Dynamic Volatility Spillover Effects
Feng-Ming Shih, Department of International Business Studies,
National Chi Nan University, Taiwan
Ming-Chieh Wang, Department of International Business Studies,
National Chi Nan University, Taiwan
ABSTRACT
This paper investigates the dynamic nature and determinants of returns and volatility spillovers
from the developed European region and the world to the five emerging European equity markets that are
not members of the European Monetary Union. Using a multi-factor model with time-varying loadings
estimated in three stages, we find the regional effect on volatility is stronger than the world effect,
however, the return spillover from the world is stronger than that from the region. Economic growth
and exchange rate can predict the volatility spillover intensities. We also find that the return spillover
effect from the world to the developed European region is stronger during a recession.
INTRODUCTION
Over the last few decades, liberalization and integration of the financial markets have increased the
co-movements of international capital markets. Particularly, stock returns in emerging markets that are
characterized by high volatility are usually influenced by developed markets. However, Binswager
(1999) indicates that the markets exert a strong influence on international linkages and uncertainty with
respect to the direction and size of price changes. Under the efficient markets hypothesis, the
movements of stock markets reflect the transmission of information between markets. Accordingly, an
examination of the volatility spillover process also enhances the understanding of information
transmission for the emerging markets. Understanding the origins and drivers of volatility in emerging
markets is important for pricing securities, determining the cost of capital, implementing global hedging
strategies, and making asset allocation decisions (Bekaert and Harvey, 1997). Recently, a large number
of empirical studies attempted to investigate how financial market shocks were transmitted across Asian
countries (see, e.g., Ng, 2000; Gallo and Velucchi, 2009) and developed European economies (Baele,
2005); however, attempts to analyze the volatility spillover effect related to economic conditions in
emerging European markets have been few. Therefore, much research remains to be undertaken.
The essential topic of international market efficiency is often viewed in terms of international
market integration or segmentation. In financial market theory, the integration of capital markets is
tackled from the perspective of asset pricing theory. Early research on integration made one of the
following three assumptions: perfectly integrated, perfectly segmented, or partially integrated with the
degree of integration being constant. Furthermore, Bekaert and Harvey (1995, 1997) combined both
extremes in a model and found that some emerging markets exhibit time-varying world integration.
Building on their work, Ng (2000) and Baele (2005) added the regional factor to the pricing model and
tested volatility spillover effects in emerging Asian and developed European markets, respectively. Thus
far, the drivers of volatility in emerging European markets have not been fully examined.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
45
If the degree of integration is truly time varying, it is important to understand the economic
determinants of shock spillover intensity. Chambet and Gibson (2008) state that regional economic
instability may influence foreign investors’ pricing of risk. Financial variables incorporate the market’s
reaction to macroeconomic news; furthermore, international spillovers may be related to the internal
situation of a country or regional (world) economic growth, although it has long been established that
business cycle conditions are intricately linked with asset returns (see, e.g., Fama and French, 1989) and
international equity correlations (Kim and Lee, 2008). Thus, there is considerable evidence that equity
market correlation and volatility are higher during recession than during growth periods, yet it is not clear
whether shock spillover intensities also exhibit this asymmetry. A number of studies have examined the
importance of economic growth for international market linkages (see, e.g., Bekaert et al., 2007;
Ragunathan et al., 1999). However, very few papers focus on the relationship between economic
conditions and the volatility effects driven by the world and the region, with the exception of Baele
(2005). Although Baele (2005) studied the issue for European Monetary Union (EMU) countries, the
results were mixed. Therefore, we investigate whether there are business cycle components in the shock
spillover intensities.
The first objective of this study is to determine the degree to which globalization and increasing
regional integration affect the interdependence of emerging European equity markets, and the second
objective is to test whether there are economic growth and exchange rate components in the shock
spillover intensities. This paper claims that the increasing impact of world and regional factors on
volatility in emerging European markets is consistent with increased global and regional integration. A
comparison between our research and previous studies is given below.
Firstly, this paper focuses on emerging European markets. A sizable increase in the capital flows
to emerging economies is one of the most prominent features of international financial markets today.
Other emerging markets such as those in Asia have been extensively studied, but evidence of stock
market integration in Central and Eastern Europe remains relatively scarce (Babetskii et al., 2007). In
practice, financial market behavior of emerging European markets can be different from other emerging
markets. Therefore, it would be interesting to gain an insight into the working of these countries’
financial markets.
Secondly, from a methodological point of view, the European situation provides an excellent case
study for regional analysis owing to the differences in culture, economy, geography, and polity compared
with Asia. The method in this paper allows for time-varying parameters and further gives us the
possibility to address world effect and regional (developed Europe) effect on emerging European markets.
Previous studies on international market linkages in emerging European markets focused mainly on the
effects of a single international market (see, e.g., Chelley-Steeley, 2005) or on the influence of the EMU
(see, e.g., Baele, 2005; Babetskii et al., 2007). However, in the case of emerging European markets,
very few papers discuss the volatility spillover effects from both the world and the region (Bekaert et al.,
2005).
