INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

ESG Uncertainty and Volatility Spillovers among BRICS Markets

Wafa HadjMohamed

La REMFiQ laboratory, IHEC of Sousse University of Sousse Sousse, Tunisia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100169

Received: 21 November 2025; Accepted: 28 November 2025; Published: 03 December 2025

ABSTRACT

This study investigates the two-way relationship between ESG uncertainty and volatility spillovers across

BRICS stock markets over the period November 2002 to March 2025. Conditional volatilities are modelled using

an E-GARCH framework, while spillover dynamics are assessed through a Time-Varying Parameter VAR

model. Granger causality tests are then employed to explore how ESG uncertainty interacts with market

interconnectedness. The results reveal significant yet asymmetric volatility spillovers, with BRICS market

connectedness intensifying during episodes of elevated ESG uncertainty. Short-run spillovers exert a strong

influence on ESG uncertainty, whereas the opposite effect is comparatively weaker, suggesting that financial

markets act as forward-looking indicators of sustainability-related risk perceptions. Evidence of bidirectional

causality between ESG uncertainty and bilateral spillovers further underscores the importance of major BRICS

economies in shaping ESG dynamics. Overall, the findings provide valuable implications for portfolio allocation,

regulatory design, and ESG risk management within BRICS markets.

Keywords: BRICS markets; ESG uncertainty; volatility spillovers; E-GARCH; TVP-VAR; Granger causality;

market connectedness; sustainability risk; asymmetric spillovers; financial integration.

INTRODUCTION

Over the past decade, sustainable investment has attracted growing attention from financial market participants.

In particular, ESG stocks that comply with Environmental, Social, and Governance criteria have been widely

adopted as a tool to mitigate the negative externalities associated with climate change and as an impact absorber

of crisis events in financial markets. This positions ESG stocks as strong competitors to traditional asset classes

in financial markets. Investors of all sizes now routinely incorporate Environmental, Social, and Governance

(ESG) criteria into portfolio selection and risk management. However, the rapid growth of rating agencies,

modelling techniques, and reporting standards has created significant uncertainty about a firm's true

sustainability profile, as well as the ambiguity surrounding a firm’s actual ESG standing. Without standardized

methodologies, different ESG scores can mislead market participants, distort perceived risk, and ultimately

impact asset prices.

Although companies in emerging markets, including the BRICS (Brazil, Russia, India, China, and South Africa)

group, have recently increased their ESG investments, the positive effects on financial performance remain

uncertain. This uncertainty is mainly due to institutional, legal, cultural, and structural differences with

developed markets, which make ESG initiatives more expensive, less transparent, and sometimes seen as

opportunistic. In particular, BRICS markets are vulnerable to global ESG uncertainty since doubts about the

credibility or effectiveness of ESG practices can lead to risk-averse behaviors among international investors.

This leads to increased financial volatility in these markets and spillover effects between them, intensified by

their growing integration and shared exposure to global ESG risk perceptions.

Since Environmental, social, and governance (ESG) issues have taken a central place in economic and financial

decisions, their uncertainty (not their level, but their instability, their unpredictability) now constitutes a

systematic risk in financial markets. This explains the new direction of research in the ESG context. Recently,

studies have attempted to detect and measure ESG risk using various methods and to examine its impact on

financial markets. Despite the contributions of these studies in answering the question of ESG risk, they remain

limited. For investors, policymakers, and academics, it is crucial to study the bidirectional relation between ESG

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

uncertainty and traditional financial markets, especially in the BRICS group. BRICS economies account for

nearly 40 percent of the world’s population and over one-quarter of global GDP. Their equity markets,

characterized by higher volatility and sensitivity to both global shocks and domestic policy shifts, present fertile

ground for examining the transmission of volatility spillovers. Although prior research has documented time-

varying and frequency-specific spillovers among BRICS markets, it has largely overlooked how ESG uncertainty

may amplify or attenuate these dynamic linkages.

This highlights the contribution of the present study, as we analyse the bidirectional relationship between ESG

uncertainty and dynamic volatility spillovers in BRICS stock markets. First, we examine daily dynamic volatility

spillovers among BRICS stock markets from November 01, 2002, to March 31, 2025, using a Time-Varying

Parameter Vector Auto regression (TVP-VAR) model by Antonakakis, Chatziantoniou, and Gabauer (2020).

Second, we identify the ESG-Based Sustainability Uncertainty Index (ESGUI) across two indices proposed by

Ongan, Gocer, and Isik (2025): The Global Equal Weighted Index and the Global GDP Weighted Index. Third,

we analyse the bidirectional relationship between volatility spillovers in BRICS markets and the ESG uncertainty

index using Granger causality tests over multiple lags.

Our objective is motivated by: First, sustainability is not only a reputational issue but also a significant financial

risk factor. Policy shifts toward decarbonisation, changing disclosure standards, green taxonomies, stranded-

asset risks, and social/governance shocks (such as labour standards and corruption events) all generate

uncertainty about future cash flows, regulation, capital costs, and investor flows. This leads markets to

increasingly incorporate sustainability information, though these signals are often noisy, incomplete, and vary

across countries. Therefore, measuring sustainability uncertainty helps identify this information risk, which can

disrupt valuations, expand risk premiums, and cause rebalancing in global portfolios. Second, volatility does not

remain confined within a single market. Through capital mobility, index co-membership, shared investor bases,

derivatives linkages, and macro-financial channels, shocks in one equity market can influence risk perceptions

and volatility in others. Measuring volatility spillovers uncovers how financial stress spreads, the level of market

integration, and the potential for contagion or risk insulation. For risk management, understanding who “exports”

versus “imports” volatility helps inform hedging strategies and portfolio decisions. Third, examining dynamic

volatility spillovers across the BRICS markets, especially, is important because the BRICS economies are large,

systemically important emerging markets with increasing influence in global portfolios and real-economy

demand. However, they vary significantly in financial market development, governance quality, regulatory

depth, commodity reliance, and ESG policy directions. These differences create a natural laboratory to examine

asymmetric cross-market risk transmission: who takes the lead, who absorbs shocks, and how disruptions spread

when fundamentals and institutional qualities differ. Because international investors often combine BRICS

exposures (fund mandates, benchmarks, ETFs), the interconnectedness is economically significant. Studying

BRICS markets is particularly relevant not only for emerging economies but also for developed markets, hence

the global economy. Recent evidence suggests a gradual convergence between BRICS and G7 economies

(BenMabrouk & HadjMohamed, 2022). The strong performance of BRICS countries has been largely driven by

substantial foreign direct investment in their private sectors, which has enhanced trade integration with the rest

of the world (Mensi et al., 2014; Ruzima & Boachie, 2018). Furthermore, several studies indicate that BRICS

nations have the potential to rival the G7 in the coming decades, with projections suggesting they could surpass

G7 countries by 2050 (Golam & Monowar, 2015; Naik et al., 2018; Plakandaras et al., 2019). Fourth, linking

sustainability uncertainty and dynamic volatility spillovers in BRICS markets helps to ask a central question in

finance: Does rising (or falling) sustainability uncertainty change the strength, direction, or net balance of

volatility transmission across BRICS? This captures a policy-relevant systemic risk dimension: sustainability

shocks may not stay idiosyncratic, but they can reshape regional or global market co-movements, affecting

diversification benefits and amplifying financial instability. Fifth, detecting and studying volatility spillovers

between BRICS markets by using a TVP-VAR (Time-Varying Parameter VAR) model is a choice. ESG

regulation and investor preferences have accelerated and evolved unevenly across the BRICS. Therefore, to

study sustainability, it is necessary to use a dynamic model that allows for market relationships to evolve. TVP

VAR model by Antonakakis, Chatziantoniou Gabauer (2020) captures evolving relationships over time, offers

a dynamic analysis of interconnected financial variables, and is suitable for markets influenced by external

shocks such as BRICS markets. Sixth, testing causality (Granger Causality tests) between sustainability

uncertainty and spillovers allows us to test the direction of causality: if sustainability uncertainty explains

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

volatility spillovers across BRICS markets or the other way around, or both directions of causality are present.

