Local and global price memory of international stock markets

Journal of International Financial Markets, Institutions and Money 9 (1999) 129 – 147

Local and global price memory of international stock markets Johan Knif a, Seppo Pynno¨nen b,* a

Hanken, Swedish School of Economics and Business Administration, P.O. Box 287, 65101 Vaasa, Finland b Department of Mathematics and Statistics, Uni6ersity of Vaasa, P.O. Box 700, 65101 Vaasa, Finland Received 1 December 1997; accepted 1 November 1998

Abstract The prime focus of this paper is on the impact of the world’s leading markets (USA, Japan, Hong Kong, UK, France, Switzerland and Germany) on the returns of the small Nordic markets (Denmark, Finland, Norway and Sweden). The order and the degree of processing both ‘local’ and ‘global’ information are uncovered using a combination of cointegration analysis and structural VAR modeling utilizing daily index returns. The results indicate that the US price changes, conditioned on the same day changes on the other markets, have an impact on all other markets during the following day, including the US market itself. Price changes on the Asian– Pacific markets are completely absorbed in price changes in Europe and do not have any direct effect on US prices. Finally, a cointegration relationship between Sweden and Norway is found, which affects also Finland. © 1999 Elsevier Science B.V. All rights reserved. Keywords: Short-term dynamics; Cointegration; Stock markets JEL classification: G15; C32

1. Introduction Since deregulation of financial markets began in the 1980s, there has been a growing interest towards the empirical study of the common behavior of interna* Corresponding author. Tel.: +358-6-3248259; fax: + 358-6-3248557. E-mail address: [email protected] (S. Pynno¨nen) 1042-4431/99/$ - see front matter © 1999 Elsevier Science B.V. All rights reserved. PII: S 1 0 4 2 - 4 4 3 1 ( 9 9 ) 0 0 0 0 3 - 7

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tional stock markets. Besides the advanced technology for worldwide information transmission and processing, the liberalization of capital movements, and the securitization of stock markets result in national markets that more rapidly react to new information from international sources. Earlier studies suggest low relationships between markets (Grubel and Fadner 1971). More resent studies, however, indicate an increasing co-movement of major stock markets. Eun and Shim (1989) in their analysis of daily index returns on nine major capital markets find substantial interrelations between markets with the USA as the most influential. Kasa (1992) claims that there is a single common factor driving the stock markets of the US, UK, Germany, Canada and Japan. Forbes (1993) applies cointegration analysis for the banking sector and finds some degree of integration of the European stock markets. Engle et al. report on the existence of a cross-market dynamic effect of news on the short-run time path of volatility, such that news revealed during the open time of one market contribute to the return of the next market to open (Engle et al., 1990, 1992). Susmel and Engle (1994) continue this kind of analysis by using intra-day data and focusing on spillovers of volatility and mean between the New York and London stock markets, especially at the hours when both markets are open. They find that the volatility spillover is minimal and has a duration lasting for  1 h. With the exception of two anomalies, they found no spillovers of mean during non-overlapping trading periods. Engle and Susmel (1993) investigate common volatility processes on international stock markets. Their data consist of weekly stock-index returns on 18 major stock markets. The results suggest a common time-varying volatility in certain groups of countries (one European and one Asian-Pacific). However, not many countries seem to share a common volatility structure, and if it exists, it is at most regional rather than global. Booth et al. (1995) find a single common factor generating volatilities on US, UK and Japanese stock-index futures markets. Karolyi (1995) investigates short-run dynamics in returns and volatility for the New York and Toronto stock exchanges on a daily basis. He finds that the inference about the magnitude and persistence of return innovations is heavily dependent on how the cross-market dynamics in volatility is modeled. Karolyi and Stulz (1996) analyze influences of the macro economic, industry, yen/dollar exchange rate, and broad-based stock market index effects on return correlations of NYSE-traded American depositor receipts and a matched-sample portfolio of US stocks on intra-day basis. They find that only large shocks to broad-based market indices positively influence both magnitude and persistence of return correlations, while changes in the other background variables have no measurable impact on the return correlations. The existence of common predictable components on regional stock markets is analyzed in Cheung et al. (1997). They report that only North American instrumental variables were able to predict excess returns in Europe and the Pacific. Koutmos (1996) investigates dynamic first and second moment interaction among four major European stock markets; UK, France, Germany and Italy, using a multivariate VAR-EGARCH model. He finds evidence of multidirectional lead/ lag relationships among returns as well as significant asymmetric volatility interac-