Unlike previous studies, our model nests two models that are composed of a pricing formula by
extending the framework of Bekaert et al. (2005) in three stages, with the world equity returns and the
European regional portfolio as the benchmarks. 1 By combining the local factors, we also examine the
asset pricing descriptions using univariable generalized autoregressive conditional heteroscedasticity
1 We extend the framework of Bekaert et al. (2005) and further allow for different impacts on the size and pattern between the
mean and volatility driven by world (European region).
46
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
(GARCH) processes with asymmetry,2 provided by Glosten et al. (1993). Then, negative news from the
world or the region may increase the volatilities of the equity returns in emerging European markets. We
add economic instruments to local and regional information sets that contain additional information on
excess returns as the proxies for local and regional risk factors. The empirical model in this paper,
allowing volatility spillover effects from the world to Europe and avoiding orthogonalization, suits the
simultaneous analysis of world and regional integration in emerging markets and allows for time-varying
parameters, latent factors, and a GJR-GARCH structure for the residuals.
Finally, this paper tests the economic circumstances that have an impact on the spillover effects
from the world and the European region. With the degree of international correlation changing over
time, a more appropriate method of investigating the behaviors of return and volatility spillovers is to
allow the spillover weight parameters to be driven by certain local information variables that might
capture the time variation in correlations (Ng, 2000). Our research focuses on the impact of the
components of economic growth and exchange rate on equity returns and volatility spillovers. The impact
of the level of economic activity on stock returns is widely documented, and research results generally
indicate that the macroeconomic variables can explain the relationship between risk and returns and
predict the excess returns.3 Moreover, previous studies show the importance of currency effects on
equity prices and volatility (see, e.g., Francis et al., 2006; Cumperayot et al., 2006). Investors focus
more on the movements of exchange rates during large shocks, which may propagate across markets.
Accordingly, we hypothesize that both exchange rate and expected economic growth have an impact upon
the degree of spillover in emerging European markets. Thus far, to our knowledge, the existing
literature has focused primarily on world market integration; however, regional integration on the basis of
economic growth conditions in emerging European markets has hardly been analyzed. Our analysis is
therefore a first look on this issue.
Our result suggests that foreign shocks from the world and the region are both important. The
volatility spillover intensities from the European region seem to be stronger than those from the world. In
contrast, the mean spillover effects from the world are greater than those from the European region.
Furthermore, this paper provides some evidence of exchange rate and economic growth effects on the
degrees of spillover in emerging European markets and the European region.
METHODOLOGY
The Model
This paper intends to test the time-varying effects of return and volatility spillovers from the world
and the European region to five emerging European markets. Considering that the degree of
international correlation is unstable, we assume that the spillover effects change over time. We extend
the two-factor model of Bekaert et al. (2005) by allowing for different degrees of spillover effects on
mean and volatility. Thus, the model takes the following general form:
reg
world
reg
Ri ,t   i i ,t 1  iworld
, t 1 uworld, t 1  i , t 1ureg, t 1   i , t 1 eworld, t   i , t 1ereg, t  ei , t
 E e
2
i ,t
2
i ,t
I t 1  i  i 
2
i ,t 1
 i e
2
i ,t 1
i 
2
i ,t 1
,
i  1,2,..N
,
(1a)
(1b)
2 Koutmos and Booth (1995) found that volatility transmission is asymmetric. Spillovers are more pronounced in the case of bad
news than good news.
3 For instance, Fama (1981) provides the evidence of a negative relationship between inflation and stock returns.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
47
where
Ri ,t
represents the stock index returns in excess of the 30-day Eurodollar rate on market i in
uworld,t 1
US dollars, and
and
ureg,t 1
denote the conditional expected excess returns on the world and

i ,t 1
European regional stock markets, respectively. The vector
includes a constant term, security
transactions per GDP, the ratio of total trade to GDP, exchange rate components, business cycle
components, and the stock indices for market i , all of which are lagged by one month.
ei ,t
the idiosyncratic return shock
The variance of
follows a GARCH process in eq. (1b) with asymmetric effects in the
conditional variance, similar to those of Glosten et al. (1993). Finally, we let
 i,t
represent the negative
shock of market i .
Parameterizations for Mean and Volatility Spillovers
This section provides two different parameterizations for the mean and volatility spillover effects.
We initially assume that the effects are constant over time and then relax this assumption. We allow the
spillover effects to be influenced by economic situation. Hence, the model can be expressed as
world
 world ireg
 reg  reg
 world
iworld
, t 1  ai  i , t 1
,t 1  bi  i ,t 1
, t 1  ci  reg, t 1
,

world
i , t 1
where
 di 
ai
,
ci
world
i , t 1
,
,
di
,

reg
i ,t 1
, and
 vi 
wi
reg
i ,t 1
The vectors of

 wi 
,
world
reg , t 1
,
are (7×1) vectors of the parameters that measure the impact on the
spillover effect from the world, while
iworld
, t 1
, and
world
reg, t 1
and
bi
 ireg
, t 1
and
vi
measure the effects from the European region.
consist of the instrument variables that capture the covariance
world
reg
, t 1
i
risks of market
with respect to the world and region portfolios, while the variable of
reflects
the risk of the European region with respect to the world portfolio.

