The study of stock market integration has been renewed with strong attention from the 2008 global financial crisis. This study examines the stock return comovements between each European Emerging markets (EEM) with the largest financial markets including US (S & P 500), UK (FTS100), German (DAX100), and France (CAC40), respectively. The European Emerging markets include Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovenia. The correlation between co-movements between each European emerging market with US, UK, Germany and France in line with economic integration and institutions in the period of 2002-2015 are presented. Such analysis offers an opportunity to investigate the economic and institutional integration of emerging countries in a European context (with US market as an indicator of a non-European environment).
In order to conduct risk hedging through global diversification in portfolio management, financial investors have to understand co-movements of stock markets and the sensitivity of the markets to exogenous shocks [
In fact, the linkages among international stock markets have been studied in a considerable number of works both in literature and empirical investigations [
Some studies, for instance [
Therefore, this study focuses on a specific environment: the European emerging markets to provide new empirical evidences about the role of institution in the stock return co-movements. In particular, this study examines whether adding the associations of the institutional quality indicators with the economic integration (including trade openness and capital flows) can explain for stock market integration.
This article offers three contributions: First, this paper offers an empirical study on the concept of European integration by examining the impact of institutions on stock return co-movements; Second, our analysis has significant contribution to the literature on the field of the interactions between institution and macroeconomic factors on financial integration, especially about the associations between institutions and European integration including trade openness, inward Foreign Direct Investment (FDI), inward Foreign Portfolio Investment (FPI) have significant impacts on stock return co-movements; Third, this study has significant contributions to the practice by the implications for the international portfolio diversification. The empirical findings imply that the investment into emerging markets with better institutional quality in along with higher trade openness and inward FDI is suggested for better diversification.
The paper is organized as following manner: Section 2 summarizes the literature of stock return co-movements; Section 3 presents the methodology and data; Section 4 provides results and discussions; And the final section gets some remarking conclusions.
This section presents the literature of stock return co-movements and its determinants, where the role of the economic integration is highlighted. In addition, we present our arguments about the impacts of institution and its associations with economic integration on stock return co-movements. Due to the deregulation, globalization and advances in information technology in communications and trading systems in recent decades, the stock return co-movement (or stock market integration) becomes a central concept to study in international finance and economics [
Even though the financial literature and empirical findings emphasize on the interaction among international financial markets, the empirical results are mixed and conflicting [
On the other hand, some empirical studies find that stock markets are segmented. For example, Roca [
Several studies on European markets showed that economic fundamentals generally play a significant role in the macroeconomic integration of emerging markets in Europe [
One of a study with a broad sample in recent year, Dorodnykh [
The institution is defined as the rules of the game in a society [
In the macroeconomic definition, stock markets are integrated if events in one market have impacts felt in other stock markets [
Regarding the connection between integration theory with the stock market integration theory, we can expect a positive impact of the better institution on stock market co-movements as following reasons: First, the better institutional quality decreases transaction cost and other barriers between stock markets, which induce better conditions for investors in buy or sell in both domestic and foreign stock markets; Second, the better institutional quality increases the efficiency of stock market due to lower asymmetric information problem, it, in turn, induces faster and more efficient transmission of events in one country to other country; Third, the better institution decreases risk in domestic market due to lower asymmetric information problem and transaction cost, which then decreases the risk premium between domestic stock market and developed stock market; hence, the price of stock should be in same direction.
In addition to the direct impacts, the institution is argued with the associations with other macroeconomic determinants on stock market integration of emerging countries, especially with the economic integration, which have increasing scales and roles in emerging markets in recent decades [
The investment is one of the main engine for economic growth thus the inward FDI provides more power for economic growth at host countries by additional capital and the technology spill over [
However, the increasing of direct investment into a country in along with the improvement in institutional quality of host country may reduce the integration of domestic stock market with international market. The better institution stimulates inward FDI going to host countries higher and more stable [
Generally speaking, the financial integration between European stock markets have been investigated through the lens of time series analyses integrating macroeconomic such as oil prices, interest rate or inflation. Interestingly, few studies explicitly associated financial integration with a combination of economic parameters such as FDI, FPI, trade openness and social indicators (institutional quality). However, these factors actually play a significant role in the financial integration especially in a European context. This article aims at filling this gap in the literature by providing an empirical analysis of the influence of these factors on European financial integration.