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tion, where negative innovations on one market increase the volatility on another market considerably more than positive innovations. The above-listed papers are all concerned with large stock markets with the exception of Engle and Susmel (1993), where some small markets are included as well. Hietala (1989), Mathur and Subrahmanyam (1990) and Malkama¨ki (1993) focus on smaller markets by analyzing relationships between the US, UK, German and Nordic markets utilizing monthly data. The general finding is that the larger markets lead the smaller Nordic ones. Malkama¨ki (1993) also finds that Finnish markets are most strongly led by the German market contrary to what previously has been suggested, i.e. that the Stockholm Stock Exchange is the main leading market for the Helsinki Stock Exchange (Malkama¨ki et al. 1993; Bos et al. 1995; Knif et al. 1995; Martikainen et al. 1993). Knif et al. (1996) investigate the existence of a common autocorrelation feature in a long history of returns from Finnish and Swedish markets. They report evidence of common autocorrelation during the period before the Second World War and after the second oil crisis. Pynno¨nen et al. (1996), Pynno¨nen and Knif (1998) extend this analysis by investigating volatility and long-term relationships between the Finnish and the Swedish stock markets. Their results indicate some evidence for volatility lead-lag relations between the two markets. However, in spite of a very similar economic structure, no support for cointegration or fractional cointegration between the two Nordic stock markets was found. No material cross-market dynamics in means in the monthly data were found, either. Contemporaneous correlation between the markets increased, however, considerably in the course of time. This suggests that virtually all information from one market is absorbed into the returns on the other within the same month. Thus we may argue that a monthly level is much too high a level of time aggregation for an analysis of inter-market dynamics. Following the approach of Eun and Shim (1989), this paper extends the literature in mainly three aspects. Firstly, by analyzing daily index returns, we try to empirically reveal possible short-term inter-market dynamics. Secondly, we look at the internationalization from a small market point of view. In particular, we intend to capture the short-term dynamic structure of these relatively small stock markets in relation to their dependence of information shocks on the major stock markets. Thirdly, in the analysis of daily close to close index returns, we explicitly model the mismatch in the open hours and the possible cointegration of some of the markets. Eun and Shim (1989) analyze the effects of the non-synchroneity in the trading hours by interpreting the contemporaneous residual correlations after fitting a VAR model to the index returns. We, however, deal with the problem explicitly by modeling the structure of different trading hours of the stock exchanges. In addition, as mentioned above, we start off with an analysis of the levels of the series in order to capture possible long-run relationships between the markets. The remainder of the paper is organized as follows. The characteristics of the data are briefly described in Section 2. Methodological aspects concerning VAR analysis and relevant causality concepts are considered in Section 3. Section 4 reports the main empirical findings. Finally, conclusions and comments on further research are presented in Section 5.

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2. Data of the study The analysis utilizes daily close to close index returns from eleven markets including the stock exchanges in New York (Dow Jones Industrial Average 30), Tokyo (Nikkei 225 Index), Hong Kong (Hang Seng), London (FT 100), Frankfurt (DAX), Zurich (SI), Paris (CAC 40), Copenhagen (KFX), Stockholm (Veckans Affa¨rer), Oslo (CSI) and Helsinki (HEX). The sample period starts on August 28, 1993 and ends August 8, 1996.1 Fig. 1 illustrates the trading hours of the markets

Fig. 1. Relative floor trading hours in eleven stock exchanges. 1 Data is obtained from Startel Oy, Finland. Consequently, data are available only for Finnish working days. In cases of national holidays in other countries, the missing index value is replaced by the last trading day’s value, which in terms of returns means zero-return.

Mean Median Standard deviation Excess Kurtosis Skewness Minimum Maximum Count a

FIN

SWE

NOR

DEN

UK

GER

FRA

SWZ

USA

JPN

HON

0.06 0.01 1.21 3.30 −0.45 −7.52 4.86 741

0.06 0.03 0.96 1.53 0.08 −3.45 4.88 741

0.05 0.04 0.83 2.47 −0.04 −3.73 4.66 741

0.03 0.00 0.76 0.99 −0.22 −3.07 2.24 741

0.03 0.01 0.75 1.38 −0.08 −3.08 3.10 741

0.04 0.06 0.92 0.99 −0.36 −4.08 3.22 741

−0.01 0.00 1.04 0.17 0.02 −3.47 3.19 741

0.05 0.06 0.77 4.86 −0.21 −3.97 5.19 741

0.06 0.03 0.65 2.12 −0.55 −3.09 2.00 741

0.00 0.00 1.25 5.78 0.44 −5.76 8.32 741

0.06 0.00 1.58 3.95 −0.53 −10.08 5.71 741

Daily returns are calculated as log differences rt =100×[ln(Pt )−ln(Pt−1)], where Pt denotes the index value at day t. Data are obtained from Startel Oy, where data is available only for Finnish working days. National holidays in other countries occurring on Finnish working days are imputed as zero returns.

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Table 1 Descriptive statistics of daily stock index returnsa

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Table 2 Contemporaneous correlations between market returnsa

FIN SWE NOR DEN UK GER FRA SWZ USA JPN HON

FIN

SWE

NOR

DEN

UK

GER

FRA

SWZ

USA

JPN

HON

1 0.48 0.43 0.35 0.28 0.41 0.28 0.31 0.11 0.11 0.19

1 0.54 0.44 0.46 0.44 0.45 0.46 0.26 0.16 0.18

1 0.44 0.41 0.50 0.39 0.47 0.13 0.23 0.26

1 0.35 0.49 0.33 0.41 0.09 0.18 0.25

1 0.42 0.60 0.42 0.26 0.14 0.20

1 0.48 0.50 0.11 0.23 0.33

1 0.44 0.24 0.14 0.16

1 0.12 0.16 0.23

1 0.02 0.10

1 0.19

1

a Daily returns are calculated as log differences rt =100×[ln(Pt )−ln(Pt−1)], where Pt denotes the index value at day t. Data are obtained from Startel Oy, where data are available only for Finnish working days. National holidays in other countries occurring on Finnish working days are imputed as zero returns.

