This article studies the relationship between macroeconomic variables and the stock markets in the BRICS countries, as well as in South Korea, Indonesia, Turkey and Mexico, determining the systematic risk for these markets and taking into account changes in four macroeconomic variables: consumer price index, industrial production, export volumes and international reserves. These were considered explanatory variables for the principal stock market indices in each economy during the time period 05/2003 to 05/2013. The methodology included a multifactorial model, vector autoregression (VAR), the variance decomposition test and the impulse-response function. The evidence obtained points to heterogeneous behavior among the various economies, implying the presence of segmentation and a weak foundation for financial integration in the short term.

THE BRICS COUNTRIES AND THEIR POTENTIAL FOR INTEGRATION

Over the past decade, Brazil, Russia, India, China and South Africa have been key players in the global economy. Multiple analyses foresee that the international economic and political leadership of these countries will continue to grow, making them not only the largest economies on the planet by 2050, but also turning them into a strong bloc as a result of increased exchange and the convergence of interests (these countries are currently the BRICS, but it is likely that in the future, other nations will join the group). Experts believe they may become the backbone of the global economy, and, specifically, the factor that finally catalyzes the development of lagging economies (Morazan, 2012). Politically, this convergence could eventually constitute the most significant counterweight to the emerging bilateral relationship between the United States and the European Union.

Despite some differences, this group of countries shares common features, such as: large economies, territories and populations, high and persistent growth rates over the past decade and strong growth predicted for the future, although to a lesser degree than in the decade prior. In addition, these economies have already struck up some specific cooperation agreements, and recently adopted a Declaration and Action Plan at the Fifth BRICS Summit.¹

As such, the group will only become more important as other countries join the bloc. South Africa is, in fact, the newest member of the original BRIC group. Some frequently mentioned candidates include Mexico, Turkey, Indonesia and South Korea. It is therefore useful to measure the similarities and differences between these economies and the BRICS group. We may even be witnessing the birth of the new BRICS+4 bloc, made up of currently emerging economies, which we might call the emerging G-9.

This group is currently economically, politically and culturally heterogeneous and, in terms of geography, it lacks common borders that would promote the integration process.² Table 1 shows these differences, as well as the potential for economic integration. Some of the most relevant data reveals that the share of the group in the global gross domestic product (GDP) is 21.3%, just below the United States (22.7%), but higher than the Eurozone (17.2%). However, there are also significant differences within the bloc. For example, the largest economy in terms of GDP is China (9.18138 trillion dollars, or 12.4% of global GDP), while South Africa, the most recent country to join, contributes only 0.5% to global production. Likewise, as a result of differences in production and population, GDP per capita is extremely disparate, and is highest, within the bloc, in Russia, at 14,818.64, and as low as 1,504.54 in India. Finally, territory size varies widely within the group: Russia and South Africa occupy 12.6% and 0.9% of the global territory, respectively.

With that said, the new members South Korea, Indonesia, Mexico and Turkey have the potential to bolster the economic and political reach of the group. There would still be differences, but the capacity of the emerging G-9 would certainly be greater, helping the group to consolidate. These four countries would add 4.17783 trillion dollars to the group in global production, with Mexico as the principal contributor with 1.25854 trillion dollars. This would raise the share of the group in global GDP to 27%, surpassing the United States (22.7%). Similarly, these new members are key participants in their regional economies, not to mention international political players, which could help strengthen the emerging G-9, both internally, as well as for negotiations with the rest of the world.

The financial markets currently trade in derivatives whose underlying assets are the major emerging stock market indices, with combined transactions of around 10.5 billion dollars in 2012, nearly 20% of the entire derivatives market. Another extremely important project that has been confirmed is the BRICS Development Bank (BDB), which could finance joint projects for the bloc, reducing dependence on other countries and organizations through the use of a currency that is not the dollar or euro. However, despite the fact that the emerging G-9 capital markets have grown significantly over the past two decades, the gap with respect to mature and developed markets is still wide. Table 2 summarizes the principal characteristics of the emerging G-9 stock markets.