world
reg, t 1
The instruments in
iworld
, t 1
and
include a constant, security transactions per GDP, the ratio of total trade to GDP, exchange rate
 reg
components (against US), and economic growth components, while i , t 1 contains a constant, the ratio
of total trade to GDP, exchange rate components (against Euro), and economic growth components.
Instrumental Variables on Spillovers
Foreign capital flows. The variable of security transactions per GDP, ST, is the data of gross
foreign purchases and sales obtained from the US Treasury International Capital (TIC) reporting system
and considered as a proxy for financial integration that is increased by liberalization. Bekaert et al. (2002)
use the data as the proxy for foreign capital flows. The mobility of international capital flows may lead
to financial market integration (see, e.g., Bekaert and Harvey, 2003).
Trade. We use the ratio of total trade to GDP as the proxy for economic integration. The
variable may affect equity return correlations through an assembly of cross-country cash flows. Chen
and Zhang (1997) found that countries with heavier external trade to a particular region tend to have
higher return correlations with that region.
48
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
Exchange rate components. Variable Ex1 indicates the movements of exchange rates, and Ex2 is a
dummy variable for the depreciation of local currency. 4 Several theoretical links between equity prices
and exchange rates are well known, and the effect of exchange rate on the relative importance of
international factors over time can be found in Bekaert and Harvey (1997) and Ng (2000).
Economic growth components. For the economic growth components, we create two dummy
variables, Gth1 and Gth2, to represent two states: the state when emerging Europe and the world
(European region) are simultaneously in recession and the state when the local business cycle is in a
recessionary phase. To explore the effect of business cycle deviations on cross-market correlations, we
include the dummy to record the economy when it is in the recessionary phase. The dummy is
calculated as follows. First, a quadratic trend is fitted for the composite leading indicator of each
country as well as for the world (European regional market). Second, the deviations from this trend are
generated. Positive deviations indicate boom; negative deviations, recession. Third, for each country, a
below-phase dummy is created. This dummy takes a value of one when the deviation of a country’s
composite leading indicator from its trend has the same sign as the world (European region), and zero
otherwise.
Estimation Method
Our model is estimated in three steps. In the first step, a univariate GJR-GARCH model is
estimated for the world return. In the second step, an extended univariate GJR-GARCH model is
estimated for the aggregate European return. The world residual from the first-step regression is
included as an explanatory variable. Finally, conditioning on the effects of the world and regional
markets, we examine the model for local markets in eqs. (1a) and (1b). To avoid non-normality in
excess returns, we adopt the quasi-maximum likelihood (QML) method to estimate the model, as
proposed by Bollerslev and Woolridge (1992). The multi-step estimation procedure provides consistent,
but not necessarily efficient, estimates. Ng (2000) and Baele (2005) use the bivariate GARCH model
for US and regional returns by two-step estimation procedure. There are no volatility-spillover effects
between the US and Europe. The three-step estimation procedure in our paper distinguishes itself by
allowing volatility spillover effects from the world to Europe and avoiding orthogonalization.
EMPIRICAL RESULTS
Data Analysis
Our data set from Datastream contains the Morgan Stanley Capital International (MSCI) equity
indices in terms of US dollars for computing monthly continuously compounded rates of return for the
world, region, and emerging European markets. The sample period is from September 1996 to December
2006. We use MSCI world index as the world market and MSCI Europe index 5 as the European regional
market. Among all emerging European markets, we select those that are not members of the EMU to test
the depreciation effect on world and regional spillover intensities. Thus, the emerging markets include the
Czech Republic, Hungary, Poland, Russia, and Turkey. The summary statistics are provided in Table 1.
4 The variables, Ex1 and Ex2, are measured by the exchange rate against US dollar for the world effect and against euro for the
regional effect.
5 The MSCI Europe Price index tracks 16 of the major stock markets in Europe by capturing approximately 85% of the market cap
of each country.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
49
In Table 1, the mean returns for all markets are positive, and emerging markets have higher returns
than world and European regional markets. Within the entire sample, including the markets that were
affected by the Asian crisis, Russia has the highest mean US dollar return of 2.935%, followed by Turkey
(2.515%), Hungary (2.022%), the Czech Republic (1.639%), and Poland (1.159%). Volatilities for
emerging European markets range from 8.192% (the Czech Republic) to 17.217% (Turkey). It is clear
that emerging European markets offer higher average returns than the world (0.602%) and European
region (0.813%); however, these high returns are also characterized by higher volatility, which is common
for emerging markets and is consistent with Bekaert and Harvey (1995, 1997).
Among the emerging European countries, the first-order autocorrelation of returns ranges from
3.1% (Turkey) to 10.3% (Poland), and the Ljung-Box statistics indicate that linear dependency is not
persistent in all markets. For the squared returns, first-order serial correlations vary between –0.103 (the
Czech Republic) and 0.382 (Russia), and the Ljung–Box statistics show strong evidence of nonlinear
dependency in Russia, the European region, and the world. Nonlinear dependency could be due to the
ARCH effect. Finally, Hungary and Russia are rejected at a 5% significance level for the normality test.