graphically. Note that New York has a 2-h overlap with London, a 1.5 h overlap with Paris, Stockholm and Zurich, and a 0.5 h overlap with Oslo and Helsinki, whereas Hong Kong and Tokyo do not overlap with New York or European stock exchanges. Table 1 presents a descriptive summary of the individual index return distributions of the markets over the sample period. The returns are defined as rt =100 × [ln(Pt ) − ln(Pt − 1)],

(1)

where Pt denotes the index value at day t. Generally, the sample period is characterized by a small positive mean return around 0.025% for the USA, Switzerland, Sweden, Finland, Norway and Hong Kong. The markets of Denmark, the UK and Germany returned between 0.011 and 0.017% whereas France and Japan report slightly negative average returns. Table 2 shows the contemporaneous correlation coefficients between the markets, as they would look if the time zone differences were not accounted for. The correlations between the European markets are close to 0.40 indicating relatively high mutual dependence. The contemporaneous correlations of Asian-Pacific markets with European and US markets give preliminary evidence about the transmission of information from Japan and Hong Kong to other markets during the day. The same is true with the correlations of European markets with US. The low correlations of Asian-Pacific markets with US and higher correlations with some European markets suggest that Asian-Pacific market information is probably fully processed on European markets. Similarly the contemporaneous correlation coefficients of UK and France with US give preliminary indications that these are the primary markets that affect the US markets during the same day.

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3. Methodological aspects Vector autoregression (VAR) has proven to be a useful tool for the analysis of short-term dynamics of several economic time series. The basic VAR model is just a multivariate generalization of the univariate autoregressive (AR) model. Formally a VAR model can be written as F(L)yt =et,

(2)

where F(L) = I −F1L − F2L 2 − ··· −FpL p is a matrix polynomial of order p. The time series vector yt is assumed to be centered for the sake of simplicity, et is a random vector of m variables, Fk is an m× m matrix (k= 1, …, p), and L is the lag operator. It is assumed that all variables in the vector et are (weak) white noise processes that, however, can be contemporaneously correlated. Formally, E(et ) =0,

for

all

t

Á Ã %, if s = t E(ese %) t =Í Ã Ä 0, s "t,

(3)

where the prime denotes transpose. Because each part of Eq. (2) has the same explanatory variables, the coefficient matrices Fk can be efficiently estimated equation by equation with ordinary least squares (OLS). Only in the case where the differences of the equations are large and at the same time there is considerable contemporaneous correlation in the residuals will a more complicated estimation method, like the method of seemingly unrelated regressions (SUR), outperform OLS. Although the main tool in this paper is VAR and related tools, we also briefly discuss the vector error correction (VEC) model in the next section to capture possible cointegration of the integrated stock indices. Furthermore, we study the response of one variable to shocks of other variables by employing impulse response analysis. In this study the impulse responses are orthogonalized utilizing the standard Cholesky decomposition.

4. Empirical results In a VAR analysis of integrated series, as stock indexes typically are, an important first step is to analyze whether the series are cointegrated. If so, the long-run dynamics such as a common trend or adjusting dynamics towards that trend should be utilized by the model. To confirm that the series are integrated we run augmented Dickey Fuller (ADF) and Phillips-Perron tests for the log-index series and corresponding first differences.

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The test results indicated that in each case the first hypotheses that the series are integrated on levels is accepted in each case even at the 10% level with both tests, and rejected for the first differences at a lover level than 1%.2 Hence, we can conclude with high confidence that all of the series are integrated of order one. In order to analyze possible common trends we first test for cointegration with the Johansen’s likelihood ratio (LR) test. The results reported in Appendix A suggest that there is only one cointegration relation between the series. This, however, does not imply that all of the series should be cointegrated. The variables are ordered according to the market capitalization in ascending order, such that Norway is first. The standardized coefficients are the regression coefficients for a model with Norway as the dependent variable and the rest of the countries as the explanatory variables. The only statistically significant coefficient in this regression is that of Sweden and also perhaps of Great Britain. However, a further check, where Norway was excluded, exposed no cointegration between the remaining stock indexes. On the other hand, when Norway, Sweden and Great Britain were analyzed alone and in pairs, it was found that the British series is not cointegrated with the other two. Furthermore, a test of equality of the absolute values of the coefficients of Sweden and Norway in the cointegration equation (we have scaled the indexes such that at the starting date, August 28, 1993, the indexes have a value of 1000) accompanied with zero coefficients for the other countries, imposed 10 restrictions, and yielded a test statistic x 2(10)= 17.3 with a p-value equal to 0.07. Hence, the restrictions are borderly accepted, and further support the result that the relation is between Sweden and Norway. In the analysis of short-term dynamics, the strong indication of cointegration between the Norwegian and Swedish series is modeled using a VEC approach, the general form of which is Dyt =m + Pyt − 1 +G1Dyt − 1 + ··· + Gk Dyt − k + et,

(4)

where in our case, P =a · b% is an 11 × 11 matrix, with the prime denoting transpose. The b-matrix contains the long-run equilibrium parameters, which here is simply an 11 ×1 matrix (vector) with ones of opposite signs for Sweden and Norway and zeros for the other series. The dimension of the a-matrix is here 11 × 1. It contains adjusting response coefficients on the discrepancies of the Swedish-Norwegian long-term equilibrium. Estimates with the associated t-values of the adjusting a-parameter are reported in the last table of Appendix A. The t-values indicate that in addition to Sweden and Norway, also Finnish stock returns react on the disequilibrium in the Swedish and Norwegian long-term trend such that if Swedish stocks are below the equilibrium, a positive return in the Finnish stock index is to be expected. The adjustment, however, is relatively low. For example, a disequilibrium of 10% would result in Norway and Sweden in a daily adjustment of 0.3%, and in Finland a daily adjustment of 0.5%. 2 In order to save space, we have not given the tables here, however, they are available from the authors upon request.

