risk and return implications from investing in emerging european stock markets

Int. Fin. Markets, Inst. and Money 16 (2006) 283–299

Risk and return implications from investing inemerging European stock markets

Theodore Syriopoulos ∗

Department of Shipping and Entrepreneurial Services, University of the Aegean, Chios, Greece

Received 10 October 2004; accepted 25 February 2005Available online 6 September 2005

Abstract

The dynamic linkages and the effects of time-varying volatilities are investigated for major emerg-ing Central European (CE) and developed stock markets. Risk and return implications for portfoliodiversification to these markets are assessed, causal lead–lag relationships are identified and asym-metric volatility effects are evaluated. The presence of one cointegration vector indicates marketcomovements towards a stationary long-run equilibrium path. Central European markets tend to dis-play stronger linkages with their mature counterparts rather than their neighbors. An asymmetricEGARCH model indicates varying but persistent volatility effects for the CE markets. Internationalportfolio diversification can be less effective across cointegrated markets because risk cannot bereduced substantially and return can exhibit a volatile reaction to domestic and international shocks.The possibility of arbitrage short-run profits, however is not ruled out.© 2005 Elsevier B.V. All rights reserved.

JEL classification: C5; F36; G11

Keywords: Emerging European markets; Market comovements; EGARCH volatility dynamics

1. Introduction

A number of Central European (CE) countries have joined the European Union (EU)as new members in May 2004, creating a dynamic financial landscape in Euroland. The

∗ Present address: 14, Sevastopoulou Str., 115 24 Athens, Greece. Tel.: +30 210 6913 594;fax: +30 210 6994 117.

E-mail address: [email protected].

1042-4431/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.intfin.2005.02.005

284 T. Syriopoulos / Int. Fin. Markets, Inst. and Money 16 (2006) 283–299

efficient financial integration of these markets with the developed European markets hasimportant implications for the smooth accession of the CE economies but also for the long-term growth prospects of the enlarged EU. Despite high growth rates seen in the emergingCE stock markets, research remains surprisingly limited and the empirical findings appearsometimes ambiguous and contradictory. As further insight would be useful, this studyattempts to fill some of the gaps in the topic and contribute a range of innovative conclusions.

Major emerging CE stock markets, namely Poland, Czech Republic, Hungary and Slo-vakia, are examined and compared to representative mature markets, Germany and theUS. Different stock market patterns are analyzed, potential dynamic linkages and inter-dependencies are assessed, cointegrating vectors and lead–lag relationships are identified,asymmetric time-varying volatility effects are detected and the implications of risk andreturn for portfolio diversification are evaluated. The presence of any cointegrating vec-tor in the emerging CE stock markets would justify possible comovements between theregion and developed markets. A six-dimensional vector error correction model (VECM)is employed to test for the temporal causal dynamics in the Granger framework and to gaininsight into lead–lag relationships of the markets under study. Furthermore, an asymmetricexponential generalized autoregressive conditional heteroscedasticity (EGARCH) modelexplores the implications of potential asymmetric time-varying volatility effects for assetallocation to the CE markets. Past literature in stock market interrelations has concentratedmainly on developed markets neglecting emerging stock markets but the conclusions havenot always been consistent (e.g. Lai et al., 1993; Fratzscher, 2001; Chen et al., 2002; Besslerand Yang, 2003; Chaudhuri and Wu, 2003; Yang et al., 2003).

The impact of stock market integration and volatility effects has considerable impli-cations for international investors and active asset management in the CE stock markets.Although international portfolio diversification can lead to efficient asset allocation andreduce risk, assets associated with similar levels of risk are anticipated to have similarlevels of return in integrated markets. If the CE stock markets share common trends, noparticular gains should be anticipated from diversification because the presence of commonfactors limits the amount of independent variation. Cointegrated markets exhibit comove-ments and a stable long-run behavior, although potential short-run profits should not beruled out. In case that the CE stock markets are not found interrelated, profitable oppor-tunities from international portfolio diversification can be exploited, leading to superiorportfolio returns for long-term investors. However, it is also important to assess the patternof volatility dynamics, as highly volatile markets increase the equity risk premium and havean adverse impact on efficient portfolio diversification. The rest of the paper is organizedas follows. Section 2 discusses briefly key characteristics of the major CE stock marketsunder study. Section 3 presents the empirical methodology and findings. The final sectionconcludes.

2. The Central European stock markets

Over the last decade, the CE economies are through a transitory phase of structuraladjustment towards a market oriented economic system (Nord, 2000). Nevertheless, duringthe last 3 years, the CE region displays robust growth rates, expanding more rapidly than the

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Table 1Foreign capital inflows in the CE marketsa

CE market Equities Bonds Portfolioinvestment

GDP (%) Otherinvestment

FDI GDP (%)

Poland 3,773 1,927 5,700 0.70 8,618 30,438 3.80Czech Republic 3,910 2,086 5,996 1.90 15,481 14,934 4.80Hungary 3,545 2,029 5,575 2.10 1,391 14,706 5.60Slovakia 148 331 479 1.40 5,814 5,814 1.70

Source: International Financial Statistics (Koke and Schroder, 2002).a mln. euros; average 1995–1999; FDI, Foreign Direct Investment.

