an investigation of the linkages between european union equity markets and emerging capital market

7/25/2019 An Investigation of the Linkages Between European Union Equity Markets and Emerging Capital Market

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An Investigation of the Linkages BetweenEuropean Union Equity Markets and Emerging

Capital Markets: the East European ConnectionbyE. Dockery*,Department of Economics, Staffordshire University, Staffordshire, ST42DF, UK.andF. Vergari, Department of Economics, Keele University, Keele, Stafford-shire, ST5 5BG, UK.

Abstract

The present paper examines whether the capital markets of three emerging East Europeancapital markets are financially integrated with two major European Union equity markets,using the Johansen approach to test for cointegration between the markets. The analysiscovers the period 1991 through to 1995. The results from these tests are consistent in pro-viding support to the integrated market hypothesis with regard to the financial markets con-

sidered and bear the implication of the existence of potential long-run benefits in portfoliorisk reduction from diversifying in East European stocks and in both the German and UKstock markets.

Key words:East European; Capital markets, Integration

JEL classification:G15; F36

1. Introduction

A cursory glance of the recent finance literature reveals a steady accumulation of researchdevoted to the degree of capital market integration among the worlds stock market indices.

Nearly always, the primary focus of attention has been on the linkages between the matureequity markets of the major industrialised economies, either between themselves or with

the capital markets of the Asia Pacific Rim. Examples in these respects include Taylor andTonks (1989), Chan et al. (1992), Kasa (1992), Arshanapalli and Doukas (1993), Byers andPeel (1993), Bekaert and Harvey (1997) and DeFusco et al. (1996). Of the studies just men-tioned, Arshanapalli and Doukas (1993) found evidence of interdependencies between theUS market and some European capital markets, such as France, Germany and UK, andother world equity markets. In contrast to the findings of Arshanapalli and Doukas, Taylorand Tonks (1989) found no such evidence of cointegration between the UK and US equitymakets. Further and more recent evidence furnished by Byers and Peel (1993) and Kasa(1992) based on a multivariate cointegration test between three European capital marketsand the markets of Canada, Japan and the US found no strong evidence of cointegration be-tween the markets examined.

The question of whether the markets of Eastern Europe are integrated with the interna-tional economy and, in particular, the linkages between their capital markets and the majorequity markets of the European Union (EU) have not yet been answered. This is in light ofthe fact that during the period marked by wholesale economic reforms and the rapid transi-tion to a market based economy, the economies of Eastern attracted a sizeable proportion offoreign direct investment, a major share of which came from major European Union coun-tries. This on the whole helped to cement their price linkages to European Union and otherinternational financial centres more closely than otherwise expected. In this study, an at-

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tempt is made to repair this neglect by exploring the degree to which markets in the CzechRepublic, Hungary and Poland are integrated with markets in Germany and the UK. Theeconomies of Germany and the UK are generally considered economically important in aEuropean context and, in particular, account for a fair share of East European trade, witheach also providing an important source of foreign direct investment. The motivation forconsidering the three East European markets arises from the following reasons. First, theliberalisation of these economies and the subsequent opening of their capital markets has,since the late 1990s, resulted in favourably equity returns that can be explained by the suc-cess of public policy measures designed to effect structural changes which improved themacroeconomic performance of their economies. This, in turn, has led to changes in theeconomic position of the East European economies in relation to trade, resultingin growinginterest from institutional investors seeking to diversify in East European stocks, and thusfinding it difficult to disregard the potential from investing in these markets. Notably also,the East European economies have benefited from the flow of inward investment from theEuropean Union, and especially from countries that have always had close political andtrading links. Second, the economies of Eastern Europe are interesting if only because they

provide an opportunity for economists to mark the presence of strong economic linkages

between their markets and the markets of the EU. Third, our interest is justified as the grow-ing tide of foreign investment in Eastern Europe has helped to tighten their price linkages toglobal financial centres within a short period of reform years. Therefore, it is only natural toask whether the importance of international investors and, especially, the activities of mul-tinational corporations can induce the long run relationship between stock prices of thesecountries and member countries of the EU. Evidence of increased market integration ofEast European countries with the markets of the EU may be found in the joint movementsofreturns realised by investors in both Eastern Europe and EU markets alike. It is with regardto this that the present paper tests for the existence of a long-term relationship among thecapital markets of Eastern Europe and the markets of Germany and the UK by way of coin-tegration analysis. Most notably, the presence of cointegration has implications for the ex-tent to which risk can be reduced by exploiting the non-zero long-run correlation amongindices when investing in these markets.