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Note that the statistical insignificance of the other a-estimates together with no cointegration relation indicates that the long-run relationship between Sweden and Norway affects only on the three Nordic countries, Finland, Sweden and Norway. A formal significance test of these coefficients accompanied with the above nocointegration restriction imposes altogether 18 restrictions and yields a test statistic: x 2(18)= 27.1 with a p-value of 0.08, which is not significant at the usual 5% level. The VAR part of the model reflects the short-term dynamics left after accounting for the specified long-run dynamics. In order to alleviate the problem with the non-synchroneity of trading hours, we introduce to the European equations as explanatory variables the same day returns of Japan and Hong Kong and to the US equation the European same day returns in addition to those of Japan and Hong Kong. The VAR model to be analyzed is of the form Dyt =m + P0yt − 1 +G0Dyt +G1Dyt − 1 + ··· + Gk Dyt − k + et,

(5)

where P0 contains the coefficient of the cointegration relationship between Sweden and Norway, and the reaction of the Finnish market on the temporary disequilibrium between Sweden and Norway. The symbol G0 denotes a coefficient matrix which accounts for the same day effects in the manner described above. Technically, we proceed by using SUR on equations of the form Swe Jpn Jpn Dy it =mi +ai D1(y Nor + g Hon Dy Hon ) t − 1 −y t − 1) +D2(g 0 Dy t 0 t Gbr Fin k i + D3(g Gbr + ··· +g Fin 0 Dy t 0 Dy t )+ %k %j g t − jDyt − j + e t

(6)

where i is running over the stock exchanges, D1 = 1 for Finland, Norway and Sweden (i= Fin, Nor or Swe) and zero otherwise, D2 = 1 for all others except Japan and Hong Kong and zero otherwise, and D3 = 1 for the USA and zero otherwise. Table 3 reports Akaike’s information criterion (AIC), Schwarz’s information criterion (SC) at different common lag lengths, the LR tests with associated p-values, and the LR difference. According to AIC, the best model has a lag length equal to one. SC suggests no lags, and the LR test a model with three lags. Nevertheless, the major drop in the test statistic occurs when moving from no lags Table 3 Akaike’s information criterion (AIC), Schwarz’s information criterion (SC), and likelihood ratio (LR) values for VAR order estimation of Eq. (6) Criterion

LR

Lag

AIC

SC

LR

p-value

Difference

df

0 1 2 3 4 5

−4.47 −4.77 −4.61 −4.47 −4.34 −4.19

−4.47 −4.02 −3.10 −2.21 −1.31 −0.41

1005.7 541.6 418.1 278.3 134.9 0

0.000 0.036 0.024 0.054 0.183 NA

NA 464.1 123.5 139.8 143.4 134.9

NA 121 121 121 121 121

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to the model with one lag. Other changes are not statistically significant. Hence, considering the above sources of information, we choose a lag length equal to one for our analysis. Many of the VAR estimates of the full model are statistically insignificant. Therefore, we re-estimated the model after deleting the insignificant variables. The estimated parameters of the reduced model are reported in Appendix 2. The results indicate that there exists both locally and globally processed information that affect stock prices. As the Asian-Pacific markets open, the changes of stock prices on these markets affect European markets during the same day. This is seen from the highly statistically significant same-day coefficients in all the European equations (except for Japan in the Finnish equation and Hong Kong in the Swedish equation). The insignificance of Japan and Hong Kong in the US equation, suggests that all US-relevant information born in the Asian-Pacific markets is already processed within the European markets. Note, however, that France and UK have direct effects on Japan and Hong Kong the next day, indicating that all information emerged in these two European markets, relevant to Asian-Pacific, are not processed in the USA in between. The markets that most affect the US market the are the UK, France and, surprisingly, Sweden, but not Germany or Switzerland.3 New market information emerging in the USA is processed first in Japan and Hong Kong. However, these markets do not absorb all the relevant information because US returns still have a highly significant 1-day lag in the European equations. Furthermore, the 1-day lag is significant (negative coefficient) even in its own equation. This mean reverting behavior is an indication of local information. Note, however, that in a univariate unconditional analysis, US daily returns are not serially correlated. The local, country-specific, information is characteristic besides for the US, also for Germany, France and Denmark. Moreover, in Germany and France their cross-country previous day returns have also highly significant coefficients. The index returns in Finland, Denmark, Norway and Sweden are not dependent on the historical returns of other European countries. Sweden and Norway have the earlier mentioned cointegration relation that affects also the Finnish market, whereas the Danish market is dependent only on its own previous day return. In summary the empirical results indicate, in line with the findings of Engle and Susmel (1993), that there seem to be four different blocs of markets. Furthermore, as reported in many papers (Cheung et al., 1997), USA is the dominating market having an effect on all other markets around the world. The empirical results suggest even an existence of a slight 1-day recoiling effect on the US market itself after the elimination of the same-day effects of other markets. Asian-Pacific markets form another group with the characteristic that the USA and some European countries influence their price changes. The direct link from some European markets, bypassing the US, indicates that all relevant information born 3 It must be noted that there is a 2-h overlap of Great Britain and Sweden with the US, and there is no overlap between Frankfurt and NYSE.