As shown in Table 2, capitalization and value of trade exchanges are a clear sign of the lack of depth in the emerging G-9. With respect to GDP, stock market capitalization is over 100% in only two countries: South Africa (159%) and Indonesia (104.5%), while it is closer to 50% for the other seven countries (Brazil, Russia, India, China, South Korea, Mexico and Turkey). Similarly, looking at the value of exchange, only in South Korea does it amount to a highly significant percentage of GDP (134%), while this ratio is extremely low in Mexico (10.4%) and Turkey (10%).

Despite the aforementioned, in light of the speed of international financial integration as a result of the growing trend towards globalization, the stock markets of the countries in this bloc could still become a key platform for the economic and financial integration of the emerging G-9. As such, the facts analyzed here make clear the need to examine the relationship between stock markets and macroeconomic variables in the emerging G-9.

Research on the BRICS countries has grown rapidly. The majority of these studies highlight the macroeconomic aspects of the group and its economic and political relationships, both within the set and with the rest of the world, as well as prospects and predictions for the future (Wilson and Purushuthaman, 2003; Jain, 2006; Cassiolato and Vitorino, 2009; Carmody, 2012; Mwase and Yoong-zhdng, 2012; Saran, Singh and Sharan, 2012). However, in economic terms, despite the fact that the integration potential of the group has been implicitly or explicitly acknowledged, the financial literature has ignored the presence of possible factors for integration. The problem is made more complex by taking into account the lack of cohesion in the group, as well as the fact that membership is still unclear. Studies related to these themes include those by Gay (2008) and Ramaprasqad and Bijana (2009).

This work aims to help elucidate the question described above, examining the relationship between macroeconomic variables and the stock markets of each country that makes up the BRICS group, as well as possible future members. The basis for this analysis resides not only in the importance of capital markets as major global economic indicators, especially in the twenty-first century, but also in the fact that this is a valuable approach to identify, based on systematic risk, the similarities and differences among the markets considered.

As such, if we look at similar variables for the BRICS countries, a similar quantification of the beta coefficients would indicate the presence of significant closeness and the potential for integration among countries that make up the bloc, while a major difference between the coefficients for systematic risk would indicate segmentation among the bloc countries, revealing little potential for integration, unless these trends are reversed with explicit integration policies and agreements.

Extending this analysis to Mexico, Indonesia, Turkey and South Korea would similarly depict the similarities or disparities within the group. Similarities would validate the importance and potential benefits (and need) of integration for these countries with the BRICS group, while the presence of segmentation would be a sign that it is unlikely that an emerging G-9 bloc would be constituted, at least for the time being. Finally, by identifying systematic risk levels for these countries, we gain valuable insight into making decisions related to building investment portfolios.

RELATED STUDIES

Many studies have examined the integration process of stock markets in different countries. The literature most closely tied to this study comes from authors such as Fuentes, Gregoire and Zurita (2005); Evans and Hanatkovska (2005); López-Herrera, Ortiz and Cabello (2007); Kazi (2009); Brugger and Ortiz (2012) and Reyez Zárate and Ortiz (2013).

Similarly, many variables have been chosen to determine the influence of macroeconomic factors on the behavior of stock markets, including: inflation rate, exchange rate, interest rate, monetary supply, investment, employment, gross domestic product – including proxy variables such as industrial production –, imports, exports and international reserves. Considering the economic structure of the countries that make up the emerging G-9 group and the availability of data, this study selected four macroeconomic variables to devise the multifactorial model, in addition to the major reference stock market indicators: changes in the consumer price index, changes in industrial production, changes in export volumes and changes in international reserves.