Furthermore, using multiple lag lengths accommodates differences in information diffusion speeds across

BRICS and allows us to detect short- vs. long-horizon predictive channels. Seventh, the choice of ESGUI by

Ongan et al. (2025) over other existing uncertainty indices, such as the Sustainable Policy Uncertainty (SPU) or

the ESG-related Economic Policy Uncertainty (EPU-ESG), is both theoretically and empirically motivated.

Unlike SPU or EPU-ESG, which mainly derive from newspaper data and concentrate on economic or policy

uncertainty, ESGUI directly measures uncertainty within the ESG framework itself. Additionally, ESGUI uses

the same standardized source (EIU reports) as the World Uncertainty Index (WUI), ensuring international

consistency and reducing methodological differences. By including data from 25 countries and providing

monthly observations, ESGUI offers greater temporal and geographical detail, making it suitable for dynamic

analyses like volatility transmission and spillover effects among emerging markets. Furthermore, ESGUI

demonstrates empirical robustness; Ongan et al. (2025) found strong correlations between it and established

uncertainty measures (WUI, EUI, and EPU), supporting the reliability and interpretive value of ESGUI as a

hybrid indicator of sustainability risk. By incorporating textual signals of ESG risk perception into a quantitative

tool, ESGUI provides a comprehensive, market-relevant measure of sustainability-related uncertainty, making

it especially useful for studying its transmission to financial volatility in the BRICS context, which explains its

importance for sustainable finance. The eighth motivation is the studied period spanning from November 01,

2002, to March 31, 2025. This period covers several critical events: (2002-2003) Enron, Sarbanes-Oxley Act,

and Corporate governance scandals, (2007-2009) Global Financial Crisis, Lehman collapse, and Occupy Wall

Street, (2010-2011) BP Oil Spill, Fukushima Disaster, and Arab Spring, (2015-2019) Paris Agreement,

Volkswagen Emissions Scandal, and Black Lives Matter, (2020-2023) COVID-19 pandemic, Green recovery

efforts, Extreme weather events, and ESG reporting mandates. This rich set of structural breaks helps identify

how sustainability uncertainty interacts with evolving inter-market risk transmission.

This study contributes to the literature on sustainability, ESG, and the interconnectedness of financial markets

in several ways. First, several studies have focused on ESG profiles as a refuge during crisis periods; however,

few have studied ESG uncertainty as a systematic risk in financial markets. Despite their growing importance

for portfolio allocation and systemic risk (Nguyen et al., 2021), ESG and sustainability uncertainty have rarely

been analysed as triggers for financial contagion or spillovers. This paper is the first to examine whether ESG

uncertainty enhances volatility spillover across markets. Second, analysing the bidirectional relationship

between ESG uncertainty and volatility spillover across markets is crucial for a better understanding of this

connectedness. Understanding whether this relationship runs from ESG uncertainty to financial markets

instability, the reverse, or both ways, adds valuable insights to ESG-finance and contagion research. It also offers

practical benefits, such as improving sustainability regulations, stabilizing markets, and helping investors

optimize their portfolios. Third, to examine emerging markets' interconnectedness, previous studies focused on

economic uncertainty and macro shocks (Bouri et al., 2018), oil shocks (Tiwari et al., 2025), crisis events (Hsiao

et al., 2024), and geopolitical conflicts (Ijaz et al., 2025), but they didn’t examine the impact of sustainability

uncertainty. Linking sustainability uncertainty to spillover dynamics, especially for BRICS markets, is another

contribution explaining otherwise instability and interconnectedness in these markets, as all emerging markets

are more vulnerable to ESG uncertainty. Fourth, through the period studied (2002-2025), we can detect how

sustainability uncertainty interacts with volatility spillovers under different conditions. This can offer theoretical

and practical implications.

This study has numerous theoretical and practical implications. Theoretically, it advances the literature on

sustainable finance and emerging market dynamics. This research clearly contributes to understanding the

complex relationships between ESG uncertainty and financial volatility in the BRICS emerging economies. The

results highlight the bidirectional and heterogeneous relationships between ESG uncertainty and market

volatility, suggesting that conventional models must integrate ESG dimensions to better capture risk dynamics.

Moreover, the analysis with multiple lags shows that effects are not immediate but can develop over several

periods, highlighting the importance of dynamic studies in modelling sustainability-related financial risks. For

the efficient markets theory and ESG, the discovery that financial volatilities precede changes in ESG uncertainty

could raise questions about how markets integrate ESG information, suggesting that it is not yet fully assimilated

into price formation. Practically, for investors, this study supports decision-making. Understanding that volatility

spillover influences ESG uncertainty helps investors to anticipate periods of high ESG risk based on market

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dynamics and adapt their sustainable portfolio management strategies. Furthermore, this study clarifies ESG

policy direction and helps to improve sustainability regulations. For policymakers in BRICS countries, our

results help to better align their ESG strategies with regional financial dynamics by identifying "influencer"

countries (e.g., China, Brazil, India) that could play a pivotal role in ESG stability. For corporate finance, this

study strengthens corporate risk management. By integrating changes in inter-market volatility as a leading

indicator of changes in the ESG environment, companies can improve their ESG risk management. More

importantly, this paper adds to the development of ESG monitoring tools. The methodology used in this study

can be incorporated into automated risk monitoring systems to continuously monitor the interactions between

financial markets and sustainability uncertainties. For investors, policymakers, and regulators, this can detect

early warning signals of ESG-driven volatility transmission. Moreover, this encourages the integration of ESG

criteria into financial analyses.

The remainder of the paper is organized as follows. Section 2 exposes previous studies in a literature review.

Section 3 presents the methodology. Section 4 describes the data used and shows the summary statistics. Section

5 reports the empirical evidence. We conclude the paper in Section 6.

LITERATURE REVIEW

Several studies have demonstrated the success of ESG profiles in financial markets. For instance, Hoepner et al.

(2019) suggested that engagement with ESG issues reduces downside risk. Furthermore, Ilhan et al. (2019)

showed that firms with poor ESG profiles, measured by higher carbon emissions, have higher tail risk. Moreover,

Albuquerque et al. (2020) developed a theoretical framework showing how firms can reduce systematic risk

exposure by using CSR investments to enhance product differentiation and diversify their product portfolios.

Otherwise, He et al. (2023) found that ESG rating significantly improves stock liquidity in the Chinese stock

market. Similarly, Zhang et al. (2024) demonstrated that ESG ratings have a positive impact on stock market

performance.

Following the outbreak of the 2008 global financial crisis and the increasing deterioration of the global

environment in recent decades, sustainability and stability studies are urgent issues for the interest of

policymakers and market regulators. This explains why some studies have examined the success of ESG profiles

during crisis periods. For instance, Cornett et al. (2016) demonstrated that during the Global Financial Crisis

(GFC), the financial performance of U.S. banks is positively related to their ESG score. Similarly, Lins et al.