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in European markets are not fully processed in the US, but some fraction is left over and directly affects the Asian-Pacific markets. The third group consists of Western European countries (France, Germany, Great Britain and Switzerland), where France and Germany are influencing the other two, and exerting a mutual effect with a 1-day lag. A fourth group consists of the Nordic countries, with Norway and Sweden having a long-term relationship that also affects the Finnish market. Denmark is neither related directly to the other Nordic markets nor to the other European markets. Appendix C contains impulse responses, and Appendix 4 the corresponding variance decompositions calculated from the residual series of the full VAR model. Generally, shocks tend to influence primarily the own market behavior and are processed during the same day. Note that there is no instantaneous effect of the US onto other markets because of the time difference that has been taken into account already in the model building stage. The same is true for the US and European markets with respect to Japan and Hong Kong. A closer investigation of Appendix C reveals that a unit shock in the UK market has the largest instantaneous effect on the other markets, especially France and Germany. Furthermore, it has a notable effect in Hong Kong the next day. This supports the earlier finding that some information born in European markets directly affect Asian-Pacific The US shocks affect virtually all markets, except its own. In the Nordic countries the effect of Sweden is dominating. This is partially due to the ordering of the series, but the Swedish shock affects also the next-day return at some magnitude, especially on the Finnish market. The main effect, however, occurs within the same day. Accordingly, the shocks are absorbed within 1-day, but in addition, given the relevance of ordering, there is evidence that the Swedish market leads, to some extent, the Finnish market by 1-day. From the variance decompositions we conclude that, generally, most of the innovation variation emerges from each country’s own market. The exceptions are France and to some extent Norway. In France, a considerable portion, 30% of total variances are explained by the UK whereas in Norway, shocks from the UK, Germany and Sweden explain  12, 8 and 7% of the variances, respectively.

5. Conclusions This paper investigates lead-lag relationships between international stock markets by taking account of the different trading hours of stock exchanges. The main findings are that New York is evidently the most influential market affecting all other analyzed stock exchanges in Europe and in the Asian-Pacific. Information born in the USA is not processed completely within the Asian-Pacific markets. A considerable fraction affects directly the European stock exchanges as well. On the other hand, from the US point of view, all local information born in the Asian-Pacific seems to be absorbed completely by the European markets and affects US prices only through the changes in European prices. A fraction of the information born in Europe, more accurately in France and Great Britain, affects directly on Japan and Hong Kong exchanges even after being processed by the US markets. The Nordic bloc, consisting of small

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exchanges, seems to constitute a separate region, that is characterized by a cointegration relationship between Norway and Sweden. Of the Nordic markets, Finland, Norway and Sweden are sensitive to deviations from this long-run relation, but Denmark and the other European countries as well as the USA and Asian-Pacific markets are not. Otherwise, the empirical results suggest that there exist two kinds of information being transferred across markets, local or region-specific and global or inter-region specific. Local information seems to be characteristic of Denmark, Germany, France and the USA. Taking account of the different trading hours, the local information is measured in terms of the residual autocorrelation in a stock index after controlling for the other stock markets’ impacts. In France, Germany and the US, own previous day change tends to a have reverse (negative) impact on the own market the next day. In the smaller Danish market the change tends to be of the same sign (positive impact). Furthermore, the larger European stock exchanges seem to be interrelated such that France and Germany affect each other and they both have an effect on the London Stock exchange with a 1-day lag. The US (Dow Jones index), is related to the changes in Great Britain and France the same day and, surprisingly, to Swedish returns even with a 1-day lag. US returns, however, are not directly related to price changes in the Asian-Pacific region. Moreover, it is interesting to note that while the Dow Jones Industrial index for the New York stock exchange is not itself autocorrelated, it is negatively autocorrelated once the impacts of the other stock exchanges have been accounted for. An impulse response analysis indicates that most of the new information is processed within 1-day. Shocks on a market have primarily a local effect. As a consequence, it seems that in order to gain a more accurate picture of the interrelations between markets, intra-day data should be used.

Acknowledgements The authors are grateful for the comments of the anonymous referees and the editor, Ike Mathur, who made several suggestions to improve the paper. The authors wish to thank the Startel Oy for providing most of the daily index values data of the study. The authors also wish to thank John Rogers for checking the English of the paper. Financial support from Jenny and Antti Wihuri Foundation is gratefully acknowledged.

