Dynamic Linkages between Brics and Other Emerging Equity Markets

In this paper, we analyze dynamic interactions between stock markets of BRICS (Brazil, Russia, India, China and South Africa) and other select emerging economies as classified by IMF [1] from January 2001 to June 2017. We employ ADCC-EGARCH model as well as block aggregation technique as suggested by Diebold-Yilmaz [2] framework and order-invariance of GVDs (Genera-lized Variance Decompositions) as developed by Greenwood-Nimmo, Nguyen, & Rafferty [3] to examine return and risk spillovers within as well as across the BRICS and other sample Emerging Market Economies (EMEs). The results suggest the cohesiveness within BRICS equity markets is moderate. Our results also show increased integration amongst BRICS economies during the global financial crisis period, implying the presence of Contagion effect. Fur-thermore, Mexico, Chile, Hungary, Turkey and Poland seem to be good candidates to be included along with BRICS for forming a larger Emerging market economic block. This expanded block will not only ensure strengthening trade and financial ties among the participating countries, but also provide a better balance between the emerging and the developed world. This paper contributes immensely to the literature on international finance dealing with financial integration, particularly for emerging markets. The study provides important implications for global policy makers, international economic agencies, investors and the academic community.

Theoretical Economics Letters loped/developing nations led to enhanced integration of financial markets that paved the way to increased international capital flows. Financial integration is the degree to which financial markets of one country is connected to another which can be at local, regional or at international level [4] [5] [6]. The frontier economies of the developing world provide risk diversification opportunities along with attractive returns as compared to the developed economies to global equity investors. The equity markets offer access to the rapidly growing sectors of the emerging economies that have benefits of larger demand creation from the increasing consumption requirements. Evidence from MSCI Emerging market index provides that in recent decades, the markets have performed much better than the world markets. After the Asian crisis of [1997][1998]  An emerging market economy (EME) is a country having some characteristics 1 of an advanced/developed market, but fails to meet all standards to be a developed market [7]. This may include countries that were developed markets earlier or have the future potential to be developed markets. Emerging markets now account for 60% of the global GDP and contribute to over 80% of the global economic growth after the financial crisis of 2008 [8]. Emerging economies are now regarded as the engines of global growth. The characteristics of emerging markets have now also been redefined with economic development and inter-connectedness of financial markets [7]. The concept of economic integration is typically based on economic cooperation among neighbouring countries or countries within the same continent often referred to as natural partners. Transaction costs have been reduced drastically by technological progress, intercontinental and integration has become more and more important in the current era of globalization [9]. BRICS as an economic block is an outcome of such a process. Jim O'Neill from Goldman Sachs in 2001 in a paper entitled "Building Better Global Economic BRICs" coined the acronym BRIC. This acronym has been widely used as a mark of an apparent shift in economic power globally from the developed economies towards the developing world [10]. Projections of the future power of the BRIC economies vary widely. Some sources suggest that they might overtake the G7 economies by 2027. BRIC countries have the eco- South Africa was included as a BRIC nation. Trade linkages 2 exist between BRIC and South Africa; however, the intensity of linkages varies across countries.