(2017) found that U.S. non-financial firms with high ESG scores have better financial performance than other

firms during this period. Furthermore, Singh (2020) examined the spillover effects across the three different

long-short portfolio indices during the COVID-19 pandemic, and they found that investors become more

attentive to corporate fundamentals, causing capital to flow away from the defensive and stocks from Europe,

Australasia, and the Far East (EAFE) portfolios to the ESG portfolio during crisis periods. They suggested that

investors find refuge in the ESG approach as it focuses on the long-run sustainability of firms. Furthermore, by

exploiting the new Morningstar ESG risk indicators introduced at the end of 2019, Ferriani & Natoli (2021)

analysed how investors receive these signals during phases of high uncertainty. They found that low ESG risk

funds attract more inflows during the COVID-19 crisis, with a marked importance of environmental risks relative

to social or governance risks. Moreover, Broadstock et al. (2021) investigated the role of ESG performance

during the COVID-19 pandemic. They found that ESG performance lowers financial risk during a crisis, and

high-ESG (performance) portfolios generally outperform low-ESG portfolios. However, by using data of the

constituents of the MSCI USA ESG leader index, Rubbaniy et al. (2021) investigated the herding behavior in

the US ESG stocks and found a significant herding behavior in the US ESG leader stocks during both bear and

bull market conditions. They documented evidence of market-wide herding during the global financial crisis,

COVID-19, lockdown, and post-lockdown episodes. Confirming the success of ESG profiles, Boubaker et al.

(2022b) highlight the role of responsible investments in reducing the adverse impacts of COVID-19-related

externalities. Furthermore, Liu et al. (2023) showed that ESG serves as a systemic stabilizer in financial markets.

They examined the relationship between sustainability (through ESG investments) and financial stability by

evaluating whether including ESG in stock indices reduces volatility spillovers among Chinese financial

markets. They found that when the ESG index is used, volatility contagion effects (total, directional, bilateral)

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decrease across the financial system, suggesting that ESG investments mitigate shock transmissions between

markets.

The frequency, ambiguity, and sometimes contradiction of sustainability regulatory changes, the rapid evolution

of investor expectations regarding sustainability, environmental, and social shocks (e.g., COVID-19, war in

Ukraine, natural disasters), and the divergence of ESG ratings explain why lately, there is growing interest in

studies to detect and measure sustainability uncertainty and to examine its impact on financial markets.

By proposing a partial approach, Ardia et al. (2023) used the Media-based Climate Change Concerns Index

(MCCC) as a daily aggregated score based on climate change news articles and the Unexpected Changes in

MCCC (UMC) extracted as the surprise (shock) component, after filtering for financial, energy, and

macroeconomic factors. This sustainability index captures unexpected variations in climate change concerns in

the American media (2003-2018) and primarily focuses on climate risk (not comprehensive ESG). Focusing on

the uncertainty linked to divergences between ESG rating agencies, Zeng et al. (2024) investigated the impact

of ESG rating uncertainty on sustainability uncertainty to examine its effect on institutional investor decisions

in China. They found that this uncertainty has a negative effect on institutional investment and weakens the

influence of ESG ratings on investment. Similarly, Sun et al. (2025) measured this risk by ESG rating

disagreements to study its effect on stock performance in the Chinese A-share market, focusing on immediate

and short-term market reactions and the risk of future stock price crashes. They found that higher levels of ESG

divergence significantly increase the risk of future stock price crashes. In the same vein, Zhang et al. (2025)

focused on the widespread confusion among investors regarding Environmental, Social, and Governance (ESG)

rankings assigned by rating agencies has underscored a critical issue in sustainable investing. They provided a

methodological framework enabling investors to make more informed decisions in the face of uncertainty related

to ESG ratings. Unlike other, more generic uncertainty indices (such as the World Uncertainty Index or the

Economic Policy Uncertainty Index), Ongan et al. (2025) developed a new ESG-based Sustainability

Uncertainty Index (ESGUI) for 25 countries by employing text mining techniques on the Economist Intelligence

Unit's monthly country reports, analysing the frequency of ESG-related keywords and uncertainty indicators.

This captures uncertainty directly linked to ESG issues, contrary to previous measures, ensuring a better

theoretical fit with research on the dynamics of sustainability, governance, or responsible investment and

avoiding informational noise linked to irrelevant forms of uncertainty (e.g., monetary, fiscal). Based on the

Economist Intelligence Unit (EIU) reports and economically relevant documents covering 25 countries, ESGUI

of Ongan et al. (2025) enriched with text mining techniques and corrected by the World Uncertainty Index

(WUI). This combination creates a robust index, contextualized at both the national and global levels,

guaranteeing strong informative value for financial markets, particularly for assessing sustainability risks. Torri

et al. (2025) provided a valuable methodological contribution to the construction of ESG indicators by

developing an axiomatic approach to measuring the risk and performance of sustainable investments, taking into

account not only financial returns, but also environmental, social, and governance (ESG) criteria. However, from

a macroeconomic and empirical perspective aimed at studying the impact of ESG uncertainty on financial

markets, the ESGUI of Ongan et al. (2025) stands out as a dynamic, global, and operational measure.

As it is important to study ESG perspectives, it is very important to examine ESG risks in financial markets. The

literature demonstrated that the success of ESG profiles is related to informational symmetry. Consequently,

ESG risk is explained by informational asymmetry, resulting in uncertainty and instability of financial markets.

By investigating the impact of ESG disclosures and institutional ownership on market information asymmetry

for 683 firms listed on the New York Stock Exchange, Siew et al. (2016) suggested that there is a statistically

significant negative relationship between ESG disclosures and bid-ask spread as a measure of market information

asymmetry. Using a Covalence EthicalQuote database, Capelle-Blancard (2019) studied market reaction to

positive and negative ESG information in environmentally sensitive industries. They demonstrated a negative

market reaction when negative ESG information circulates (via companies, media, NGOs), which underlines the

importance of transparency and reliability of ESG information. Furthermore, Avramov et al. (2022) analysed the

implications of uncertainty on the ESG profile of companies, and they found that higher uncertainty leads to a

higher market premium, reduced demand for shares, an increase in CAPM alpha, and effective beta. It illustrates

how ESG uncertainty constitutes an obstacle to sustainable investment.

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Other studies attributed ESG uncertainty to crisis periods. For instance, Yi et al. (2021) applied the event study

method and econometric models to investigate the impacts of COVID-19 on China's green bond market for the

first time. They found significant impacts of the COVID-19 pandemic on China's green bond market by

increasing the cumulative abnormal return (CAR) of the green bonds greatly, signalling that these assets have

not functioned as a shield against financial turmoil. Similarly, Liu (2022) examined the risky side of the green

bond market by measuring their reaction to extreme negative shocks (e.g., COVID-19), assessing and forecasting

the volatility of this market, and identifying the determining factors of this volatility. They found a high

instability of the green bond market during the pandemic; hence, the green characteristics of a financial

instrument do not mitigate risk under extreme conditions. They added that green bond volatility is primarily

influenced by the traditional bond market, followed by foreign exchange, equity, and green infrastructure

investments, and markets with higher spillover effects allow for better volatility forecasts, but accuracy decreases

when correlations become unstable.

