500 2000 2500 3000 3500 RSD30_RONUSD
.00 .02 .04 .06 .08 .10 .12
500 1000 1500 2000 2500 3000 3500 RSD30_RSDUSD
.00 .04 .08 .12 .16 .20
500 1000 1500 2000 2500 3000 3500 RSD30_RUBUSD
.00 .05 .10 .15 .20 .25 .30
500 1000 1500 2000 2500 3000 3500 RSD30_TRYUSD
Fig. 4 (a) Rolling standard deviations of exchange rate returns against the EUR—30 days
window, 1999–2013. Note: The first observation is February 15, 1999 and the last observation is
December 31, 2013. ( b) Rolling standard deviations of exchange rate returns against the USD—30
days window, 1999–2013. Note: The first observation is February 15, 1999 and the last observation
is December 31, 2013 158 A. Horobet et al.
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indicated stationarity in level, but the PP test confirmed non-stationarity. As a
result, all SD series are I(1).
The next step in identifying a fitted ARIMA model was to study series
correlogram and, based on the autocorrelation and partial autocorrelations, to try
various values for p and q. We show in Table 12 the best-fit ARIMA(p,d,q) models
for the monthly standard deviation series for all exchange rates, based on three
model diagnostic indicators (Schwartz criterion, Adjusted R 2and SEE). We
observe that all series have AR terms, but not all of them have MA terms—MA
terms are found only in the case of CZKEUR, CZKUSD, HRKEUR, HUFEUR,
HUFUSD, PLNEUR, PLNUSD, RONEUR, RONUSD and RUBUSD. This result
indicates, on one hand, that exchange rate volatility “has memory”, sometimes even
for 7 or 9 months (as is the case with HRKUSD and TRYUSD)—but all standard
deviations have a memory of at least 1 month, while some exchange rates are more
prone to the persistent effects of shocks in volatility (the most interesting case is the
RONUSD exchange rate, where a shock in volatility seems to persistent for
4 months!). .00 .01 .02 .03 .04 .05
500 1000 1500 2000 2500 3000 3500 RSD90_CZKEUR
.000 .004 .008 .012 .016 .020 .024
500 1000 1500 2000 2500 3000 3500 RSD90_HRKEUR
.00 .01 .02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 3500 RSD90_HUFEUR
.01 .02 .03 .04 .05 .06 .07 .08
500 1000 1500 2000 2500 3000 3500 RSD90_PLNEUR
.00 .02 .04 .06 .08 .10 .12 .14
500 1000 1500 2000 2500 3000 3500 RSD90_RONEUR
.00 .01 .02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 3500 RSD90_RSDEUR
.00 .02 .04 .06 .08 .10 .12 .14 .16
500 1000 1500 2000 2500 3000 3500 RSD90_RUBEUR
.00 .04 .08 .12 .16 .20 .24
500 1000 1500 2000 2500 3000 3500 RSD90_TRYEUR
Fig. 5 (continued)Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 159
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4 Conclusion
Our research examined exchange rate volatility for a number of eight currencies
from the Balkans and Eastern Europe, with the aim of observing short-run versus
long-run patterns in volatility, as well as the influence of past volatility on current
volatility levels and the persistence of shocks in volatility. Our main findings point
towards significant differences in volatility patterns among the currencies under
investigation. First, there are currencies such as the CZK, HRK, HUF and PLN that
experienced decreasing currency volatility from 1999 to 2006, followed by increas-
ing volatility until the end of 2009 and subsequent declines in volatility until the end
of 2013. Second, the RON, RUB and TRY had more stable volatilities between
1999 and 2013, which strongly indicates a serious concern of these countries ’
central banks regarding exchange rate fluctuations and a success of these central
banks in terms of exchange rate volatility management. Third, the RSD (for which .01 .02 .03 .04 .05 .06 .07 .08 .09
500 1000 1500 2000 2500 3000 3500 RSD90_CZKUSD
.01 .02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 3500 RSD90_HRKUSD
.02 .03 .04 .05 .06 .07 .08 .09 .10 .11
500 1000 1500 2000 2500 3000 3500 RSD90_HUFUSD
.00 .02 .04 .06 .08 .10 .12
500 1000 1500 2000 2500 3000 3500 RSD90_PLNUSD
.00 .02 .04 .06 .08 .10 .12 .14
500 1000 1500 2000 2500 3000 3500 RSD90_RONUSD
.01 .02 .03 .04 .05 .06 .07 .08 .09 .10
500 1000 1500 2000 2500 3000 3500 RSD90_RSDUSD
.00 .02 .04 .06 .08 .10 .12 .14 .16
500 1000 1500 2000 2500 3000 3500 RSD90_RUBUSD
.00 .04 .08 .12 .16 .20 .24
500 1000 1500 2000 2500 3000 3500 RSD90_TRYUSD
Fig. 5 (a) Rolling standard deviations of exchange rate returns against the EUR—90 days
window, 1999–2013. Note: The first observation is May 14, 1999 and the last observation is
December 31, 2013. ( b) Rolling standard deviations of exchange rate returns against the USD—90
days window, 1999–2013. Note: The first observation is May 14, 1999 and the last observation is
December 31, 2013160 A. Horobet et al.