It is important to emphasize that the literature does not report unequivocal results with respect to the direction and signs of beta coefficients of macroeconomic variables; there is also a chance that conflicting results may be produced, even if they are statistically robust.³

In summary, the literature has reported heterogeneous results regarding the relationship between macroeconomic variables and stock market performance, and these outcomes are supported by opposing theories. Even so, there are two main effects that can explain these disparities:1) different periods of study (even for the same economy) and 2) reliability of the data, which is certainly less so in some emerging economies.⁴

VARIABLES CHOSEN AND THE DATA

In light of the economic structure of the emerging G-9 and the availability of information, this study focuses on four macroeconomic variables and the principle stock market indices in each of the countries: changes in the consumer price index, changes in industrial production, changes in export volumes and changes in international reserves, as well as the following stock market indices: Ibovespa (Brazil); RTS (Russia), BSE Sensex 30 (India), HangSeng (China), J203 – Alsi (South Africa), KOSPI 200 (Korea), IDX Composite (Indonesia), IPC (Mexico) and ISE 100 (Turkey).⁵

Both the stock market indices and the macroeconomic variables consist of monthly series for the period 05/2003 to 05/2013. The series for the macroeconomic variables were obtained from Data/World Bank, reported by the World Bank, while the stock market indices were obtained from Yahoo Finance, Invertia,⁶ and the Johannesburg stock market portal.

ECONOMETRIC MODELING

Keeping in mind that this is an empirical study, the econometric methodology began, as mentioned earlier, with a multifactorial model to evaluate the similarities and differences in the relationships between macroeconomic factors and the stock markets of the emerging G-9. The advantage of this model is that it is flexible, that is, it is not restricted by equilibrium conditions, which allows for the development of a concrete comparative framework in which it is possible to measure systematic risk in complex samples.

The measurement of systematic risk is complemented with a vector autoregression (VAR) model whose approach allows us to estimate and complement the systematic risk measurement with decomposition analysis of the variance and the impulse-response function. The foundation established by Sims (1980) is to model a general VAR without restrictions; this consists of regressing each non-lagged variable with respect to all other variables with various lags based on the following expression:

(1)

Where X_t is a vector (N x 1) corresponding to the current values of all variables included in the model as matrices (N x N), and ε_t is a white noise vector that satisfies the customary relations of orthogonality,⁷ and p is the number of lags considered. Each of the N variables of the vector autoregression can be decomposed into addends, an optimal linear predictor based on all of the variables in p periods and innovation ε_t. The identification phase involves searching for the N variables that represent the relations of the model in question, on the one hand, and on the other, choosing the number of lags to which the vector autoregression is extended (Lütkepohl, 2007). The next step is to reduce the number of lags by p and use the a priori information to reduce the number of parameters to estimate in the matrices.

This final equation considers that ε_t is the random error column vector, assuming that they are contemporaneously correlated, but not autocorrelated, with the error term, it is consistent to estimate equation by equation through least ordinary squares (LOS), because only the lagged variables of the endogenous variables are on the right side of the equation (Lütkepohl, 2007). With a re-expression, the model can be formulated as follows:

(2)

More simply, the VAR can be explained as a regression of the variable x explained by itself and some other variable. As such, the historical behavior of a specific phenomenon later influences itself.

The impulse-response analysis indicates the dynamic response of the dependent variable in the VAR system in response to shocks in the error term or innovations in all of the endogenous variables, excluding the effects of the variables that are expressly assigned as exogenous. A shock in a certain variable in period i directly affects that same variable and will be transmitted to the rest of the variables explained through a dynamic structure represented by the VAR model (Ben-Arfa, 2012). In this way, as a function of s is called the impulse-response function, which describes the response of y_it+s to an impulse in y_jt, where all the other variables of the period t or the former remained constant.

The variance decomposition test serves to describe the dynamics of the VAR system of equations and is an important complement to the impulse-response analysis, as it allows us to measure, over different time periods, the percentage of volatility of a variable in response to the shocks of the rest of the variables that make up the model (Lütkepohl, 2007). As such, the relative participation of a disturbance in y_j at time t(ε_jt) over the variability of variable y_i at the moment t + s (y _it + s ) is given, according to Arias Montoya (2006), by:

(3)

Where , represents element ij in the matrix C_s, belonging to the polynomial matrix C(L), identifying the effect of the shock in the system.

In this way, the variance decomposition provides information regarding the relative importance of each random innovation of the variables in the VAR model. As such, if a significant share of the variance of a variable is explained by the contributions of the disturbances themselves, this variable will be relatively more exogenous than the others.