EU average and catching up in terms of productivity, especially in manufacturing. Poland,Czech Republic, Hungary and Slovakia are among the top performers (Hanousek and Filer,2000). The projected annual growth rate in Central and Eastern European economies isestimated at an average of about 3% in both 2003 and 2004, outstripping the Euro-zoneagain. Inflation continues to drop to single-digit annual rates throughout the region, withPoland and the Czech Republic moving below average EU rates (Havlik, 2003). The effectsfrom CE participation in the EU are anticipated to be smooth. There developments areexpected to have important implications for the short- and long-run behavior of the CEstock markets, their dynamic linkages with the developed markets and the impact of marketvolatility.

The CE stock markets have a rather brief history compared to the mature markets ofEurope and the US.1 Despite robust growth rates and reasonable valuations, the CE stockmarkets remain small in terms of capitalization, turnover, number of traded securities andliquidity compared to developed markets (Pajuste, 2001; Koke and Schroder, 2002). Atthe end of 2001, the CE stock markets together amounted to 0.2% of world stock marketcapitalization and to 9.2% of the German stock market. The Warsaw stock market is thelargest and most developed market, covering approximately 40% of the capitalization ofthe whole region (Koke and Schroder, 2002). The worldwide recessionary slowdown since2000 has affected the CE stock markets as well. Market capitalization as a percentageof GDP has fluctuated from 3.7% (Poland) to 26.1% (Hungary) over 1995–2000 (Kokeand Schroder, 2002). In 2002, these figures ranged from 14% (Poland) to 19% (Hungary,Slovakia), according to the European Central Bank. The CE stock markets with higherliquidity and market capitalization are more attractive to international investors. Foreigndemand for CE assets shows upward trends, as reflected in the CE foreign investment ratios(Table 1).

The financial literature on the CE stock markets remains thin. Linne (1998), for instance,reports some evidence of cointegration was detected only between the CE markets but notbetween the CE and mature markets. Jochum et al. (1999) assess the impact of the 1997–1998Russian crisis on the long-run relationships between Vyshegrad countries (Poland, CzechRepublic, Hungary), Russia and the US. Bivariate cointegration relationships in the sample

1 The Budapest Stock Exchange (Hungary) was re-opened in June 1990, the Bratislava Stock Exchange (Slo-vakia) in March 1991, the Warsaw Stock Exchange (Poland) in April 1991 and the Prague Stock Exchange (CzechRepublic) in April 1993 (Budapest Stock Exchange, 2003; Bratislava Stock Exchange, 2003; Warsaw StockExchange, 2003; Prague Stock Exchange, 2003).


for the pre-crisis period ceased for all but two pairs of markets after the crisis. This wasattributed to the predominance of the short-run over the long-run dynamics in the post-crisisperiod, resulting to a change in the long-run relationship. Verchenko (2000) provides anempirical analysis of potential portfolio diversification across Eastern European and formerSoviet Union stock markets. Profitable diversification opportunities were concluded, dueto the absence of cointegration and independence of stock market movements. Scheicher(2001) documents the pivotal influence of global factors on the Hungarian stock market,related to the high share of active international investors in this market. MacDonald (2001)studies the CE stock market indices as a group against each of three developed markets (US,Germany, UK) and concludes significant long-run comovements for each of the groupings.Gilmore and McManus (2002), on the contrary, find no long-term links between the threemajor CE markets (Poland, Czech Republic, Hungary) and the US; however, the relationsof the CE stock markets with major European counterparts were not considered. Serwa andBohl (2003) investigate contagion effects to European capital markets, which are associatedwith seven important financial shocks between 1997 and 2000. The study uses correlationanalysis and compares a number of developed European markets (Germany, UK, France,Ireland, Spain, Portugal, Greece) with major Central and Eastern European markets (Poland,Czech Republic, Hungary, Russia). Weak evidence of increased cross-market linkages fol-lowing these crises was found, whereas emerging market returns did not converge to thoseof the developed markets. CE stock markets were concluded to still offer considerablerisk diversification opportunities. Voronkova (2004) investigates the long-run relationshipsbetween CE stock markets (Poland, Czech Republic, Hungary), developed European stockmarkets (Germany, France, UK) and the US, incorporating a structural break in the model.Evidence of long-run linkages between the CE emerging markets and the mature marketswas found, implying limited diversification benefits for international investor portfoliosallocated to these markets.

3. Empirical methodology and findings

3.1. Data description

The statistical data used in this study consist of the daily stock index closing prices infour major CE stock markets, Germany and the US. The stock market indices of interest areWIG of Poland, PX50 of the Czech Republic, BUX of Hungary, SAX of Slovakia, DAXof Germany and SP500 of the US. These are the main stock indices in the markets understudy and represent well-diversified domestic stock portfolios that adequately cover totalmarket capitalization. The inclusion of Germany and the US is important for two reasons.First, these markets serve as reasonable proxies for the mature European and North Ameri-can stock markets, respectively, in depicting possible linkages with the emerging CE stockmarkets. Second, due to the investment flows and transactions turnover in these developedmarkets, they are expected to play an influential role in international stock market move-ments including the CE region. The sample period is from January 1, 1997 to September 20,2003, totaling 1747 daily observations for each series. These high frequency data includeinformation on short-run market interactions that may be absent in lower frequency data.