Appendix A. Johansen’s cointegration test results Johansen’s LR test for cointegration of 11 national stock markets: USA, Great Britain, Germany, France, Switzerland, Japan, Hong Kong, Denmark, Sweden, Norway and Finland. Daily observations on the sample period September 1993– August 1996 are obtained from Startel Oy, where data are available only for Finnish working days. National holidays in other countries occurring on Finnish working

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days are imputed as previous day’s index values (zero returns). Five lags were used in the VEC model for testing the number of the cointegration equations (CE). Eigen-value

0.068 0.064 0.061 0.046 0.041 0.028 0.024 0.018 0.012 0.011 0.001

LR statistic

Critical value

282.5 230.7 182.1 135.5 100.7 70.0 49.2 31.0 17.7 8.6 0.5

No. CE(s)

5%

1%

277.7 233.1 192.9 156.0 124.2 94.2 68.5 47.2 29.7 15.4 3.8

293.4 247.2 206.0 168.4 133.6 103.2 76.1 54.5 35.7 20.0 6.7

None* 51 52 53 54 55 56 57 58 59 510

* Significant at 5% level. Coefficients for the one statistically significant cointegration vector normalized for Norway.

FIN DEN SWE

SWZ

HON GER GBR FRA JPN USA Const

Coefficient 0.12 −0.30 −1.24 −0.14 −0.14 0.30 0.56 0.10 0.03 0.03 −2.17 S.E. 0.10 0.16 0.26 0.17 0.10 0.18 0.23 0.12 0.05 0.15

Short-term adjusting parameter estimation results for the cointegration analysis. Alpha FIN SWE NOR DEN GBR GER FRA SWZ USA JPN HON

0.05 0.03 − 0.03 0.00 0.00 0.02 0.00 −0.01 0.01 −0.01 0.01

t-values 3.04 1.98 −2.27 0.14 0.05 1.71 −0.23 −1.11 0.84 −0.57 0.25

142

Finland Constant (N-S)t−1 FIN FINt−1 SWE SWEt−1 NOR NORt−1 DEN DENt−1 GBR GBRt−1 GER GERt−1 FRA FRAt−1 SWZ SWZt−1 USAt−1 JPN HON HONt−1 R2 s

Sweden

Norway

0.27 (0.00) 0.22 (0.00) −0.10 4.22 (0.00) 3.22 (0.00) −2.27

Denmark (0.08) 0.01 (0.83) (0.01)

Great Britain 0.01

(0.68)

Germany

France

0.02 (0.56) −0.04

Switzerland USA (0.34) 0.04 (0.17)

0.10 (0.01)

Japan

Hong Kong

0.05

(0.03) −0.02

(0.70) 0.01 (0.91)

0.14 0.07

(0.00) (0.01)

0.18

(0.00)

0.12 (0.00) 0.46 (0.00) 0.06

(0.02) − 0.16 (0.00)

0.10

(0.02) 0.05 (0.07) 10.0

−0.09

(0.00)

0.18 (0.00) −0.12

(0.00)

(0.00) (0.00) (0.00)

0.35 (0.00) 0.09 (0.00) 0.12 (0.00)

0.21 0.09 0.07

(0.00) 0.19 (0.00) −0.13 (0.00) 0.06 (0.01) (0.00) 0.07 (0.00)

0.25 0.80

0.06 1.01

0.47 (0.00) 0.43 (0.00) 0.07 (0.00) 0.08 (0.00)

0.26 0.05 0.08

(0.00) 0.22 (0.00) (0.01) 0.07 (0.00) (0.00) 0.08 (0.00)

0.16 0.05 0.07

0.12 1.14

0.14 0.77

0.13 0.71

0.07 0.72

p values in parentheses.

0.12 0.90

0.09 0.74

0.10 0.65

(0.00)

(0.00)

0.15

(0.00)

0.29

(0.00) 0.59 (0.00)

0.05 1.22

0.14 1.47

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Appendix B. Estimated model with non-significant coefficients deleted

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143

Appendix C. Innovation responses Days after

Response to shock in USA

USA

GBR 0.00 0.15 0.00 0.00

GER 0.00 0.28 −0.01 0.01

JPN

FRA

SWZ

HON

SWE

DEN

FIN

NOR

0 1 2 3

0.63 −0.01 0.02 0.00

0.00 0.18 0.03 0.00

0.00 0.19 0.00 0.00

0.00 0.17 0.01 0.00

0.00 0.37 0.05 0.01

0.00 0.30 −0.02 0.01

0.00 0.17 0.01 0.00

0.00 0.32 0.01 0.00

0.00 0.21 0.00 0.00

0 1 2 3

Response to shock in GBR 0.17 0.71 0.31 0.00 0.00 −0.01 0.19 0.16 0.01 0.01 −0.04 −0.01 0.00 0.00 0.02 0.00

0.57 0.00 0.02 −0.01

0.27 0.05 0.01 0.00

0.00 0.44 −0.02 0.01

0.37 0.07 −0.02 0.00

0.21 0.11 −0.01 0.00

0.26 0.18 −0.02 0.01

0.27 0.10 −0.01 0.00

0 1 2 3

Response to shock in Germany 0.01 0.00 0.73 0.00 0.01 0.03 −0.07 0.09 −0.01 −0.01 0.02 −0.01 0.00 0.00 −0.01 0.00

