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Global Stock Market Integration and the Determinants of Co-movements: Evidence from developed and emerging countries Author(s): Asma Mobarek1 Abstract This study examines the extent of cross-country returns co-movement between the stock markets of five developed benchmark countries [US, UK, Japan, Germany and France] and five emerging benchmark countries [Brazil, Russia, India, China and South Africa] countries, vis-à-vis a total country sample composed by 20 countries. The Geweke (1982) Measure of Feedback methodology along with a set of pooled cross-country time-series regressions is used to identify and explain the changes in stock market integration. The general findings for the Geweke contemporaneous feedback measures provide supportive evidence of increased stock market integration. Our pooled cross-country time-series regression analysis has shown that countries’ economic integration, as measured by the explanatory variables explain almost 32 percent of the variation in the contemporaneous Geweke feedback measure on a global scale. This explanatory power becomes stronger for the group of developed markets (49.74 percent) and for countries that are part of the European economical and political union (69.82 percent). The results also reported that several variables as significantly associated with the evolution of stock markets integration over time. These statistically significant variables include, on a global level, import dependence, stock markets’ size differential and their relative size, difference in annual GDP growth rate as well as the time trend. 1 Associate Professor, Stockholm University Business School. The author acknowledges NASDAQ OMX Foundation for financially supporting the project. The author also thankful to Angelo Fiorante and also Federica Vitali, for their efforts as a research assistant in the project. 1. Introduction The movement towards a synchronized stock market landscape has gained momentum, especially during the past two decades, where tighter economical and financial linkages among developed economies have grown stronger. However, the rises of many important emerging markets, which have been a major driver of global growth the past decades, have opened up additional channels for cross-border relations. Other causes behind the rapid increase in world trade, capital movements, and foreign investments between world economies are due to market liberalization/deregulation, technological advances and removals of statutory controls. Many of these factors have contributed to more interlinked economies, which in turn, are said to have given rise to a higher degree of stock market synchronization, especially in volatile time periods, e.g. eruption of a financial crisis, war, or political instability. The aftermath of historical financial crises, including the latest one in 2007, have opened up a tremendous interest for determining the underlying factors that might explain how stock markets are correlated with one and other for better understanding the causes of the sudden and simultaneous deterioration of wealth that occurs during crises periods. To investigate the propensity of one country to be affected by global shocks have enormous value for preventing future crises. The extent of financial and economical integration between a country-pair may indeed be reflected by the degree of stock markets co-movement that they exhibit. In fact, the dynamic structures of international economies have clearly intensified the complexity behind stock market performances. As our countries become more economically interlinked, explaining the formation of price co-movement between stock markets on an international level is significant for better understanding this higher interdependency and integration. However, the contemporary research in stock market integration has not sufficiently focused on determining the driving forces behind co-movement although this information would be most effective for policy-makers and investors that are keen to know how economic linkages may influence the countries financial stability, diversification possibilities and what types of common and specific shocks stock markets are most vulnerable against. This study assesses how stock market integration and the co-movement between country-pairs, distinguishing between developed and emerging markets, has been affected in terms of timing and intensity during 1995 – 2010. A 2 greater degree of co-movements in stock prices is seen as a reflection of greater stock market integration. It also takes a closer look at the outcome of significant financial meltdowns occurring within this period, e.g., the Asian crises, the dotcom-bubble, the financial crisis of 2007 and other country-specific crises. It investigates plausible economical and financial underlying factors that are said to characterize and influences the extent of integration between pairs of countries. A two-step analysis is employed to assess which underlying factors can explain stock market integration and the degree of co-movement. First, the Geweke (1982) feedback measure, outlined in Section 3.1, is estimated between country pairs. By considering a dynamic interrelationship between two countries’ daily stock market returns, the year-on-year feedback measures demonstrate how co-movement evolves over time, reflecting changes in stock market integration by increases or decreases in the measures (see Bracker et al., 1999; Johnson & Soenen, 2002, 2003). Second, the estimated feedback measures are employed in a pooled cross-country time-series regression, outlined in section 3.2, including significant economical, financial and country-specific factors hypothesized to influence the degree of stock market integration. The data sample covers 20 countries - ten developed and ten emerging – from 1995 to 2010. The major findings of the paper are as follows: The general findings for the Geweke contemporaneous feedback measures provide supportive evidence of increased stock market integration. A reasonably clear time trend is identified, where the extent of contemporaneous co-movement across markets has intensified over time, especially for emerging countries, which consequently suggests that greater market efficiency is being fostered at the international level. On the other hand, the results of the Geweke unidirectional feedback measures indicate a tendency that some markets are more likely to