The series are converted in euros, to avoid implications from any currency risk distortionsthat could affect international investors’ decisions on the CE markets.2

3.2. Contemporaneous correlations

Time series non-stationary variables can lead to spurious regressions and misleadinginferences unless at least one cointegration vector is present. As a preliminary step, theCE stock price indices were transformed into natural logs, their integrated properties wereinvestigated and their plots were examined. A range of descriptive statistics in the CE stockmarket returns that are of prime interest to international portfolios is analyzed (Table 2).In most cases, significant kurtosis, negative skewness (long left tail) and high Jarque-Berastatistics indicate rejection of normality in stock return distributions.

The contemporaneous correlations matrix of stock returns indicates positive but lowpairwise correlations between the CE, German and US stock market returns, whereas the cor-relations for SAX returns are particularly low with all markets (Table 2). The low correlationcoefficients of stock index returns indicate weak short-term contemporaneous interactionsbetween these markets. This may be related to the relatively short active life of the CEstock markets since their reopening and the absence of substantial market depth, in terms ofcapitalization, turnover and listed companies. In order to test for the presence of stochasticnon-stationarity in the data, the integration order of the individual time series is investigatedemploying standard unit root tests, the Augmented Dickey-Fuller (ADF) and the Phillipsand Perron (PP) tests. The results from the ADF and PP tests indicate that, for every stockprice index series, the null hypothesis of a unit root is not rejected at the 5% significancelevel by both tests.3 To verify that the order of integration is I(1), the presence of a unit rootin the first difference of the stock price indices was also tested but no unit root was found(Table 3).

3.3. Cointegrating vectors

As the null hypothesis of unit roots cannot be rejected, multivariate models can be built toenable investigation of short- and long-run linkages among the emerging CE stock marketsand developed counterparts. Testing for the presence and number of cointegrating vectors4

in the six markets jointly is based on a vector error correction model (VECM), applyingthe procedure advanced by Johansen (Johansen, 1988; Johansen and Juselius, 1990, 1992).Defining a vector zt of n potentially endogenous non-stationary variables, it is possible

2 Empirical evidence indicates that currency risk can have a significant impact on time-varying dynamics offinancial integration in Europe. Reduction of exchange rate uncertainty has resulted to increased market cointe-gration in the EU (e.g. Fratzscher, 2001).

3 The optimal lag structure was based on the minimization of the Akaike Information Criterion (AIC) and wasset at four lags for the ADF test and at seven lags for the PP test.

4 If two or more variables are cointegrated, then stationary linear combinations of these variables may exist eventhough the variables themselves are individually non-stationary. The absence of cointegration suggests that thevariables involved have no long-run interdependence and can drift arbitrarily away from each other (e.g. Engleand Granger, 1987; Johansen, 1988).

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Table 2Stock market return statistics

SP500 DAX WIG PX50 BUX SAX

Mean 0.0002 0.0001 0.0002 0.0000 0.0004 −0.0001Median 0.0000 0.0002 0.000 0.0000 0.0001 0.0000Maximum 0.0797 0.0755 0.1353 0.0582 0.1361 0.1049Minimum −0.1245 −0.0887 −0.1354 −0.0708 −0.1790 −0.1148Standard deviation 0.0135 0.0182 0.0180 0.0132 0.0198 0.0149Skewness −0.4427 −0.1295 0.0397 −0.1317 −1.0312 −0.2160Kurtosis 9.8654 4.6843 11.7932 4.6291 15.5674 11.7177Jarque-Bera 348.605 211.252 562.545 198.1269 117.995 554.256Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Q(12) 16.413 (0.173) 10.934 (0.535) 33.926 (0.001) 27.869 (0.006) 43.645 (0.000) 16.271 (0.179)

Q2(12) 104.77 (0.000) 929.21 (0.000) 586.08 (0.000) 401.18 (0.000) 575.95 (0.000) 140.98 (0.000)

Correlation matrixSP500 1.000DAX 0.497 1.000WIG 0.146 0.273 1.000PX50 0.170 0.327 0.330 1.000BUX 0.167 0.397 0.440 0.409 1.000SAX 0.030 0.008 0.053 0.007 0.002 1.000

The correspondence between stock markets and indices is: SP500: US; DAX: Germany; WIG: Poland; PX50: Czech Republic; BUX: Hungary; SAX: Slovakia.Q(12) and Q2

(12) Ljung-Box test for stock returns and squared stock returns (12-lags); (·): p-values.