This article aims at examining the impact of institutional quality and some macroeconomic elements (FDI, FPI, trade openness) on the stock return co-movements between 9 European emerging markets with the largest European markets and the US one. The institutional quality will be quantified thanks to the World governance indicators dataset (World Bank) that has provided annual series since 2002. We also collect annual data of foreign direct investment, foreign portfolio investment, trade openness, stock market capitalization, stock market turnover, inflation, interest rate of 9 European emerging markets1 in the period of 2002-2015 to control for macroeconomic determinants of stock return co-movements.
This study examines the determinants of stock return co-movements between emerging markets with largest stock markets by examining the relationship between stock correlations and the economic factors including FPI, FPI, Trade openness, and the institutions. In which, we use a unique data of daily return correlations between each index in 9 emerging market indices with FTS100; DAX100; CAC40 and S & P 500 indexes in the period from 1/Jan/2002 to 31/Dec/2015, which calculated yearly by ourselves to proxy for the stock return co-movements (
In detail, we calculate the correlation between each emerging market with FTS100, DAX100, CAC40 in a same date for both indices; while we use the index of each emerging market in day t with the S & P 500 in day t-1 since the US market is seen as the leading market. While, other variables are collected from World Development Indicators and Worldwide Governance Indicators (World Bank) (
Country | Index name | Period |
---|---|---|
US | S & P 500 | Jan/2/2002-Dec/31/2015 |
Bulgaria | SOFIX Index | Jan/2/2002-Dec/31/2015 |
Czech Rep. | Prague Stock Exchange Index | Jan/2/2002-Dec/31/2015 |
Estonia | OMX Tallinn Index | Jan/2/2002-Dec/31/2015 |
Hungary | Budapest Stock Exchange Budapest Stock Index | Jan/2/2002-Dec/31/2015 |
Latvia | OMX Riga Index | Jan/2/2002-Dec/31/2015 |
Lithuania | OMX Vilnius Index | Jan/2/2002-Dec/31/2015 |
Poland | Warsaw Stock Exchange WIG Total Return Index | Jan/2/2002-Dec/31/2015 |
Romania | Bucharest Stock Exchange Trading Index | Jan/2/2002-Dec/31/2015 |
Slovenia | Ljubljana Stock Exchange Slovenian Blue-Chip SBITOP Index | Apr/2/2003-Dec/31/2015 |
UK | FTS100 | Jan/2/2002-Dec/31/2015 |
Germany | DAX100 | Jan/2/2002-Dec/31/2015 |
France | CAC40 | Jan/2/2002-Dec/31/2015 |
Variable | Definitions | Calculation | Sources |
---|---|---|---|
Corr | The stock return correlation | Annual correlation of daily returns between each emerging market with S & P 500 | Author’s calculation from data of Bloomberg |
Trade | The trade openness (%) | The ratio of total trade value to GDP | World Development Indicators dataset |
Fdi | The flow of direct investment (%) | The ratio of net foreign direct investment inflow/outflow to GDP | World Development Indicators dataset |
Fpi | The flow of portfolio investment (%) | The ratio of net foreign portfolio investment inflow/outflow to GDP | World Development Indicators dataset |
Bilateral trade | The bilateral trade between each emerging market with US, UK, Germany and France | The ratio to GDP or logarithm of bilateral trade | Trade Map |
Control of corruption, Government effectiveness, Rule of law, Regulatory quality, Politic stability, Voice and Accountability | WGI |
The correlation between each emerging market with US, UK, Germany, and France then is analyzed in along with the economic integration (trade, FDI, and FPI) and institutions.
In this study, we calculate the pair-correlation between each European Emerging market (EEM) with the large markets including US, UK, German, and France, respectively. The European Emerging markets include Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovenia. At the first sight, EEMs have positive correlations with the large markets, especially all markets have positive correlations in the period of the 2008 global financial crisis (GFC). Moreover, the value of correlations is increased in the GFC with a more concentration in the whole sample in 2008 (
The correlations are going to decrease slightly in the period of post-GFC, but the values are still higher than the pre-GFC period.