Most market returns are left skewed and leptokurtic, and all returns are shown to be stationary by the
ADF unit root tests.
World and Regional Models
A correct specification for the models is important for investigating the influence of world and
European regional shocks on emerging European markets. Table 2 presents the results of the
specification tests for the world and regional models. All the models for the world and regional markets
exhibit no autocovariance in the standardized residuals and squared terms. The Jarque-Bera tests for
normality indicate that the standardized residuals for all models are generally normally distributed. All the
results of the joint tests are far above their critical values. Negative news from the world may increase
the volatilities of the equity returns in the regional market and emerging European markets. For the
world model estimation, the likelihood ratio test (at a 10% level) and the Wald test (at a 5% level) reject
the null hypothesis of no asymmetry. We find no evidence of asymmetric volatility in the regional
model estimation. The results are similar to Bekaert et al. (2005), who find strong asymmetric volatility
in the United States, yet fail to reject symmetry in the European, Asian, and Latin American regional
portfolios.
Table 1: Summary Statistics for Equity
LB 4
LB 2 4 Skewness Ex. Kurtosis Jarque-Bera
2
Mean Std. dev. 
ADF
Czech
1.639
8.192 0.074 7.283 -0.103 1.440
0.019
2.782 0.253
-10.212***
Hungary 2.022
9.366 0.051 4.732 0.131 3.809
-0.559
3.722 9.151** -10.422***
Poland
1.159
9.651 0.103 11.414* -0.023 4.592
-0.156
3.085 0.540
-10.109***
Russia
2.935 15.763 0.022 2.741 0.382 21.602***
-0.463
5.234 30.223*** -10.121***
Turkey
2.515 17.217 0.031 9.940 0.106 3.319
0.470
3.419 5.477* -10.850***
World
0.602
4.146 0.055 0.933 0.126 10.919**
-0.169
3.586 2.359
-10.411***
Europe
0.813
4.595 0.073 1.038 0.249 11.082**
-0.235
3.513 2.504
-10.088***
All monthly log returns from the MSCI database on Datastream are calculated in US dollars and
expressed in percentages. Where  and  2 are the first-order serial correlations of returns and squared
returns. LB 4 and LB 2 4 are the Ljung-Box statistics for returns and squared returns with four lags,
50
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
respectively. The normal density is assumed for the Jarque-Bera statistics. The ADF unit root tests for
a random walk. ** and *** denote statistical significant at 5% and 1% level, respectively.
Market
World
Europe
LB 4
Model
Asy/Sym
Symmetry
Asymmetry
Symmetry
Asymmetry
LB
Table 2: Specification and Normality Test
Specification Tests
Asymmetry Test
4
LB 2 4
Jarque-Bera Joint test
LR test
Wald test
1.825
2.941
3.921 25.108***
3.112
1.253
4.969* 22.008***
3.579*
6.531**
5.113
1.025
1.435 215.168***
4.323
0.599
1.723 191.272***
1.257
1.023
and LB 2 4 are the Ljung-Box statistics for returns and squared returns with four lags,
respectively. The normal density is assumed for the Jarque-Bera statistics. LR is the likelihood ratio
tests for asymmetry. ** and *** denote statistical significant at 5% and 1% level, respectively.
Constant Spillover Effects
To understand the existence of spillover effects, we first constrain all mean and volatility spillovers
to be constant and then consider the time-varying case. In Table 3, we find that the world effect on mean,
iworld , is positive and ranges from 1.402 (Hungary) to 0.844 (Russia), but the regional effect on mean,
 ireg ,
is very small for most emerging European markets and even negative for Hungary (–0.026),
Poland (–0.064), and Turkey (–0.183). For all emerging European returns, there are significant and
positive world effects (at the 1% level), but no significant regional effects. Thus, we can conclude that
emerging European markets are more integrated with the world than the region with respect to returns.
 wo rld
 reg
In emerging European markets, i
and i
represent the dependences of local shock on the
world and European regional shocks, with ranges from 1.942 (Turkey) to 0.479 (the Czech Republic) and
from 1.613 (Russia) to 0.745 (Turkey), respectively. All the parameters for volatility spillover are
positive. Both world effect and regional effect on volatility are positive for all emerging European
markets. Moreover, there are significant volatility spillovers from the world to the European region and
emerging European markets; all the return volatilities in emerging European markets, with the exception
of Turkey, are significantly driven by innovations from the European region at the level of 5% or 10%
(Russia).
 world
 reg
 wo rld  reg
In table 3, the parameter estimates present that | i
|>| i | and | i
|<| i | for most
countries, which indicate that emerging European markets are more influenced by world than by regional
factors with respect to returns and that the regional factor is more important in volatility spillover. The
Wald statistics indicate that the world factor has significant explanatory power for the European region
and all emerging European markets.
Our results are similar to Miyakoshi (2003) but contrary to Ng (2000). Both studies use the US and
Japan equity markets as the proxies for the world and regional factors. Miyakoshi (2003) shows that
only the influence of the United States is important for Asian market returns; there is no influence from
Japan. However, the volatilities of Asian markets are influenced more by Japan than by the United States.
Ng (2000) finds that the world shock of the United States is stronger than the regional one of Japan.