Table 3Unit root tests

ADF PP

zt �zt zt �zt

(1) (2) (1) (2) (1) (2) (1) (2)

SP500 −2.153 −2.115 −20.191 −20.268 −2.216 −2.166 −44.787 −44.859DAX −1.668 −2.004 −19.243 −19.351 −1.699 −2.030 −42.240 −42.323WIG −2.280 −2.222 −20.349 −20.353 −2.390 −2.352 −38.341 −38.334PX50 −1.877 −1.729 −18.054 −18.103 −1.696 −1.541 −38.466 −38.483BUX −3.110 −3.155 −18.429 −18.428 −3.299 −3.316 −40.415 −40.411SAX −1.059 −0.702 −19.511 −19.728 −1.022 −0.676 −43.948 −44.122

zt: variables in levels; �zt: variables in first differences. (1): without trend; (2) with trend.Critical values—without trend: −3.437 (1% level); −2.864 (5% level); −2.568 (10% level).Critical values—with trend: −3.969 (1% level); −3.415 (5% level); −3.129 (10% level).

to specify the following data generating process and model zt as an unrestricted vectorautoregression (VAR) involving up to k-lags of z:

zt = A1zt−1 + A2zt−2 + · · · + Akzt−k + ut ut IN (0, Σ) (1)

where zt is a (n × 1) matrix and each of Ai is a (n × n) matrix of parameters. Eq. (1) can bereformulated into a VECM form:

�zt = Γ1�zt−1 + Γ2�zt−2 + · · · + Γk−1�zt−k+1 + Πzt−k + ut (2)

or

�zt =k−1∑i=1

Γi∆zt−i + Πzt−k + ut (3)

where Γ i = −(I − A1 − ··· − Ai) (i = 1,. . ., k − 1), Γ i are interim multipliers andΠ = −(I − A1 − ··· − Ak). If the coefficient matrix Π has reduced rank r < n, there exist(n × r) matrices α and β each with rank r such that Π = α β′ and β′zt is stationary. Testingfor cointegration hence is related to the consideration of the rank of Π, that is finding thenumber of r linearly independent columns in Π (cointegrating vectors). The hypothesis ofthe existence of r cointegrating vectors can be tested by the ‘trace’ test, i.e. the LR teststatistic that there are at most r distinct cointegrating vectors against a general alternative:

λtrace(r) = −2 log (Q) = −T

n∑i=r+1

log(1 − λ̂i) (4)

where i = r + 1, . . ., n, are the (n − r) smallest squared canonical correlations, r = 0, 1, 2, . . .,n − 1 and λtrace (r) = 0, when all λi = 0. Alternatively, the ‘maximum eigenvalue’ test canbe used to compare the null hypothesis of r cointegrating vectors against the alternative of(r + 1) cointegrating vectors. The LR test statistic for this hypothesis is given by:

λmax(r, r + 1) = −2 log(Q) = −T log(1 − λ̂r+1) (5)

where r = 0, 1, 2, . . ., n − 1.


Table 4Tests for the presence of cointegrating vectors

Null Eigenvalues λtrace Test Critical values at 95%

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

r = 0 0.0242 0.0242 0.0243 105.42 104.13 117.10 102.14 94.15 114.90r ≤ 1 0.0133 0.0130 0.0148 62.81 61.53 74.23 76.07 68.52 87.31r ≤ 2 0.0120 0.0117 0.0118 39.41 38.76 48.28 53.12 47.21 62.99r ≤ 3 0.0055 0.0054 0.0064 18.33 18.17 27.59 34.91 29.68 42.44r ≤ 4 0.0046 0.0045 0.0049 8.86 8.72 16.49 19.96 15.41 25.32r ≤ 5 0.0005 0.0006 0.0045 0.82 0.81 7.88 9.24 3.76 12.25

H1(r) against H1(n).Model 1: model with a constant restricted to the cointegrating space.Model 2: model with unrestricted constant.Model 3: model with a linear trend in the cointegration vector. Critical values are obtained from Osterwald-Lenum(1992).

Table 5Tests for the number of cointegrating vectors

Null λmax Test Critical values at 95%

n − r Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

r = 0 42.61 42.60 42.87 40.30 39.37 43.97r = 1 23.41 22.77 25.95 34.40 33.46 37.52r = 2 21.08 20.59 20.68 28.14 27.07 31.46r = 3 9.47 9.45 11.10 22.00 20.97 25.54r = 4 8.04 7.90 8.61 15.67 14.07 18.96r = 5 0.82 0.81 7.88 9.24 3.76 12.25

H1(r) against H1(r + 1).Model 1: model with a constant restricted to the cointegrating space.Model 2: model with unrestricted constant.Model 3: model with a linear trend in the cointegration vector.Critical values are obtained from Osterwald-Lenum (1992).

Three alternative models are compared and contrasted for the CE and developed stockmarkets of interest: a model with a constant restricted to the cointegrating space; a modelwith unrestricted constant; and a model with a linear trend in the cointegration vector(Tables 4 and 5).

The empirical findings support the presence of one cointegrating vector in the marketsunder study, in all three versions of the model.5 The null hypothesis that the four CE stockmarkets, Germany the US are not cointegrated (r = 0) against the alternative of one or morecointegrating vectors (r > 0) is rejected, since the λmax(0) statistic exceeds the critical valueat the 5% significance level. However, the λmax and λtrace statistics suggest no more than onecointegrating vector, since H0 of r ≤ 1 is not rejected, as λmax(1) is less than the critical valueat the 5% significance level. The significance of each stock market in the cointegrating vector

5 The optimal lag structure was chosen by the AIC minimization and the absence of autocorrelation in the VARresiduals; four lags for the levels of variables were included. The order the stock indices are entered into the modelis based on their market capitalization (all other orderings are also analyzed in supplementary models).


