Convergence in major euro-zone stock markets: evidence from monthly data.

Caples, Stephen ; Hanna, Michael E. ; Perdue, Grady 等

ABSTRACT

This paper investigates whether the introduction of the euro as a common currency promotes integration among the major Euro-zone stock markets. To carry out this investigation, the dynamics of the stock markets of Germany, France and Italy are studied. Monthly data on stock returns from February 1994 through December 2003 are employed. There is some evidence of increasing integration among these markets. Presumably, the post-euro sub-sample period may be too short to reveal the true extent of enhancing market integration.

INTRODUCTION

Eleven European countries introduced a common currency (euro) since January 1, 1999 replacing their own national currencies. They gave up their monetary authority to create the European Central Bank (ECB) that issues the euro and implements a common monetary policy for them. Adoption of the euro makes it easier for multinational corporations to design plans, pricing policies and invoicing. It eliminates exchange rate risk and facilitates the comparability of cross-border prices.

Reduced risk and lower cross-border currency conversion costs promote the flows of trade and investments among member countries and should bring about greater integration of Europe's capital, labor and commodity markets. Consequently, a more efficient allocation of resources is also induced within the region as a whole. Increased trade, in turn, has intensified Europe-wide competition in goods and services inspiring a wave of corporate restructurings including transnational mergers and acquisitions. The euro is thus expected to initiate necessary restructurings of the Euro-zone economy making it flexible, dynamic, productive, and better able to rival the mega U.S. economy.

Currently, governments of twelve Euro-zone countries can also issue bonds in euros, just as individual American states can issue dollar bonds. Likewise, Euro-zone corporations can issue stocks and bonds including other financial assets in euros. As a result, the euro is expected to enhance capital market convergence among the twelve Euro-zone countries. Although the full impact of Euro is yet to unfold, its effects have already been discernible.

The focus of this paper is the major Euro-zone equity markets and the issue of convergence using monthly data for the pre-and post- euro sub-periods. Since there is not a single European stock market, the main objectives of this paper are to determine if the euro introduction affects the integration of the above markets, and to determine if the integration has increased during the post-euro sub-period relative to the pre-euro sub-period.

BRIEF SURVEY OF RELATED LITERATURE

Many studies (theoretical and empirical) analyze the linkages among many national stock indices. If national stock markets were integrated, the lags of the price adjustments in these stock markets would be reduced (Koch and Koch, 1991). The empirical results usually depict significant correlation between markets in near geographic areas. The relaxation of controls on capital movements and foreign exchange transactions, improvements in computer and communication technology, expansion in multinational operations of major corporations, and above all globalization of financial transactions make stock markets increasingly synchronized and shorten the adjustment delays in international prices [Gelos and Sahay (2000), Jeon and Chiang(1991)].

There have been several studies about linkages and dynamic interactions among international stock markets with conflicting evidence. The results vary depending upon the choice of markets, the sample period, the frequency of observations (daily, weekly or monthly) and the different methodologies employed. Jafe and Westerfield (1985), Schollhammer and Sand (1985), and Arshanapalli and Doukas (1993) find substantial increases in the degree of international co-movements among stock price indices of the U.S., U.K., France and Germany excepting Japan. Hamao et al. (1997), Susmel and Engle (1994), and Booth et al. (1997) using ARCH models find linkages and spillovers in stock markets. Ayuso and Blanco (2000) using GARCH methodology discover increased linkages during 1995-1999 as compared to 1990-94 among the stock markets of the USA, Japan, U.K., France, Italy, Spain and Germany. Moreno and Olmeda (2002) conclude that the European stock markets became more integrated during 1999-2001; and the German market has increased its leadership into the euro-area because of its dominant role in the European monetary policy. In contrast, Roll (1988), Dwyer and Hafer (1988), Maldonado and Saunders (1981), Chan et al. (1992), De Miguel et al. (1998), and Moreno and Olmeda (2002) find evidence against stock market linkages for several countries.