0.27 0.03 −0.01 0.00

0.24 0.04 −0.01 0.00

0.00 0.02 0.02 −0.01

0.18 −0.02 0.00 0.00

0.22 0.05 0.00 0.00

0.26 −0.04 0.00 −0.01

0.23 −0.01 0.00 0.00

0 1 2 3

Response 0.00 0.04 0.00 0.00

0.12 0.04 0.01 0.00

0.08 −0.05 0.01 0.00

0.17 −0.03 0.02 0.00

0.09 −0.04 0.02 0.00

0.08 −0.04 0.01 0.00

0.05 −0.05 0.02 0.00

0.13 −0.04 0.02 0.00

0.77 −0.04 0.02 −0.01

0.09 0.04 0.00 0.00

0.00 0.10 −0.01 0.01

0.13 0.04 −0.01 0.00

0.04 0.08 0.00 0.00

0.06 0.07 −0.02 0.01

0.06 0.06 −0.01 0.00

0 1 2 3

Response to shock in Switzerland 0.01 0.00 0.00 0.00 0.00 −0.02 −0.04 −0.02 0.00 −0.09 0.00 0.00 −0.03 −0.03 0.00 0.00 0.00 0.01 0.00 0.00

0.63 −0.04 −0.01 0.00

0.00 0.05 0.04 0.00

0.18 −0.05 0.01 0.00

0.11 0.00 −0.01 0.00

0.11 −0.03 −0.02 0.00

0.14 −0.04 −0.01 0.00

0 1 2 3

Response 0.06 −0.01 −0.01 0.00

0.12 0.04 0.00 0.00

1.45 0.08 0.00 0.00

0.08 −0.04 −0.01 0.00

0.11 −0.02 −0.01 0.00

0.11 −0.02 −0.02 0.00

0.12 0.00 −0.01 0.00

0 1

Response to shock in Sweden 0.10 0.00 0.00 0.00 0.06 0.03 0.07 0.04

0.00 0.04

0.00 0.05

0.75 0.07

0.12 0.06

0.30 0.16

0.20 0.07

to shock in Japan 0.08 0.13 1.21 0.00 -0.06 −0.04 0.01 0.04 0.01 0.00 −0.01 0.00

Response to shock in France 0 1 2 3

0.09 0.00 0.01 0.00

0.00 −0.01 0.01 0.00

0.00 0.19 −0.05 0.02

0.00 0.13 0.00 0.00

to shock in Hong Kong 0.12 0.17 0.00 0.12 0.00 0.02 −0.02 −0.01 0.00 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00

0.00 0.03

144

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Appendix C. (Continued) Days after

Response to shock in USA

USA 2 3

GBR

0.01 0.00

GER

0.02 0.00

0.02 0.00

JPN

FRA

SWZ

HON

SWE

DEN

FIN

NOR

0.03 0.00

0.02 0.00

0.02 0.00

0.04 0.01

0.03 0.00

0.02 0.00

0.04 0.01

0.02 0.00

0 1 2 3

Response to shock in Denmark −0.03 0.00 0.00 0.00 −0.01 −0.01 −0.01 0.00 0.00 0.00 −0.01 −0.01 0.00 0.00 0.00 0.00

0.00 −0.03 0.00 0.00

0.00 −0.03 −0.01 0.00

0.00 −0.01 −0.02 0.00

0.00 −0.01 −0.01 0.00

0.61 0.07 0.00 0.00

0.08 −0.02 −0.01 0.00

0.08 −0.01 −0.01 0.00

0 1 2 3

Response −0.02 0.03 0.00 0.00

in Finland 0.00 0.00 0.02 0.05 0.03 0.02 0.00 0.00

0.00 0.05 0.00 0.00

0.00 0.02 0.00 0.00

0.00 −0.03 0.03 0.00

0.00 0.07 0.02 0.00

0.00 0.02 0.01 0.00

1.01 0.08 0.03 0.00

0.09 0.04 0.02 0.00

0 1 2 3

Response to shock in Norway −0.02 0.00 0.00 0.00 0.00 0.00 −0.04 0.06 0.00 −0.01 0.00 −0.01 0.00 0.00 0.00 0.00

0.00 0.01 −0.01 0.00

0.00 0.00 −0.01 0.00

0.00 −0.01 0.00 0.00

0.00 0.00 −0.01 0.00

0.00 −0.02 −0.01 0.00

0.00 −0.08 −0.01 0.00

0.61 0.00 0.00 0.00

to shock 0.00 0.04 0.00 0.00

Appendix D. Variance decompositions Days ahead

S.E.

Decomposition of variance for USA

USA

GBR

GER

6.4 6.3 6.3 6.4

0.0 0.1 0.1 0.1

1 2 3 10

0.67 0.68 0.68 0.68

88.5 87.3 87.3 87.3

1 2 3 10

0.73 0.75 0.75 0.75

Decomposition 0.0 96.1 3.8 91.4 3.8 91.3 3.8 91.3

JPN 0.0 0.3 0.3 0.3

of variance for 0.0 1.1 0.2 1.0 0.2 1.0 0.2 1.0

FRA

SWZ

HON

SWE

DEN

FIN

NOR

1.9 1.9 1.9 1.9

0.0 0.2 0.2 0.2

0.7 0.7 0.7 0.7

2.0 2.7 2.7 2.7

0.2 0.2 0.2 0.2

0.1 0.2 0.2 0.2

0.1 0.1 0.1 0.1

2.8 2.7 2.7 2.7

0.0 0.2 0.2 0.2

0.0 0.0 0.0 0.0

0.0 0.3 0.3 03

0.0 0.0 0.0 0.0

Great Britain 0.0 0.0 0.0 0.4 0.1 0.4 0.1 0.4

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145

Appendix D. (Continued) Days ahead

S.E.