The larger return spillover from world may be caused by the huger international capital flows. For
example, the boom in global economic growth brings high economic growth in emerging Europe.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
51
Consequently, the intensity of world information spillover to emerging European markets will become
stronger. In contrast, foreign investors make massive capital withdrawal from emerging Europe owing
to slack economic growth during recession period. Then, equity returns decline in emerging European
markets. On the other hand, regional effects seem to be larger than world effect on volatility. It may be
inferred that intra-regional trade and real economic activities, such as direct investment in emerging
European markets, enhance regional integration in volatility. Therefore, in the next section, this paper
uses foreign capital flows and trade as two controlled variables and tests growth effect and exchange rate
effect on mean and volatility spillover from the world and the European region.
Table 3: Constant volatility spillover effects
mean spillover
volatility spillover

Europe
Czech
Hungary
Poland
Russia
Turkey
world
i

reg
i

world
reg

wo rld
i

reg
i
0.254
1.316***
1.402***
1.181***
0.844***
1.354***
0.073
-0.026
-0.064
0.094
-0.183

wo rld
reg
0.993***
0.479*** 1.079**
0.895*** 0.979***
0.874*** 1.243***
1.328*** 1.613*
1.942*** 0.745
Wald test
World
Region
1028.250***
30.958*** 4.966*
91.439*** 16.357***
37.327***
8.984**
30.668*** 5.269*
42.960***
1.327
Note: ** and *** denote statistical significant at 5% and 1% level, respectively.
Time-Varying Spillover Effects
In this section, we examine the time-varying impact of world and regional factors by using the
latent variables model. Some studies have found that correlations change over time and that the
time-varying correlations are linked to economic activity. Consequently, we test whether the economic
growth and exchange rate components are included in the spillover intensities by adding security
transactions and trade as controlled variables. The parameter estimations for the economic determinants
of mean spillover intensities are reported in Table 4.
For the European region, an increase in security transactions seems to strengthen the mean spillover
effect from the world at a 10% level, albeit to a very small extent. The coefficient of the ratio of trade to
GDP is negative but not significant. The currency depreciation remarkably mitigates the mean spillover
effects from the world. It is probable that the European region becomes more segmented owing to hot
money outflows when currency depreciates. In addition, the parameter estimations for both the
dummies of economic growth components are positive and significant. Contrary to Ragunathan et al.
(1999),6 our result indicates that the mean spillover effect from the world to the European region is
stronger when the world and European regions are simultaneously in recession or when only the
European region is in recession. Further, the Wald tests state that exchange rates and economic growth
are both significantly related to time-varying world beta at the 5% and 1% levels for the European
regional market, respectively.
In the Czech Republic, the world effect on mean is stronger when the total trade with the world
decreases and currency depreciation occurs, while the regional effect on mean is greater when the trade
within this region increases. The Wald tests show that the currency depreciation is a significant driver of
world return integration in the Czech Republic. In Hungary, currency depreciation increases the world
6 Ragunathan et al. (1999) study the correlations and integration of the Australian and US markets and provide the conclusion that
the markets are segmented when the business cycles are in the contraction phase.
52
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
effect on mean at the level of 10%; however, the other instruments shown in this table have no significant
impact on the return spillover for Hungary. In Poland, currency depreciation magnifies the world effect
(at a 5% level) and the European region effect (at a 10% level) on returns. Furthermore, for Russia, the
world effect on returns diminishes with the size of trade with the world; the European effect is smaller
when currency depreciation occurs. Finally, the Wald tests suggest that the depreciation changes the
European effect on Russia and Turkey.
In sum, different from the result in the regional market, the relationship between exchange rate and
mean spillover intensities from the world is positive for the majority of emerging European markets,
although the coefficients are not all significant. Thus, the importance of world effect for emerging
European markets becomes more profound when the currencies depreciate. However, it is mixed for the
relationship between exchange rate and European mean spillover intensity. Relative literature from Ng
(2000) points out that exchange rate may influence the spillover effect from the United States and Japan
to Asian emerging markets, yet the direction of the influence is not unification. An explanation is that
exchange rates should affect the competitiveness of firms, and thus their market values. This has an
impact on the expected cash flows of firms. Ma and Kao (1990) propose that currency appreciation has
a negative effect on the stock market in an export-dominant economy, whereas currency appreciation
promotes the stock market in an import-dominant economy. However, we cannot find clear evidence of
the impact of economic growth components on the mean spillover intensities from the world and the
European region.
Table 5 reports time-varying volatility spillover effects on the European regional market and
emerging European markets. In the European regional market, the volatility spillover from the world
decreases when the currency depreciates or when the European economy is in recession. However, we
find that the world effect becomes stronger when the world and European regions are simultaneously in
recession or when the size of trade with the world increases in the European region. The Wald tests
indicate that exchange rate and economic growth may significantly influence the world effect.
In the Czech Republic, the result shows that the world effect on volatility is larger when security
transactions decline or when the trade with the world increases. However, the European effect is weaker
when the Czech economy is in recession (at a 10% level) and is stronger when currency depreciates, or
when the European region and the Czech Republic are simultaneously in recession. Then, the Wald tests
show that exchange rate can influence the world effect (at a 10% level), and both exchange rate (at a 5%
level) and economic growth (at a 1% level) can change the European effect.
