Integration and Volatility Spillover Among Environmental,Social and Governance Indices: Evidence from BRICS Countries

Abstract

The current research examines the integration of environmental, social and governance (ESG) equity indices among emerging markets, that is, Brazil, Russia, India, China and South Africa (BRICS). Daily data of the ESG equity index from 1 January 2012 to 31 December 2021 are collected from Morgan Stanley Capital International (MSCI). The article employs Johansen’s co-integration test for long-term co-movement and Granger causality tests for causality among ESG equity indices. The study also used the BEKK model to investigate the volatility spillover among the ESG indices. Further, the study also calculated hedge ratios and portfolio weights. The results indicate that none of the ESG indices is co-integrated and short-run bi-directional causality exists across the four ESG indices. All the indices are significantly affected by their past shock and volatility. However, India’s ESG index is influenced by the past shock of South Africa and the past volatility of China. The findings suggest that the flow of information between the ESG indices of emerging countries is not developed yet to the point where they may be integrated into the BRICS countries. As a result, these sustainable equity indices must be promoted even more to become fully integrated.

Keywords

BRICS countries ESG equity index Johansen’s co-integration sustainable index volatility spillover BEKK model

Introduction

Sustainable investment is a strategy for achieving financial goals while being aware of the environmental, social and governance (ESG) consequences. It is an approach that adversely weeds out firms that are not striving to achieve sustainable goals. Thus, sustainable investment is significant in a global context concerned with preserving scarce and irreplaceable resources (Tripathi & Kaur, 2020). Current economic trends have persuasively proven that sustainable investment is necessary for a better future existence. A rising number of investors, governments and financial institutions engage in a productive discussion about the relationship between economic development and environmental protection. Financial market participants collaborate with the UN environment programme finance initiative¹ to better comprehend ESG problems, why they matter to finance, and how to tackle them (Derbentsev et al., 2020). The underlying assumption of ESG-based investment is to identify and evaluate the economic value possessed by socially conscious, environmentally sustainable companies with robust governance policies in place. The financial crisis of 2008, as well as increasing social consciousness, have stimulated interest in investment-linked to sustainability (IISL, 2018). According to the reports of the global sustainable investment alliance (GSIA) 2020, sustainable investment has reached USD 35.3 trillion globally, registering a growth of 15% in 2 years.

Every investment needs a benchmark. The growing acceptance of ESG investment has necessitated the creation of ESG indices and rating organizations. The emergence of stock indices based on sustainability assessment allows investors to switch to an investment focused on sustainability benchmark at a reasonable price which is more feasible for institutional investors (Rehman et al., 2021). ESG ratings and indices are an essential part of how businesses are handled in the twenty-first century. They guarantee that critical business challenges are reflected in company evaluations, tracked by appropriate corporate benchmarks and investment indices, and eventually become feasible to investors and other stakeholders. There is a shift in corporate attitude toward sustainable investment, which is increasingly seen as beneficial to the success of an organization. Sustainability captures the concerns of two key market players: customers and shareholders. Customers are becoming increasingly socially conscious, and they value firms that adhere to long-term development goals. Shareholders are expressing their interest in firms that participate in socially responsible activities to maximize their wealth (Malik & Yadav, 2020). More than 100 rating agencies, including significant data suppliers such as Morgan Stanley Capital International (MSCI), Bloomberg Professional, Governance Metrics International Rating and Thomson Reuters, contribute to ESG ratings, rankings and indices. This information provides researchers and analysts with their first impression of a company’s ratings on ESG aspects such as governance, environmental effect, fraud, human rights concerns and others. It also assists in formulating an opinion on the anticipated investment choice (Efimova, 2018). From a financial standpoint, a company listed on any sustainable stock index can acquire alternative financing from non-traditional investors interested in long-term growth and social well-being. Companies have accessible incentives in this context to include sustainable standards in their strategic management (Ortas Fredes & Moneva Abadía, 2011).

The sustainable indices have emerged as a critical investment criterion throughout the world. The governments of different countries are designing policies, guidelines and procedures; with the active convening of conventions and committees related to growing worldwide human resource and environmental issues. The concept of sustainable investment has moved to the forefront in advanced economies such as the United Kingdom (UK), the United States America (USA), Japan, Australia and other European countries (Tripathi & Kaur, 2020). To address the rising demand for such investments, internationally prominent index providers like FTSE and Dow Jones launched sustainable indices. On the other hand, the Socially Responsible Index (SRI) is still in its beginnings in developing countries. Despite the importance of sustainable investment, its market in emerging countries remains restricted and narrow. Our research attempts to uncover the integration and spillover relationship among the sustainable indices in the BRICS (Brazil, Russia, India, China and South Africa). The linkage of ESG indices is essential as it assists in the risk evaluation and opportunities in business and portfolios. Global investors see the BRICS as an excellent place to diversify their portfolios (Naresh et al., 2017). According to estimates, BRICS countries will outperform the majority of the world’s advanced economies by 2050 due to their high growth rates in comparison to others (Patra & Panda, 2019; Wilson & Purushothaman, 2003). This research is contemporary and important since BRICS countries are growing significantly faster than the rest of the globe, providing excellent chances for investors to earn a better rate of return on their assets while reaping the benefits of diversity. BRICS countries wield considerable power over the world. According to a World Bank study, BRICS countries account for more than 30% of global land coverage, about 42% of the world population, and contribute more than 20% of the gross world product (Shen et al., 2017). These countries have gained considerable attention in the business community in recent years as an attractive avenue for foreign investment and other issues concerning the quality of life of these countries, including environmental factors. With the focus on sustainability, the study investigates the integration and spillover among the ESG indices of BRICS countries because academicians and researchers call these five countries the top emerging markets of the world.