lead other markets than vice versa. However, there is a less distinctive time trend in the movements of the annual twoways unidirectional feedback measures, suggesting that leader-follower relationships are likely to change over time periods. These alterations might be due to possible changes in a country’s economy and market conditions, but also the stability of global markets. Nevertheless, the highly sophisticated market of the US and the emerging markets of Brazil and Russia appear to 3 affect other rather than be affected. However, the distinction with previous studies is that not only is the contemporaneous measures larger on average, but higher significance levels are reported for the unidirectional measures of feedback, suggesting that cross-market adjustment persist over time more often than occasionally for this study’s time period 1995-2010. The study also reports that countries’ economic integration, can explain almost 32 percent of the variation in the contemporaneous Geweke feedback measure on a global scale over the 15-year period, 1995-2009. This explanatory power becomes stronger for the group of developed markets (49.74 percent) and for countries that are part of the European economic and political union (69.82 percent). The results also point out several variables as significantly associated with the evolution of stock markets integration over time. These statistically significant variables include, on a global level, import dependence, stock markets’ size differential and their relative size, difference in annual GDP growth rate as well as the time trend. The rest of the paper is organized as follows: section 2 reviews the literature on stock market integration, section 3 outlines the already mentioned research method more in detail, section 4 presents the empirical results, and section 5 provides the conclusions. 2. Literature review: The current state of literature offers numerous studies that examine the presence of stock market integration, with the notion that markets have been exhibiting tighter co-movements with one and other, and that they are more integrated than never due to closer financial and economical linkages. However, it is clear that less has been said concerning the determinants of stock market co-movement and economic integration, which makes it still an intriguing research topic where there seems to be many missing pieces of the puzzle. A good number of studies on the correlation between stock markets at an international level have been concerned with measuring the extent and direction of the co-movement by using multivariate GARCH, vector auto-regression (VAR), Unit root test, and various co-integration tests. Several of these studies report that during periods of financial crisis the stock market comovement is greater than before the crisis occurred. Liu et al. (1998) employs a vector autoregressive analysis to examine the dynamic structure of international transmission in stock 4 returns for six countries – the U.S, Japan, Hong Kong, Singapore, Taiwan and Thailand – for the period 1985-1990 capturing the October 1987 stock market crash. They conclude that the degree of interdependence among the Asian-Pacific markets increased substantially after the 1987 stock market crash and where the U.S market possesses an influential role affecting these markets. In addition, the risk reduction benefits of international portfolio diversification have been reduced due to the higher interdependence that has been observed in these markets. Similarly, Arshanapalli et al. (1995), conclude that the co-integration structure that links these markets increased substantially after the 1987 collapse. However, Longin & Solnik (1995) examines the correlation for seven major European countries over the period 1960-90 indicating that not only is the international covariance and correlation matrices unstable over time, but that correlation rises in periods when the conditional volatility of markets is large. Karolyi & Stulz (1996) explores the co-movements between the Japanese and U.S stock markets from 1988-1992, showing that correlation and covariance are high when markets move a lot, hence demonstrating the shortcomings of international diversification in times of high volatility which is when it is most needed. In light of the benefits of international portfolio diversification (see e.g. Solnik, 1995), there is a range of studies that deals with emerging stock markets, which are said to have lower exposure to world factors, thus having lower levels of integration and therefore may offer greater opportunities for risk diversification across countries. Moreover, Ampomah (2008) presents evidence that African stock markets are still segmented from global markets offering strong diversification benefits. Another type of studies has provided evidence on which markets dictates over other markets. An early study by Eun & Shim (1989) highlights the influence and power that the U.S stock market has on the stock markets of eight other developed countries. Findings indicate that a substantial amount of interdependence exists, where the U.S stock market represents the most influential world economy having by far a dominant position when it comes to producing valuable information that affects world stock markets. Empirically they found that innovations in the US stock market were rapidly transmitted to the rest of the world, whereas innovations in other markets did not have much effect on the US market. 5 A very few studies evidence on the determinants of stock market co-movement has been presented by Pretorius (2002), which examined ten emerging stock markets for the period 1995–2000 by employing a cross-section and a time-series model. The major findings showed that only bilateral trade and the industrial production growth differential were significant for explaining the correlation between two countries on a cross-sectional basis. Similar results were achieved by the time-series regression. The model explained 40% of the variation in the correlation coefficient, thus 60% could be due to contagion or other explanatory variable that was not included in the analysis. Forbes & Rigobon (2002) pointed out that traditional tests for contagion based on cross-market correlation coefficient are problematic due to the bias introduced by changing volatility in market returns, i.e. heteroskedasticity. During a crisis period when stock market volatility increases, the estimates of