The followingsection presents the data and some preliminary statistics. Section 3 con-tains a summary of the methodology employed, while section 4 outlines the model and re-

ports results pertaining to the analysis of market linkages. Concluding remarks are set forthin the final section of the paper.

2. Data and descriptive statistics

This study employs weekly data sets for the stock indices of the markets in the Czech Re-public (PX50), Hungary (BUX), Poland (WIG), Germany (FAZ General), and the UK (FTAll Share).1 The period under investigation extends from 28 April 1991 through to 30 April1995. For the Czech republic the sample period begins at 28 April 1994 because data for thePrague stock exchange index are discontinuous prior to that time period. The indices are ex-

pressed in natural logarithms and are calculated from prices expressed in local currency.Accordingly, continuously compounded (percentage) returns, denoted by Rt, are calculatedas the difference of the (log) closing index value (Pt), i.e., Rt = 100(lnPt - lnPt-1). The indicesare value weighted and not adjusted for dividends. Thus, on the basis of the evidence fur-nishedby French et al. (1987)and Poon andTaylor (1992), it is expected that adjustment fordividends would not affect the results reported in this paper. Preliminary statistics on the

percentage returns are reported in Table 1.

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Over the period considered, mean returns are positive except for the Czech Republicand larger than returns in UK and Germany. A look at the variances explains why. Rangingfrom 3.007 (Czech Republic) to 9.404 (Poland), from which it may be concluded that theemerging Eastern European markets are riskier and, consequently, command, in the least, ahigher risk premium. Even so, the Sharpe ratio suggests that both Poland and Hungary farewell when compared to the more established EU markets of Germany and the UK. With theexceptionof Hungary, whose index is positively skewed, all series exhibit significant nega-tive skewness. The positive skewness may, however, be due to some extreme positive re-turn that the Budapest market realised over the sample period examined. Further evidenceof this can be gleaned from Fig. 1.

Coupled with the evidence from the excess kurtosis measure and the studentisedrange, there is clear indication that the distributions of returns are not normal. When for-mally tested, the normality hypothesis is firmly rejected by the Jarque-Bera test at all levelsof statistical significance.


Table 1.

Descriptive Sample Statistics on Weekly Stock Returns (1)

Staistic Czech Rep. Hungary Poland Germany UK

Mean -0.334 0.148 0.942 0.19 0.151

Std. Dev. 3.007 3.446 8.861 2.992 2.357

Sharpe ratio (2) -0.11 0.04 0.11 0.06 0.06

Skewness -.637*** 2.3*** -.448*** -1.12*** -2.65***

Excess Kurtosis 2.8*** 19.3*** 4.0*** 4.98*** 28.7***

Studentised Range 7.73 11.63 8.74 9.86 14.98

Normality (3)

2 (2)83.7*** 3678.9*** 157.34*** 724.2*** 20712.1***

Autocorrelation (4)

Q(12)

16.92 20.38* 14.04 13.44 16.53

ARCH(5) 3.52*** 1.33 2.28*** 7.17*** 0.327

Sample Period 1994/04/10

1995/08/06

- 1991/01/06

- 1987/04/30

- 1991/04/14

- 1995/08/06

- 1987/07/12

- 1999/02/07

- 1987/07/12

- 1999/02/07

Notes: (1) Defined asRt= 100 ln(Pt/Pt-1) , (2) Ratio of Mean to Standard Deviation, (3)

Jarque-Bera statistic; (4) Ljung-Box statistic. (5) F-test of no ARCH effects with 12 lags. ***, **

and * denote significance at 1, 5 and 10%, respectively.