Our study question is: are ESG equity indices connected? To address this, the authors chose the BRICS countries because of their distinct characteristics in comparison to other developing markets. This economic union consists of one each from African countries and South America (South Africa and Brazil) and other three countries from most developing economies of the world (Russia, India and China). The distinctive BRICS mix of faraway and neighbouring countries may assist us in evaluating financial market co-movement since experts believe that co-movement and spillover are stronger in neighbouring countries than those farther away. The author collected data from the MSCI ESG index introduced in 2010. This research will assist us in locating empirical evidence to support the widely held notion that sustainable indices are not internationally interconnected as conventional equity indices. The current research contributes to the existing research in three aspects. First the study analyses the long-term relationship and causality among the ESG indices of BRICS. Second, the study investigates the volatility spillover effects among these ESG indices. Last, this study also computes both hedge ratios and optimal portfolio weights since evaluating the potential of developing economies for portfolio diversification is critical.

The structure of the current study is as follows: The second section discusses prior literature, the third discusses research methods, the fourth discusses analysis and results, the discussion of the study presents in the sixth section, and the last section concludes the article.

Literature Review

Sustainable indices have been investigated and examined from numerous perspectives. One such approach is discovering a connection between sustainability practices and organizational performance as measured by returns. Sadiq et al. (2020) researched the relationship between the ESG index and corporate value. The authors utilized regression models in the first stage to explore ESG disclosure and the relationship between ESG strength, concern and disclosure; regression models in the second stage analyse the insider implications of ESG operations and ESG disclosure. Garcia et al. (2017) tried to answer whether the financial structure of organizations is associated with ESG performance in BRICS countries. There is a vast amount of research on integrating traditional market indices, stock prices, commodity prices and macroeconomic variables. Kaur and Dhiman (2021) used autoregressive distributive lag (ARDL) and Toda and Yamamoto approach to measure the long-run and causality between the agri commodities and prices of consumer goods stock. The authors found no causal and co-integrating relationship between agri commodities and prices of FMCG stock. The same ARDL approach has been used by Kaur, 2021 to test the long-term relationship between the two macroeconomic variables, that is, inflation and fiscal deficits. Several pieces of research investigated the relationship between return and volatility of conventional and sustainability indices. Ghosh and Kanjilal (2016) used the threshold co-integration test to explore the dynamic long-run relationship between crude oil, Sensex and other sectoral indices. The authors also used the Toda and Yamamoto approach to investigate their causality. Maitra and Dawar (2019) employed Johansen’s co-integration test to examine the long-run trend among stock index, commodity futures and foreign exchange rates. Some argue that sustainable investing can contribute to the resolution of social and environmental concerns through reforming equity markets to boost business accountability. Thenmozhi and Maurya (2020) built a hedging strategy among the spot and futures prices of the commodity using the hedge ratios derived from the Baba–Engle–Kraft–Kroner (BEKK) model. Srivastava (2007) investigated the long-term co-integrating relationship between Asian and USA markets. Derbentsev et al. (2020), Armin Razmjoo et al. (2019) compare the complexity of typical market indices with sustainable indices. Sherwood and Pollard (2018) look at the integration of ESG and non-ESG indices in the emerging stock market. Jawadi et al. (2019) used asymmetric generalized autoregressive conditional heteroskedasticity (AGARCH) modelling to evaluate uncertainty in Dow Jones Islamic index, Dow Jones sustainability and Dow Jones industrial index. The study findings reveal that conventional and sustainable investments have equivalent levels of uncertainty and present evidence of uncertainty spillover. Santis et al. (2016) investigated companies’ financial and economic performance included in the sustainability index and the conventional S&P 500 index. The relationship between the sustainable index of Australia and 14 other markets has been investigated using the dynamic conditional correlation (DCC) method (Tularam et al., 2010) and found a significant rise in correlation among the indices during the market crises. However, Mousa et al., 2022 found that the ESG index is less volatile and safer during the crisis period. Maraqa and Bein (2020) found the existence of spillover relationship among the sustainable stock index, stock index of European countries and crude oil prices using DCC multivariate generalized autoregressive conditional heteroskedasticity (MGARCH). Kang and Yoon (2019) found spillover relationship between the future of stock and commodities. The authors calculated portfolio weights and hedge ratios to build mixed commodity and stock portfolios to build the hedging strategies and portfolio weights among the equity sectors. Hammoudeh et al. (2009) found out the spillover relationship among the sectoral indices of Saudi Arabia, Qatar, Kuwait and UAE.