cross-market correlation will be biased upward. The paper reevaluates several crisis periods with a method that corrects for this heteroskedasticity, finding that there was no contagion during these periods of turmoil. They conclude that the higher levels of market co-movement during the observed periods are mostly due to interdependence, which depend on the linkages that economies have with each other. Wälti (2005) follows Forbes and Rigabon (2002) correction model for determining the macroeconomic variables underlying co-movements between stock market return for fifteen industrialized countries for the period 1973–1997. Results show that trade and financial integration contributes positively to stock market synchronization, while a fixed exchange rate regime increases co-movements. Other factors such as the similarity of economic structure across countries, informational asymmetries and a common language also contribute to stock market synchronization. Serra (2000) found that emerging markets’ returns are mainly driven by country specific factors and less by industry specific factors. Cross-market correlation is not affected by the industrial composition of the indices, making cross-market diversification a better option than cross-industry diversification. However, significant loss of diversification benefits may occur if the industrial mix is totally ignored. Morgado & Tavares (2007) examines the impact of bilateral indicators of economic integration on the correlation of stock return of 40 developed and emerging markets for the period 1970–1990. Results showed that bilateral trade intensity affects the correlation positively, whereas the asymmetry of output growth, the dissimilarity of export structure and the real exchange rate volatility have negative effects on 6 stock return correlation. Lin & Cheng (2008) apply a non-linear Multinominal Logit Model (MNLM) in which co-movement is categorized in three outcomes: (i) negative co-movements, (ii) positive co-movements and (iii) no co-movements. The empirical analyze of the economic determinants that affect the stock market co-movement relationship between Taiwan and its four major trading partners (Mainland China, US, Japan and Hong Kong) are stock market return volatility, the rate of change in exchange rate and interest rate differentials. Other types of studies, such as Bracker & Koch (1999) suggest that countries that experience greater economic integration should also experience greater co-movement in their respective capital markets. Their study addresses questions whether, how and why, the correlation structure changes over time. By testing the stability of the correlation matrix over different periods and modeling potential economic determinants of the correlation structure for ten national stock indices during 1972–1993, they provide evidence of the dramatic evolution in the correlation matrix over both short- and long-time horizon. Results indicate that the degree of international integration (measured as the magnitude of the correlation structure) is positively associated with (1) world market volatility and (2) trend; while it is negatively related to (3) exchange rate volatility, (4) term structure differential across markets, (5) real interest rate differentials, and (6) the return on a world market index. However, it is concluded that further analyzes on potential economic determinants of the correlation structure is needed to fully understand what makes market move in tandem. Other similar studies like, Bracker et al., (1999); Johnson & Soenen, (2002, 2003) investigate how and why different pairs of international stock markets display differing degree of co-movement over time. The main empirical results from these studies show that; (Bracker et al., 1999) Several macroeconomic factors are significantly associated with the extent of stock market integration over time, e.g. trade, geographic distance, stock market size differential, time trend, and real interest differential; (Johnson & Soenen, 2002) Asian stock markets become more integrated with the Japanese stock market over time, especially since 1994, where increased export share from Asian economies to Japan and greater foreign direct investment from Japan to other Asian economies contributes to greater co-movement; (Johnson & Soenen, 2003) Indicating that a high share of trade with the US has a strong positive effect on stock market co-movements for 7 equity markets of the Americas, whereas increased bilateral exchange rate volatility and a higher ratio of stock market capitalization relative to the US contribute to lower co-movement. In short, the main emphasis of previous mentioned studies has been determining how integrated markets are by examining the extent of the co-movements that stock markets exhibit. By flipping the coin, we find a smaller amount of studies that attempt to determine why stock markets are integrated. The main objective of this research paper is to fill the gap is to unfold the determinants and the driving forces behind stock market relationships including both developed and emerging markets, which may indeed be of greater value for investors that struggles with portfolio-diversification choices and for policy-makers and regulatory bodies that are keen to know what types of determinants and treaties with other countries that might affect the national stock market, especially during turmoil periods. 3. Methodology and Data Sample 3.1 Measuring Stock Market Integration: Geweke (1982) Measures of Feedback Geweke (1982) provides a cardinal methodology for measuring the degree of co-movement (or interrelationship) between pairs of stock markets, which indicates how integration between country pairs evolves over time (Bracker et al., 1999; Geweke, 1982, 1984). An increase (or decrease) in the year-to-year feedback measure reflects an increase (or decrease) in the extent of stock market integration. The measure of feedback technique has been chosen since it has certain advantages over other means (e.g. VAR2 or Granger Causality3) that might be used for testing the relationship between two stock markets. It identifies not only the presence of significant information flows between two markets, but also the extent of this feedback. Moreover, it reveals how integration, as well as how the leader/follower relationship changes over time. 2 “The VAR approach is deficient in its failure to incorporate potential long-term relations and, therefore, may