EMPIRICAL RESULTS

The multifactorial model proposed for each of the economies is as follows:⁸

Where RIB_bra, RIB_rus, RIB_ind, RIB_chi, RIB_sud, RIB_kor, RIB_indo, RIB_mex and RIB_tur correspond to the series of stock yields for the markets in Brazil, Russia, India, China, South Africa, South Korea, Indonesia, Mexico and Turkey, respectively. RIPC, RPI, RRES and RX refer to the first differences of the macroeconomic variables of consumer price index, industrial production, international reserves and export volumes, respectively.

To prevent spurious results and ensure that the residuals of the model passed the correct specification tests (autocorrelation, heteroscedasticity and normality),⁹ the series were adjusted, implementing dummy variables (such as softening, trend, pulse and stationary) where applicable. Residual correction was possible for all series. Thus, in general, for all of the economies, the principle shocks in the series were due to the delayed spread of the United States financial crisis that began in 2007, as the majority of the effects were felt in the period September 2008-January 2009. The effects on each variable were of a different magnitude and appeared on different dates, which is why it was necessary to insert different dummy variables depending on the characteristics of each series. The results of the adjusted models that passed the diagnostic tests for the emerging G-9 group are presented in Table 3.

Table 3 shows that the systematic risk factors produced diverse impacts, both in terms of sign and magnitude, on each of the economies. Reserves were the factor with the greatest magnitude, and a positive sign, for five of the nine economies. In China and Mexico, given the nature of their economies, where the industrial sector, manufacturing and the maquiladora industry play a major role, industrial production was the factor that most impacted stock market movements.

On the other hand, the economies of Turkey and Brazil were similar not only in the sense that the Consumer Price Index (CPI) was the factor with the greatest impact on capital market movements in these two countries, but also because all of the macroeconomic variables had a positive impact on capital market movements. It should also be noted that besides Turkey and Brazil, the Russian and South Korean economies demonstrated similar results, not in terms of magnitude, but rather in terms of the sign of each macroeconomic variable. The relationship between export volumes and capital markets was positive in Brazil, India, China, South Korea, Indonesia and Turkey, and especially in China, with the highest beta coefficient (0.275809), consistent with the high value reported for the coefficient of international reserves. Finally, we note that the economy with the highest systematic risk factors affecting the stock market was China, followed by South Korea and then Russia, in that order.

RESULTS OF THE APPLICATION OF VAR

Initially, selection criteria were applied for an optimal number of lags (modified sequence LR statistical test, FPE: final prediction of error, AIC: Akaike information criteria, SC: Schwarz information criteria, Hannan-Quinn information criteria), choosing a number of lags considered as optimal based on the majority of criteria, verifying that the correct specification tests were approved and corroborating through an inverse roots test for the polynomial that each and every one of the VAR models in question were stable and stationary at a 95% level of significance. In this way, the correctly specified VAR models that met the conditions of stationarity are reported in the following table.

In the same way that the results for the previous tests were diverse, the number of optimal lags varied between three, two and one, for three economies each, for a total of nine economies. It took approximately three lag periods for the effects of the principal variables to permeate the capital markets of Russia, China and South Korea, which could be due, among other factors, to the low percentage of capitalization as percentage of GDP, at 43.4%, 45.2% and 44.9%, respectively.

On the other hand, the economies that reacted the most quickly to stock market movements were in India, Indonesia and Turkey, while the Brazilian, South African and Mexican economies required an intermediate timeframe of two periods to make these adjustments.

As shown in Table 5, the results obtained for the economies are rather disparate. Of the independent variables that explain the behavior of each market, the beta for inflation was the highest for four of the nine economies, which mainly reacted pursuant to what economic theory would expect, that is, negatively. Some of the positive signs presented for this factor can be explained by the lack of information available to investors. Apparently, the factor to which three stock markets in the group were most sensitive (Brazil, India and Indonesia) was industrial production. Meanwhile, the stock markets of South Korea and Turkey were more sensitive to changes in reserves and exports, respectively.

The South Korean market was the most sensitive to reserves. The majority of the results for South Korea, two out of the three lags, matched what would be theoretically expected, as they were positive. The lags with a negative sign are surely due to an over-accumulation of reserves, a poor macroeconomic symptom that discourages investors.