Balcilar et al. (2017) got the evidence of transmission of volatility spillover from traditional indices to the sustainable indices in European countries, and a dynamic correlation existed among them. Sadorsky (2014) has used the MGARCH technique to measure the volatility among the SRI, oil price and gold price. The authors also computed time-varying conditional correlation to test the dependency, hedge ratios for hedging strategies and optimal portfolio weights for portfolio building. Curto and Vital (2014) considered the daily return of four conventional and 10 sustainable indices. The authors applied the co-integration and Granger causality tests and found that sustainable indices outperformed conventional indices. However, Mynhardt et al. (2017) mentioned that conventional market indices are more efficient than sustainable indices. Baykut and Kula (2019) used the BEKK methodology to empirically investigate both shock and volatility transmission between the traditional and sustainable index of Borsa Istanbul and found bi-directional spillover among these indices.

While the literature on the success of sustainable indices in emerging economies is scarce, its growing worldwide importance has resulted in the creation of restricted research on the nature, features and trends of sustainable indices in developing countries. The relationship between the ESG and MSCI indices of developing and developed markets was determined using Granger’s causality and Johansen’s co-integration approach (Jain et al., 2019). The GARCH modelling has been used to evaluate the existence of volatility spillover between the sustainable and traditional market indices. The research shows a link between sustainable and conventional indices. The Vector Autoregression-Asymmetric BEKK (VAR-ABEKK) and VAR-DCC-AGARCH model suggests a strong volatility spillover relationship among the conventional index of BRIC countries (Singh & Singh, 2017). Tasşdemir and Yalama (2014) found evidence of volatility spillover among the indices of Brazil and Turkey. Tripathi and Kaur (2020) employed numerous risk-adjusted indicators and conditional volatility measures to compare the performance of BRICS countries’ SRI to that of their conventional counterparts. During times of crisis, India outperforms the rest of the world. Rehman et al. (2021) explored the integration of traditional and ESG indices of the BRICS countries. Johansen’s co-integration test analysed the MSCI ESG and MSCI composite indices, followed by vector error correction model (VECM) to evaluate causality among the indices. All ESG equities indices appear to be integrated with traditional market indices in all BRICS countries. The results also indicate that long-run correlation exists between India’s conventional and ESG indices.

Several studies evaluated different sustainable indices of developed markets, but no studies examine the integration and volatility spillover among the sustainable indices of developing markets, specifically in BRICS countries. So, there is a need for a greater understanding of the integration and spillover among ESG equity indices of BRICS countries.

Research Design

Data

Our work is on the co-integration and volatility spillover among sustainable indices in BRICS countries. Daily data of conventional and ESG indices of five BRICS countries are collected from the database of MSCI2 for the period of 10 years spanning from 1 January 2012 to 31 December 2021. All the indices are quoted in USD to prevent local inflationary pressures and currency swings on the indices. The log return of ESG stock index prices is used. The formula used to calculate log-returns is R_t = ln(P_t/P_t₋₁).

Unit Root Test

Simple regression on non-stationary variables will generate misleading results since statistical tests such as t-statistics and F-statistics are not statistically acceptable. As the prerequisite condition of the co-integration test, the variables should be non-stationary (Chowdhury & Masih, 2015), so the non-stationarity of each series is investigated by examining for the existence of a unit root. The study used the augmented Dickey–Fuller test (ADF test) proposed by Dickey and Fuller (1979) and the Phillips–Perron test (PP test) developed by Phillips and Perron (1988) unit root tests. The null hypothesis is accepted if the series has a unit root. The ADF test estimated the below equation:

{Δ Y}_{t} = θ_{0} + θ_{1} Y_{t - 1} + \sum_{m = 1}^{n} β_{m} {Δ Y}_{t - m} + ε_{t}

(1)

The PP test uses a non-parametric adjustment to eliminate probable correlation in the first difference of the series and allows for a non-zero mean and a deterministic linear trend (Dhawan & Biswal, 1999). The regression equation of the PP test is shown below:

{Δ Y}_{t} = θ_{0} + {β Y}_{t} + ε_{t}

(2)

Johansen’s Co-integration Test

Co-integration tests are useful when working with non-stationary series and investigating the long-term relationship. Co-integration tests investigated the common stochastic movement among the variables (Chittedi, 2010). The study uses Johansen’s approach, which allows for co-integration among sustainable indices of BRICS. This test employs on the non-stationary data. If the investigated series are integrated at the same level, the integrated variables can be evaluated for a long-run relationship (Madhusoodanan & Kumar, 2008). Co-integration tests are carried out using the method profounded by Johansen (1988), and Johansen and Juselius (1990). The maximum likelihood approach proposed by Johansen (1988) was used to determine the presence of the number of co-integrating vectors.