South Africa's real output and imports are considerably impacted by shocks from all BRIC nations. South Africa and Russia are highly linked to China, whereas India and Brazil have linkages of moderate intensity with China [11] [12].
BRICS together hold only eleven percent of total IMF voting rights, The World Bank is traditionally headed by Americans, and IMF by Europeans. Some experts believe that establishment of New Development Bank (NDB) by BRICS, Emergency Reserve Fund, along with Asian Infrastructure Investment Bank (AIIB) provide a viable alternative to the developing world, for funding apart from the World Bank and the IMF, keeping a check to their growing monopoly power [13] [14].
To ensure capital market integration it is inevitable to develop the capital markets, ensure consistency and connected markets with adequate infrastructure by reducing costs to ensure increased cross border trade and an increase in international investments of global financial instruments causing improved savings and investments. Capital account liberalization is important to ensure financial integration and it promotes flow of capital from surplus countries to the deficient ones [15]. Sehgal, Pandey, & Deisting [16] have suggested fiscal position, external position and governance among the Macroeconomic factors, stock market performance among the Market-related factors and trade linkages among the Trade factors as the fundamental drivers of equity market integration of the East Asian Economic Community region. The study has been made taking some of the advanced economies of Asia and Pacific. BRICS specifically is an economic group and not a regional group. Its members are characterized by heterogeneous political, economic and social structures. In the last two decades, equity markets of almost all the members of BRICS have seen tremendous rise, much higher than the developed markets. The move towards liberalization and opening of the markets has caused increased flow of capital and introduction of a wide variety of financing instruments with options available to domestic investors to invest/raise capital abroad. Visalakshmi & Lakshmi [17] point out the increase in growth of BRICS in the past decade has impacted capitalization of the stock markets along with dependence with other equity markets. The correlation between the developed and the emerging markets is low which has encouraged international investors to invest around the globe and exploit the benefits of diversification opportunities. The fast pace of advancement of the emerging markets has paved a way of these markets to be industrialized economies in the near future. Lately, there has been an increase in cross listed firms in financial centers across BRICS. Cross-listing ensures informational efficient environment thereby reducing the cost of capital. American Depositary Receipts (ADRs) is one of the methods of cross listing which permits companies in BRICS to trade shares in the US capital market. Closely linked equity markets may have Theoretical Economics Letters serious spillover implications for others.
This recent slowdown of the BRICS has changed its internal cohesiveness and with other EMEs. BRICS interaction with other emerging economies may provide some vital insights about potential entrants into this economic block in future. It may be interesting to study how this economic block and the economic/trade integration among BRICS and with other developing economies affect the internal cohesiveness. This may further strengthen integration in this block and bring positive effects in its trade, financial integration and policy coordination within the group and off the group with other emerging economies. Thus, this paper examines dynamic co-movements for BRICS stock markets and other select EMEs through price-based measures of financial integration.
The rest of this paper is framed as follows: Section 2 provides a brief review of literature. Section 3 describes the data along with its properties. Section 4 describes the methodological framework adopted. Section 5 provides the empirical results and Section 6 summarizes the conclusions and suggests policy implications. Tables and Figures are also presented at the end of the paper.