Table 5 shows that in Hungary, the world effect is weakened when the total trade with the world
increases and when the world and Hungary are simultaneously in recession. Nevertheless, the European
effect becomes more important when the trade increases, when Hungary is in recession, and when the
European region and Hungary are simultaneously in a recessionary phase. The Wald tests show that
exchange rate (at a 10% level) and economic growth (at a 1% level) are the important determinants for
the shock spillover intensity from the world, and economic growth (at a 5% level) is important for the
European effect.
The world effect on volatility of Poland diminishes when the world and Poland are simultaneously
witnessing recession or when only the economy of Poland faces recession. However, the European
effect on volatility of Poland decreases during currency depreciation and increases when the economy of
Poland is in recession. The Wald tests indicate that economic growth is an important driver of world
effect; furthermore, exchange rate and economic growth have a significant impact on the European effect
in Poland.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
53
For the Russian market, the world effect extends when the total trade with the world reduces or
when currency depreciates. Additionally, less trade with the European region (at a 5% level) or currency
depreciation (at a 10% level) degrades the European effect.
In Turkey, currency depreciation declines the European regional volatility spillover. The Wald test
indicates that exchange rate effect is important for the European effect at a level of 5%.
Interestingly, Table 5 also shows that the absolutes of the coefficients of the latent variables on the
European effect are larger than those on the world effect in most of the emerging European markets.
That is, the variables of trade, exchange rate, and business cycle components exhibit a stronger impact on
shock spillover intensities from the European region than on those from the world.
In sum, the size of trade appears to have a positive effect on the shock spillover intensities from the
European region and a negative effect on the volatility spillover from the world to emerging European
markets, except the Czech Republic, although the coefficients are not all significant. The possible
reason is that developed European countries are the largest export markets for emerging European
markets. As a result, increases in the trade with the European region leads to less diversified trade
structure for emerging European markets. Chambet and Gibson (2008) find that countries with less
diversified trade structure have more integrated stock markets.
Furthermore, in Table 5, the coefficients of business cycle components on world effect have a
negative sign, indicating that when the world and emerging European markets are simultaneously in
recession or when only emerging European markets are in recession, there is a lower degree of integration,
except in Turkey, although the coefficients are not all significant. The result conforms to Ragunathan et
al. (1999), who study the correlation and integration of the Australian and US markets by using
single-factor methodology. On the other hand, our paper reveals that growth effect can predict the
regional effect in the Czech Republic, Hungary, and Poland, and most signs are positive. In other words,
the regional effect is stronger during recession. The Czech Republic, Hungary, and Poland joined the
European Union on May 1, 2004. Thus, a possible explanation for the result may be that increased
economic integration with the European region and the links connecting regional trade lead to more
integration with European region for Czech, Hungary and Poland.
Table 6 displays the summary statistics of the Wald tests for the time-varying spillover effects. All
emerging markets as well as the European regional market have significant spillover effects from the
world market with respect to mean and volatility. The European region effect in the time-varying model
is significant only with respect to volatility but not mean. These results are consistent with the constant
spillover model. Moreover, in our study, full tests for the null hypothesis of no mean and volatility
spillover effects from the European region are rejected at the significant level of 5% for all emerging
European markets. Thus, the world and the European factors are both important for emerging European
market integration.
Table 4:Time-varying Mean Spillover Effects
World
European Region
Wald test
S. T.
Trade
Ex 1 Ex 2
Gth
Gth 2
Trade Ex1 Ex 2 Gth1 Gth 2 W(ex)
W(gw)
Eu(ex)
1
Eu(gw)
Europe 0.006* -0.004 -0.014 -0.080*** 0.182*** 0.182***
7.238** 21.589***
Czech -28.171 -36.408*** 0.147*** 0.298 0.033 -0.376
50.731 0.112 0.179 0.016 -0.135 11.692*** 1.366 2.808 0.216
**
Hungary -43.769 3.412 0.097*
0.182 -0.294 0.117
0.829 -0.009 0.037 -0.052 -0.173 3.155
2.256 0.230 0.561
Poland -742.575 1.026 0.063
-0.0540 -0.093
-65.722 0.083* 0.244 -0.185 0.197 5.733*
0.089 3.273 1.905
0.550**
Russia
0.120 -0.083** 0.121
0.477 0.011 1.421
-0.028 -0.037 -0.607** 0.366 -0.800 3.058
3.272 6.811** 2.943
54
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
Turkey -51.782
-2.037
-0.033
0.169 -0.348
0.901
-26.820 0.040 0.504 -0.280 -0.038
3.058
3.272
6.811** 1.935
Note: a “S. T.” represents the securities transactions per GDP. b “Trade” is the ratio of trade to GDP. c “EX1” is the changes of
exchange rate. d “EX2” includes a dummy indicating the currency depreciation. e “Gth1” displays the states of the local and world
(region) are simultaneously in economic recession. f “Gth2” is a dummy equal one when the local business cycle is below the phase.