Δ Y_{t} = ϕ + \sum_{n = 1}^{k - 1} Γ_{j} Δ Y_{t - n} + \prod Y_{t - k} + ε_{t}

(3)

From the above equation, Y_t is a non-stationary variable, k and Ø are the lag length and constant, and ∇ is used as the difference operator. Two parameters that need to be estimated are Π and Γ. The long-term relationship among the variables is determined by Π. α and β contain adjustments and the number of co-integrating vectors indicated by Π (Soni, 2014). The co-integration test is based on two tests, that is, trace test and the maximum eigen-value test; the following formulas are used to calculate these two tests:

λ_{t r a c e} = - T \sum_{k = r + 1}^{n} \ln (1 - λ j) (2)

(4)

λ_{m a x} = - T I n (1 - λ_{r + 1}) (3)

(5)

The null hypothesis of trace test tests r co-integrating vectors tested against n co-integrating vectors (Sahoo & Kumar, 2021), and the null hypothesis of maximum eigenvalue test tests r co-integrating vectors tested against r + 1 co-integrating vectors (Gupta & Guidi, 2012; Kishor & Singh, 2016). The selection of lag length is a critical issue in performing the co-integration test and causality test developed by the Granger (Gujarati, 2004, p. 696). The Akaike information criterion (AIC) is used for the optimal lag selection.

VAR Granger Causality Test

Methods of conducting causality tests, or more precisely Granger causality test, have been studied for the VAR. Return series, which is stationary, is considered for the Causality test. As a result, Granger’s test examines the direction of causality among the variables. According to Granger (1969), the test determines whether the Y series causes another X series or the X series causes the Y series. The following models are estimated to examine the Granger causality:

If Y causes X

X_{t} = \emptyset_{x} + \sum_{n = 1}^{k} β_{n} x_{t - n} + \sum_{n = 1}^{k} γ_{n} y_{t - n} + μ_{x t}

(6)

If X causes Y

Y_{t} = \emptyset_{y} + \sum_{n = 1}^{k} β_{n} x_{t - n} + \sum_{n = 1}^{k} γ_{n} y_{t - n} + μ_{y t}

(7)

Where k is the positive integer, β_n and γ_n are parameters, constants terms are Ø _x and Ø _y , and µ_t is the error term with the constant means and variance. The null hypothesis in the above equations states that Y does not cause X and vice versa.

BEKK GARCH

The current study employed the BEKK-MGARCH model to investigate the integration of ESG indices by using dynamic variance decomposition, also known as volatility spillover among the ESG indices of the BRICS countries. The BEKK-MGARCH is broad enough to allow for an appealing amount of flexibility in building the conditional variance matrix while maintaining the covariance matrix’s positive definiteness. Due of its capacity to directly remark on the volatility level of two assets, the BEKK parameterization has been regularly used in research exploring volatility spillover (McCullough et al., 2018). Below mentioned volatility model has been formed to model conditional volatility.

H_{t} = W^{'} W + \sum_{j = 1}^{m} A^{'} ℇ_{t - j} ℇ_{t - j}^{'} A_{j} + \sum_{j = 1}^{m} B_{j}^{'} H_{t - j} B_{j}

(8)

where W, A and B are the matrices. W as the lower triangular matrix ensures the positive definiteness of the H_t (Chevallier, 2012). ARCH and GARCH are the matrix of parameters represented by A and B (Thenmozhi & Maurya, 2020). ARCH parameters represent the degree of innovations from one asset to another asset and own innovations. While GARCH parameters measure the volatility between two different markets/assets and their own volatility persistence.

Hedge Ratio and Optimal Portfolio Weights

The ultimate goal of portfolio management between the two assets is to demonstrate how portfolio diversification benefit investors. Hedge ratio and optimal portfolio weights are the two fundamental strategies to mitigate inherent uncertainties (Tule et al., 2017). Buying the first asset can be hedged against selling the second asset to produce the best hedging ratio (Mensi et al., 2013). The below-mentioned formula calculates the hedging ratio for two assets i and j in the portfolio. It is as follows:

β_{i j t} = \frac{h_{i j t}}{h_{j j t}}

(9)

where β_ijt represents the hedge ratio between two different assets i and j, the conditional covariance between the assets i and j are characterized by h_ijt and the conditional variance of the same assets j is represented by h_jjt (Thenmozhi & Maurya, 2020). To manage investors’ risk, the study computes the asset weight of a portfolio from the conditional variance of the BEKK-MGARCH model.

ω_{i j t} = \frac{h_{j j, t} - h_{i j, t}}{h_{i i, t} - {2 h}_{i j, t} + h_{j j, t}}

(10)

The following would be the portfolio weight for asset i:

ω_{i j t} = \{\begin{matrix} 0, i f ω_{i j t} < 0 \\ ω_{i j t} i f 0 \leq ω_{i j t} \leq 1, \\ 1, i f ω_{i j t} > 1 \end{matrix}

where W_ijt denotes the weight of the first asset in a $1 portfolio. The portfolio consists of two assets i and j, at time t (Thenmozhi & Maurya, 2020). The conditional variance between the two different assets indicated by h_ijt, and h_jjt represent the conditional variance of assets j (Sadorsky, 2014). The 1−W_ijt formula is used to compute the weight of the second asset (Kang & Yoon, 2019).