Literature Review
International stakeholders have shown great interests in the dynamic relationships between returns and volatilities in the emerging markets. Thus, there is a considerable research and literature in this area focusing on economic interaction between emerging economies including BRICS.
Bhar & Nikolova [18] examine the level of integration and the dynamic relationship between the BRIC countries, their respective regional economies and the world. It is found that India exhibits the highest global and regional integration among the BRIC countries, which is followed in the order Brazil, Russia and China. Sheu & Liao [19] demonstrate both long-run time-varying nonlinear cointegration relationships and short-run time-varying Granger-causality relationships pertaining between the equity markets of US and each of the BRIC countries with these relationships altered in the financial crisis related to subprime mortgage in the short-run. Xu & Hamori [20]

Data
Various sources provide a list of "Emerging Markets". However, one of the most used sources by researchers is the list published by the IMF, which includes Ar-  [48] to check the problem of non-synchronous trading bias, we use the two-day average returns, Daily returns are calculated as the first difference of log transformed price index series. The non-trading days vary across the indices because of difference in holidays. The value of index on such days or the day on which data is unavailable due to any other technical reason is presumed to remain the same, equal to its closing value on the last trading day before such day.

Asymmetric Dynamic Conditional Correlation (ADCC-EGARCH) Model
Asymmetric dynamic conditional correlation (ADCC) model proposed by Cappiello et al. [39] allows for series specific news impact along with smoothing parameters. It also permits for conditional asymmetries in dynamics of correlation.
ADCC specifications are suited to study dynamics of correlation among varied asset classes and also investigate the presence of asymmetric response in the conditional variances as well as correlations to negative returns. We employ the ADCC model to analyze and study the behavior of the sample EMEs including BRICS.
Integration across varied sub periods cannot be measured by a static measure of correlation. Therefore, we use Asymmetric DCC-EGARCH (ADCC-EGARCH) model given by Cappiello et al. [39] accounting for heteroscedasticity and also continuously adjusting for time varying volatility. ADCC takes into account the correlations asymmetry which is observed to rise more post-joint negative shock as compared to a positive shock as pointed by Baumohl [49], Exponential GARCH (E-GARCH) model accommodates the asymmetries in conditional variances of asset returns, as the bad news have greater impact than good news [50]. The ADCC-EGARCH as discussed above has been estimated by modifying a program in EViews 9 econometric analysis software as per the requirements of the study to derive and analyze dynamic correlations for our sample countries. Pair wise ADCC is conducted for 20 EMEs. Hence we obtain 190 ( 20 C 2 ) such pair-wise correlation series over the entire sample period. Mean ADCC values for each pair are then presented in a matrix. Since this study focuses on BRICS and other EMEs related to BRICS, the average correlation of BRICS has been calculated. The average correlation of each of the EMEs with BRICS has also been calculated to see the associations and interaction of BRICS with each of the EMEs. To capture the dynamics over the pre/during/post crisis period average ADCC values of each of the BRICS with other BRICS members and identified sample economies are also calculated. However, ADCC-EGARCH methodology used for this study as developed by Cappiello et al. [39] is also presented in brief as follows: The mean equation is specified as an AR (1) process (based on SIC criteria): , , , Estimation results show presence of long run volatility persistence as is indicated by significant value of ψ coefficient. Estimation results are not shown due to scarcity of space. Both ARCH (φ) and GARCH (δ) term measuring size and leverage effect, respectively are also found to be significant for all sample markets. EGARCH model is justified as negative and significant value of δ coefficient indicates asymmetric effect caused by news on volatility factor which increases more post a negative shock as compared to a positive shock.
Correlation equation as evolved in ADCC model [39] is given by: and the asymmetric term g captures the periods where both markets jointly experience negative shock. The scalar parameters θ 1 and θ 2 are non-negative and satisfy 1 2 1 θ θ + < . Finally, dynamic correlation matrix among the two series is represented by:  is diagonal matrix with entries as the square root of i th diagonal elements of Q t .
ADCC as a measure of interactions only gives correlations but not spillovers or dominance, however, to study the "to and from" linkages in details, block aggregation technique is used in this study as given under Diebold-Yilmaz framework that was enhanced by Greenwood-Nimmo et al. [3].

Diebold and Yilmaz (2012) Spillover Index
Diebold and Yilmaz [2] have proposed the Spillover index methodology which is based on vector autoregressive (VAR) framework, which allows us to examine spillovers across variables. The contribution of shocks from and to each variable in terms of each variable's forecast error variance through variance decomposition analysis is quantified thereby providing the magnitude and direction of spillovers. Diebold and Yilmaz [2] use generalized VAR frame-work of Pesaran and Shin (1998) and Koop et al. (1996) which yields forecast-error variance decompositions that are invariant to ordering of variables.
Following the spillover index methodology by Diebold and Yilmaz [2]. The authors estimate the results using RATS10.0 econometric analysis software. The pro-4 EGARCH (1, 1) is chosen as the preferred model in interest of parsimony of parameters (see Kim and Wang, 2006). Theoretical Economics Letters gram of the said methodology is downloaded from their webpage 5 which has been modified to analyse the spillovers among the sample markets. Sample series of returns and conditional volatilities derived from EGARCH (1, 1) process along with US returns and conditional volatility are provided as input into the software. However, methodology as developed by Diebold and Yilmaz [2] and Greenwood-Nimmo, Nguyen, & Rafferty [3] used for this study is briefly discussed below.
The N variable VAR of p th order can be written as: Its moving average representation is written as: The H-step ahead forecast error variance decomposition of i th variable which can be attributed to shocks for j th variable is: where Σ is the estimated variance matrix for the error term of VAR, ij σ is standard deviation for the error term of the i th equation and i e is the selection vector with one for the i th element and zero otherwise. Each forecast error variance decomposition is normalized given by the row sum as: Connectedness matrix can thus be constructed using the forecast error variance decompositions as follows: measures the pairwise spillover from variable j to variable i.