** and *** denote statistical significant at 5% and 1% level, respectively.
Europe
Czech
Hungary
Poland
Russia
Turkey
S. T.
-0.004
-118.854***
97.275
-306.075
-0.004
116.056
Trade
0.010*
21.574**
-25.626***
-16.386
-0.064**
-0.207
Table 5: Time-varying Volatility Spillover Effects
World
European Region
Wald test
Ex 1
Ex 2 Gth 1 Gth 2
Trade
Ex1
Ex 2
Gth1 Gth 2 W(ex) W(gw) Eu(ex) Eu(gw)
-0.056*** -0.099 0.217*** -0.165**
16.093*** 20.770***
0.076 -0.196 -0.372 -0.232
51.166
0.555 2.608** 2.486*** -1.125* 5.378* 2.590 6.424** 20.059***
-0.134 0.102 -0.689*** -0.589
196.089*** -0.554* -1.438 1.998** 1.307** 5.756* 9.649*** 3.550 7.841**
-0.211 -0.748 -1.058*** -0.873*** 129.168 -0.585** -0.941 0.570 1.635** 2.433 18.670*** 11.398*** 6.157**
0.208* 0.661 -0.038 -1.005
0.282** -0.246 -2.454* -0.206 -1.873 3.444
1.935
3.223
2.044
0.010
0.706 0.685 0.845
31.250
-0.138 -6.078*** -1.568 -0.070 0.454
2.011
8.894** 1.356
Note: a “S. T.” represents the securities transactions per GDP. b “Trade” is the ratio of trade to GDP. c “EX1” is the changes of
exchange rate. d “EX2” includes a dummy indicating the currency depreciation. e “Gth1” displays the states of the local and world
(region) are simultaneously in economic recession. f “Gth2” is a dummy equal one when the local business cycle is below the phase.
** and *** denote statistical significant at 5% and 1% level, respectively.
Table 6: Wald Tests Table
Spillover from Region
Exchange Rate
Spillover from World
Mean
Europe
Czech
Hungary
Poland
Russia
Turkey
43.037***
94.082***
36.081***
85.464***
39.732***
16.696**
Economic Growth
Volatility
Full test
Mean
Volatility
Full test
Mean
Volatility
Full test
Mean
2703.896***
61.305***
89.934***
58.382***
30.987***
71.687***
2940.165***
140.928***
130.863***
145.003***
70.083***
100.767***
10.763*
2.768
8.415
12.049*
2.985
56.691***
50.205***
33.690***
14.966**
14.071**
68.911***
58.960***
42.670***
27.434***
22.638**
16.149***
3.227
7.473
8.682*
2.359
11.729**
10.800**
15.284***
4.597
9.522**
28.905***
15.786**
27.192***
16.240**
14.500*
10.946**
3.514
2.913
3.875
6.712
Volatility
Full test
20.908***
14.601***
24.596***
4.201
3.311
39.299***
21.413***
31.235***
7.676
9.961
Note: Table 7 provides the wald tests to examine the spillover effects from the world (European region) and the effects of exchange
rate components and economic growth components on spillover intensities from the world and European region. ** and *** denote
statistical significant at 5% and 1% level, respectively.
The effects of currency depreciation can predict the volatility spillover intensities at the 5% level in
four of five emerging European countries. Furthermore, the full test suggests that the exchange rate
components have significant predictive power at the 10% level for all emerging European countries
included in our research. Finally, the Wald tests presented in the last three columns of Table 6 show that
shock spillover intensities are significantly related to the state of the business cycle in three emerging
European countries at the 1% level; however, the return spillover intensity related to economic growth is
found only in the Czech Republic at the 5% level. At the significant level of 1%, a full test reports that
business cycle components can influence the spillover intensities for the Czech Republic, Hungary, and
Poland.
CONCLUSIONS
This paper measures the mean and volatility spillover effects from the world and the region for five
emerging European equity markets. Unlike previous research, we extend the Bekaert et al. (2005) model
estimated in three stages. This method is suitable for the analysis of emerging market integration.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
55
Our results suggest that there are significant volatility spillover effects for emerging Europe from
the world and the European developed region, but return spillover effects only from the world. The
positive volatility spillover intensities from the European region seem to be stronger than those from the
world. However, the mean spillover effects from the world are greater than those from the European
region. Moreover, both mean and volatility spillovers from the world to the European developed region
are significant.
This study also provides evidence of exchange rate effect on the degree of spillover for all emerging
European markets and growth effect on the shock spillover intensities from the European region for the
Czech Republic, Poland, and Hungary. The findings may reflect that economic integration can change
financial integration in the three markets. Emerging markets of various geographical regions have
different international trading partners and regulations as well as cultures and political environments.
Economic activity in a small country could be geographically localized and stocks in large countries
might move more independently than those in smaller countries.
Finally, for the European region, currency depreciation remarkably mitigates the mean and
volatility spillover effects from the world. The returns spillover from the world to the European region
increases when the business cycle is in recession. Additionally, we suggest that world and regional
economic instability may also change the degree of the world effect on European regional volatility.
Overall, our results are helpful for understanding the sources and behavior of volatilities in emerging
European markets. The findings in this paper are also important for portfolio managers and international
investors to price securities and to manage risk.