The capital market in Turkey reacted negatively to changes in exports, producing results contrary to what economic theory would expect.

Another factor to which stock market behavior is less sensitive with respect to changes is the stock index itself, which apparently can impact future behavior depending on market trends. As such, for some markets, the impact was negative, especially in South Korea, for which the beta result was negative for one, two and three lags.

GLOBAL ANALYSIS OF THE IMPULSE-RESPONSE TEST

Using this tool, we can obtain the response of the dependent variable to the shocks of the endogenous variable in the VAR model. In this way, a shock in a certain variable will directly affect it and be transmitted to the rest of the variables, explained by a dynamic structure. We also analyze the sign, intensity and time it takes to return to stability.

The following figures describe the impulse-response test at one year in percentage terms, with the X-axis expressing the number of months considered in an annual timeframe.

With respect to the first shock to the CPI and its relation to the stock market, in Brazil, Russia, South Korea and, to a lesser extent, South Africa, the results were very similar, as inflation initially had a negative effect on the stock market in the economies of Russia, India and South Korea, where the value of the shock reached -0.015%, and the minimum point in South Africa was -0.003%. In India, China, Indonesia and Mexico, and to a much lesser extent in Turkey, the results were similar with a positive shock in the first months, as high as 0.005% for China, 0.0015% in India, 0.004% in Indonesia, 0.003% in Mexico and 0.001% in Turkey.

The second impact describes how the stock markets reacted to industrial production, for which nearly all countries except China had similar behavior with a positive impact in the first months, with Brazil, Russia at 0.012%, India at 0.004%, South Africa at 0.002%, South Korea at 0.01%, Indonesia at 0.006%, Mexico at 0.08% and Turkey at 0.003%. In China, meanwhile, the first month was very close to zero, but a negative impact appeared in the third month with a value of -0.008%, before recovering in the long term.

The third impact refers to the behavior of stock indices with respect to reserves. Behavior was similar in China and Brazil, with the first impact positive, at 0.003% for Brazil and 0.009% for China. On the other hand, in the first two months, South Africa saw behavior very close to zero, and experienced the greatest impact in the third month at 0.003%, before falling and stabilizing in the long term. Finally, the rest of the countries displayed negative sensitivity initially. In Russia, the figure was -0.003%, in India -0.004%, in Indonesia -0.003%, in Mexico -0.007% and in Turkey -0.002%, and finally in South Korea, -0.002%, which later showed a positive shock of around 0.005%, followed by a return to equilibrium in the long term.

The fourth and final shock was the sensitivity of the stock market to exports. Brazil, China, Indonesia and Turkey initially experienced a negative response: Brazil -0.004%, China -0.01%, Indonesia -0.001% and Turkey -0.018%, although this last later experienced a positive result of 0.004%. On the contrary, the economies of Russia (0.021%), India (0.003%) and South Korea (0.006%) saw higher positive impulses. In the Mexican economy, the impulse was positive in the first few months, followed by a negative impulse, the greatest of all (-0.0035%) and finally a positive impulse of the same magnitude as the first (0.002%).

As such, this global analysis of the impulse-response function confirms that the financial integration of the emerging G-9 group is only partial, in light of the major differences in how the stock markets responded to the behavior of each of the macroeconomic variables.

GLOBAL ANALYSIS OF THE VARIANCE DECOMPOSITION
FOR THE EMERGING G-9 GROUP

The following figures show the variance decomposition of the residuals of the VAR models for the group of emerging G-9 countries, on a monthly basis, although the data is only presented for each quarter to make it easier to handle the information.

The above data reveals that the variability of stock market performance is entirely dominated by the disturbances of the market itself, as the proportion of the variance explained by this type of variation in the first period was 100% for all countries, although the magnitude slowly falls, reaching the lowest value for China (79%). The highest values for the four periods shown were found in India, Indonesia and South Africa, with values above 97%.