Although companies in emerging markets, including the BRICS, have recently increased their ESG investments,

the positive effects on financial performance remain uncertain. This uncertainty is mainly due to institutional,

legal, cultural, and structural differences with developed markets, which make ESG initiatives more expensive,

less transparent, and sometimes seen as opportunistic. This explains why some studies suggest that ESG

uncertainty is greater in emerging markets than in developed markets. According to Feng et al. (2022), the growth

of ESG investment in emerging markets, particularly in China, faces challenges stemming from weak regulation

and low transparency. Confirming by He et al. (2022), emerging markets like China have a less developed

institutional and regulatory framework. In these markets, investor protection is weaker, laws and regulations

related to governance and transparency remain incomplete, and fraud control and sanction systems are less

stringent, reducing the incentive to adopt high-quality ESG. He et al. (2022) noted that in emerging markets,

ESG is sometimes used as a camouflage tool to conceal opportunistic behavior or mismanagement. Similarly,

Hao & He (2022) presented several indices of ESG uncertainty in emerging markets such as China. A recent

regulation, high information asymmetry, strategic use of CSR, and variable quality of reports are ESG

uncertainty indices.

Despite the significant contribution of these studies, all of them have focused on the unidirectional link from

ESG uncertainty to the financial market and have overlooked the reverse direction of this relationship.

Motivated by these previous studies, we examine the bidirectional relationship between sustainability

uncertainty and instability in emerging markets. For this, we connect ESG uncertainty with dynamic volatility

spillover across BRICS markets in two directions over a period spanning November 01, 2002, to March 31,

2025.

METHODOLOGY

To examine the relationship between sustainability uncertainty and dynamic volatility spillovers across BRICS

markets, we implement a three-stage approach. First, we apply the E-GARCH model to estimate the daily

conditional volatility of each BRICS market, allowing us to capture the persistence and asymmetry of market

shocks. Second, we use a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to investigate

how volatility spillovers between markets evolve, capturing dynamic interconnections that may change due to

external shocks. Finally, we conduct Granger causality tests to examine whether ESG-based sustainability

uncertainty can predict changes in market volatility, and vice versa.

E-GARCH model

The Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH) model, introduced by

Nelson (1991), extends the standard GARCH model by allowing for asymmetries in volatility. This means that

negative shocks (“bad news”) may affect market volatility differently than positive shocks (“good news”). The

2

E-GARCH(p,q) model expresses the logarithm of the conditional variance

as a function of past shocks and

past variances, ensuring that volatility is always positive. We choose E-GARCH because it effectively models

the high persistence and leverage effects commonly observed in financial markets. The E-GARCH(p,q) model

2

expresses the logarithm of the conditional variance

as:

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|

−

2

−

(

)

∑

(

)

∑

~

ln

The return series is defined as = µ + ,

is a constant term.

=

+

ln

+

(

−

[

]) +

(1)

=1

(

)

=

, where

0,1 . .

measures volatility persistence (ARCH effects). captures the symmetric impact of

past shocks (shock magnitude). The parameter captures the asymmetric impact of shocks. If < 0, negative

shocks (bad news) increase volatility more than positive shocks of the same magnitude. Since the logarithm of

2

(

)

variance ln

is modelled, the conditional variance

is always positive. The model captures persistent

volatility and shock aggregation often observed in financial markets.

TVP-VAR model

The Time-Varying Parameter Vector Autoregression (TVP-VAR) model (Antonakakis, Chatziantoniou, &

Gabauer, 2020) allows us to examine how the relationships between BRICS market volatilities change over time.

Unlike standard VAR models, TVP-VAR accommodates structural shifts and evolving interconnections among

markets. This is important because volatility transmission between markets is not constant and can be affected

by external shocks. The model estimates how shocks in one market influence other markets over time, providing

a dynamic map of risk transmission.

Mathematically, the model can be represented as

=

₋₁+ , where the coefficients

vary over time to

capture changing relationships. To analyze the spillover effects, we transform the model into a Vector Moving

Average (VMA) representation, which enables the computation of Generalized Impulse Response Functions

(GIRF) and Generalized Forecast Error Variance Decompositions (GFEVD). These tools measure the directional

impact of shocks from one market to another and the total connectedness within the BRICS market system.

Positive values indicate a market acts as a risk transmitter, while negative values indicate it is a risk receiver.

Their time-varying parameters and systemic perspective make the TVP-VAR exceptional models. TVP-VAR

permits the coefficients that describe how one variable affects another to evolve across time; this explains how

we chose this model to study risk transmission across BRICS markets. Indeed, the TVP-VAR model allows us

to follow and control the evolution of risk transmission and interconnectedness between BRICS markets. The

TVP-VAR model can be formulated as follows:

|

=

+ ,

(0, ∑ )

(0,

(2)

−1

͠

(

)

(

)

|

=

+ ,

)

(3)

−1

͠

−1

1

2

′

−2

(

=

)

(

)

With

=

.

−1

.

−

Where ₋₁represents all information available until t-1. and ₋₁represent m×1 vectors respectively. and

are, respectively, m×mp and m×m dimensional matrices. is an m×1 vector. is with dimension ( ²p×1).

2

The time-varying variance-covariance matrices ∑ and

are, respectively, m×m and

dimensional

×

2

(

)

matrices. Furthermore,

is the vectorization of , which is

_×1

vector.

To investigate dynamic behavior, the TVP-VAR model transforms into a Vector Moving Average (VMA)

representation. VMA representation facilitates the computation of both the Generalized Impulse Response

Function (GIRF) and the Generalized Forecast Error Variance Decomposition (GFEVD). The GIRF represents

the responses of all variables j, following a shock in the variable i. This representation incorporated a specified

forecast horizon for forward-looking analysis. In turn, the GFEVD represents the pairwise directional

connectedness from j to i and illustrates the influence variable j has on variable i in terms of its forecast error

variance share. These variance shares are then normalized, so that each row sums up to one, meaning that all

variables together explain 100% of the variable i’s forecast error variance. This is calculated as follows:

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2

−1

=1

∑

,

( )

=

,

2

−1

=1

∑

=1

,

∑

( )

∑

( )

With

= 1 and

=

. The denominator illustrates the cumulative effect of all the

,

=1

, =1

shocks, while the numerator shows the cumulative effect of a shock in the variable i. Based on the GFEVD, the

Total Connectedness Index (TCI) is represented as follows:

∑

_{, =1}, ≠

( )

,

( )

=

× 100

This connectedness approach shows how a shock in one variable is transmitted to other variables. We explain

the connectedness approach in three steps: in the first step, we look at the case where variable i transmits its

shocks to all other variables j, which is called total directional connectedness to others and is defined as:

∑

₌₁, ≠

( )

,

( )

→ ,

=

× 100

∑

( )

,

=1

In the second step, we calculate the directional connectedness variable i receives from variable j, which is called

total directional connectedness from others and is defined as:

∑

₌₁, ≠

( )

,

( )

← ,

=

× 100

∑

( )

,

=1

In the third and final step, we calculate the net total directional connectedness by subtracting the total directional

connectedness to others from the total directional connectedness from others. The net total directional

connectedness can be interpreted as the influence variable i has on the analysed network.