suffer from specification bias”. (Mukherjee & Naka, 1995) 3 The Granger Causality (1969) test for casual relation can only reveal if the hypothesis under consideration holds or not. 8 The objective of implementing Geweke’s feedback measures on stock market co-movement is to capture the degree to which daily stock returns (i) move together in the two countries on the same day and (ii) the degree to which daily stock returns in the two countries lead and lag each other. The Geweke contemporaneous and unidirectional feedback measures are calculated annually between pairs of countries using daily stock market returns. In the first stage of the analysis, the model specification considers a dynamic interrelationship between the daily stock market return of country i and j, (rit and rjt), to hypothetically depend upon: (i) past returns in the other market, (ii) its own past returns, and (iii) the idiosyncratic noise. The restricted regression equations (1) and (2) are specified as follows: M2 M1 k 1 k 1 rit 0 ak rjtk bk ritk it , M2 M1 k 1 k 1 rjt 0 c k ritk dk rjtk jt , v ar( it ) 2i [1] v ar( jt ) 2j [2] wi t h t he va r i-acnc ova e r i a nc e m a t r i x of re i nd dua j )lt :s ( i s t a C Y D i t2i i o v 2 Y j ti j j e t e j rY c m i ,o j) t v ti The residuals εit and εjt are assumed to be white noise, i.e., normally distributed where z = i or j and ( n N(0,σεz2), Cov(εzt, εz,t-1)=0. Despite the fact that the residuals εit and εjt are assumed to be serially uncorrelated, they may exhibit contemporaneous correlation4 with each other. The regression equations [1] and [2] can be solved by applying the Seemingly Unrelated Regression (SUR), a technique that account for the contemporaneous correlation among the residuals (Judge et al., 1988). The initiative behind Eq. [1] and [2] measures the nature and extent of the interrelationship between daily stock returns in the two countries, e.g. coefficient 4 Estimates derived with OLS techniques may be inefficient when error terms may exhibit contemporaneous correlation. See Zellner (1962). 9 a n ak display how the second market (j) leads the first market (i) across days, while coefficient ck display how the first market (i) leads the second market (j) across days (Bracket et al., 1999). Following Bracket et al. (1999) the lag length of M1 and M2 are chosen to be 10 and 5 business days, respectively. In the second stage of the analysis, it is assumed that there is no interrelationship among the price series of the two different stock markets, (i.e. coefficients ak and ck will be equal to zero for k = 1,2,…,M2) hence, the unrestricted regression equations [3] and *4+ incorporates only the country’s own lagged returns to explain its current daily return, and they can be estimated with ordinary least squares (OLS). M1 rit = a + å bk´ rit-k + mit , ´ 0 var(mit ) = s m2i [3] var(m jt ) = s m2 j [4] k=1 M1 rjt = b + å dk´ rjt-k + m jt , ´ 0 k=1 The residuals variance, µit and µjt are independently and identically distributed with zero means and where z = i or j, and Cov(i,t , j,t ) 0 i.e., the residuals does not exhibit contemporaneous correlation, thus Ordinary Least Squares (OLS) technique is appropriate for solving Eq. [3] and [4]. At this stage, three null hypotheses may be identified from the considerations related with the above analysis. They are formulated as follows: H1: There is no contemporaneous relation between rit and rjt on the same day. H2: There is no unidirectional relationship from rjt to rit across days (i.e. ak = 0, for any k) H3: There is no unidirectional relationship from rit to rjt across days (i.e. ck = 0, for any k) According to Geweke (1982) Measure of Feedback, the interrelationship among the stock markets of two different countries can be measured by the following Log-likelihood Ratio statistics: 10 GMFi* j = (n)ln éë(s m2i ´ s m2 j ) / Y ùû ~ c12 under H1; GMFj®i = (n)ln (s m2i / s e2i ) ~ c M2 2 under H2; GMFi® j = (n)ln (s m j / s 2 2 ej a a ) a ~ c M2 2 under H3; The yearly Geweke measures demonstrate how the co-movement of daily returns between a pair of countries evolves over time, e.g. where an increase (or decrease) in GMF from year t1 to t1+n (n=1,2…T) reflects an increase (or decrease) in the extent of stock market integration for that pair of countries. The likelihood-ratio test statistics forms the Geweke feedback measure, and it is calculated for each country pair and for each year from the residual variances and covariances from the restricted [Eq. 1 and 2] and unrestricted [Eq. 3 and 4] country pair regressions. 3.2 Modeling for Determinants: Pooled Cross-Country Time-series Regression The second step of this analysis is specifically aimed at investigating the statistical significance of various macroeconomic and financial factors, indicators of economic integration between two countries, in explaining the evolution of the degree of co-movement between their stock markets over time. At this purpose, a pooled cross-country (more specifically, cross-country pair) time-series regression has been estimated with the contemporaneous Geweke measure of feedback CGMFij,t for countries i and j at time t acting as dependent variable, across pairs of the 20 countries included in the study. The pooled regression model representing the potential determinants of equity markets interdependence takes the following form: A B C a 1 b 1 c 1 CGMFij,t 0 a Tradeta b Macrotb c Developmenttc Trendt t [5] The explanatory variables included in the regression model [5] outlined above are described in Table 1. 