UK, Germany, and France have the highest values of correlations with the US, whereas the German is the higher correlation one (average correlation is 0.6291), while Poland is the market with the highest correlation in one single year (0.7722). Among the EEM, the Czech Republic, Estonia, Hungary, and Poland are the highest correlated markets with all US, UK, German, and France. Meanwhile, the Bulgaria, Latvia, and Slovenia are among the lowest correlated markets with all these large markets (
Country | Obs. | Mean | Std. dev. | Min | Max | |
---|---|---|---|---|---|---|
Correlation with US markets | ||||||
Bulgaria | 14 | 0.1368 | 0.2164 | −0.1065 | 0.5151 | |
Czech Republic | 14 | 0.3653 | 0.1008 | 0.2128 | 0.5554 | |
Estonia | 14 | 0.3114 | 0.1196 | 0.0908 | 0.5135 | |
Hungary | 14 | 0.2883 | 0.1101 | 0.1498 | 0.5156 | |
Latvia | 14 | 0.1582 | 0.1487 | −0.0341 | 0.3993 | |
Lithuania | 14 | 0.2545 | 0.1369 | 0.0272 | 0.4708 | |
Poland | 14 | 0.3274 | 0.0798 | 0.2205 | 0.5365 | |
Romania | 14 | 0.2382 | 0.2139 | −0.1235 | 0.5606 | |
Slovenia | 14 | 0.2697 | 0.1736 | 0.0667 | 0.6210 | |
France | 14 | 0.6027 | 0.1062 | 0.4143 | 0.7572 | |
Germany | 14 | 0.6291 | 0.1064 | 0.3637 | 0.7559 | |
UK | 14 | 0.5686 | 0.1089 | 0.3297 | 0.7413 | |
Correlation with Germany market | ||||||
Bulgaria | 14 | 0.1093 | 0.1539 | −0.1056 | 0.3911 | |
Czech Republic | 14 | 0.5246 | 0.1486 | 0.2743 | 0.7547 | |
Estonia | 14 | 0.5246 | 0.1486 | 0.2743 | 0.7547 | |
Hungary | 14 | 0.4663 | 0.1562 | 0.1984 | 0.6908 | |
Latvia | 14 | 0.1330 | 0.1258 | −0.0379 | 0.3969 | |
Lithuania | 14 | 0.2149 | 0.1607 | 0.0078 | 0.5201 | |
Poland | 14 | 0.5435 | 0.1516 | 0.3586 | 0.7722 | |
Romania | 14 | 0.2712 | 0.2219 | −0.0856 | 0.6213 | |
Slovenia | 14 | 0.1642 | 0.1287 | −0.0333 | 0.4962 | |
Correlations with UK market | ||||||
Bulgaria | 14 | 0.0966 | 0.1528 | −0.0989 | 0.4053 | |
Czech Republic | 14 | 0.5175 | 0.1397 | 0.2412 | 0.7614 | |
Estonia | 14 | 0.5154 | 0.1397 | 0.2412 | 0.7614 | |
Hungary | 14 | 0.4649 | 0.1487 | 0.2886 | 0.7519 | |
Latvia | 14 | 0.1334 | 0.1261 | 0.0151 | 0.4542 | |
Lithuania | 14 | 0.2271 | 0.1419 | 0.0384 | 0.5322 | |
Poland | 14 | 0.5240 | 0.1691 | 0.2519 | 0.7687 | |
Romania | 14 | 0.2710 | 0.2196 | −0.0263 | 0.6589 | |
Slovenia | 14 | 0.1574 | 0.1424 | −0.0748 | 0.5489 | |
Bulgaria | 14 | 0.1200 | 0.1527 | −0.0984 | 0.4084 |
---|---|---|---|---|---|
Czech Republic | 14 | 0.5438 | 0.1437 | 0.3166 | 0.7391 |
Estonia | 14 | 0.5438 | 0.1437 | 0.3166 | 0.7391 |
Hungary | 14 | 0.4835 | 0.1595 | 0.2493 | 0.7515 |
Latvia | 14 | 0.1264 | 0.1348 | −0.0837 | 0.4521 |
Lithuania | 14 | 0.2321 | 0.1554 | 0.0150 | 0.5417 |
Poland | 14 | 0.5434 | 0.1430 | 0.3458 | 0.7519 |
Romania | 14 | 0.2747 | 0.2267 | −0.0641 | 0.6370 |
Slovenia | 14 | 0.1735 | 0.1408 | −0.0313 | 0.5672 |
Dividing into periods of 2002-2007, 2008-2012, and 2013-2015, we find that the average correlations between EEMs with all large European markets are higher than the correlations with the US market except the Bulgaria, Latvia, and Slovenia in the period of pre-GFC. This story is the same for the subsequent periods. Reminding that, these above markets have the average value of correlation lowers than all other markets, which means that the stronger integration of each stock market with the large market, the higher dependence of them into the near large market. Meanwhile, the lower integrated one depends much more in the US market such as Bulgaria, Latvia, or Slovenia.