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The presence of intertemporal dependencies in the returns is tested by means of the Ljungand Box portmanteau test (LB) for (up to) 12th -order correlation. Overall, there is little evi-dence of correlation in stock returns over the period. Thereby confirming that there is wide-spread evidence of dependence in conditional second moments. This also validates theindication of the leptokurtosis by the excess kurtosis measure of which the ARCH tests - forup to 12 lags - rejects the null of no ARCH effects in the case of Czech Republic, Poland and

Germany.

3. Methodology

The degree of market integration or linkage between twoor more markets may be examinedby investigating the existence of a long-run relationship between the stock market indicesby drawing on Granger (1986) and Engle and Grangers (1987) concept of cointegration.The methodology of cointegration requires that long-run equilibrium constraints be im-

posed on short-run asset return equations allowing estimation of market dynamics. Here,Engle and Granger (1987) instruct us that multivariate time series models which excludelong-run constraints, such as vector autoregressive models (VAR) of stock returns, will bemisspecified if stock prices are cointegrated. They note, in particular, that imposing long-run equilibrium constraints results in efficiency gains and improved multi-step forecasting.

And since the cointegration methodology does not distinguish, at least in principle, be-tween exogenous and endogenous variables, there is therefore no simultaneity bias.

If a variable is stationary after first differencing that it is integrated of order one, I(1).On this basis and, in the multivariate context of this paper, we consider the vectorzt

p ofstock price indices generated at time t, on which observations t =1, ..., T are available and letZ t= (z1,z2,...,zt) bethe(tp) matrix of price index histories. Here, we assume a linear pro-



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cess2 forztand letits conditional density D(zt|Zt-1,) be multivariate normal. In other words,zt|Zt-1,Dt~NID(A1zt-1+...+Akzt-k++Dt,), where zk+1,... z0are fixed, Dtis a vector ofnon-stochastic variables - including seasonal and, if needed, intervention dummies - and

possibly weakly exogenous variables, zt is at most I(1),3 and the parameters (A1,...,Ak,,.) are unrestricted. The implied vector auto-regressive (VAR) model can then be rewrit-ten in vector error-correction form (VECM) as follows:

z z z z D

... , (1)

where

A I A

,

and

I

. If cointegrated, this sys-

tem incorporates a structure whereby short-run dynamics in stock returns as given by (zt)respond to deviations from the long-run equilibrium relationship among stockindex levels.

The hypothesis of cointegration is formulated as a reduced rank condition on the ma-trix . Specifically, for r = 0,1, ...p, the hypothesis of at most rcointegrating vectors is thusdefined:

H

: (2)

where and are(pr) matrices of full rank. The hypothesisHrimplies that the process ztis stationary, meaning that ztis non-stationary, and the vector of linear cointegrating rela-tions ztis stationary (see Johansen, 1991). Using the reduced rank condition, the VECM

becomes:

z z z z

D

... (3)

This parameterisation offers a clearer interpretation of the coefficientsiwhich de-scribe the short-term dynamics of the process, while the effect of the levels is isolated in thematrix. The rcointegrating relations contained in ztcan be considered as the long-runrelationships among stock price indices, whilst the loadings included in the feedback ma-trix summarise the behaviour of stock returns when deviations from long-run equilibriumoccur.

In our subsequent analysis, we are interested notonly in the features of the long-run re-lationship(s) zt, but also in the nature of the market response to departures from it (). Soassuming the cointegrating rankris known, we can achieve our objective by partitioningthe VECM, which will then allow us to deliberate the exogeneity properties of the stockmarkets considered in this study. In particular, the partitioning of the , and matrices tocorrespond to the partitioning ofztinto two subsets of stock price indices, say xtand yt, re-sults in the following:

, ,

and =

.