11 Table 1. Potential determinants of stock market integration (A.) Measures of the nature and extent of bilateral trade relationships Xi = (Xij /Xi ) t Exports from country i to country j , relative to i 's total export Xj = (Xji /Xj ) t Exports from country j to country i , relative to j 's total export Mi = (Mij /Mi )t Imports of country i from country j , relative to i 's total import Mj = (Mji /Mj )t Imports of country j from country i , relative to j 's total import (B.) Macroeconomic factors I = (πi -πj )t Inflation differantial between markets i and j RI = (ri -rj )t Real interest rate differential between markets i and j Gr = (gi -gj) t GDP annual growth rate differential between country i and j (C.) Measure of financial development S = (size i -size j )t Percent of world equity market share of country i minus that in j MV = (MV j /MV i )t Ratio of stock market capitalization of country j to that of country i , expressed in US dollars T Variable for the time trend t (i.e. t = 1,2,…,T years) In terms of bilateral trade relationships, four different variables have been considered in order to encompass the point of view of both countries in each pair, hence revealing the two different sides of the same coin. Indeed, although theoretically total exports from country i to j should equal total import of country j from i, Xij=Mji, the measures used in this study are relative measures, as also specified by Bracker et al. (1999). Export from country i to j is compared to country i’s total export and, vice versa, import of country j from i is compared to j’s total import, so that the theoretically same amount of bilateral trade Xij=Mji becomes relatively more important for one of the two trading parties. The importance of including four measures of bilateral trade relationships relies in the fact that each of the four could have a different impact on the co-movement of two given stock markets. While export from the point of view of both countries seems to be always positively related to the sensitiveness of one country’s stock market to its partner’s stock market activities, the same is not valid for import. As fully explained by Bracker et al. (1999), stock market performance is considered an indicator of the future economic outline of a country, so that the possibilities of increasing export to that country should always be positively linked to its stock market movements. On the other hand, an increasing import dependence of country i on j (and vice versa) may entail positive stock market co-movements whereas a decreasing dependence may generate a negative effect. Indeed, when the economy of the importing country performs well, this country is likely to 12 import more from its partner thus boosting the latter’s economic performance as well. Hence, larger import dependence between two countries should be positively associated with greater co-movements between their stock markets. A reduction in the import dependence may boost the ability of exporting firms in the less dependent importing country to compete on the global market with the exporting firms of its partner country, thus driving their stock markets apart. Hence, the degree of relative import dependence may have either a positive or a negative effect on stock markets integration. The macroeconomic factors included, inflation rate differential, real interest rate differential and GDP annual growth rate differential, are expected to be negatively related to the co-movements in a stock markets’ pair. Indeed, the larger these differences become, the larger the divergence between the economies of the two countries and hence the less their stock markets will be influenced by each other. The third group of variables includes indicators of the stage of stock markets’ development, such as stock markets size differential and relative size. More specifically, the stock market capitalization of a country may be a measure of the ease or difficulty, in terms of liquidity and costs, of trading on that stock market. While a large difference in market size for a pair of countries may determine less co-movements between their respective stock markets, the relative size of the two markets in the pair have opposite effects. Last, a time trend is included in the regression to encompass the possibility that stock market inter-dependence has increased over time, due to the advanced communications technology, the eased flow of information, trade and capital across borders and the increasing cross-listing of stocks and mergers between stock markets of different countries. As a preliminary test of poolability, we found the applicability of pooled regression model using the following, (Kunst, 2009): H01: yit= α + βXit + vit………………………………………………………………………………………………………………….(6) H11: yit= α + βXit + µi + vit…………………………………………………………………………………………………………(7) Or H01: µi=0 i=1995, 1996,……….2009 The test Statistic is 13 F= ……………………………………………………..(8) Follows F distribution with (T-1), (N-1)(T-K) df SSR = Residuals sum squares under the null hypothesis SSU= Residuals sum squares under the alternative hypothesis T= Number of years, N= Number of observations, K= Number of parameters=10 F= Our regression analysis extends the works of Braker et al. (1999) and of Johnson and Soenen (2002; 2003) in one fundamental way, which is the large span of countries included in our analysis. Whereas the number of explanatory variables considered is on average the same, no previous study has incorporated as many as twenty countries. In addition, the regression model presented above has been estimated for groups of markets in the same geographical area as well as by differentiating markets according to the most distinctive characteristic, which is their level of development. 3.3 Data Sample and Summary Statistics The 20 countries included in the data sample are specified in Table 2. Out of these 20 countries, 10 are used in the analysis of stock market integration. These are referred as the base country group, which consists of five developed [US, UK, Japan, Germany and France] and five emerging [Brazil, Russia, India, China and South Africa] countries. The sample covers a 16-year period from 1995-2010. Daily stock returns are calculated as the log change in the daily index closing price as follows: rz P ( 100 t ln z,t /P z,t 1) where z = market i or j, and Pz,t represent the closing price of the markets on day t. The daily MSCI stock index time-series expressed in US dollars have been extracted from the Thomson Financial DataStream. The pooled cross-country time-series regression uses yearly data, also 14 extracted from Thomson Financial DataStream, consisting of cross-sections of country-pair observations covering a 15-year period from 1995-2009 (2010 has been excluded due to nonavailability of dataset). Table 2. Description of data sample and price indices Table 3, provides descriptive statistics for the daily stock index returns for the 20 sample countries, where the test of normality is rejected for all return series. Table 4, presents the correlation coefficient of returns. The highest correlation coefficients are mostly found between developed markets, especially between European countries. 