was tested by using an LR test statistic, which is asymptotically distributed as χ2(1) (Johansen,

1991). Based on that, all markets except from Slovakia were found statistically significant tothe long-run relationship (5% significance level). The Slovakian stock market appears to fol-low an autonomous path, as it does not influence and is not influenced by the other markets.This stock market is characterized by particularly small capitalization, limited market depthand thin trading activity. As a result, Slovakia was excluded from the final version of the coin-tegrating vector and the relevant error correction model. The magnitude of the coefficients ofthe cointegrating vector is informative on the respective role of the individual stock marketsin the group. The results indicate positive long-run linkages among the stock markets, exceptfor Poland and Hungary (Table 6). The presence of one cointegrating vector and of five com-mon stochastic trends indicates that common dynamics bring the CE markets, Germany andthe US towards a long-run equilibrium path. This evidence suggests that future volatility inone stock market can be determined or predicted to some extent by movements in the othermarkets. The presence of an equilibrium relationship could be attributed to the growinginflow of foreign portfolio investments in the CE markets, the extensive trade and financiallinkages and the common economic policies applied. Stock price fluctuations in the maturemarkets, especially in the US, appear to have a significant impact on the emerging CE stockmarkets. These findings imply that long-run investors who diversify their portfolios acrossthe CE stock markets should expect rather short-run modest portfolio gains, as investmentrisk cannot be reduced and portfolio returns can exhibit a volatile behavior to market shocks.

3.4. Error correction and Granger causality

Following the presence of one cointegrating vector for the markets under study, a dynamicVEC model can be estimated. The VECM depicts the feedback process and adjustment speedof short-run deviations towards the long-run equilibrium path and reveals the short-run (uni-or bi-directional) Granger causalities (causal flows) and lead–lag relationships in any CEstock market relative to the other markets (Granger, 1988). For two cointegrated stock priceseries xt and yt, the ECM can take the form:

�xt = α1 +m1∑i=1

β1i�xt−i +m2∑i=1

β2i�yt−i + γ1zt−1 + u1t (6)

�yt = α2 +m3∑i=1

β3i�xt−i +m4∑i=1

β4i�yt−i + γ2zt−1 + u2t (7)

The magnitude of the coefficients γ1 and γ2 determines the speed of adjustment backto the long-run equilibrium following a market shock. When these coefficients are large,adjustment is quick so z will be highly stationary and reversion to the long-run equilibriumwill be rapid.

Robust and persistent short-run US stock market effects are found to Granger causeGerman and CE stock market reactions (Table 7).6 The international leading role of the US

6 A four-lag VEC model was initially estimated and a parsimonious representation was finally reached byappropriately reducing the statistically insignificant terms.

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Table 6Normalized cointegrating vector

SP500 DAX WIG PX50 BUX C TREND

Model 1 1.000 2.103 (0.500) −7.630 (0.740) 6.448 (0.631) −0.232 (0.647) 2.524 (0.000)

Model 2 1.000 2.118 (0.152) −7.695 (0.750) 6.487 (0.638) −0.199 (0.636) 2.520 (0.000)

Model 3 1.000 2.066 (0.535) −7.386 (0.740) 6.249 (0.652) −0.228 (0.719) 2.437 (0.000) 0.000 (0.000)

Model 1: model with a constant restricted to the cointegrating space.Model 2: model with unrestricted constant.Model 3: model with a linear trend in the cointegration vector.The Slovakian stock market was initially found statistically insignificant and was subsequently excluded from the cointegrating vector. (·): asymptotic standard errors.

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Table 7Lead–lag Granger causalities

Stock index �SP500 �DAX �WIG �PX50 �BUX

�SP500(−1) −0.108* (−3.780) 0.408* (10.899) 0.450* (12.682) 0.202* (7.415) 0.442* (11.029)

�SP500(−2) −0.064* (−2.026) 0.154* (3.730) −0.079* (−2.027) −0.037 (−1.243) −0.033 (−0.749)

�SP500(−3) −0.012 (−0.392) 0.107* (2.566) 0.064 (1.630) 0.056 (1.860) 0.089* (2.001)

�DAX(−1) 0.067* (2.880) −0.172* (−5.648) −0.028 (−0.983) −0.023 (−1.044) −0.038 (−1.167)

�WIG(−1) 0.006 (0.294) −0.013 (−0.451) 0.026 (0.979) −0.003 (−0.156) 0.001 (0.005)

�PX50(−1) −0.009 (−0.320) 0.024 (0.665) 0.065* (1.889) 0.051* (1.929) 0.071* (1.920)

�PX50(−2) 0.086* (2.200)

�BUX(−1) −0.016 (−0.783) −0.037 (−1.422) 0.066* (2.667) 0.023 (1.228) −0.018 (−0.626)

�BUX(−2) 0.048* (2.491)

ECT(−1) −0.0001 (−0.570) −0.001 (−0.559) −0.005* (−2.670) −0.007* (−4.837) −0.002 (−0.895)

C 0.001 (0.635) 0.005 (0.128) 0.007 (0.153) −0.005 (−0.017) 0.003 (0.784)

Determ. resid. covariance 0.00003Log likelihood 2463.77Akaike AIC −28.155

VECM based on Model 2; �: first differences (short-term impact); ECT: error correction term; (·): t-statistics.* 5% Significance level.