The basic VECM can now be rewritten as

x z z

D

, (4)



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y z z

D

, (5)

Conditional on past history,xtand ytcan be reformulatedfrom equations(4) and(5) as

y z z

D

( ) ~ ~ ~ ~ ,

(6)

where

,~

,~

, ~

and~ ,

.

with variance If

and (6) become 0 4,( )

x z

D and (7)

y x z z

D

~ ~ ~ ~ (8)

so that equation (7) can now be treated as a marginal model, whilst equation (8) remainsconditional. Under1 = 0, this parameterisation operates a sequential cut (Engle, Hendryand Richard, 1983) in that it allows us to examine the conditional model by taking the mar-ginal as given.3 In this case, if1 = 0, the stock price processes in xtare said to be weakly ex-ogenous for the long-run parameters of interest (2 ,). Hall and Wickens (1993) and Halland Milne (1994) for example interpret weak exogeneity in the context of cointegrated sys-tems as long-run causality. In practical terms, weak exogeneity for a subset of stock price

indices rather implies that the relevant stock markets are only affected by short-run fluctua-tions in the system. Notwithstanding, national markets that are not weakly exogenous mayalso be affected by other markets, both as a result of short-run fluctuations and by devia-tions from the long-run equilibrium.

4. The Model and Empirical Results

This section examines the linkages between the three East European capital markets -Czech Republic, Poland and Hungary - and the two major European Union equity markets,namely Germany and the UK. The first step in our cointegration analysis is to assess the sta-tionarity of each series. In table 2, we perform a series of tests in order to assess the extent towhich shocks to each process are (not) reversed at any finite horizon. Drawing on the Aug-mented Dickey-Fuller (ADF) (1979) and the Phillips-Perron (PP) (1998) tests of the null

hypothesis of a (univariate) unit root, table 2 reveals widespread acceptance of the null hy-pothesis, except for the UK.

To uncover more about the dynamic properties of the processes under consideration,we adopt a number of alternative methodologies in order to shed further light on the behav-iour of our stock markets at different horizons. Specifically, the Variance Ratio (VR) test asadvanced by Lo and MacKinlay, (1988, 1989) are carried out on returns obtained over a pe-



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riod (k) of 2, 4 and 12 weeks. Using heteroskedasticity-consistent variances for testing, theresults fail to denounce the null of a random walk process for two-week returns for all mar-kets. Over the much longer horizon, however, non-random walk behaviour can be detectedover 12 periods for the Czech, Polish and Hungarian markets. In the case of the Hungarianmarket, it is notable that the market also fails the test on four-week returns. This aspect isfurther analysed under a different null hypothesis: that of no long-range dependence, other-wise known as fractional integration. Here, the Normalised Rescaled Range (R/S) (Lo,1991) is obtained for windows (q) of 0, 4 and 12 weeks. The first case is the Classical Rangemeasure, whilst the other two (q = 4,12) correspond to Los Modified Range. The test is for-mulated so as to be more sensitive to long-run correlation, and so any rejection of the nullhypothesis should be ascribed to slow mean-reverting or mean-averting behaviour, ratherthan to short-term activity. It is well known that the Classical measure is relatively moresensitiveto correlation at lowlags than Los measures (see Lo, 1991). Butinterestingly, Ta-

ble 2 shows that any significant rejection occurs under the Classical Range measure. Takinginto consideration the VR results, the R/S results would seem to bear some implications forthe (weak-form) efficiency of the stock markets under consideration. From this, it may beargued that over longer horizons, in particular, the emerging East European markets exhibit

dynamic properties that may be inconsistent with the efficiency hypothesis5 and, further-more, the perceived mean dependence is likely to occur at shorter horizons. In addition, thegeneral conclusion about inefficiency is also buttressed - for Hungary and the Czech Re-

public - by a non-parametric procedure such as the Runs test.

Having uncovered some univariate features, we now turn our attention to the multi-variate dynamics of the markets under study. It is in this connection that we perform twosets of analyses. In the first case, the vectorzt includes all five markets and tspans a periodof just over a year (April 1994-April 1995). While in the second analysis, we exclude the


Table 2.

Unit Roots and Efficiency

Market ADF (

1

) PP (

2

) Runs Variance Ratios (

3

) Normalised Rescaled Range

k = 2 k = 4 k = 12 q=0 q = 4 q = 12

Czech

Rep.