Greenwood-Nimmo [3] Block Aggregation Framework
While Diebold-Yilmaz framework provides the measure of pair wise directional spillovers among individual markets, it does not quantify the spillovers between a group of variables, Greenwood-Nimmo et al. [3] extend Diebold-Yilmaz framework by exploiting block aggregation of the connectedness matrix which applies an aggregation routine for grouping sets of individual variables.
We, therefore, adopt the same methodology to examine linkages amongst the EME including BRICS. We examine the linkages among 20 EMEs and US market representing the global market system, wherein each market encompasses two variables-return and conditional volatility in a similar manner as in Greenwood-Nimmo, Nguyen and Shin [3] but with only returns and conditional volatilities. The variables are arranged in the order ( ) Generalized VAR framework adopted in Diebold and Yilmaz [2] ensures that forecast-error decomposition is not sensitive to the variable re-ordering and hence supports any desired block structure. Therefore, the connectedness matrix is arranged in the following form: Total within market forecast error variance contribution for market i is given as: and the total pairwise directional spillover from market j to market i (i ≠ j) at horizon H is given as: where m is the number of variables that each group is composed of (in this case, m = 2) and m e is 1 m × vector of ones.
Hence, the aggregated connectedness matrix following Greenwood-Nimmo et al. [3] can be written as: and cross-variable effects, , which are given as follows: Gross and Net directional spillover of market i can be obtained as follows: Similarly, total directional spillover of market i to/from BRICS markets along with Gross and Net directional Spillovers of market i (in terms of BRICS) in the model is also estimated from the connectedness matrix.
ADCC as a measure of interactions only gives conditional correlations but not spillovers or dominance, thereby not showing flow of information from where and to where. Therefore, to study the "to and from" linkages over the first two moments in detail, we utilize the Diebold-Yilmaz framework. While Diebold-Yilmaz framework provides the measure of pair wise directional spillovers among individual markets, it does not quantify the spillovers between a group of variables, Greenwood-Nimmo framework extend Diebold-Yilmaz framework by exploiting block aggregation of the connectedness matrix which applies an aggregation routine for grouping sets of individual variables and thereby presenting a holistic view over the two moments also providing information regarding openness and dominance of a country by Gross and Net Spillovers respectively.

Descriptives
The descriptive statistics for the sample countries are shown in Table 2. The highest daily mean return is for Colombia (0.0815%) and lowest for Poland (0.008%). The daily standard deviation as a measure of volatility is highest for Turkey (1.95%) and lowest for Columbia (0.976%) followed by Chile (0.987%).
All return series exhibit negative skewness and high kurtosis (leptokurtic) implying that they are fat-tailed distributions. There is violation of normality assumption as shown by Jarque-Berra (JB) statistics. All sample series are serially correlated as indicated by Ljung-Box Q-Statistic. ARCH LM test also shows strong evidence of conditional hetroscedasticity implying volatility clustering in   Table 3 reveal that all sample return series are integrated at level.

ADCC Full Sample Mean Correlation Results
The dynamic conditional correlation (ADCC Model) is estimated using two-day moving average of log-returns to measure the co-movements between the markets. Sehgal, Gupta, & Deisting [42] find that correlation coefficients for the advanced economies of the European Economic and Monetary Union (Belgium, France, Germany, Italy, Netherlands and Spain) remained over 0.84 during all the sub-periods of their study. In contrast, we find that the correlations between EMEs including BRICS are moderate.       ADCC is a measure of association only. It does not show interactions and hence, analysis cannot be made on information spillovers or dominance. The results from the study of "to and from" linkages in details using the Block aggregation technique under Diebold-Yilmaz framework as enhanced by Greenwood-Nimmo et al. [3] is presented in the next sub-section.

Diebold and Yilmaz [2] Spillover Index Results
We employ full sample Diebold and Yilmaz [2] spillover index methodology The analysis is done by estimating the connectedness matrix under Diebold-Yilmaz framework wherein optimal lag length is determined by minimizing Schwarz Information Criterion (SIC) and forecast horizon is set to H = 10 days.