Analysis and Results

Descriptive Statistics

This article computes descriptive statistics of the sustainable indices of BRICS countries presented in Table 1. The table includes the mean, median, skewness, kurtosis and standard deviation to understand the behaviour of the series. The Jarque–Bera statistics check the normality of the considered series. The significance of Jarque–Bera statistics is indicated by its p-value.

Table 1.

Summary Statistics.

	Brazil	Russia	India	China	South Africa
Mean	−0.000241	0.000080	0.000420	0.000347	0.000050
Median	0.000000	0.000348	0.000330	0.000123	0.000580
Maximum	0.131729	0.121594	0.097855	0.066825	0.087052
Minimum	−0.192356	−0.142568	−0.147360	−0.076682	−0.118176
Standard Deviation	0.020549	0.018516	0.012064	0.013920	0.018064
Skewness	−0.679278	−0.291781	−0.973328	−0.288699	−0.479029
Kurtosis	12.05200	10.52721	17.75454	5.232323	6.524623
Jarque–Bera	9108.050***(0.0000)	6196.314***(0.0000)	24077.35***(0.0000)	577.9646***(0.0000)	1450.261***(0.0000)
ADF	−18.7259***	−36.7969***	−15.5370***	−30.5347***	−49.8946***
PP	−53.0000***	−48.9288***	−52.3330***	−49.4969***	−50.0173***
L-B Q² Stat (5)	103.01***	117.94***	161.35***	104.38***	418.03***
ARCH LM (5)	97.4610***	117.98***	139.1650***	96.6516***	332.1838***

Source: Authors’ estimates/calculations.

Notes: The table presents the descriptive statistics of returns. ADF and PP test indicates the results of the unit root tests. L-B Q² test and ARCH-LM test reject the null hypothesis of no serial correlation and heteroskedasticity.

(***) Significant at the 1%.

It can be evident from the results that India has given the highest average return among all indices while Brazil has given negative returns. The sustainable index of India exhibits the lowest variation, followed by China, while the sustainable index of Brazil exhibits the highest variation, followed by Russia; higher variation indicates indices react abnormally to the events. The sustainable indices of all countries are negatively skewed. The negative skewness suggests the series has a long left tail. The estimated kurtosis value of all series is greater than the threshold value of three, implying the series are higher peaked, that is, leptokurtic. The probability value of Jarque–Bera statistics indicates that no indices are normally distributed. ADF and PP test of stationarity suggests that all indices are stationary at the level which fulfils the prerequisite condition to run GARCH type models. The significant value of the Ljung-Box test and ARCH LM test at lag five confirms the existence of serial correlation in the squared residuals and heteroskedasticity in all the considered return series.

Co-integration Test Results

Johansen’s co-integration analysis is performed to discover their long-run co-integration. The six lag is chosen based on the lower AIC value. Table 2 presents the results of the co-integration test. The critical values of both the trace and maximum eigen-value test are larger than the respective trace and max-eigen statistic, indicating the acceptance of the null hypothesis of no co-integrating equation. The findings suggest no long-run association among the sustainable indices of BRICS. The VAR Granger causality test further explains the causality relationship.

Table 2.

Johansen’s Co-integration Results.

Hypothesized No. of CE(s)	Trace Test				Maximum Eigenvalue Test
Hypothesized No. of CE(s)	Eigen Value	Trace Statistic	Critical Value at 0.05	Prob.	Eigen Value	Max-Eigen Statistic	Critical Value at 0.05	Prob.
None	0.012518	57.74740	69.81889	0.3110	0.012518	32.79093	33.87687	0.0670
At most 1	0.004383	24.95647	47.85613	0.9198	0.004383	11.43522	27.58434	0.9530
At most 2	0.002777	13.52124	29.79707	0.8664	0.002777	7.238688	21.13162	0.9439
At most 3	0.002065	6.282556	15.49471	0.6621	0.002065	5.380933	14.26460	0.6932
At most 4	0.000346	0.901624	3.841466	0.3423	0.000346	0.901624	3.841466	0.3423

Source: Authors’ estimates/calculations.

Notes: Both trace and max-eigen value test indicate no co-integrating equation(s) at 0.05 level.

VAR Granger Causality Test Results

After indicating no long-run relationship among the variables, the VAR Granger causality test determined the short-term causal effect. The causality test considers return series to satisfy the prerequisite condition. Table 3 shows the proof of the bi-directional causality between the sustainable indices of Brazil-Russia, Brazil-South Africa, Brazil-India, and China-India; however, the sustainable index of China is caused by Brazil and South Africa but not the other way around. There is one-way short-run causality from India to Russia. A bi-directional causal relationship exists between South Africa and India.

Table 3.

VAR Granger Causality Test.