-2.75 -3.338* -2.985*** 1.029 1.155 1.4** 1.753* 1.404 1.294

Poland -1.16 -1.16 -1.35 1.08 1.143 1.468*** 1.993** 1.827* 1.361

Hungary -1.91 -1.524 -2.646*** 1.175 1.509*** 2.054*** 1.998** 1.41 1.256

Germany -2.23 -2.23 .144 .945 1.01 1.118 1.129 1.095 1.079

UK -4.1*** -4.1*** -1.176 1.028 1.111* 1.026 .942 .917 1.095

Notes: (1) Augmented Dickey-Fuller t-test of the unit-root null in log stock price indices.

Number of lags chosen by the Schwartz Information Criterion. (2) Phillips-Perron t-test of

the unit-root null in log stock price indices. (3) Uses heroskedasticity-adjusted asymptotic

variances. ***, ** and * denote significance at 1, 5 and 10%, respectively.


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Prague stock market in an attempt to obtain more robust results by virtue of allowing the se-ries to extend for approximately four years, from April 1991 through to April 1995. In allcases,Dtcontains (centred) seasonal dummies to allow for monthly fluctuations in condi-tional means. The results are summarised in Table 3, panels A and B.

Our approach follows five separate steps, reflected in five separate sections in Table 3.First, we choose the optimal lag length for the VAR model outlined in the previous section.Contrary to common practice, we do not select the VAR lag length by conventional meth-ods such as the Akaike or Schwartz Information Criteria. Instead, we follow Johansen(1995a) and select the number of lags, which then allows us to obtain reasonably well-

behaved (multivariate) residuals. The second step involves the determination of the rank (r)of the matrix. And following the reformulation of the VAR into a VECM,the rank is thendetermined by first using Johansens (1998, 1991) Full Information Maximum Likelihood(FIML) procedure to estimate . From which we then proceed to test the hypotheses aboutits rank by means of the Trace and Maximum Eigenvalue statistics (see Johansen and Juse-lius, 1990). In both cases,6 however, the evidence points towards the existence of one long-run relationship among stock indices.

As a third step, we use the VECM framework to carry out multivariate2 tests of thenull hypothesis of stationarity for each of the series. This is accomplished by imposing, inturn, zero restrictions on all but one of the elements of-vector, i.e. by evaluating whetherthe cointegrating vector consists of solely one market index, rather than a combination ofsome; see Johansen and Juselius (1990) and Johansen (1995a). This procedure differs fromthe traditional univariate tests in two important respects. First, it takes into account the dy-namics of other processes as well as those of the series being tested. Second, and unlike uni-variate procedures, its null hypothesis is one of stationarity, and not of unit root. The resultsforcefully reject the null and confirm (except for the UK market) the conclusion obtainedfrom univariate tests that the five indices are indeed non-stationary during the periods con-sidered.

Our next step in this process is to uncover the nature of the dynamic interactions - ifany - among the five stock markets, and to test each component ofz for weak exogeneity tothe long-run parameter vector.7 This is achieved by permitting x in equation (7) to containthe stock price index being tested, and allowingyin equation (8) to contain the remainingseries and, in theprocess, testing the zero restriction on therelevant element of. Thelikeli-hood ratio (LR) tests for the 5-equation system (panel A) provide partly unexpected results.As Table 3A shows, both the London andFrankfurtmarkets are exogenous to the system; inaddition, the Budapest and Prague markets also appear exogenous - thus leaving the War-sawmarketas the only market whose short-runbehaviour is affected by departures from thelong-term equilibrium relationship among the stock markets considered. This limited evi-dence in favour of cross-border feedback may well be due to the limited horizon used in theanalysis. When the Prague market is excluded and the analysis repeated, the results aremore in line with expectations (panel B). Over a much longer horizon, both the Budapestand Warsaw markets appear endogenous to the system. In fact their short-run behaviour ap-

pears to be affected by any price fluctuations that propels the markets out of equilibrium.Once identified the markets that reveal themselves as being weakly exogenous to the sys-tem, their exogeneity is (jointly) imposed 8on the VECM and the cointegrating relationshipis finally made explicit. The identified vectors are presented in the Restricted CointegrationAnalysis section of Table 3.