EMPIRICAL METHODOLOGY

The methodology involved in this empirical study is outlined as follows: First, the nature of the data distribution of each variable is examined by using the standard descriptive statistics (mean, median, standard deviation, skewness and kurtosis). Second, a correlogram of the yearly averages of the explanatory variables is computed to identify the extent of their bilateral linear relationship and the existence of multicollinearity including its severity. Third, the time series property of each variable is investigated in terms of the ADF (Augmented Dickey-Fuller) test for unit root (nonstationarity) following [Dickey and Fuller (1981), and Fuller (1996)].

The simple ADF test, as outlined in (Dickey and Fuller, 1981), is implemented by using the following regression models:

[DELTA][Y.sub.t] = [alpha] + [[beta].sub.0] [Y.sub.t-l] + [L.summation over (i=l)] [[beta].sub.i][DELTA][Y.sub.t-i] + [U.sub.t] (1)

[DELTA][X.sub.t] = [alpha]' + [[beta]'.sub.0] [X.sub.t-l] + [L.summation over (i=l)] [[beta]'.sub.i][DELTA] [X.sub.t-i] + [U'.sub.t] (1)

[DELTA][Z.sub.t] = [alpha]" + [[beta]".sub.0] [Z.sub.t-l] + [L.summation over (i=l)] [[beta]".sub.i][DELTA][Z.sub.t-i] + [U'.sub.t] (1)

where:

Y = percentage change of German stock index (DAX)

X = percentage change of French stock index (CAC 4016)

Z = percentage change of Italian stock index (MIBTEL)

[DELTA] = first difference operator

L = number of optimum lags

t = time subscript

U = random disturbance term

All these stock indices are in U.S. dollar terms. The aforementioned major stock indices have been selected because of their high market capitalizations, vibrancy and dominance in the Euro-zone. Moreover, Germany, France and Italy have comparable developed, maturing and sophisticated stock markets within the Euro-zone.

The ADF test is performed to accept or reject the null hypothesis of unit root in the following test:

[H.sub.0]: [b.sub.0] = 0 (or [b.sub.0]' = 0 or [b.sub.0]" = 0)

[H.sub.a]: [b.sub.0] < 0 (or [b.sub.0]' < 0 or [b.sub.0]" < 0).

Some definitive inferences on the stationarity/nonstationarity property for each variable of interest are drawn to determine the appropriate estimating statistical procedure

In view of the evidence of stationarity in each variable, it is appropriate to implement the Vector Autoregressive (VAR) model, which is augmented by the inclusion of a dummy variable. The estimating models are specified as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (4)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (5)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (6)

The dummy variable D is included to capture the effects of qualitative variables and major policy changes. For the post-euro sub-period, D = 1; otherwise D = 0.

The appropriate lag-structures k, l, and m are determined by the Akaike (1969) information criterion. The VAR models without a dummy variable are also separately estimated for the pre-euro (March, 1994 through December, 1998) and post -euro (January, 1999 through December, 2003) sub-periods. The objective is to identify whether the lagged delay has decayed during the post-euro sub-period as compared to the pre-euro sub-period. Monthly data on the stock indices from February 1994 through December 2003 are employed. The data have been obtained from www.Datastream.com.

RESULTS

Table 1 provides the mean, median, standard deviation, skewness and kurtosis for the monthly market returns in each country.

As observed in Table 1, the data distribution of each variable is near-symmetric. The stock market rates of return in Germany, France and Italy are significantly positively correlated as shown in Table 2.

The pair-wise simple correlation coefficients vary from 0.657 to 0.858 indicating a high degree of market integration. The German and French stock markets have a relatively higher degree of integration when compared to that of the Italian stock market.

Next, the time series property of each variable is examined invoking the simple ADF (Augmented Dickey-Fuller) test for unit root (nonstationarity). The ADF unit root test results are reported in Table 3.