The data (
Country | Periods | Average correlation with | |||
---|---|---|---|---|---|
US | German | UK | France | ||
Bulgaria | 2002-2007 | 0.0166 | −0.0090 | −0.0087 | 0.0011 |
2008-2012 | 0.3172 | 0.2622 | 0.2384 | 0.2662 | |
2013-2015 | 0.0764 | 0.0910 | 0.0710 | 0.1142 | |
Czech Republic | 2002-2007 | 0.3045 | 0.3994 | 0.4303 | 0.4256 |
2008-2012 | 0.4550 | 0.6431 | 0.6168 | 0.6532 | |
2013-2015 | 0.3373 | 0.5776 | 0.5263 | 0.5981 | |
Estonia | 2002-2007 | 0.2503 | 0.3994 | 0.4303 | 0.4256 |
2008-2012 | 0.4089 | 0.6431 | 0.6108 | 0.6532 | |
2013-2015 | 0.2712 | 0.5776 | 0.5263 | 0.5981 | |
Hungary | 2002-2007 | 0.3178 | 0.3723 | 0.3989 | 0.3949 |
2008-2012 | 0.2886 | 0.6356 | 0.6179 | 0.6593 | |
2013-2015 | 0.2290 | 0.3720 | 0.3420 | 0.3678 | |
Latvia | 2002-2007 | 0.0831 | 0.0651 | 0.0753 | 0.0676 |
2008-2012 | 0.2280 | 0.2227 | 0.2186 | 0.2276 | |
2013-2015 | 0.1923 | 0.1190 | 0.1075 | 0.0753 | |
Lithuania | 2002-2007 | 0.1697 | 0.0889 | 0.1049 | 0.1076 |
2008-2012 | 0.3648 | 0.3986 | 0.3772 | 0.4060 | |
2013-2015 | 0.2401 | 0.1609 | 0.2210 | 0.1916 | |
Poland | 2002-2007 | 0.2741 | 0.4273 | 0.4318 | 0.4484 |
2008-2012 | 0.3994 | 0.7134 | 0.6878 | 0.6963 | |
2013-2015 | 0.3140 | 0.4926 | 0.4354 | 0.4785 | |
Romania | 2002-2007 | 0.0477 | 0.0694 | 0.0645 | 0.0618 |
2008-2012 | 0.4280 | 0.5192 | 0.5097 | 0.5214 | |
2013-2015 | 0.3028 | 0.2617 | 0.2859 | 0.2896 | |
Slovenia | 2002-2007 | 0.1470 | 0.0918 | 0.0754 | 0.0933 |
2008-2012 | 0.3673 | 0.2379 | 0.2322 | 0.2535 | |
2013-2015 | 0.3117 | 0.1622 | 0.1693 | 0.1739 |
but still higher than the period 2002-2007. This observation shows that GFC had a global impact on countries even those who were not the less correlated with the largest markets like Bulgaria, Latvia and Slovenia. In addition to this, it is worth mentioning that this observation has been prolonged in time.