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As can be seen from Table 3, panel A and B, none of the markets in our sample appearredundant in the cointegration space, since all the long-run coefficients are significant atconventional significance levels.9 This evidence is taken as a clear indication of financialmarket integration, at least with reference to the markets we consider. Furthermore, thelong-run coefficient vector also bears some clues as to the nature of such links among

price index levels. Here, conditional on movements in other stock markets, a positivemovement in Frankfurt, for example, may be deemed to translate in a negative shock to theWarsaw exchange if equilibrium is to be maintained in the long run. On a more generalnote, the evidence which stems from the 4-equation system points toward positive partialcorrelation between the Warsaw, London and Frankfurt markets, whilst the Budapest mar-ket appears negatively (partially) correlated to all the other markets, in the long run.

Our VECM formulation also allows us to draw some firm conclusions as to the impactthat departures from equilibrium have in the short run. As the Frankfurt and London equitymarkets reveal themselves as weakly exogenous, violations of the long-run equilibriumwill not produce any direct impact. But in the case of the Budapest market, there is, ostensi-

bly, (i) strong evidence of mean reversion (error correction) when the shock originates in

that market and (ii) indication of positive feedback to positive shocks to stock prices in theWarsaw, London, and Frankfurt markets. Interestingly, the evidence on the short-run be-haviour of the Warsaw market points towards mean aversion (lack of error correction), aswell as positive responses to surges in index levels in both the Frankfurt and London equitymarkets.

The last part of Table 3 sheds further light on the role played by cross-border short-rundynamics. For instance, the LR test results assess the possibility that shocks to any one mar-ket only affects the market from which they originate. Specifically, the tests impose zero re-strictions on, and the relevant subset of

~

in equation (8) above. If the LR test fail to

reject, then the market may be said not to cause any other in the system, in the short run.The evidence summarised in Table 3 is quite telling. Here, the general impression gleaned

from the results is that once the long-run effects are accounted for, there is little role for (un-conditional) shocks originating across the border. Interestingly, while the 5-equation sys -tem reserves a leading role to the London market, even in the short run, this role is assignedto the Warsaw market when the Prague market is excluded and a longer time horizon is con-sidered.

Overall, the above findings furnish a clearer picture of the extent to which the threeemerging East European stock markets are linked with the German and UK equity marketsand of the nature of such linkages both in the short and in the long run. The evidence pointsstrongly towards market integration and also highlights therole playedby these markets be-yond their national borders. The implications of this evidence can be appreciated from a

portfolio diversification perspective. Indeed, it is interesting to mention that for some timenow, institutional investors world-wide have closely observed the investment opportuni-ties arising in East European markets as a result of macroeconomic policies and widespreadliberalisation and privatisation programmes. Yet, the dynamic behaviour of such marketsremains largely unknown. The presence of integration documented in this paper suggeststhe existence of long-run gains in risk reduction from diversifying in East European stocksas well as stocks in the two major European Union markets considered. In addition, thedocumented short-run behaviour will also prove helpful, especially in evaluating short-term investment strategies.



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Table 3: Cointegration tests based on the Johansen approach

PanelA:

Dynamic Analysis in the 5-equation system Sample period (24 April 1994-30 April 1995)

VAR Specification(1

)Lag Length:

Vector Autocorrelation2 (25):2

34.78 [.09]

Rank Determination(2)

H0:

r= 0

r=1

Trace statistic

67.00 (6)

34.73

Multivariate Stationarity Test Results(3)

Stationarity (4)

YCZE

YPOL

YHUN

YGERYUK

Restricted Cointegration Analysis

Weak Exogeneity Tests2 (2): LR test p-valueYCZE

YPOL

YHUN

YGER

YUK

.03

17.51

.84

1.54

.40

.98

.00

.66

.46

.82

Cointegrating Relationships (5):

Vector 1: YPOL = 1.32 YCZE - .759 YHUN - 1.32 YGER + 3.674 YUK(.181) (.296) (.181) (.807)