As depicted in Table 3, the calculated ADF value of each variable is much larger than the critical values at 1 percent and higher levels of significance. Their comparison clearly rejects the null hypothesis of unit root (nonstationarity). On the evidence of data stationarity, Vector Autoregressive (VAR) models (4), (5) and (6) are estimated with a dummy variable to capture the structural changes during the post-euro sub-period. The estimates of VAR model (4) are shown in Table 4.

Table 4 shows that the numerical coefficient of the dummy variable is quite low and statistically insignificant indicating a lack of discernible convergence. However, the lagged delays seem to have reduced over time. This is somewhat in contrast with the empirical evidence on the dummy variable.

The estimates of VAR model (5) are shown in Table 5.

As shown in Table 5, the coefficient of the dummy variable is not significant. Thus, there is no evidence of increasing market convergence during the post-euro sub-period. However, the lagged delays of other variables decayed over time indicating increasing convergence among German, French and Italian stock markets.

The estimates of VAR model (6) are reported in Table 6.

The evidence with regard to the dummy variable is almost identical to that in the preceding cases. There is evidence of decaying lagged delays suggesting market convergence. However, the results are not statistically significant in most cases. The estimates of models (4) through (6) reveal the dominance of the German stock market as expected.

VAR model (4) is estimated with an exclusion of the dummy variable for pre-euro and post-euro sub-periods. The estimates are provided in Tables 7, 8, and 9.

The estimates in Table 7 confirm some increase in market convergence as reflected through an improvement in the numerical coefficients and the associated t-values during the post-euro sub-period. Similar conclusions can be drawn when comparing the numerical values of the adjusted [R.sup.2] and F-statistic for the pre-and post-euro sub-periods.

Table 8 shows the same results in terms of the numerical coefficients of the relevant variables with the associated t-values, adjusted [R.sup.2], and the F-statistic. Again, the German stock market seems to dominate other markets within the region.

A comparison of the estimates in Table 9 reveals increasing convergence in the stock markets of Germany, France and Italy.

CONCLUSIONS

The data on stock returns for Germany, France and Italy are stationary. The dummy variable approach sheds no additional light on convergence of the stock markets in Germany, France and Italy. However, there is evidence on decaying lagged delays indicating increasing market convergence. The estimates of the VAR models (4) through (6) without a dummy variable for pre-euro and post-euro sub-periods imply similar conclusions. In closing, the post-euro sub-sample period is possibly too short to divulge the true extent of market integration.

REFERENCES

Arshanapalli, B. & J. Doukas (1993). International stock market linkages: evidence from the pre-and post-October 1987 period. Journal of Banking and Finance, 7(1), 193-208.

Ayuso, J. & R. Blanco (2002). Has financial market integration increased during the 1990s? BIS Conference paper N. 8, International financial markets and the implications for monetary and financial stability, March 2000, 175-195.

Booth, G.G., Martikainen, T., & Y. Tse (1997). Price and volatility spillovers in Scandinavian stock markets. Journal of Banking and Finance, 21, 811-823.

Chan, C., Benton, E.G., & M. Pan (1992). An empirical analysis of stock prices in major Asian markets and the United States. International Review of Economics and Finance, 5(4), 281-307.

De Miguel, M., Mora, A., Olmeda I., & Yagiurre, J. (1998). Integraci'on de las principales bolsas de la Unio'n Europea: Un an'alisis reciente, Actualidad financiera (nueva E'poca), 3 (7), 3-21.

Dickey, D.A. & Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 35, 315-329.

Dwyer, G.P. & R.W. Halfer (1988). Are national stock markets linked? Federal reserve Bank of St. Louis Economic Review, 70, 3-14.

Fuller, W.A. (1996). Introduction to statistical time series, New York: John Wiley and sons.

Gelos, G. & R. Sahay (2000). Financial markets spillovers in transition economics. Working paper. International Monetary Fund.

Hamao, Y; Masulis, R.W. & V. Ng (1990). Correlations in price changes and volatility across international stock markets. Review of Financial Studies, 3(2), 281-07.

Jaffe, J. & R. Westerfield (1985). The weekend effect in common stock return: The international evidence. Journal of Finance 40(June), 433-454.