stock market is supported by its market depth (capitalization and turnover) as well as theforeign direct capital inflows and investors’ equity transactions in the CE markets (Hanousekand Filer, 2000). This is in line with past findings (e.g. Masih and Masih, 1997). The short-runGerman market impact on the US market indicates bi-directional causal linkages (feedbackeffects) between the two developed markets and may reflect the active asset allocation ofGerman funds to the US stock market. International financial crises and contagion effectsmay also induce comovements between the stock markets (Ratanapakorn and Sharma,2002). Short-run CE stock market interrelations indicated uni-directional Granger causaleffects primarily from the Czech and Hungarian markets to the Polish stock market andbi-directional ones between the Czech and Hungarian stock markets. This seems plausibleas these CE countries share high trade activity and their economies follow similar structuraladjustments and a common growth path. Both domestic and external forces affect the CEmarkets in the short-run.

The error correction term, derived from the cointegrating vector, reflects long-run causalflows and the proportion by which the long-run equilibrium in the dependent variable isbeing corrected in each short period. The ECT is found to be statistically significant mainlyin the cases of Poland and the Czech Republic, the largest CE stock markets in the sample.Hence, it is responses from the Polish and Czech markets that adjust to clear deviationsfrom the equilibrium. The US and the German stock markets appear to be statisticallyexogenous to the system, as they are not particularly influenced by any short-run CE effectsand the respective error correction terms are not found statistically significant. This outcomeindicates an autonomous path for the mature stock markets, shaped by the dominance ofcountry-specific factors over common international factors. Overall, since the coefficientsof the ECT are small, adjustment is expected to take some time and reversion to the long-runequilibrium is slow.

3.5. Conditional volatility

In order to further understand the CE stock market dynamics and assess the relative riskand return trade-off, the time-varying implications of return variance are investigated. Thisis an important issue for asset valuation, optimal portfolio allocation and dynamic hedgingstrategies. Expanding on the VECM framework, an EGARCH model is tested whether itadequately describes volatility behavior, taking into account asymmetric (leverage) dynam-ics (Engle, 1982; Bollerslev, 1986; Nelson, 1991). Negative shocks to stock returns canpotentially generate more volatility than positive shocks of equal magnitude (e.g. Paganand Schwert, 1990; Engle and Ng, 1993). Under the EGARCH(1,1) the conditional vari-ance of the i stock market return, hit, is given by:

log(hit) = ω + α

[εit−1√hit−1

−√

2/π

]+ β log(hit−1) + γ

εit−1√hit−1

(8)

where ω, α (ARCH effect), β (GARCH effect) and γ (asymmetric shocks) are constantparameters; γ typically enters with a negative sign, indicating that bad news (εit < 0) generatemore volatility than good news (εit > 0).7

7 The term εit can be treated as a collective measure of ‘news’ on the i stock return (Engle and Ng, 1993).

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Table 8EGARCH model

SP500 DAX WIG PX50 BUX SAX

ω −0.386 (−2.914) −0.362 (−5.224) −0.573 (−5.957) −0.648 (−4.132) −0.866 (−2.241) −0.977 (−2.074)

α 0.063 (2.257) 0.150 (5.624) 0.499 (4.705) 0.190 (5.237) 0.363 (2.872) 0.195 (3.186)

β 0.961 (7.238) 0.971 (3.394) 0.114 (0.556) 0.943 (5.963) 0.927 (2.461) 0.900 (5.730)

γ −0.157 (−5.445) −0.073 (−3.271) −0.034 (−0.566) −0.036 (−1.364) −0.080 (−1.463) −0.042 (−1.013)

L.L. 5194.805 4786.016 4748.018 5229.636 4715.797 4930.221Sk −0.643 −0.091 0.537 −0.013 −0.887 −0.524Ku 7.134 3.375 12.074 4.450 13.168 11.945Q(12) 13.781 7.016 17.959 8.408 43.117 14.782

{0.315} {0.857} {0.117} {0.753} {0.000} {0.254}Q2

(12) 3.860 26.744 15.201 19.539 7.320 10.462{0.986} {0.008} {0.000} {0.076} {0.000} {0.575}

ω: constant; α: ARCH effect; β: GARCH effect; γ: asymmetric (leverage) effect. L.L.: Log Likelihood; (·): robust z-statistics; {·}: p-values. Sk: standardized residualsskewness; Ku: standardized residuals kurtosis. Q(12): Ljung-Box test, standardized residuals; Q2

(12): Ljung-Box test, squared standardized residuals.