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we have observations only since the end of 2007) experienced a decreasing
volatility until the end of 2013.
Regarding the short-run versus long-run volatility, spikes in monthly volatility
were present for all currencies, although for some of them they are observable
throughout the entire period (CZK, HRK, HUF, PLN), while for others (RON,
RUB, TRY) they were present in 1999–2000 and afterwards only in 2008. When
long-run volatility is considered, there are exchange rates that experienced sudden
increases in volatility over the entire period, but rather quickly corrected (in 3–4
months) and, on the other hand, there are currencies that, after experiencing higher
volatilities at the beginning of the period, have remained at reasonable levels of
volatility throughout the remaining period. The results of applying ARIMA model-
ling to currency volatility series indicate that exchange rate volatility “has mem-
ory”, sometimes even for 7 or 9 months—but all standard deviations have a
memory of at least 1 month, while some exchange rates are more prone to the
persistent effects of shocks in volatility, such as the RON/USD.
Overall, our research demonstrates that currency volatility remains a strong issue
for the countries in the region and that all central banks have attempted to properly
manage it, particularly after the global financial crisis that emerged in 2008. At the
same time, even if inflation targeting as a monetary policy rule has been adopted by
almost all the countries in the Balkans and Eastern Europe, their economic .010 .015 .020 .025 .030 .035
500 1000 1500 2000 2500 3000 3500 RSD360_CZKEUR
.006 .007 .008 .009 .010 .011 .012 .013 .014 .015
500 1000 1500 2000 2500 3000 3500 RSD360_HRKEUR
.015 .020 .025 .030 .035 .040 .045 .050
500 1000 1500 2000 2500 3000 3500 RSD360_HUFEUR
.01 .02 .03 .04 .05 .06
500 1000 1500 2000 2500 3000 3500 RSD360_PLNEUR
.01 .02 .03 .04 .05 .06 .07 .08
500 1000 1500 2000 2500 3000 3500 RSD360_RONEUR
.02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 3500 RSD360_RSDEUR
.00 .02 .04 .06 .08 .10 .12
500 1000 1500 2000 2500 3000 3500 RSD360_RUBEUR
.00 .02 .04 .06 .08 .10 .12 .14
500 1000 1500 2000 2500 3000 3500 RSD360_TRYEUR
Fig. 6 (continued)Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 161
alexandra.horobet@gmail.com

specificities make the adoption and implementation of a less flexible exchange rate
regime that would pave the way for Euro adoption, at least for some of them, a real
challenge. Our results have implications both for central banks and governments ’
policies, as well as for private investors that have to deal with currency risk as part
of the wider range of risks they are exposed to in the region.
As any other research, our approach has limitations which can be further
addressed by extending the scope of our enterprise in various directions, such as:
(i) modelling currency volatility with instruments that specifically take into account
the “volatility clustering” phenomenon, such as ARCH or GARCH; (ii) studying
currency volatility within the overall period, during specific time intervals that are
relevant for shifts in exchange rate volatility; or (iii) contrasting our results with
similar results for other emerging countries, with the aim of better understanding
the issue of currency volatility in a wider perspective.