Jeon, B.N. & T.C. Chiang (1991). A system of stock prices in world exchanges: common stochastic trends for 1975-1990. Journal of Economics and Business, 43, 329-338.

Koch, P. & T. Koch (1992). Evolution in dynamic linkages across daily national stock indexes. Journal of International Money and Finance, 10, 231-251.

Maldonado, R. & A. Saunders (1981). International portfolio diversification and the intertemporal stability of international stock market relationship, 1957-78. Financial Management, (Autumn), 54-63.

Moreno, J.D. & J.I. Oleda (2002). El efecto euro en la integracio'n e las bolsas europeas. Boletin ICE Econo'mico, 2715, 21-28.

Roll, R.W. (1988). The international crash of October 1987. In R. Kamphius, R. Kormendi, & J. W. H. Watson (Eds), Black Monday and the future of financial markets, Mid-American Institute (PP. 35-70), Homewood, IL: Dow-Jones Irwin, Inc.

Schollhammer, H. & O. Sand (1985). The interdependence among the stock markets of major European countries and the United States: An empirical investigation of interrelationships among national stock price movements. Management International Review, 25(Jan.), 17-26.

Susmel, R. & R.F. Engle (1994). Hourly volatility spillovers between international equity markets. Journal of International Money and Finance, 13, 3-25.

Stephen Caples, McNeese State University

Michael E. Hanna, University of Houston - Clear Lake

Grady Perdue, University of Houston - Clear Lake

Matiur Rahman, McNeese State University

Table 1: Descriptive Statistics

Descriptors Germany France Italy

Mean 0.774 0.656 0.986
Median 1.112 0.671 0.071
Std. Dev 6.746 6.151 6.940
Skewness -0.271 -0.054 0.303
Kurtosis 4.337 3.120 3.031

Table 2: Correlogram

 Germany France Italy

Germany 1,000 0.858 0.657
France 0.858 1.000 0.675
Italy 0.657 0.675 1.000

Table 3: ADF Unit Root Test

Variable ADF Statistic ADF Critical Values

Y -11.430 -3.486
X -11.832 -2.886
Z -11.409 -2.580

Variable Level of significance

Y 1%
X 5%
Z 10%

Table 4: Estimates of Model (4)

Variable Coefficient t-Statistic Prob.
[D.sub.1] -0.612 -0.949 0.345
[Y.sub.t-1] -0.119-1.263 0.209
[Y.sub.t-2] 0.050 0.522 0.603
[Z.sub.t] 0.158 2.482 0.015
[Z.sub.t-1] 0.122 1.895 0.061
[Z.sub.t-2] -0.024 -0.382 0.703
[X.sub.t] 0.825 11.697 0.000
[X.sub.t-1] 0.036 0.343 0.732
[X.sub.t-2] -0.026 -0.240 0.811

Adjusted [R.sup.2] = 0.747, F = 39.454, DW = 2.011, n = 118

Table 5: Estimates of Model (5)

Variable Coefficient t-Statistic Prob.

[D.sub.t] 0.465 0.794 0.429
[X.sub.t-1] -0.129 -1.353 0.179
[X.sub.t-2] -0.086 -0.895 0.373
[Z.sub.t] 0.165 2.883 0.005
[Z.sub.t-1] -0.058 -0.982 0.328
[Z.sub.t-2] 0.018 0.314 0.754
[Y.sub.t] 0.678 11.697 0.000
[Y.sub.t-1] 0.124 1.451 0.150
[Y.sub.t-2] 0.040 0.462 0.645

Adjusted [R.sup.2] = 0.751, F = 40.222, DW = 2.004, n = 118

Table 6 Estimates of Model (6)

Variable Coefficient t-Statistic Prob.