Dependent	Independent Wald χ² Statistics
Dependent	Brazil	Russia	India	China	South Africa
Brazil		12.6625**(0.0487)	13.6802**(0.0334)	10.2233(0.1156)	20.2543***(0.0025)
Russia	38.3304***(0.0000)		13.1184**(0.0401)	15.6194**(0.0159)	14.6558**(0.0231)
India	68.5481***(0.0000)	9.8736(0.1301)		13.9547**(0.0301)	45.6368***(0.0000)
China	33.5604***(0.0000)	5.1048(0.5304)	28.2929***(0.0001)		41.2535***(0.0000)
South Africa	39.1172***(0.0000)	5.9301(0.4311)	31.7297***(0.0000)	8.1725(0.2257)

Source: Authors’ estimates/calculations.

Notes: Values presented without brackets are coefficients, and values given in the brackets are the p-values, indicating the coefficient significance.

(**) Significant at 5%; (***) Significant at 1%.

BEKK GARCH Results

Table 4 depicts the results of the BEKK MGARCH model. This model captures the own and cross volatility spillover among the ESG indices of the BRICS market. The BEKK model estimates coefficients that quantify the influence of shocks originating in one market and other markets on the conditional variance of assets. The coefficients of matrix A reflect short-term volatility, also termed ARCH effects. The significant diagonal element of co-efficient matrix A indicates that past shocks and innovations have impacted the current volatility. However, the off-diagonal element of co-efficient matrix A suggests that none of the past shocks and innovations affected the current volatility of any index. The past shock of South Africa’s index affected the current volatility of India’s ESG index. On the other side, the coefficients of matrix B indicate the impact of past volatility on the current volatility. The diagonal element of matrix B suggests that the past volatility significantly and positively influences all the ESG indices. The past volatility of China positively impacts the current volatility of the ESG index of India; however, South Africa is negatively affecting India. An important observation is that no other indices have volatility spillover effect other than the specified pairs.

Table 4.

BEKK Results.

Variable	Coefficient	Standard Error	T-Statistics	Significance
Mean (BRAZIL)	0.0002390	0.0004325	0.5525500	0.5805744
Mean (RUSSIA)	0.0004878	0.0003464	1.4083300	0.1590330
Mean (INDIA)	0.0006291	0.0002368	2.6564100	0.0078978
Mean (CHINA)	0.0007107	0.0002783	2.5538500	0.0106541
Mean (SOUTH AFRICA)	0.0005504	0.0003291	1.6725700	0.0944121
A(1,1)	0.2224752	0.0441131	5.0432900	0.0000005
A(1,2)	−0.0188493	0.0713327	−0.2642400	0.7915912
A(1,3)	0.0240234	0.0223453	1.0751000	0.2823302
A(1,4)	0.0269999	0.0235830	1.1448900	0.2522556
A(1,5)	0.0693206	0.0744208	0.9314700	0.3516112
A(2,1)	0.0252331	0.0316004	0.7985000	0.4245776
A(2,2)	0.2999940	0.0348207	8.6153900	0.0000000
A(2,3)	0.0016271	0.0294715	0.0552100	0.9559711
A(2,4)	0.0164577	0.0241558	0.6813100	0.4956725
A(2,5)	−0.0112508	0.0309654	−0.3633300	0.7163553
A(3,1)	−0.0144248	0.0946926	−0.1523300	0.8789240
A(3,2)	−0.0027196	0.1328167	−0.0204800	0.9836637
A(3,3)	0.2094499	0.0534343	3.9197700	0.0000886
A(3,4)	0.0171931	0.0497707	0.3454500	0.7297591
A(3,5)	−0.0090175	0.1511512	−0.0596600	0.9524276
A(4,1)	−0.0474337	0.0484904	−0.9782100	0.3279709
A(4,2)	−0.1103282	0.0657780	−1.6772800	0.0934876
A(4,3)	−0.0193338	0.0220755	−0.8758000	0.3811373
A(4,4)	0.1080273	0.0283150	3.8152000	0.0001361
A(4,5)	−0.0130863	0.0567026	−0.2307900	0.8174795
A(5,1)	0.0351668	0.0258163	1.3621900	0.1731374
A(5,2)	0.0261143	0.0367046	0.7114700	0.4767918
A(5,3)	0.0334431	0.0142929	2.3398400	0.0192918
A(5,4)	0.0152142	0.0185083	0.8220200	0.4110631
A(5,5)	0.1807518	0.0348798	5.1821400	0.0000002
B(1,1)	0.9603972	0.0143046	67.1388300	0.0000000
B(1,2)	0.0053382	0.0195887	0.2725100	0.7852272
B(1,3)	−0.0095816	0.0056295	−1.7020300	0.0887494
B(1,4)	−0.0097499	0.0072906	−1.3373300	0.1811145
B(1,5)	−0.0223644	0.0233932	−0.9560200	0.3390610
B(2,1)	0.0109574	0.0155389	0.7051600	0.4807115
B(2,2)	0.9314492	0.0215365	43.2498400	0.0000000
B(2,3)	0.0159867	0.0141933	1.1263600	0.2600137
B(2,4)	0.0042050	0.0083338	0.5045700	0.6138627
B(2,5)	0.0279105	0.0119060	2.3442300	0.0190663
B(3,1)	0.0132439	0.0568525	0.2329500	0.8157985
B(3,2)	0.0081351	0.0831108	0.0978800	0.9220260
B(3,3)	0.9639488	0.0264129	36.4954300	0.0000000
B(3,4)	−0.0085337	0.0253977	−0.3360000	0.7368683
B(3,5)	0.0219159	0.0900438	0.2433900	0.8077022
B(4,1)	0.0320396	0.0218641	1.4654000	0.1428128
B(4,2)	0.0149581	0.0427014	0.3502900	0.7261174
B(4,3)	0.0237849	0.0071194	3.3408800	0.0008351
B(4,4)	1.0052602	0.0045181	222.4959100	0.0000000
B(4,5)	0.0405290	0.0347836	1.1651800	0.2439479
B(5,1)	−0.0445740	0.0372190	−1.1976100	0.2310672
B(5,2)	0.0064892	0.0777660	0.0834500	0.9334973
B(5,3)	−0.0378402	0.0142177	−2.6614800	0.0077797
B(5,4)	−0.0230779	0.0143714	−1.6058300	0.1083117
B(5,5)	0.9132812	0.0626291	14.5823900	0.0000000