15 Table 3: Summary statistics on daily index returns (%) 16 Table 4: Correlation coefficient on daily index returns 17 4. Empirical Results 4.1 Stock market integration analysis: Geweke (1982) Measures of Feedback The result statistics of the annual Geweke measures of feedback (GMF), contemporaneous and two-way unidirectional feedback, estimated with regression [1] – [4] during the sample period 1995-2010 between the base countries – United States, United Kingdom, Japan, Germany, France, Brazil, Russia, India, China and South Africa – vis-à-vis the total country sample (see section 3.3) are reported in Appendix [A] and summarized in Table [5]. The corresponding results for the base countries are illustrated in Figures [1] – [3], which summarizes the average annual GMF across time as well as presenting a marketwise differentiation – total country sample average, developed country average and emerging country average. The contemporaneous feedback measures results, summarized in Table [5]: Geweke 1, report high percentages [94% - 99%] of significance present across the country sample. The highest average country-pairs contemporaneous feedback measures is found in France, Germany and the United Kingdom, with each exceeding 100, whereas India’s average is the lowest one at just over 40, and the remaining eight countries fall in the range of 49-82. The year-on-year contemporaneous measure of feedback [See Appendix A for selected countries] clearly indicate that stock market integration have intensified, where larger measures denote greater contemporaneous relationship between stock return patterns from country pairs. The trend towards a global stock market landscape that takes into account information flows from other markets has clearly gain momentum the past decade. Figure [1] reflects this evolution of stock market integration during the 16-year [1995 – 2010] sample period. It illustrates how stock markets have been witnessing stronger co-movement with time. The estimated measures indicate significant inter-market relationship across the base country group and the total country sample. Although the overall results are highly significant across countries, the developed markets seem to be more extraordinary affected by each other. As seen from Figure [1], the average contemporaneous feedback measures for developed markets consistently exceed the average contemporaneous feedback measures for emerging markets, in particular for UK, Germany and France. 18 Table 5. Summary of Geweke Measure of Feedback (GMF) According to Johnson & Soenen (2009), this greater extent of stock market integration may be attributed to the presence of more favorable economic and political climate towards business in developed markets. Nonetheless, from 2005 and onwards, emerging markets, in particular Brazil, Russia, China and South Africa, have enjoyed a tightening of co-movement. This greater extent of co-movement appears to be rightful considering the increased importance of these countries’ economies. One might argue that emerging markets have become more sophisticated and efficient with time. Furthermore, the notion that financial crises periods change the co-moving behaviors of stock markets seems to be also present in the results. A further investigation of today’s global financial crisis (2007-) and previous economic meltdowns, e.g. Asian crisis (1997-98) and the dot-com bubble (2000-02), are to a certain degree reflected by the feedback measures, which are apparent as upward peaks during the climax period of the crisis, followed by a stabilizing or a slight plunge in the measures. However, crisis periods seem to foster a new and higher equilibrium level of co-movement, evidence that are in line with previous studies, e.g. Liu et al. (1998); Longin and Solnik (1995); and Arshanapalli et al. (1995). 19 Figure 1. Geweke 1 – Contemporaneous feedback measures Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each country pair. 20 The average unidirectional feedback measures, summarized in Table [5]: Geweke 2, from each base country to all others are considerably lower than the contemporaneous feedback levels, with all of them being within the range [5.2–19.7]. However, a fairly high percentage of the year-by-year unidirectional feedback measures from each country to the others are significant at the 5% level, with the minimum being 6% (for Japan) and the maximum being 65% (for Brazil) and the rest are within 15% - 50%. Surprisingly, it appears that there is more often than occasionally a delay with which these stock markets fully incorporate information from other markets. The United States, Brazil and Russia appears to have higher influence compared to other markets, since the 50%, 65% and 40% significance of the annual unidirectional feedback measures, respectively, is fairly higher. However, the other base markets, besides Japan and China, show also a relatively high percentage of significance. Figure [2] illustrates the unidirectional feedback measures from the ten base countries to the total country sample. As mentioned above, interesting features are particularly present in US, Brazil and Russia. Information flows from these markets in particular are demonstrated to be significant across days. Additionally, the unidirectional feedback measures variation across time appears to increase substantially across periods of financial meltdowns. The uncertainty arising from crises, shown by an alteration in volatility, is clearly reflected by the unidirectional feedback measures, which illustrate how markets continue to exhibit co-movement across days. For Russia, this is clearly illustrated by the peak in the feedback measure, which represents the “Ruble crisis” that hit the country in 1998, triggered by the Asian crisis that erupted one year before. Furthermore, the booming economy of Brazil reveals further how unidirectional feedback has intensified over the sample period, especially during the years prior to the financial crisis of 2007. The nature of crises and market uncertainty appears to extend periods of co-movement between country pairs, which are more pronounced during financial crises and/or booming years. The results of financial crises or booming economies seem to add complexity in how efficient markets are able to incorporate or transmit information flows. However, identifying a clear time trend, as for the contemporaneous feedback measures, the unidirectional appears to fluctuate more around crisis periods, but the increase does not persist with time. 21 Figure 2. Geweke 2 – Unidirectional Measure of Feedback (Base country Others) Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each country pair. 22 The