( )

← ,

=

→ ,

−

,

If

is positive, this means that variable i influences the network more than it is influenced by itself (variable i

,

is a transmitter of risk). Conversely, if

a receiver of risk).

is negative, meaning variable i is driven by the network (variable i is

,

Finally, we break down the net total directional connectedness even further to examine the bidirectional

( ) ( )

−

relationships by computing the net pairwise directional connectedness NPDC: NPDC

( )

= (

,

) × 100

,

( )

H

If NPDC_ijH ˃0 , it means that i dominates variable j. Conversely, if NPDC_ij

< 0, it means that variable j dominates variable i

Granger causality tests

Granger causality tests (Granger, 1969) were conducted to clarify the direction of causality between ESG

Uncertainty Indices (ESGUI) and BRICS volatility spillovers. In simple terms, the test examines whether past

values of one variable (e.g., ESGUI) can help predict another variable (e.g., market volatility). We test whether

ESG uncertainty “causes” changes in volatility spillovers and whether volatility spillovers can “cause” changes

in ESG uncertainty.

Data and descriptive statistics

Date

The data includes daily closing prices of BRICS market indices (Brazil, Russia, India, China, South Africa),

which are, respectively, BM & FBovespa, IMOEX, BSE Sensex, Shanghai Composite, and TOP40. They are

collected from DATASTREAM. BRICS returns are calculated as follows:

=

, −1

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The ESG uncertainty indices (ESGUI) of Ongan et al. (2025) are collected from the website of economic policy

uncertainty (http://www.policyuncertainty.com/). ESGUI indices are Global Equal Weighted and Global GDP

Weighted. Our data covers the period from November 1, 2002, to March 31, 2025.

ESG-Based Sustainability Uncertainty Index (ESGUI)

The ESG-based Sustainability Uncertainty Index (ESGUI), developed by Ongan, Gocer, and Isik (2025),

represents the first global composite measure specifically designed to quantify uncertainty related to

sustainability and ESG factors. The index provides a systematic way to capture how uncertainty stemming from

environmental (E), social (S), and governance (G) dimensions is reflected in macroeconomic dynamics and

investment behavior. Its construction integrates the conceptual framework of the World Uncertainty Index

(WUI) by Ahir et al. (2022) with ESG-related textual indicators, thereby filling a methodological gap in the

literature on sustainability risk measurement.

The ESGUI is constructed using text mining techniques applied to the Economist Intelligence Unit’s (EIU)

monthly country reports for a panel of 25 developed and developing economies covering the period 2002M11–

2024M09. These reports were selected due to their standardized structure, regular frequency, and global

comparability, reducing ideological and linguistic inconsistencies across countries. The construction of ESGUI

involves three main steps:

First step: Building the ESG sub-index

Separate indices are computed for environmental (E), social (S), and governance (G) dimensions based on the

relative frequency of selected ESG-related keywords within each monthly Economist Intelligence Unit EIU

report.

The keywords were extracted through natural language processing (NLP) using the PyMuPDF (Fitz) and Natural

Language Toolkit NLTK libraries. Common stop words were removed, and advanced models such as Latent

Dirichlet Allocation LDA and Bidirectional Encoder Representations from Transformers BERT were tested but

rejected in favor of a transparent keyword-based approach to maintain interpretability and replicability.

For each report, the frequency of ESG-related terms is divided by the total number of words in the text, producing

normalized monthly series:

(

)

(

)

(

)

=

,

=

,

=

These three dimensions are equally weighted to form the composite ESG sub-index:

1

=

(

+

)

3

Each sub-series is normalized using a Min–Max scaler (range 0–100), following the approach of Dang et al.

(2023) and Chung et al. (2022), to ensure comparability over time and across countries.

Second step: Building the Uncertainty sub-index (UI)

Following Ahir et al. (2022), the uncertainty component is derived from the frequency of the words “uncertain,”

“uncertainty,” and “uncertainties” within the same reports:

(

)

=

Third step: Constructing the ESGUI composite

The final index integrates the ESG and uncertainty components with equal weights:

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1

=

+

2

The global ESGUI is then computed in two forms:

(i) Equally weighted, giving the same importance to each country, and

(ii) GDP-weighted, using each country’s share in the total GDP of the 25-country sample.

Descriptive statistics

Table 1 provides a statistical summary of the daily returns in the BRICS markets and the ESG Uncertainty

Indices (Global Equal Weighted and Global GDP Weighted) of Ongan et al. (2025).

Table 1 shows that the average returns are positive for all markets considered. This indicates that all markets are

profitable, although the Russian market is the least profitable among them. The Jarque-Bera test rejects the null

hypothesis of normality at the 1% significance level for all series (p-value = 0). The skewness and kurtosis values

support the non-normality of the series, with non-zero skewness and high kurtosis. Most return distributions

exhibit significant deviations from normality, with extreme kurtosis values for Russia (~758), indicating heavy

tails. Additionally, skewness values reveal strong asymmetries in the Russian market.

The ARCH-LM test (Engle, 1982) reveals a significant ARCH effect in Brazil, India, China, and South Africa,

but not in the Russia series. Although the ARCH-LM test revealed no statistically significant ARCH effect for

the Russian stock market series, we adopt the Exponential GARCH (E-GARCH) framework (Nelson, 1991) for

all BRICS markets for several methodological and theoretical reasons. First, the absence of a significant ARCH

effect based on a preliminary diagnostic test does not rule out the presence of conditional heteroscedasticity in a

more flexible model such as E-GARCH. Unlike traditional GARCH models, the E-GARCH specification

captures asymmetric volatility effects (also called leverage effects), which are common in financial time series,

even when symmetric ARCH-type behavior is not identified. Second, E-GARCH models the logarithm of the

conditional variance, which relaxes the non-negativity constraint on variance parameters. This feature provides

extra flexibility and robustness, especially during volatile market conditions or structural breaks. Third, for

consistency and comparability across the BRICS markets, it is methodologically sound to use the same volatility

modeling framework for all series. Applying a unified model enables consistent examination of volatility

dynamics and cross-market spillovers, which is especially important in a multivariate or systemic context.

Therefore, using the E-GARCH model for all BRICS countries is justified by its statistical robustness and driven

by the research goals, including capturing asymmetric volatility responses to shocks and maintaining coherence

in cross-market analysis.

The Augmented Dickey–Fuller (ADF) unit root test of Dickey & Fuller (1981) and the Phillips–Perron (PP) unit

root test of Peter et al. (1988) indicate that all return series are stationary. Nevertheless, Global Equal Weighted

is not stationary according to the ADF and PP tests. Hence, we use the first difference for this series to obtain a

stationary series, as indicated by the ADF and PP tests.

Table 1 Descriptive statistics of BRICS and ESGU indices

Index

N

Mean

Max

Min

Std.De

v

Skew

Kurt

J-B stat

LM-stat

ARCH

effect

ADF-stat

PP-stat

BRAZIL

5853

0.0004

0.1344

-0.1577

0.0158

0.0248

0.0132

-

13,3100

26127,4193^***

1732.3(<2

.2e-16)

Yes

-

0,4581

17.422^***(0

.01)

5931.2^***(0

.01)

RUSSIA

INDIA

5853

0.0002

0.0005

1.1306

0.1642

-0.4047

-0.1374

14,845

7

757,7373

16,3398

139133118,0418^***

43559,8721^***

1.5252

(0.9999)

No

-

23.808^***(0

.01)

5746.4^***(0

.01)

-

721.07

(<2.2e-16)

Yes

-

0,4080

16.425^***(0

.01)

5594.1^***(0

.01)

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CHINA

5853

0.0004

0.1404

-0.1283

-0.0948

0.0153

0.0123

-

10.8949

15202,06^***

6502.2

(<2.2e-16)

Yes

-

0.0408

3.1749^***(0

.01)

1493.1^***(0

.01)

SOUTH

AFRICA

0.0003

0.0723

-

7,4575

4,4950

4,5381

4936,0055^***

751,2540^***

1618,2625^***

1453.1

(<2.2e-16)

Yes

-18.946^***

(0.01)

-5157.5^***

(0.01)

0,3043

Global Equal 5853

Weighted

28.6600

27.6700

46.8000

51.1200

17.6100 4.9900

16.6900 5.8000

0,4597

1,0332

-

-2.2935

(0.4541)

-8.6829

(0.6256)

Global GDP 5853

Weighted

-

3.5393^**(0.