Source: Authors’ estimates/calculations.

Notes: The values presented in the table are the coefficients matrix of the BEKK MGARCH model. The model is applied to the return series of the BRICS indices.

Time-varying Conditional Correlations

Figure 1 depicts the time-varying conditional correlations calculated from the BEKK-MGARCH model for all possible pairs. The study observes that all the plots present a varying pattern in the dynamic correlation path. The study indicates that Russia and India stock market return plotted BEKK correlations have the lowest value, while India and South Africa have the highest correlations among all pairs. It can be evident from the plot that there is a sudden spike which represents the COVID period. The results indicate that correlations remained stable and positive during the starting of the period; however, it increased dramatically when crises took place. The outcomes recommend that diversification benefits remain the same in pre and post crises, but international diversification benefits became lower during the crises.

Hedge Ratio

The hedge ratios of different pairs have been computed using conditional volatility estimates of the BEKK model. Table 5 presents the mean value of different hedge ratios. The average value of the calculated hedge ratio between South Africa/India, and South Africa/China is 0.59. It indicates that a $1 buying in South Africa can be hedged against selling in India and China for 0.59 cents. The cheapest hedge is a $1 long position in India with a short position in Brazil for 0.14 cents. The second cheapest hedge is between India/Russia, and China/Brazil, where a $1 buy can be hedged against 0.16 cents in sell. Notice that the Russian and Indian pairs vary with hedge ranges between −0.40 and 2.82. This pair is also one of the most expensive pairs for hedging. So, the study can conclude that the reasonable hedge is long in India with the short in Brazil, whereas the most expensive hedge is long in South Africa with the short position in India and China.

Table 5.

Summary Statistics of Hedge Ratio.

Series	Mean	Standard Deviation	Min	Max
Brazil/Russia	0.39	0.18	−0.20	1.11
Brazil/India	0.40	0.28	−0.48	2.21
Brazil/China	0.33	0.27	−0.11	2.46
Brazil/South Africa	0.49	0.16	0.04	1.51
Russia/Brazil	0.33	0.20	−0.12	1.42
Russia/India	0.42	0.35	−0.40	2.82
Russia/China	0.36	0.25	−0.12	2.18
Russia/South Africa	0.47	0.20	0.06	1.78
India/Brazil	0.14	0.10	−0.13	0.61
India/Russia	0.16	0.11	−0.18	0.56
India/China	0.33	0.15	0.01	1.63
India/South Africa	0.24	0.11	0.05	0.86
China/Brazil	0.16	0.08	−0.07	0.52
China/Russia	0.22	0.10	−0.19	0.56
China/India	0.49	0.14	0.01	1.04
China/South Africa	0.36	0.11	0.12	0.76
South Africa/Brazil	0.41	0.12	0.04	0.80
South Africa/Russia	0.48	0.14	0.05	1.12
South Africa/India	0.59	0.20	0.10	1.56
South Africa/China	0.59	0.16	0.25	1.81

Source: Authors’ estimates/calculations.

Notes: The tables depict summary statistics of hedge ratios of assets i and j. The summary statistics include the mean, standard deviation, min and max.

Optimal Portfolio Weights

Table 6 reports the summary statistics of optimal portfolio weights estimated from the BEKK GARCH model. The average portfolio weight for Brazil/Russia and Brazil/South Africa is 0.42, which means that for a portfolio of $1, 0.42 cents must be invested in the Brazil index and 0.58 cents invested in the Russian and South Africa index. The mean portfolio weight for Russia/South Africa and India/South Africa is 0.52 and 0.82, respectively. So, for the portfolio of $1, 0.52 cents is to be invested in Russia in Russia/South Africa pair and 0.82 cents is to be invested in India in India/South Africa pair, while 0.48 cents and 0.18 cents to be invested in South Africa in both pairs respectively. The mean values of portfolios for Russia/India and Russia/China are 0.24 and 0.37, suggesting that for portfolio values of $1, 0.76 cents to be invested in India in the Russia/India pair, and 0.63 cents to be invested in China in Russia/China pair, respectively.