Decomposition of variance for USA

USA

GBR

Decomposition 0.0 14.3 9.3 15.8 9.2 15.9 9.2 15.9

GER

JPN

of variance for 79.0 2.4 63.9 2.3 63.3 2.4 63.3 2.4

FRA

SWZ

HON

SWE

DEN

FIN

NOR

4.3 3.5 3.5 3.5

0.0 0.5 0.6 0.6

0.0 0.0 0.0 0.0

0.0 0.1 0.2 0.2

0.0 0.2 0.2 0.2

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.1 0.2 0.2

0.0 0.0 0.0 0.0

0.0 0.1 0.2 0.2

0.0 0.3 0.3 0.3

0.0 0.8 0.8 0.8

1.5 1.4 1.4 1.4

0.0 0.1 0.1 0.1

0.0 0.1 0.1 0.1

0.0 0.2 0.2 0.2

0.0 0.0 0.0 0.0

Germany 0.0 0.0 4.3 0.1 4.5 0.1 4.6 0.1

1 2 3 10

0.82 0.92 0.92 0.92

1 2 3 10

1.21 1.25 1.25 1.25

1 2 3 10

1.01 1.03 1.03 1.04

Decomposition 0.0 31.8 3.4 30.3 3.3 30.2 3.3 30.2

of variance for France 7.3 1.4 58.0 7.0 1.5 55.2 7.0 1.5 55.2 7.0 1.5 55.2

1 2 3 10

0.74 0.77 0.77 0.77

Decomposition 0.0 13.1 4.7 12.5 4.7 12.5 4.7 12.5

of variance for 10.6 1.2 10.2 1.6 10.2 1.6 10.2 1.6

Switzerland 1.6 70.7 1.8 66.0 1.8 66.0 1.8 65.9

2.8 2.8 2.8 2.8

0.0 0.3 0.3 0.3

0.0 0.1 0.1 0.1

0.0 0.1 0.1 0.1

0.0 0.0 0.0 0.0

1 2 3 10

1.46 1.58 1.58 1.58

Decomposition 0.0 0.0 5.5 7.7 5.6 7.7 5.6 7.7

of variance for 0.0 1.3 0.0 1.2 0.0 1.2 0.0 1.2

Hong Kong 0.0 0.0 0.4 0.1 0.4 0.2 0.4 0.2

98.7 85.0 84.7 84.7

0.0 0.1 0.2 0.2

0.0 0.0 0.0 0.0

0.0 0.0 0.1 0.1

0.0 0.0 0.0 0.0

1 2 3 4 10

0.89 0.95 0.95 0.95 0.95

Decomposition 0.0 17.0 9.7 15.5 9.7 15.5 9.7 15.5 9.7 15.5

of variance for 4.2 1.1 3.7 1.1 3.7 1.2 3.7 1.2 3.7 1.2

Sweden 2.0 2.0 2.0 2.0 2.0

4.0 3.8 3.8 3.8 3.8

0.8 0.9 0.9 0.9 0.9

70.9 62.8 62.6 62.6 62.6

0.0 0.0 0.0 0.0 0.0

0.0 0.6 0.6 0.6 0.6

0.0 0.0 0.0 0.0 0.0

1 2 3 10

0.71 0.75 0.76 0.76

Decomposition 0.0 8.4 5.3 9.4 5.3 9.4 5.3 9.4

of variance for 9.6 1.4 9.1 1.5 9.0 1.5 9.0 1.5

Denmark 0.3 2.2 1.5 2.0 1.5 2.0 1.5 2.0

2.5 2.3 2.3 2.3

2.9 3.1 3.2 3.2

72.8 65.7 65.5 65.5

0.0 0.1 0.1 0.1

0.0 0.1 0.1 0.1

Decomposition of variance for Japan 0.0 2.2 2.2 2.2

0.0 1.6 1.6 1.6

0.0 0.5 0.5 0.5

100.0 94.1 93.9 93.9

0.0 1.0 1.0 1.0

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146

Appendix D. V(Continued)

Days ahead

S.E.

Decomposition of variance for USA

USA

1 2

1.13 1.21

3 10

1.21 1.21

1 2 3 10

0.78 0.82 0.82 0.82

GBR

GER

JPN

FRA

SWZ

HON

SWE

DEN

FIN

NOR

0.9 0.8

7.3 8.2

0.5 0.5

79.5 69.8

0.0 0.4

0.9 0.9

0.8 0.8

8.3 8.3

0.5 0.5

69.6 69.6

0.4 0.4

Norway 0.6 3.4 1.0 3.3 1.0 3.4 1.0 3.4

2.5 2.3 2.3 2.3

6.7 6.8 6.9 6.9

0.9 0.8 0.8 0.8

1.3 1.5 1.5 1.5

61.3 55.4 55.3 55.3

Decomposition of variance for Finland 0.0 5.3 5.1 0.2 0.3 0.9 7.1 6.9 4.6 0.3 0.6 0.8 7.0 7.0

6.9 6.9

Decomposition 0.0 11.8 6.3 12.0 6.3 12.0 6.3 12.0

4.5 4.5

0.4 0.4

of variance for 8.6 2.7 7.8 2.6 7.8 2.7 7.8 2.7

0.6 0.6

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