Feedback: Coefficient t-value

YCZE

YPOL

YHUN

YGER

YUK

-

-.685

-

-

-

-

-5.633

-

-

-

Short-run Dynamics

Non-causality Tests2(4) LR test p-valueYCZE

YPOL

YHUN

YGER

YUK

3.89

5.85

7.72

4.33

9.41

.42

.21

.10

.36

.05


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Table 3: Cointegration tests based on the Johansen approach

Panel B:

Dynamic Analysis in the 4-equation system Sample period (28 April 1994-30 April 1995)

VAR Specification(1)

Lag Length:

Vector Autocorrelation2 (16):2

13.80 [.61]

Rank Determination(2)

H0:

r= 0

r=1

Trace statistic

54.69***

30.41**

Multivariate Stationarity Test Results(3)

Stationarity (4)

YPOL

YHUN

YGER

YUK

Restricted Cointegration Analysis

Weak Exogeneity Tests2 (2): LR test p-value

YPOL

YHUN

YGER

YUK

8.42

7.89

.37

.02

.01

.02

.83

.99

Cointegrating Relationships (5):

Vector 1: YPOL = 33.974 YHUN - 38.755 YGER - 33.974 YUK(6.883) (11.734) (6.883)

Feedback: Coefficient t-value

YPOL

YHUN

YGER

YUK

.004

.001

-

-

3.467

3.523

-

-

Short-run Dynamics

Non-causality Tests2(3) LR test p-value

YPOL

YHUN

YGER

YUK

9.15

1.11

4.62

3.98

.03

.77

.20

.26


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5. Summary and Concluding Remarks

This paper empirically examined the linkages between three emerging East European capi-

tal markets and two large European Union equity markets, using cointegration techniquesfor theperiod from 1991 to 1995. Themain findings strongly suggest that the emerging EastEuropean capital markets are cointegrated with the equity markets of Germany and the UK.This result holds for the entire period, and stands in contrast to previous evidence related tothe linkage between EU markets and other groups of markets in Asia, Latin America, andPacific Basin. The evidence of cointegration between the emerging markets of EasternEurope and the two major EU equity markets in our sample implies the existence of long-run benefits from diversifying in East European stocks and stocks in EU equity markets ex-amined. This result, moreover, can be viable not only to investors and financial institutionsholding long-run investment portfolios, but also in helping to improve understanding of themarkets short-term behaviour, which can also be understood within the reference of short-term portfolio management.



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Endnotes

*Corresponding author. E-mail address: [email protected]

1. Data on Germany and the UK have been extracted from Datastream. The series on East-ern European Stock Market have been obtained from the relevant Exchanges.

2. Non-linear cointegration is a distinct alternative butwill notbe taken up in this paper.

3. The treatment of I(2) variables requires different likelihood analysis and ensuing distri-butions. See Johansen (1992a, 1995a,b).

4. Since the parameters of the marginal model arem =( ,..., ,1,1, )and the onesfor the conditional model arec = (2,,

~,...,

~, ~ ,

~, , ),

the whole parame-

ter space is=(2,,1,...,k-1, , ).

5. Of course, this claim is subject to all the limitations typical of studies in the market effi-

ciency literature such as, for example, time-varying expected returns and market micro-structure factors (although to a lesser extent, given that our returns are computed over arelatively long period for microstructure effects to survive).

6. In the 4-equation system, the null thatr 1 could not be rejected at the 1% significancelevel. In the 5-equation system, the Trace statistic fails to provide evidence of cointegrationwhilst the Maximum Eigenvalue statisticsuggests the presence of one cointegrating vector.Accordingly, we selectr= 1.

7. Note that, as there only exists one cointegrating vector, identification is trivial and isachieved by imposing a normalisation restriction on one of the elements of.

8. The joint restrictions are not rejected by the LR test.

9. Moreover, tests for long-run exclusion were carried out on each of the indices in the unre-stricted VECM and the exclusion null was always rejected.



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an investigation of the linkages between european union equity markets and emerging capital market

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