Table 6.

Summary Statistics of Optimal Portfolio Weights.

Series	Mean	Standard Deviation	Min	Max
Brazil/Russia	0.42	0.17	0.00	1.00
Brazil/India	0.19	0.10	0.00	0.74
Brazil/China	0.30	0.11	0.00	0.64
Brazil/South Africa	0.42	0.13	0.00	0.71
Russia/India	0.24	0.13	0.00	0.76
Russia/China	0.37	0.17	0.00	0.74
Russia/South Africa	0.52	0.20	0.00	1.00
India/China	0.66	0.16	0.00	1.00
India/South Africa	0.82	0.10	0.13	1.00
China/South Africa	0.70	0.13	0.29	1.00

Source: Authors’ estimates/calculations.

Notes: The tables depict summary statistics of portfolio weights of assets i and j. The table presents the average values of the portfolio weights.

Discussion

ESG equity indices are becoming more popular among investors concerned with long term and responsible investment. These indices require companies to demonstrate better sustainability performance and excel their counterparts in an extensive review of environmental, social and economic factors. As a result, these companies are regarded as sustainability leaders. This article evaluates the co-integration and spillover relationship among the recently constructed ESG equity indices of MSCI in the BRICS; it is necessary to assess cross-border financial market integration due to their economic and financial cooperation. ADF and PP tests suggest that all series are stationary, L-B Q² stat indicates serial correlation, and the ARCH test confirms the evidence of heteroskedasticity. Further, Johansen’s co-integration test confirms evidence of no long-run relationship among the BRICS countries’s ESG indices. However, our finding suggests a short-run causality among the ESG indices of all BRICS countries.

The analysis shows the existence of bi-directional causality among the four ESG indices and unidirectional causality from Brazil, South Africa and India to China. BEKK model for volatility spillover suggests that the current shock of the ESG index of India is influenced by the past shock of the index of South Africa. However, South Africa’s volatility negatively impacts the ESG index of India. In addition to this, the past volatility of China’s ESG index significantly affects India’s ESG index. It is also worth mentioning that all countries’ ESG indices of all countries are significantly influenced by their past shock and volatility. Furthermore, the conditional correlation suggests that the opportunity for diversification decreased during the crisis period. The hedge ratio, which helps in building hedging strategies, indicates that buying the index of India and selling the index of Brazil will be the cheapest hedge while purchasing the index of South Africa and selling the index of India and China will be the most expensive hedge. The study also computed the optimal portfolio weights, which helps investors build an optimal portfolio considering past shock and volatility.

The findings of the study suggest that socially responsible stocks are now as appealing to investors as traditional stocks because investors’ participation in socially responsible practices has increased in emerging countries. Simultaneously, our findings demonstrate that investors who utilize ESG indices for diversification would benefit from such advantages over a more extended period due to the lack of integration across BRICS indices. This also explains the establishment of the BRICS union to encourage economic relations in terms of globalization and incorporate ESG concerns into investment and portfolio construction.

Conclusion

ESG investing is becoming extremely popular throughout the world. Our research looks at the integration of ESG indices in the BRICS union. This alliance of all countries is exceptional as it includes three of the world’s top growing economies from Asia, one from South America and one from Africa. The researchers further expand coverage by examining the volatility spillover among the ESG indices of BRICS countries. The results confirm that ESG indices are not integrated and the existence of short-run causality. The findings suggest that ESG equity indices have not yet advanced to the point where they can be integrated in the BRICS countries. As a result, these sustainable equity indices must be promoted even more to become thoroughly integrated into the capital market of these countries.

Investors will find it easier to change their investment strategy if they can evaluate the behaviour of one index using the information of another index. Asset managers and experts can gain additional meaningful insights into their investment strategies and change their portfolios by comparing stock indices for risk diversification and hedging. Investors should ensure a long-term investment return and obligation to the environment and society and contribute to the economy by setting long-term development paths. Furthermore, the study findings are important for policymakers as they respond to increased cross-border financial interactions. Governments can utilize this information while developing non-traditional financial market policies.

Furthermore, the findings are relevant in light of India’s and China’s economic and political changes and how they will affect sustainable markets both within and outside the borders. However, like with every study, even this study has certain limits. First and foremost, the study focused on the BRICS countries only. Further research should look at the co-integration and volatility spillover among ESG indices with traditional market indices in both emerging and developed capital markets and the influence of structural breaks on integration can be studied. Second, as individual behavioural choices are influenced by social and cultural environment, future research should evaluate the impact of ESG in the context of investing. Future studies might investigate portfolios based on traditional indices and their ESG counterparts. To assess the benefit of ESG in terms of diversity, these portfolios may be compared to a traditional index. Investment performance may be evaluated using metrics like value-at-risk and projected deficit.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Note

ORCID iD

Satyaban Sahoo

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