REFERENCES
Babetskii, I., Komárek, L., & Komárková, Z. (2007). Financial integration of stock markets among new EU member states and the
Euro area, Finance a úvěr-CZECH Journal of Economics and Finance, 57 pp.341-362.
Baele, L. (2005). Volatility spillover effects in European equity markets, Journal of Financial and Quantitative Analysis, 40
pp.373-401.
Bekaert, G., & Harvey, C. R. (1995). Time-varying world market integration, Journal of Finance, 50 pp.403-444.
Bekaert, G., & Harvey, C. R. (1997). Emerging equity market volatility, Journal of Financial Economics, 43 pp.29-77.
Bekaert, G., Harvey, C. R. & Lumsdaine, R. L. (2002). The dynamics of emerging market equity flows, Journal of International
Money and Finance, 21 pp.295-350.
Bekaert, G., & Harvey, C. R. (2003). Emerging market finance, Journal of Empirical Finance, 10 pp.3-55.
Bekaert, G., Harvey, C. R, & Ng, A. (2005). Market integration and contagion, Journal of Business, 78 pp.1-32.
Bekaert, G., Harvey, C. R., Lundblad, C., & Siegel, S. (2007). Global growth opportunities and market integration, Journal of
Finance, 62 pp.1081-1137.
Binswanger, M. (1999). Stock markets, speculative bubbles and economic growth. Edward Elgar Publishing: London.
Bollerslev, T., & Wnnlridge, J. M. (1992). Quasi-Maximum Likelihood estimation and inference in dynamic models with
time-varying covariances, Econometric Reviews, 11 pp.143-172.
Brooks, R., & Negro, M. D. (2004). The rise in co-movement across national stock markets: market integration or IT bubble?
Journal of Empirical Finance, 11 pp.659-680.
Chambet, A., & Gibson, R. (2008). Financial integration, economic instability and trade structure in emerging markets, Journal of
International Money and Finance, 27 pp.654-675.
Chelley-Steeley, P. L. (2005). Modeling equity market integration using smooth transition analysis: A study of Eastern European
stock markets, Journal of International Money and Finance, 24 pp.818-831.
56
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
Chen, N., & Zhang, F. (1997). Correlations, trades and stock returns of the Pacific-Basin markets, Pacific-Basin Finance Journal, 5
pp.559-577.
Cumperayot, P., Keijzer, T., & Kouwenberg, R. (2006). Linkages between extreme stock market and currency returns, Journal of
International Money and Finance, 25 pp.528-550.
Fama, E. (1981). Stock returns, real activity, inflation and money, American Economic Review, 71 pp.545–565.
Fama, E., & French, K. (1989). Business conditions and the expected returns on stocks and bonds, Journal of Financial Economics,
25 pp.23–50.
Fisman, R., & Love, I. (2004). Financial development and intersectoral allocation: A new approach, Journal of Finance, 59
pp.2785-2807.
Francis, B. B., Hasan, I., & Hunter, D. M. (2006). Dynamic relations between international equity and currency order flow, Journal
of Business, 79 pp.219-257.
Gallo, G. M., & Velucchi, M. (2009). Market interdependence and financial volatility transmission in East Asia, International
Journal of Finance and Economics, 14 pp.24-44.
Glosten, L. R., Jagannathan, R., & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal
excess return on stocks, Journal of Finance, 48 pp.1770-1801.
Kim, S-W, & Lee, B-S. (2008). Stock returns, asymmetric volatility, risk aversion, and business cycle: some new evidence,
Economic Inquiry, 46 pp.131-148.
Kim, S. J., Moshirian, F., & Wu, E. (2005). Dynamic stock market integration driven by the European Monetary Union: An
empirical analysis, Journal of Banking & Finance, 29 pp.2475-2502.
Koutmos, G., & Booth, G. G. (1995). Asymmetric volatility transmission in international stock markets, Journal of International
Money and Finance, 14 pp.747-762.
Ma, C. K., & Kao, G. W. (1990). On exchange rate changes and stock price reactions, Journal of Business Finance and Accounting,
17 pp.441-450.
Miyakoshi, T. (2003). Spillovers of stock return volatility to Asian equity markets from Japan and the US, Journal of International
Financial Markets Institutions & Money, 13 pp.383-399.
Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific-Basin, Journal of International Money and Finance,
19 pp.207-233.
Ragunathan, V., Faff, R., & Brooks, R. (1999). Correlations, business cycles and integration, Journal of International Financial
Markets, Institutions & Money, 9 pp.75-95.
Syriopoulos, T. (2004). International portfolio diversification to Central European stock markets, Applied Financial Economics, 14
pp.1253-1268.
Syriopoulos, T. (2006). Risk and return implications from investing in emerging European stock markets, Journal International
Financial Markets, Institutions & Money, 16 pp.283-299.
Voronkova, S. (2004). Equity market integration in Central European emerging markets: A cointegration analysis with shifting
regimes, International Review of Financial Analysis, 13 pp.633– 647.
The Journal of Human Resource and Adult Learning Vol. 5, Num. 2, December 2009
57