[D.sub.1] -0.726 -0.767 0.445
[Z.sub.t-1] -0.155 -1.634 0.105
[Z.sub.t-2] -0.050 -0.537 0.592
[X.sub.t] 0.341 2.482 0.015
[X.sub.t-1] -0.032 -0.227 0.821
[X.sub.t-2] -0.216 -1.564 0.121
[Y.sub.t] 0.433 2.883 0.005
[Y.sub.t-1] 0.212 1.372 0.173
[Y.sub.t-2] 0.295 1.920 0.058

Adjusted [R.sup.2] = 0.471, F = 12.596, DW = 2.032, n = 118

Table 7: Pre-and Post-Euro Comparisons (Dependent Variable: Y)

 Pre-Euro Sub-period

Variable Coefficient t-Statistic

[Y.sub.t-1] -0.296 -2.126
[Y.sub.t-2] 0.048 0.342
[Z.sub.t] 0.111 1.581
[Z.sub.t-] 0.156 2.520
[Z.sub.t-3] -0.025 -0.360
[X.sub.t] 0.637 6.830
[X.sub.t-1] 0.059 0.450
[X.sub.t-2] -0.003 -0.024

 Post-Euro Sub-period

 Coefficient t-Statistic

[Y.sub.t-1] 0.084 0.596
[Y.sub.t-2] 0.040 0.282
[Z.sub.t] 0.146 1.163
[Z.sub.t-] 0.054 0.421
[Z.sub.t-3] 0.0002 0.001
[X.sub.t] 0.999 8.378
[X.sub.t-1] -0.081 -0.447
[X.sub.t-2] -0.093 -0.524

Adjusted [R.sup.2] = 0.610, F = 12.129, DW =2.045,
n = 58; Adjusted [R.sup.2] = 0.844, F = 40.871, DW =2.010, n = 60

Table 8: Pre-and Post-Euro Comparisons (Dependent Variable: X)

Variable Coefficient t-Statistic

[X.sub.t-1] -0.227 -1.627
[X.sub.t-2] -0.130 -0.955
[Z.sub.t] 0.116 1.502
[Z.sub.t-1] -0.101 -1.282
[Z.sub.t-2] -0.004 -0.056
[Y.sub.t] 0.765 6.830
[Y.sub.t-1] 0.362 2.398
[Y.sub.t-2] 0.005 0.030

Variable Coefficient t-Statistic

[X.sub.t-1] 0.089 0.646
[X.sub.t-2] -0.030 -0.217
[Z.sub.t] 0.293 3.341
[Z.sub.t-1] 0.018 0.183
[Z.sub.t-2] 0.021 0.226
[Y.sub.t] 0.580 8.378
[Y.sub.t-1] -0.145 -1.377
[Y.sub.t-2] 0.044 0.411

Adjusted [R.sup.2] = 0.609, F = 12.080, DW = 2.000, n = 58;
Adjusted [R.sup.2] = 0.870, F = 50.644, DW = 2.002, n = 60

Table 9: Pre-and Post-Euro Comparisons (Dependent Variable: Z)

 Pre-Euro Sub-period

Variable Coefficient t-Statistic

[Z.sub.t-1] -0.153 -1.069
[Z.sub.t-2] -0.015 -0.105
[Y.sub.t] 0.437 1.581
[Y.sub.t-1] -0.191 -0.666
[Y.sub.t-2] -0.373 -1.362
[X.sub.t] 0.379 1.502
[X.sub.t-1] 0.382 1.505
[X.sub.t-2] 0.368 1.518

 Post-Euro Sub-period

Variable Coefficient t-Statistic

[Z.sub.t-1] -0.193 -1.397
[Z.sub.t-2] 0.074 -0.541
[Y.sub.t] 0.177 1.623
[Y.sub.t-1] 0.206 1.354
[Y.sub.t-2] -0.142 -0.913
[X.sub.t] 0.613 3.341
[X.sub.t-1] -0.042 -0.208
[X.sub.t-2] 0.218 1.222

Adjusted [R.sup.2] = 0.239, F = 3.238, DW = 2.027, n =
58; Adjusted [R.sup.2] =0.704, F = 18.558, DW =2.049, n = 60