0385)

21.966^**(0.

0466)

Note: Table 1 presents descriptive statistics of BRICS indices' daily returns and ESG Uncertainty indices (Global

Equal Weighted and Global GDP Weighted) of Ongan, Serdar, Gocer, Ismet, and Isik, Cem (2025), and

preliminary tests for the used data. The data range is from November 01, 2002, to March 31, 2025. ^***Indicates

statistical significance at the 1% level through the Jarque-Bera (JB) test. ADF and PP denote the statistics of

Augmented David et al. (1981) and Peter et al. (1988) unit root tests, respectively. ^***and ^**indicate the stationary

level at 1% and 5%.

Source: Author’s work

Empirical evidence

The conditional volatility estimation

Table 2 shows the estimates of the E-GARCH (1,1) model for BRICS returns. The results indicate a very high

β for all series, suggesting that all BRICS markets exhibit strong volatility persistence. This means that

volatility shocks have long-lasting effects, which is important for spillover analyses. γ₁is positive and significant

for all series, indicating that negative news increases volatility more than positive news in all BRICS markets.

We note that Russia and India present the strongest asymmetry, reflecting high political or macroeconomic risk

sensitivity. α₁, which indicates shock sensitivity, shows that South Africa and Russia respond the most to the

size of past shocks. However, China has the lowest α₁, indicating a more stable volatility response to events.

Furthermore, all markets (except South Africa) show positive and significant mean returns, reflecting higher

growth potential or risk premiums in these markets above all in India and China.

These premium results motivate us to understand volatility transmission dynamics among BRICS and to examine

the relationship between ESG uncertainty (ESGUI) and this spillover. This is the objective of the following

section.

Table 2 E-GARCH model estimates

−

=

+ β ln

+

|

⁻¹| +

−1

2

(

ln

)

(

)

−1

1

−

r = µ + ε_t,

Markets

ε_t~N(0, σ²_t)

ω(ct)

-0.1664^***

α (ARCH)

β (GARCH)

0.982 ^***

0.962^***

γ (Asymmetry)

0.126^***

µ

BRAZIL

0.0003^***

0.0004^***

0.0006^***

0.0005^***

0.0002

-0.0554 ^***

-0.0854^***

-0.0731 ^***

-0.0278^***

-0.0944 ^***

RUSSIA

-0.3092^***

-0.133^***

0.2046^***

0.170 ^***

INDIA

0.980 ^***

0.984^***

CHINA

-0.4775^***

-0.2019^***

0.167^***

SOUTH AFRICA

0.979 ^***

0.119 ^***

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Note: Table 2 presents the estimates of the E-GARCH model (Nelson, D.B. 1991) that is presented as follows:

|

−

2

−

***

(

ln

)

∑

(

ln

)

∑

=

+

(

−

[

]) +

.

indicates the significance level

=1

−

at 1%.

Source: Author’s work

BRICS volatilities and ESGU indices

Figure 1 shows BRICS volatilities and ESGUI indices (Global Equal Weighted and Global GDP Weighted) from

November 01, 2002, to March 31, 2025. Analysing this figure, we observe several key points.

The period spanning 2002 to 2003 is characterised by a peak in ESGUI and generally a high volatility in BRICS

markets. This peak in volatility is more marked in India and China, but less marked in Brazil and South Africa,

and it is negligible in Russia. This simultaneity in BRICS volatilities and ESGUI behavior can be explained as

follows. Enron, Sarbanes-Oxley Act, and corporate governance scandals (2002-2003) events that are marked in

Ongan et al. (2025) can be an explanation. A crisis of confidence caused by corporate governance scandals such

as Enron, WorldCom, and Tyco explained this peak on ESGUI. These events revealed major failures in internal

control and financial transparency systems, leading to a major regulatory reaction with the adoption of the

Sarbanes-Oxley Act in 2002. This American law increases the perceived ESG uncertainty in emerging

economies such as the BRICS, leading investors to be more afraid about their future cash flows, hence increasing

market volatility. These observations confirm the work of Capelle-Blancard (2019) and Siew et al. (2016), who

highlight that the dissemination of negative information on governance increases uncertainty and volatility via

an increase in information asymmetry. They also agree with Avramov et al. (2022) that increased uncertainty

about governance raises the risk premium and reduces demand for shares.

Between 2008 and 2009, a high level of volatility was observed in all BRICS markets. This peak is higher in

China, Brazil, India, and South Africa, and relatively higher in Russia. Simultaneously, a sudden rise in ESGUI

is detected. This period covers the Global Financial Crisis, leading to financial markets becoming more volatile.

During this period, uncertainty about financial governance, corporate accountability, and regulatory reform has

reached unprecedented levels. Emerging markets such as the BRICS were particularly affected by the global

capital flight and the collapse in commodity demand. As ESG concerns intensify, investors have re-evaluated

the institutional risk of developing economies, increasing market volatility. These observations are consistent

with Cornett et al. (2016), Lins et al. (2017), and Broadstock et al. (2021), who show that ESG plays a relatively

stabilizing role, but that increased uncertainty about corporate governance and responsibility in times of crisis

amplifies volatility. Also, they align with Liu et al. (2023), suggesting that in times of high uncertainty, if ESG

is not integrated or perceived as credible, volatility spillovers remain strong.

During 2010-2011, due to ongoing instability from the global financial crisis, the developing Eurozone sovereign

debt crisis, increasing geopolitical tensions such as the Arab Spring, the explosion of the Deepwater Horizon rig,

and a major nuclear disaster resulting in radioactive leaks, ESG uncertainty stayed high. These events raised

concerns about political and institutional risks, especially in emerging economies. As ESG frameworks

developed, uncertainty about the direction and enforcement of sustainability-related regulations also increased

investor caution. This leads BRICS markets to experience renewed volatility, which was aggravated by external

shocks and fluctuations in commodity markets. These observations are in line with Yi et al. (2021) and Liu

(2022), who show that “green” or ESG-strong assets do not always neutralize risk under extreme shocks,

particularly in emerging markets like the BRICS group.

By observing the period from 2015 to 2016, we note a peak in ESGUI, which is attributed to policy risk stemming

from regulatory shifts, such as the Paris Agreement, as well as reputational and legal risks (as seen in the

Volkswagen case), and social unrest and governance concerns (as exemplified by Black Lives Matter activism).

Faced with this uncertainty, BRICS markets, often perceived as riskier, experienced capital withdrawals,

portfolio adjustments, and increased responsiveness to ESG information, leading to higher conditional volatility.

This aligns with Zeng et al. (2024), Sun et al. (2025), and Zhang et al. (2025) that the negative impact of ESG

divergences and controversies on investment decisions increases perceived risk and volatility.