The relationships between stock market correlations and trade openness are tested by the ratio of total trade value to GDP. The empirical trend shows that there is slightly positive relationship between stock market correlation and the openness in trade activities. Interesting, these relationships are stronger in the correlations of EEMs with the German, UK, and France market since the fitted values line is stepper. These observations confirm the existence of a relative financial integration in a European context (
Furthermore, the correlations between EEMs with the large markets are more concentrated when the trade openness is in the range from 100% of GDP to nearly 150% GDP. Based on these results, we decided to investigate further the situation by focusing on the bilateral trade between each EEMs with each large market, respectively (
Notably, the correlations between each EEMs in the relationship with the bilateral trade are more concentrated and stepper in line. This suggests that the more dependent in bilateral trade between two markets, the higher integration between them. In fact, the higher values of bilateral trade between two markets, the more concentration in the correlations in the stock markets.
In the relationship with the capital flow, we first graph the correlation and the FDI inflow in both current and lag one. There is interesting finding. We find no significant relationship between the correlations between two stock markets with the current or lag FDI inflow. However, there is a slightly positive relationship between stock market correlation and lag of FDI inflow suggesting that the impacts of FDI inflow on the stock market co-movement are the long-term effects as shown on the following graphs (
We observe the same trend for FDI outflow in relationship with the co-movement correlations between EEMs with the large market. Furthermore, the graph shows less related between two variables than the case of FDI inflows, as suggested on the following graphs (
Our analysis also revealed that the higher net FPI flow into a country, their stock market is seemly less dependent to the large stock market. In fact, the graph shows that the higher FPI net flow is in line with less correlation between each EEMs with the large stock markets. This point is important for policy makers since it suggested that a FPI oriented policy might increase the financial independence of emerging economies to foreign stock markets (
We observe a positive relationship between stock market co-movement and the control of corruption. This implies that the better in corruption controlling of a country, the higher co-movement of this stock market with the large stock market. This result is easily understandable since investors coming from large market (US, UK, Germany, France) are more likely to spend money in a country where corruption will not ruin their investment. In the same vein, investors are sensitive to an environment in which they feel that safer on several aspects. However, the relationship is not concrete concentration as shown hereafter (
The story is the same with the Government effectiveness. Notably, the relationship is more concentrated when the government effectiveness has the value around 0.5 to 1. This means that the country with a certain level of government
effectiveness has the same pattern in the stock co-movement with the large market (
The sensitivity of investors and the importance of stability in the economic environment are emphasized in the observations related to correlations between emerging stock markets the political stability (
Interestingly, the relationship between stock co-movement and regulatory quality is concretely strong and concentrated in the range of regulatory quality from 0.5 to 1.5. The story is same with the more concentrated in the range of 0.5 to 1 for the Rule of law and Voice and accountability as shown on the following graphs (Figures 9-12).
This paper offers an empirical study on financial integration of European emerging market by taking into account of institutional quality. Several findings can be outlined here. First, our analysis indicates that European emerging markets have higher co-movements with their European partners (UK, Germany and France) showing the existence of a relative financial integration in European Union.
A second finding of our research revealed that co-movements between European emerging markets increased during the last global financial crisis. Although this movement decreased after the crisis, the trend is still higher than the precrisis period. This result contrasts with some studies [
Our empirical analysis also emphasizes the importance of trade openness of emerging markets for the financial integration while FDI inflows appear to have a long-term effect on this integration. Our analysis on the statistical link between the institutional quality and the financial integration shows that institutions play a significant role in the cross-border investment enhancing the co-movements and the financial integration in European Union. Another important result of our study refers to the role played by the FPI net flow since this parameter might
temper the financial integration.
In the light of these results, some policy implications can be suggested for emerging economies policy makers. First of all, institutions are a key determinant in the financial European integration implying that economic/financial development goes hand in hand with a policy improving the institutional quality. Such policy also improves the economic environment favoring the trade openness and the attraction of inflow FDIs. Interestingly, our empirical study
suggests that policy makers can also use FPI to temper financial integration. This observation might have an important implication, especially during a financial crisis since a FPI policy oriented might be appropriate to decrease the dependence of emerging markets to large financial markets facing a crisis.
Canh, N.P., Thai, N.V.H. and Schinckus, C. (2018) Stock-Return Co-Movements and Institutional Quality: An Empirical Investigation of the European Emerging Markets. Theoretical Economics Letters, 8, 820-843. https://doi.org/10.4236/tel.2018.85058