average unidirectional feedback measures in the opposite direction, summarized in Table [5]: Geweke 3, from all other markets to each base markets, reveals additional country-specific differences. Besides reporting relatively lower estimates than the contemporaneous feedback levels, the number of significances is slightly lower than the previous unidirectional feedback measure. The range for the mean values is between [23.03-3.68], where Japan, in this case, have the highest percentage of significant estimates, 56%, followed by China [38%]. In contrast, the estimates for US, Brazil and Russia, only 19%, 4% and 11%, respectively, are significant. Hence, these patterns imply that some markets may have greater tendency of leading other markets, whereas the opposite is true for others. This seems to be the cases for the latter mention countries, whereas for Japan and China there is a higher tendency that they are being lead by others. The leader-follower relationship is subtler and less pronounced for UK, Germany, France, India and South Africa. Although a good number of significant estimates are reported for the two-way unidirectional feedback measures, the differences for these countries are less striking. In Figure [3], it is clear that Japan, as of 2007, has been affected to a higher degree by the delayed influence of the stock markets of other countries. Moreover, South Africa was also affected in 1998 by what seems to have been spillover effect from the Asian crisis that started in 1997, which is illustrated by the spike in the average unidirectional feedback measures from emerging countries. 23 Figure 3. Geweke 3 – Unidirectional Measure of Feedback (Others Base Country) Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each country pair. 24 Moreover, the preliminary analysis of F test statistic of pool ability is significant at 1% level as the tabulated F value with (14, 945) df is 2.10. So the null hypothesis is rejected, which supports the use of pooled regression model with the dataset. 4.2 Pooled Regression Analysis The empirical results from the pooled regression analysis over the 15-year period 1995-2009 are presented in Table 5.a for all 190-country pairs, for 45 pairs of developed countries and 45 pairs of emerging markets. The separation between developed and emerging countries finds its rationale in the fact that the different level of development may entail economic, financial, political and regulatory conditions that are distinctive and typical for each of the two groups but that are not directly measurable and hence could not be included in the regression as explanatory variables. The pooled cross-pair time-series regression has also been estimated for group of countries located in the same geographical zone. The rationale lies in the fact that stock markets which have over-lapping trading hours, are more likely to systematically co-move with each other on the same day than with markets in distant regions. Three regional areas have been defined5: Europe including 21 pairs, Asia with 28 pairs and the Americas consisting of 10 pairs. Table 5.b presents pooled regression results for these three regions. Two models have been estimated: - Model 1 with all explanatory variables. - Model 2 with all explanatory variables, except real interest rate differential, which proved to be insignificant in the univariate regression results. The results in Table 5.a and 5.b show that goodness-of-fit statistics, the adjusted R2 and the F-statistic, indicate that the explanatory variables included in model 1 explain a significant portion of stock markets co-movement on the same day. It should be noted that, for model 2, where the insignificant variable Real interest rate differential is removed, these measures 5 Europe: France, Germany, Italy, Sweden, UK, Russia, South Africa. Asia: Australia, China, Japan, Hong Kong, Malaysia, India, Indonesia, Korea. Americas: Argentina, Brazil, Chile, Canada and USA. 25 of goodness of fit always increase; implying that model 2 better fits the underlying data. Considering the all country-pairs regression, model 2 is able to explain almost 32 percent of the variation in the contemporaneous Geweke feedback measure. When comparing this measure between developed and emerging markets, it appears clear that the economic integration among developed markets, as represented by the explanatory variables as well as by intangible characteristics proper of this group, explains almost 50 percent of their stock markets’ integration on the same day. The same is not valid for emerging markets, for which only 30 percent of their same-day financial markets co-movement is explained by economic integration. As suggested by Johnson & Soenen (2009), this greater extent of developed countries’ stock market integration, apart from economic integration as proved by regression results, may also be attributed to the presence of a more favorable economic and political climate towards business in these countries compared to the emerging ones. Table 5.a - Results of the pooled regressions on contemporaneous Geweke measures Expected sign ALL COUNTRIES DEVELOPED + Model 1 -0.120 3.681 Model 2 -15.884 *** -48.065 Model 1 1.397 -182.458 *** + 30.563 106.214 728.302 *** 1546.485 *** Intercept Xi Xj Mi ? 139.015 *** 157.836 *** Mj Size log(MV) Infllation GDP growth Real interest T # of obs ? ? + 226.081 -19.560 3.736 0.076 1.042 -0.120 6.600 2450 384.402 -45.226 4.966 0.024 1.730 Adjusted R2 F-statistic *** *** *** *** *** *** *** *** 8.812 *** 2774 29.09% 101.456 *** 31.69% 143.950 *** 56.058 366.240 127.165 10.913 -0.805 -1.552 -1.059 9.080 538 Model 2 -38.485 *** -433.392 *** 203.865 ** *** ** 209.265 108.828 ** 6.663 -1.915 -0.548 EMERGING Model 1 -12.131 ** 135.516 Model 2 -9.012 168.748 158.823 173.553 26.949 *** 13.913 *** 666 218.517 582.777 *** 5.542 ** -0.077 0.444 0.106 5.919 *** 632 35.90% 31.072 *** 49.74% 74.110 *** 29.62% 27.554 *** 3.207 206.651 563.765 *** 4.435 -0.029 0.233 29.88% 31.639 *** 5.724 *** 648 ** Significance at the 0.05 level. *** Significance at the 0.01 level. When considering the three regional blocks, the adjusted R2 for model 2 is highest in Europe with 70 percent of contemporaneous co-movement explained by economic integration. This result is not surprising if one considers that European countries, excluding Russia and South Africa, are part of an economic, political and monetary union. In the Asiatic region, economic factors can explain almost 51 