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Between 2020 and 2025, Figure 1 shows that sustainability uncertainty has experienced a continuous increase,

explained by several critical events such as the COVID-19 pandemic, Black Lives Matter, Green Recovery

Efforts, Extreme weather events, and ESG reporting mandates. This contributed to higher volatility levels across

BRICS financial markets through capital flows, investor sentiment, and global supply chains. These observations

converge with Ferriani & Natoli (2021), Broadstock et al. (2021), and Boubaker et al. (2022), who show that

ESG perception strongly influences capital flows in times of high uncertainty, but also with Hao & He (2022)

and Feng et al. (2022), who highlight that in the BRICS, institutional weakness amplifies these effects.

Fig. 1 BRICS volatilities and ESGU indices

Note: Figure 1 presents E-GARCH Conditional volatility of BRICS markets returns and ESGU indices that are

Global Equal Weighted and Global GDP Weighted by Ongan, Serdar, Gocer, Ismet, and Isik, Cem (2025) from

November 2002 to March 2025.

Source: Author’s work

Volatility spillover among BRICS markets

In this paragraph, we examine volatility spillovers among BRICS markets by applying the TVP-VAR model of

Antonakakis, Chatziantoniou, & Gabauer (2020). Table 3 presents ADF and PP stationarity tests of BRICS

volatilities. The results demonstrate that all BRICS volatility series are stationary.

Table 3 Stationarity tests of BRICS volatilities

Market volatility

BRAZIL

ADF

PP

-7.779994^***

-6.242132^***

-8.248202^***

-6.305515^***

-7.971513^***

-110.96670^***

-344.74825^***

-148.19730^***

-95.65445^***

-143.15921^***

RUSSIA

INDIA

CHINA

SOUTH AFRICA

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***

Note: Table 3 reports the results of the ADF and PP stationarity tests for BRICS volatilities.

indicates the

stationarity of the series at 1% level.

Source: Author’s work

Figure 2 presents the Total Connectedness Index (TCI) across BRICS markets' volatilities. As illustrated in Fig.

2, the TCI value exhibits significant temporal variation throughout the observation period. A Strong

interconnectedness of BRICS markets during the period spanning 2003 to 2004, since TCI has an extremely high

level (˃75%) following the accounting crisis (post-Enron), in a context of governance reforms. The period

spanning 2007 to 2009 shows another peak of TCI (~ 65%), tracing the effect of the global financial crisis. New

peaks of TCI are observed between 2011 and 2012 that are linked to the Eurozone sovereign debt crisis,

geopolitical tensions (Arab Spring), and Fukushima, confirming studies of Hsiao et al. (2024) that crisis events

explain markets' interconnectedness, and confirming Ijaz et al. (2025) that geopolitical conflicts enhance the

spillover effect.

A strong peak of TCI is detected in the period spanning 2020 to 2022 (~ 70%), reflecting the impact of the

COVID-19 pandemic and ESG uncertainty resulting from Green recovery and ESG mandates. Our finding

emphasizes a new role, ESG uncertainty, which is added to health crises to fuel spillovers. This aligns with

Ongan et al. (2025), who state that new ESG obligations and the “green recovery” have added a new channel of

uncertainty. Between 2023 and early 2025, the TCI index first declined, then recovered. This is due to a gradual

decline following the COVID crisis (normalization effect) in 2023 (Diebold & Yilmaz, 2012), but in 2024–2025,

a clear recovery is visible. This rebound is linked to the resurgence of geopolitical tensions (such as the prolonged

Russia-Ukraine war), the acceleration of ESG policies, and increasing extreme weather events. This recovery

aligns with work showing that geopolitical uncertainty and climate risks become lasting catalysts for spillovers

(Broadstock & Zhang, 2021; Nguyen et al., 2023; Ijaz et al., 2025). These results motivate us to explore the

dynamic relationship between ESG uncertainty and BRICS connectedness within the volatility context. That is

the objective of the following sections.

Fig.2. Dynamic Total Connectedness (TVP–VAR (0.99,0.99) with three lags and a 12-step-ahead forecast)

Note: Figure 2 presents the Dynamic Total Connectedness Index (TCI), which is estimated using a Time-

Varying Parameter Vector Autoregression (TVP–VAR) model with forgetting factors set at 0.99 for both the

state and covariance equations, incorporating 3 lags and a 12-step-ahead forecast horizon. This specification

captures the evolving spillover effects and connectedness dynamics among the BRICS markets over the sample

period.

Source: Author’s work

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Table 4 presents averaged connectedness measures. Diagonal elements denote idiosyncratic shocks, while off-

diagonal elements signify interdependencies between variables. Analysis of each BRICS series indicates that the

majority of market volatility fluctuations (˃ 67%) originate from internal shocks. This suggests that BRICS

markets remain largely self-determined in terms of volatility, confirming Diebold & Yilmaz (2012) that

emerging markets, although interconnected, retain a high proportion of self-generated volatility, often linked to

domestic factors (monetary policy, institutional instability, market structure).

Table 4 shows that China and South Africa transmit, respectively, 32% and 33% volatility to other markets,

indicating that they are the main emitters of volatility in the BRICS group. However, Russia, India, and Brazil

are the weakest emitters (29%, 28%, and 28% respectively). Due to its dependence on raw materials and its high

financial integration, South Africa is often a source of shocks. Despite capital controls, China influences the

BRICS through its economic size and trade ties (Mensi et al., 2014, and Bouri et al., 2020). Russia, South Africa,

and Brazil receive, respectively 38%, 37%, and 35% of the volatility from other markets. They are the most

vulnerable markets to external shocks, while India and, above all, China are less affected (27% and 12%).

Analysing the results of net spillover (TO-FROM), we suggest that Russia, Brazil, and South Africa (+9%, +7%,

and +4% respectively) are net volatility issuers, but China and India (- 20% and -1% respectively) are net

volatility receivers. This shows that China and India appear as volatility dampeners rather than catalysts,

confirming Mensi et al. (2014). However, Russia appears as a net contributor, especially during episodes related

to sanctions or energy volatility, justifying the results of Tiwari et al. (2025) that markets highly exposed to

commodity prices (such as oil and gas for Russia) and geopolitical tensions often act as structural sources of

volatility.

Table 4 Average Connectedness Table

BRAZIL RUSSIA

INDIA

0.06

CHINA SOUTH AFRICA FROM

0.72

0.09

0.08

0.06

0.11

0.35

+ 0.07

0.10

0.71

0.08

0.09

0.11

0.38

+ 0.09

0.03

0.68

0.04

0.12

- 0.20

0.10

0.11

0.09

0.08

0.67

0.37

+ 0.04

0.28

0.29

0.28

0.32

0.33

1.49

BRAZIL

0.06

RUSSIA

0.72

INDIA

0.08

CHINA

0.07

SOUTH AFRICA

TO

0.27

- 0.01

NET(TO-FROM)

Note: Table 4 shows the averaged connectedness results obtained from TVP–VAR (0.99,0.99) with three lags

and a 12-step-ahead forecast. Diagonal elements denote idiosyncratic shocks, while off-diagonal elements

signify interdependencies between variables.

Source: Author’s work

To conclude, Table 4 shows a significant but asymmetric interconnectedness of volatility among the BRICS,

with China acting as the net receiver (absorbing shocks), Russia as a major source of volatility, and the other

countries playing more balanced roles. Asymmetric connectedness is a well-documented phenomenon (Baruník

et al., 2017), reflecting differences in market size, financial openness, and risk profile.