The empirical findings indicate evidence of ARCH effects,8 whereas volatility clusteringis shown in stock price and return plots, as large changes in returns are followed by furtherlarge changes. The estimated coefficients ω, α, β, and γ (γ < 0) in the EGARCH modelare found statistically significant at the 5% level in most cases (Table 8). The coefficient α

measures the extent to which volatility shocks today feed through into next period’s volatil-ity. Large α coefficients mean that volatility reacts quite intensively to market movements.Large β coefficients indicate that volatility is persistent and shocks to conditional variancetake a long time to die out. The (α + β) term measures the rate at which this combined effectdies out over time. This effect is found higher than unity in most CE stock markets, exceptfrom Poland, indicating a persisting volatile behavior over the sample period. Asymmetricshocks (γ < 0) were mainly found for the mature markets, as for most CE stock marketsthe leverage effects were not found statistically robust.9 Even in this latter case, modelingvolatility with an EGARCH model has considerable advantages. The absence of furtherautoregressive conditional heteroskcedasticity effects in the EGARCH standardized inno-vations (zit = εit/

√hit) and the squared standardized innovations (z2

it) was also tested andthe relevant Ljung-Box statistics indicated acceptance of the null hypothesis in all cases(Table 8).

4. Conclusions

The risk and return implications from investing in emerging Central European stockmarkets have been studied in depth. Emphasis has been placed on the short- and long-runlinkages of major CE and developed stock markets (Poland, Czech Republic, Hungary,Slovakia, Germany and the US), employing cointegration methodology. The time-varyingvolatility behavior of these markets has also been investigated, incorporating asymmet-ric EGARCH models. The CE states have recently joined the EU and examination of theseissues remains important and timely. The empirical findings enrich the thin body of literatureon the emerging CE stock markets and have direct implications for the assessment of riskand return in portfolio diversified to these markets. The evidence supports the presence ofone cointegration vector and indicates a stationary long-run relationship. The relevant VECmodel showed some Granger causal effects and lead–lag relationships between emergingand mature stock markets. Short-run deviations from equilibrium are found to exert a feed-back effect on the CE markets, resulting to reversion towards their long-run path. Althoughboth domestic and external forces have an impact on that, this process appears to be slow.

The individual CE markets tend to display stronger linkages with their mature counter-parts rather than the other CE neighbors. This may be related to the short active life of theCE stock markets and the absence of substantial market depth, in terms of capitalization,turnover and listed companies. Direct foreign investment inflows and asset allocation by

8 As the relevant (12-order) Ljung-Box statistics show in the stock market return and squared return series(Table 8); the critical value of χ2

(12) is 21.026%, at the 5% significance level.9 The quasi-maximum likelihood (QML) estimator employed may affect EGARCH results, as it has poor finite

sample properties when the data generating process has conditional excess kurtosis (Deb, 1995). The currentmarket regime may also have an impact on volatility (Hamilton and Susmel, 1994).


international portfolios towards the CE markets have also increased recently. The US marketholds a leading influential role, as it can induce strong movements in the CE markets butis not influenced by them, whereas a feedback impact is seen between the US and Germanmarkets. The Polish, Czech and Hungarian stock markets are more sensitive to shocks trans-mitted from the mature markets and short-term causal linkages are found between these CEmarkets. In case of short-run departures from the long-run path, it is mainly responses fromthe Polish and Czech stock markets that adjust in order to clear the disequilibrium. TheSlovakian stock market remains small in terms of capitalization and turnover and exhibitsan autonomous behavior relative to its CE peers and the mature counterparts.

The EGARH model was found to be a statistically satisfactory representation to study theimplications of volatility effects for CE stock market returns. The empirical findings supportpersistent volatility effects, since once volatility increases it can remain high over severalperiods in the future. However, the impact of volatility on the CE stock market returns isnot uniform, and Poland exhibits relatively lower reactions. This may indicate that sectoraland/or market-specific fundamentals can also be important when investors decide on assetallocation to the CE markets. Portfolio diversification to the CE stock markets may notpresent an attractive low risk investment opportunity, as CE market returns can exhibitpersistent volatility over time. The (negative) asymmetric (leverage) effects are found tobe significant mainly for the developed stock markets, indicating that a negative shock isanticipated to potentially cause volatility to rise more than a positive shock of the samemagnitude.

The Central European markets follow a common path of adjustment and growth andbecome gradually more integrated with the international developed markets. These link-ages are anticipated to strengthen in the medium term, especially following participationin the European Monetary Union (EMU). Evidence indicates that stock market cointegra-tion has strengthened in the countries that have adopted the euro, despite volatility effects(e.g. Fratzscher, 2001). Major driving forces for that have been monetary policy conver-gence and reduction of exchange rate risk. The presence of cointegrating relationships hasimportant implications for active portfolio management. In the short-run, the CE stockmarkets may offer opportunities for short-run arbitrage profits. In the long-run howevermarket comovements imply that the potential for attaining superior portfolio returns maybe limited. International portfolio diversification is less effective across the cointegratedmarkets because the investment risk cannot be reduced and portfolio returns can exhibita volatile behavior to internal and external shocks. This outcome was reinforced by thefindings that the risk level for assets allocated to the CE markets appears relatively high, aspersistent volatilities may adversely affect investment returns. Further research in the CEstock markets following their accession in the EU could enrich the present findings.

References

Bessler, D.A., Yang, J., 2003. The structure of interdependence in international stock markets. Journal of Interna-tional Money and Finance 22, 261–287.

Bollerslev, T., 1986. Generalised autoregressive conditional heteroskedasticity. Journal of Econometrics 31,307–327.


Bratislava Stock Exchange, 2003. Fact book (www.bsse.sk).Budapest Stock Exchange, 2003. Fact book (www.bse.hu).Chaudhuri, K., Wu, Y., 2003. Random walk versus breaking trend in stock prices: evidence from emerging markets.