Although the factor that dominated through disturbances in all of the stock markets of these countries was the performance of the stock markets themselves, after this factor, countries experienced different orders of variation for each of the factors. For China and South Korea, the second-most important shock in terms of the variability of stock market performance was a disturbance in inflation, while for Russia, Turkey and South Africa, it was a disturbance in exports. In Mexico, India, Indonesia and Brazil, the second-most important shock in terms of the variation of stock market performance was a disturbance in industrial production. The third-most important factor affecting stock market performance was determined by the following disturbances: inflation for Russia, Brazil, Mexico, South Africa and Indonesia; industrial production for South Korea and Turkey; exports for China; and reserves for India.

The fourth most-important factor affecting stock market performance was disturbances in: reserves for China, South Africa, South Korea, Indonesia, Mexico and Turkey; exports for Brazil and India; and industrial production for Russia.

Finally, the factors with the least impact on the variability of the stock market through disturbance were: reserves for Brazil and Russia, inflation for Turkey and India, industrial production for China and South Africa and exports for South Korea, Indonesia and Mexico.

To summarize, we can observe that of the nine countries analyzed, the factor that most affected variations in the stock market was a disturbance in stock market performance itself. Similarly, upon examining the difference in the importance of the four macroeconomic factors in the composition of variance for each market, it can be concluded that the foundation for integration in these markets is still weak. Likewise, the evidence obtained through the variance decomposition test confirms the conclusion found previously through the various tests conducted in this study, which is that there is a high degree of segmentation among the stock markets of the economies that make up the emerging G-9 group.

CONCLUSIONS

The main objective of this work was to detect to which macroeconomic variables the systematic risks of the stock markets of the BRICS group and four potential members – South Korea, Indonesia, Mexico and Turkey, possible future participants – respond.

The evidence obtained through the multifactorial model, as well as the adjusted model (with the inclusion of some dummy variables), reveals significant differences in the magnitude and the nature of the beta parameters associated with each macroeconomic variable in each of the economies. The factor of variations in reserves was the variable to which the stock markets of five economies proved most sensitive. For the other four economies, industrial production and inflation were the most impactful factors, for two and two, respectively. Exports were the only factor that, through an initial measurement with the model, did not reveal any specific or significant impact. Finally, through an estimate of the VAR and the application of the number of optimal lags, we concluded that the principal factor to which four of the nine stock markets were most sensitive was inflation; the second-most common factor was industrial production, for three economies, while reserves and exports corresponded to each of the other two economies, respectively.

The impulse-response function revealed that the volume and sign of the impulses of the stock market in response to shocks in the macroeconomic variables varied for each of the economies.

Looking at the variance decomposition test, it could be said that changes in the performance of stock market indices were primarily dominated by disturbances in the stock markets themselves (100% for all markets in the first period). The second-most important factor was industrial production (in four of the nine economies), followed by exports (for three countries) and finally inflation (two economies).

In conclusion, the results of the statistics tests offer evidence of only a shallow presence of common features among the capital markets that make up the BRICS group and its four potential members. The varied responses of these markets to macroeconomic factors reflect not only important differences in the nature of these economies and their stock markets, but are also a sign of the disparate responses of the various economies to exogenous shocks derived from global economic dynamics, which influence cyclical behavior, which in turn entails financial turbulence throughout the study period of 2003-2013. It should be reiterated that despite the fact that the same five economies (the BRIC group plus Mexico) were addressed in a previous study (Sosa and Ortiz, 2014), with a different period, but whose time lapse falls in the same years of study (2003-2009), the results were different for the same economies, which can likely be attributed to the recent crisis in the United States and Europe.

In any case, we can conclude that the BRICS+4 group is currently only integrated through trade, thanks to the process of globalization in which these economies engage. In terms of financial integration, there is still a long way to go. In this regard, one suggestion for these nine countries would be to promote financial integration through strategic stock market alliances; it would also be beneficial for these countries to forge closer political and commercial relations, taking advantage of the multiple international organizations that exist, such as the Asia-Pacific Economic Cooperation (APEC).

Future research might involve conducting an analysis of financial integration based on the stock market indices of the major trading partners of England, as well as the United States, to compare and analyze the impact on the behavior of macroeconomic variables and financial markets. Finally, future research might also explore other econometric models.

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