percent of the evolution of the Geweke feedback measure over 26 time whereas in the Americas only 32 percent of the variation in the contemporaneous relationships over the 15-year sample is explained by economical integration. Table 5.b - Results of the pooled regressions on contemporaneous Geweke measures Expected sign EUROPE Intercept Xi + Model 1 Model 2 -28.251 -112.982 *** 3211.827 *** 3704.471 *** Xj + -193.390 Mi ? Mj Size log(MV) Inflation GDP growth Real interest T # of obs ? ? + Adjusted R2 F-statistic -105.938 -1968.281 *** -1728.129 *** 384.582 882.419 ** 18.830 -0.078 5.740 ** 0.400 16.257 *** 214 289.103 604.941 -2.980 0.289 10.909 *** 52.53% 24.575 *** ASIA Model 1 -12.414 ** 507.601 *** AMERICAS Model 2 -12.977 ** 518.355 *** Model 1 40.778 ** 26.099 Model 2 48.495 *** 59.441 98.431 107.607 274.763 249.975 -86.261 -105.775 -23.015 -33.097 67.810 8.522 ** -0.483 1.186 ** 25.013 *** 297 -40.099 65.630 8.961 *** -0.773 1.192 ** -0.639 8.117 *** 413 8.106 *** 413 -143.147 11.670 8.115 0.577 -1.321 -0.100 7.046 *** 142 69.82% 77.103 *** 50.81% 43.550 *** 50.75% 48.178 *** 30.40% 7.157 *** -33.315 -189.185 68.012 11.843 ** 0.116 -1.731 6.749 *** 150 31.88% 8.749 *** ** Significance at the 0.05 level. *** Significance at the 0.01 level. The variables included in the pooled regressions are always jointly significant at the 1 percent level and, on average, four of the ten explanatory variables enter into model 2 at 5 percent level of significance. More specifically, one or several measures of bilateral trade relationships are able to significantly influence stock market integration over time in the developed markets’ group, in the European and Asiatic regions as well as in the all countries’ sample. This finding does not conflict with the empirical results provided by Braker et al. (1999) nor with those presented by Johnson and Soenen (2002; 2003). In these studies, indicators of bilateral trade are found to be significantly associated with the evolution of stock market integration over time. The interesting result that inflation rate differential and real interest rate differential are never statistically significant in influencing the variation of the contemporaneous Geweke measures is also in accordance with these studies, excluding Johnson and Soenen (2002) that found greater differential inflation and differential real interest to be significant in reducing the co-movement among Asian markets. When the measures of financial development are considered, results are less consistent with those from other studies. Size differential is found 27 to be statistically significant in the all countries’ sample as well as in the developed and emerging markets’ groups, whereas it is not in the regional blocks. Bracker et al. (1999) show that, in a group of developed markets, size differential is statistically significant in explaining the degree of contemporaneous co-movement. However, in the study conducted by Bracker et al. (1999), a greater size differential negatively affects stock markets’ interdependence, as expected, whereas in our study this effect is positive. The relative size indicator (natural logarithm of the variable MV, as described in table 1) is positively associated with greater contemporaneous co-movement in the all countries’ sample, in the Pacific region and in the Americas. Conversely, this indicator is negatively related to stock markets’ integration in the American block according to Johnson and Soenen (2003) and it is not significant among Asian countries according to Johnson and Soenen (2002). Finally, all regions and all groups of countries always exhibit a significant trend towards increasing same-day co-movement over time throughout the 15-year sample, as also empirically demonstrated by other studies. 5. Conclusions This study examines the degree of cross-country returns co-movement between the stock markets of five developed [US, UK, Japan, Germany and France] and five emerging [Brazil, Russia, India, China and South Africa] countries, vis-à-vis a total country sample composed by 20 countries. The Geweke (1982) Measure of Feedback methodology along with a set of pooled cross-country time-series regressions is used to identify and explain the changes in stock market integration. The general findings for the Geweke contemporaneous feedback measures provide supportive evidence of increased stock market integration. A reasonably clear time trend is identified, where the extent of contemporaneous co-movement across markets has intensified over time, especially for emerging countries, which consequently suggests that greater market efficiency is being fostered at the international level. On the other hand, the results of the Geweke unidirectional feedback measures indicate a tendency that some markets are more likely to lead other markets than vice versa. However, there is a less distinctive time trend in the movements of the annual two-ways unidirectional feedback measures, suggesting that leader-follower relationships are likely to change over time periods. These alterations 28 might be due to possible changes in a country’s economy and market conditions, but also the stability of global markets. Nevertheless, the highly sophisticated market of the US and the emerging markets of Brazil and Russia appear to affect other rather than be affected. Similar findings have been reported for the US by Eun and Shim (1991), Bracker et al. (1999) and Johnson and Soenen (2003). However, the distinction with previous studies is that not only is the contemporaneous measures larger on average, but higher significance levels are reported for the unidirectional measures of feedback, suggesting that cross-market adjustment persist over time more often than occasionally for this study’s time period 1995-2010. Our pooled cross-pair time-series regression analysis has shown that countries’ economic integration, as measured by the explanatory variables included in model 2, can explain almost 32 percent of the variation in the contemporaneous Geweke feedback measure on a global scale over the 15year period, 1995-2009. This explanatory power becomes stronger for the group of developed markets (49.74 percent) and for countries that are part of the European economic and political union (69.82 percent). The results also point out several variables as significantly associated with the evolution of stock markets integration over time. 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