Journal of Banking and Finance 27, 575–592.Chen, G., Firth, M., Rui, O., 2002. Stock market linkages: evidence from Latin America. Journal of Banking and

Finance 26, 1113–1141.Deb, P., 1995. Finite sample properties of maximum likelihood and quasi-maximum likelihood estimators of

EGARCH models. Econometric Reviews 15, 51–68.Engle, R.F., 1982. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom

inflation. Econometrica 50, 987–1006.Engle, R.F., Granger, C.W., 1987. Co-integration and error correction: representation, estimation and testing.

Econometrica 55, 251–276.Engle, R., Ng, V., 1993. Measuring and testing the impact of news on volatility. Journal of Finance 48, 1749–1778.Fratzscher, M., 2001. Financial market integration in Europe: on the effects of EMU on stock markets, Working

Paper No. 48 (European Central Bank).Gilmore, C.G., McManus, G.M., 2002. International portfolio diversification: US and Central European equity

markets. Emerging Markets Review 3, 69–83.Granger, C.W.J., 1988. Some recent developments in a concept of causality. Journal of Econometrics 39, 199–211.Hamilton, J.D., Susmel, R., 1994. Autoregressive conditional heteroscedasticity and changes in regime. Journal

of Econometrics 64, 307–333.Hanousek, K., Filer, R.K., 2000. The relationship between economic factors and equity markets in Central Europe.

Economics of Transition 8, 99–126.Havlik, P., 2003. Transition countries in 2003: reforms and restructuring keep the global economic slowdown at

bay, Working Paper (Vienna Institute for International Economic Studies).Jochum, C., Kirchgasser, G., Platek, M., 1999. A long-run relationship between Eastern European stock markets?

Cointegration and the 1997/98 crisis in emerging markets. Weltwirtschaftliches Archiv 135, 455–479.Johansen, S., 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12,

231–254.Johansen, S., 1991. Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive

models. Econometrica 59, 1551–1580.Johansen, S., Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration with application

to the demand for money. Oxford Bulletin of Economics and Statistics 52, 169–210.Johansen, S., Juselius, K., 1992. Testing structural hypotheses in a multivariate cointegration analysis of the PPP

and UIP for UK. Journal of Econometrics 53, 211–244.Koke, J., Schroder, M., 2002. The prospects of capital markets in Central and Eastern Europe, Working Paper No.

02-57 (Centre for European Economic Research).Lai, M., Lai, K.S., Fang, H., 1993. Dynamic linkages between the New York and Tokyo stock markets: a vector

error correction analysis, Journal of International Financial Markets. Institutions and Money 3, 73–96.Linne, T., 1998. The integration of the Central and Eastern European equity markets into the international capital

markets: a cointegration analysis, Working Paper (Institut fur Wirtschaftsforschung Halle).MacDonald, R., 2001. Transformation of external shocks and capital market integration. In: Schroder, M. (Ed.),

The New Capital Markets in Central and Eastern Europe. The Centre for European Economic Research,Springer Verlag, pp. 210–245.

Masih, A.M.M., Masih, P., 1997. Dynamic linkages and the propagation mechanic driving major internationalmarkets: an analysis of the pre- and post-crash era. Quarterly Review of Economics and Finance 37, 859–885.

Nelson, D.B., 1991. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59, 347–370.Nord, R., 2000. Central and Eastern Europe and the new financial architecture. IMF Finance and Development

37, 1–7.Osterwald-Lenum, M., 1992. A note with quartiles as the asymptotic distribution of the maximum likelihood

cointegration rank test statistics. Oxford Bulletin of Economics and Statistics 54, 461–472.Pagan, A., Schwert, G., 1990. Alternative models for common stock volatility. Journal of Econometrics 45,

267–290.

http://www.bsse.sk/

http://www.bse.hu/


Pajuste, A., 2001. Corporate governance and stock market performance in Central and Eastern Europe: a study ofnine countries, 1994–2001, Working Paper (Stockholm School of Economics).

Prague Stock Exchange, 2003. Fact book (www.pse.cz).Ratanapakorn, O., Sharma, S.C., 2002. Interrelationships among regional stock indices. Review of Financial

Economics 11, 91–108.Scheicher, M., 2001. The comovements of stock markets in Hungary, Poland and the Czech Republic. International

Journal of Finance and Economics 6, 27–39.Serwa, D., Bohl, M.T., 2003. Financial contagion: Empirical evidence on emerging European capital markets,

Working Paper No. 5-03 (European University Viadrina Frankfurt).Verchenko, O., 2000. Potential for portfolio diversification across Eastern European stock markets, Dissertation

for MA in Economics (National University Kiev-Mohyla Academy).Voronkova, S., 2004. Equity market integration in Central European emerging markets: a cointegration analysis

with shifting regimes. International Review of Financial Analysis 13, 633–647.Warsaw Stock Exchange, 2003. Fact book (www.wse.com.pl).Yang, J.J., Min, I., Li, Q., 2003. European stock market integration: does EMU matter? Journal of Business Finance

and Accounting 30, 1253–1276.

http://www.pse.cz/

http://www.wse.com.pl/

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