Given the specific long-term endeavour of the countries in the region, which
deals with the Euro adoption, another possible extension of our study might be .01 .02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 RSD360_CZKUSD
.015 .020 .025 .030 .035 .040 .045
500 1000 1500 2000 2500 3000 RSD360_HRKUSD
.02 .03 .04 .05 .06 .07 .08
500 1000 1500 2000 2500 3000 RSD360_HUFUSD
.02 .03 .04 .05 .06 .07 .08
500 1000 1500 2000 2500 3000 RSD360_PLNUSD
.02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 RSD360_RONUSD
.02 .03 .04 .05 .06 .07
500 1000 1500 2000 2500 3000 RSD360_RSDUSD
.00 .02 .04 .06 .08 .10 .12
500 1000 1500 2000 2500 3000 RSD360_RUBUSD
.00 .02 .04 .06 .08 .10 .12 .14
500 1000 1500 2000 2500 3000 RSD360_TRYUSD
Fig. 6 (a) Rolling standard deviations of exchange rate returns against the EUR—360 days
window, 1999–2013. Note: The first observation is May 26, 2000 and the last observation is
December 31, 2013. ( b) Rolling standard deviations of exchange rate returns against the USD—
360 days window, 1999–2013. Note: The first observation is May 26, 2000 and the last observation
is December 31, 2013 162 A. Horobet et al.
alexandra.horobet@gmail.com

Table 11 Unit root tests for monthly standard deviations
ADF PP
Constant Trend and constant Constant Trend and constant
SD_CZKEUR /C07.126* /C07.144* /C07.352* /C07.379*
SD_HRKEUR /C04.910* /C05.016* /C08.586* /C08.707*
SD_HUFEUR /C06.867* /C07.249* /C06.867* /C07.237*
SD_PLNEUR /C08.357* /C08.721* /C09.153* /C09.530*
SD_RONEUR /C04.456* /C06.020* /C010.779* /C012.939*
SD_RSDEUR /C01.743 /C04.616* /C04.144* /C04.883*
SD_RUBEUR /C04.318* /C03.522** /C05.998* /C07.928*
SD_TRYEUR /C04.388* /C04.493* /C07.436* /C07.536*
SD_CZKUSD /C04.487* /C06.018* /C06.177* /C06.231*
SD_HRKUSD /C03.313** /C03.280*** /C04.706* /C04.670*
SD_HUFUSD /C05.342* /C05.716* /C05.342* /C05.543*
SD_PLNUSD /C06.631* /C06.884* /C07.123* /C07.392*
SD_RONUSD /C05.780* /C05.766* /C012.020* /C012.004*
SD_RSDUSD /C03.732* /C04.746* /C03.662* /C04.753*
SD_RUBUSD /C03.963* /C03.752** /C05.907* /C06.115*
SD_TRYUSD /C04.388* /C04.493* /C07.436* /C07.536*
Note: ADF and PP are Augmented Dickey-Fuller and Phillips-Perron unit root tests. Test equations
include either a constant or a constant and a trend. The lag length is chosen using the Schwarz
information criterion for the ADF test, and the Newly West kernel estimator for the PP test
*Rejection of the null hypothesis at the 1 % levels
**Rejection of the null hypothesis at the 5% levels
***Rejection of the null hypothesis at the 10% levels
Table 12 Best-fit ARIMA models for monthly standard deviations series
ARIMA model (p,d,q) Schwartz criterion Adjusted R2SEE
SD_CZKEUR (1,1,1) /C09.7262 0.1762 0.0018
SD_HRKEUR (2,1,1) /C011.0532 0.4040 0.0009
SD_HUFEUR (1,1,1) /C09.0988 0.2098 0.0025
SD_PLNEUR (3,1,1) /C08.8860 0.4064 0.0027
SD_RONEUR (2,1,1) /C08.6295 0.4485 0.0031
SD_RSDEUR (4,1,0) /C09.1031 0.3404 0.0023
SD_RUBEUR (1,1,0) /C08.0802 0.3483 0.0042
SD_TRYEUR (6,1,0) /C04.2102 0.2569 0.0281
SD_CZKUSD (1,1,1) /C06.2338 0.1469 0.0103
SD_HRKUSD (7,1,0) /C07.1370 0.1411 0.0065
SD_HUFUSD (1,1,1) /C05.8844 0.1382 0.0123
SD_PLNUSD (3,1,1) /C05.5072 0.3015 0.0145
SD_RONUSD (2,1,4) /C05.7798 0.5899 0.0124
SD_RSDUSD (3,1,0) /C05.6703 0.1009 0.0133
SD_RUBUSD (1,1,10) /C05.3252 0.5619 0.0157
SD_TRYUSD (9,1,0) /C04.1931 0.2757 0.0280Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for . . . 163
alexandra.horobet@gmail.com

represented by a larger framework of analysis that would include not only monetary
variables, but also socio-economical and political variables. These would allow for
a better control and robustness test of our results and would permit the observation
of other features of exchange rate policies in these countries.
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