Return and Volatility Spillovers
Spillover index methodology as proposed by Diebold and Yilmaz [2] is employed to return and conditional volatility of 20 sample markets along with US as a global factor that gives a 42 × 42 connectedness matrix. The matrix quantifies magnitude of pairwise linkages between the first two moments of each market in the sample. The connectedness matrix depicting pairwise spillovers across return and volatility for all 21 markets is presented in   1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44 COUNTRY, RETURN/ VOLATILITY Row No.  ARR  ARV  BGR  BGV  CLR  CLV  COR  COV  HUR  HUV  IIDR  IIDV  MCR  MCV  MYR  MYV  PER  PEV  PHR  PHV  PKR  PKV  PLR  PLV  ROR  ROV  THR  THV  TRR  TRV  BRR  BRV  RUR  RUV  INR  INV  CNR  CNV  ZAR  ZAV  usR    Column 43 of Table 5 labeled "contribution from others" sums the directional spillovers to market "I" from rest of the sample markets. The row 43 labeled "contribution to others" represents the directional spillover from market "i" to other markets in the model. Similarly, Column 44 labeled "contribution from BRICS excluding own" sums the directional spillovers to market "i" from BRICS markets and row 44 labeled "contribution to BRICS excluding own" represents the directional spillover from market "i" to BRICS markets in the model.
Dominance of financial markets can be inferred by directional spillovers "to" and "from" other markets that are the two key aspects based on the combined effect. Dominance of a country's market can be established if it influences in transmitting information to other markets, but is relatively less influenced from them. Therefore, calculating and analyzing difference between "contribution to others" and "contribution from others" also known as net spillovers of the financial markets is an important tool to evaluate dominance/subordination of a given market. In addition, we estimate Gross spillover which is the sum of "contribution to others" and "contribution from others".

Block Aggregation Approach under Diebold-Yilmaz Results
Greenwood-Nimmo et al. [3] have given a novel method in which block aggre-  Table 5. This helps us to examine linkages among the emerging markets flowing through risk and return in a common framework providing a comprehensive picture to elucidate their interactions. of Gross contribution. High value of Gross contribution of these markets also reflects higher level of Globalization/Openness (Row 28).

Rolling-Sample Analysis: Time Varying Spillover Results
The static spillover results are supplemented with rolling window analysis to capture the time-varying characteristics of the spillover indices. We present results with h = 10 days and a rolling window of 250 days.

Conclusion
Based on the findings of this paper, it is advisable to extend the BRICS into a Theoretical Economics Letters broader and more relevant block as "Emerging Market Economic Community" including Mexico, Chile, Poland, Hungary and Turkey. The new block will account for a much wider market, with Brazil, Mexico and Chile representing the emerging markets in South America. Mexico is one of the most looked after countries in Latin America and is also a member of NAFTA. Further Mexico's mixed blessing of sharing its border with US can prove to be an important member of the group. China, Russia and India represent Asia. South Africa is the strongest economy in the African continent. Hungary, Poland and Turkey represent the chief emerging economies in Europe. Turkey is also regarded as a gateway to Middle East. Turkey's strategic location between Europe, Middle East and Central Asia and its fast modernizing and industrializing economy will increase the group's influence in Europe and Middle East.

Policy Implications
The study has important implications for policy makers, international economic agencies, investors and academia. For policy makers around the globe, the study is of particular relevance as it suggests expanding the existing economic block by including Mexico, Chile, Poland, Hungary and Turkey. The expanded block will be more capable in achieving political influence, enhanced economic and trade linkages amongst economies as well as developing a coordinated response to global risks including financial contagion. For international economic agencies like World Bank and International Monetary fund the setting up of New Development Bank by BRICS will challenge the monopoly of these institutions by providing alternative sources of finance at ease to the emerging economies. Inclusion of new members will increase the bank capital base and ease the problems faced by emerging economies in procuring finance. The proposed group shall be a combination of heterogeneous markets and for investors, this expanded group will provide risk diversification opportunities as the block currently exhibits relatively low correlations as compared to developed economies.
The expanded block will also be able to compete with the G7 economies in the near future, thereby challenging their influence in the world economic order and international institutions. This proposed group can emerge as the new growth engine of the world and provide balance in economic power between the developed and the developing world. For academic community, this paper is useful in better understanding of the information linkages between the geographically dispersed economic block with member counties exhibiting different political and cultural environment. The study contributes to the existing literature by examining candidacy of EMEs and the possibility of expanding the existing block to form "Emerging Markets Economic Community" as a more viable Emerging Market Block in the future.