Figure 3 traces a network of BRICS volatility spillover. Visual inspection of Fig. 3 reveals that Brazil transmits

high volatility to South Africa. This confirms Brazil's position as a net transmitter, observed in the table (Net =

+0.07). Russia also transmits a lot to South Africa. This reinforces its status as the largest net emitter of volatility

(Net = +0.09). These two dominant relationships place South Africa in a major receiver position, which perfectly

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corresponds to the fact that South Africa has a high FROM (0.33), confirming that it receives more volatility

than the others. However, the dynamic connectedness between other markets is less intense.

Figure 3 visually confirms what the Average Connectedness Table reveals numerically. Russia and Brazil are

the main emitters of volatility, but South Africa is the main receiver, with strong links from both countries. This

can be explained on one side by the strong dependence of Russia and Brazil on raw materials, which have highly

volatile prices and are more vulnerable to global shocks. This can be a trigger of market risk, hence sources of

regional and international contagion. Furthermore, the period studied covers several critical events that help to

explain volatility spillovers, such as the war of Russia-Ukraine, the COVID-19 pandemic, and other uncertainty

sources (Bouri et al., 2021; Tiwari et al., 2025). We must note also that Brazil, as a large emerging market in

Latin America, is often a regional proxy for investors. Consequently, a shock in Brazil can trigger chain reactions

in other BRICS markets. On the other hand, South Africa is a relatively small and open economy, more

dependent on foreign investment and international trade, explaining its vulnerability to external shocks (Mensi

et al., 2014, and Baruník et al., 2017), particularly those coming from major trading partners such as Russia and

Brazil. What makes the South African market more sensitive to external shocks is its significant public debt and

recurring social tensions (inequalities, strikes, political instability). China is relatively isolated as a net receiver.

Several reasons can explain this result. The Chinese market is still partially closed to foreign capital, with

controls on financial flows and the exchange rate, limiting the transmission of its volatility to the external market

(low spillover "TO") (Diebold & Yilmaz, 2014). Furthermore, the Chinese economy is more diversified and run

by the state, which absorbs the magnitude of shocks to financial markets. However, China remains exposed to

external shocks (notably via global trade), which explains why it receives more volatility than it transmits (net

= –0.20 in the table). Consequently, overall dynamic interconnectedness is moderate, but some bilateral

relationships are dominant and asymmetric (Diebold & Yilmaz, 2012; Baruník et al., 2017). These results are

consistent with the literature on shock transmission in emerging markets, which highlights that countries rich in

natural resources or exposed to geopolitical tensions are often emitters of volatility, while more fragile or

dependent economies are receivers.

Fig. 3. A network of Volatility spillover

Note: Figure 3 illustrates the directed interactions within a network of BRICS market volatilities. Node size

represents the magnitude of net pairwise directional connectedness.

Source: Author’s work

We continue this investigation by quantifying total directional connectedness to differentiate between net

transmitters (positive index values) and net receivers (negative index values) of volatility (risk) shocks over the

time series. Figure 4 shows this characterization. Notably, the Brazilian market is a highly volatile emitter during

several periods (2008, 2010-2012, post-2018). We note also that it transmits more volatility than receives,

reflecting its role as a shock propagator, particularly during crises (global financial crisis, local political

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instability, pandemic). Concerning the Russian market, we note a one-time net contributor, but it often tends to

be close to zero. In 2022, the peaks could be explained by the war in Ukraine and sanctions. The Indian market

shows an alternation between the role of transmitter and receiver, without clear dominance. A strong volatility

transmission of this market is detected in 2008, 2013, 2020, and 2022-2023, linked to the financial crisis, the

COVID-19 pandemic, its growing integration into global markets, and ESG tensions. However, the Chinese

market presents a volatile role. More frequently, it is a net receiver until 2015, with occasional emitter episodes

during specific stresses (stock market crisis of 2015-2016, trade tensions with the US) (Baruník et al., 2017). For

the South African market, we observe an almost constant net emission of volatility, with very significant peaks

(2008, 2020), reflecting its structural sensitivity to external and ESG shocks. The South African market plays a

pivotal role in transmitting volatility within the BRICS. These asymmetric results call for differentiated hedging

strategies across BRICS markets.

Fig. 4. Dynamic net total directional connectedness

Source: Author’s work

Note: Fig. 4 shows the dynamic net total directional connectedness between BRICS volatilities in a period

separately from November 01, 2002, to March 31, 2025

Source: Author’s work

To analyse the impact of market i on market j, we use Net Pairwise Directional Connectedness among BRICS

markets. Figure 6 shows the results of this measurement. The Brazilian market experienced several peaks of

volatility transmission to the Russian market (in 2003, 2006, 2009, 2014, 2018, 2023). Conversely, during these

times, the Russian market was a strong receiver of risk. Several key events explain this pattern, such as the global

financial crisis and commodity crisis. However, volatility spillover between Brazil and India seems more

contained. Episodes where Brazil becomes a net emitter are evident around 2008, 2013 (taper tantrum), and 2020

(COVID). Looking at the Brazil-China dynamic through volatility shows moderate variability but generally stays

around zero, indicating a balanced bilateral relationship. Between Brazil and South Africa, spillover is highly

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volatile, especially around 2008, 2011, and 2020, suggesting Brazil often acts as a net transmitter of volatility to

South Africa. Similarly, for the Russia-India relationship, Russia appears to be a net transmitter during certain

periods. However, the Russia-China dynamic remains relatively stable, with some significant dips during times

of geopolitical crisis. High directional volatility between Russia and South Africa during 2008, 2014, and 2020;

periods when Russia appears to be a net emitter of shocks. The India-China net directional connectivity varies

greatly, especially between 2005 and 2015. India often appears to be a net transmitter, except during periods of

crisis when this reverses. Between India and South Africa, a strong asymmetry (very positive peaks) in

directional connectedness is observed, indicating that India appears to play a dominant role as a net transmitter

of volatility to South Africa, especially around 2008 and 2020. The net pairwise directional connectedness

between China and South Africa is highly volatile, with periods where China receives a significant amount of

risk (negative values around 2003-2004 and 2008). After 2010, China became a net transmitter more frequently.

Generally, these results show that Brazil, India, and China often appear as net sources of volatility for the other

BRICS. However, South Africa is frequently a net receiver. Russia plays a mixed role, but often appears to be a

net receiver during periods of geopolitical tension. These findings suggest that periods of global crises (2008,

2011, 2020) generally amplify directional connectivity and reveal structural roles.

Fig. 5. Net Pairwise Directional Connectedness.

Note: Fig. 5 shows the net pairwise directional connectedness between BRICS volatilities in a period separately

from November 01, 2002, to March 31, 2025.

Source: Author’s work

ESG Uncertainty and Volatility Spillover

To examine the relationship between ESG Uncertainty and Volatility Spillover among BRICS markets, we apply

the Granger causality tests for several lags. First, we test the relationship between the Global Equal Weighted,

as an index of ESG uncertainty (Ongan et al., 2025), and the Total Connectedness Index TCI, as a measure of

spillover, for several lags. Second, we test the relationship between the Global GDP Weighted, as a second index

of ESG uncertainty (Ongan et al., 2025), and the Total Connectedness Index TCI for several lags. Third, we

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apply Granger causality tests between the Global Equal Weighted and the Net Pairwise Directional

Connectedness for several lags.

The results of these tests are shown in Tables 5 and 6. First, Table 5 reveals a unidirectional causality from

BRICS TCI to ESG Uncertainty (Global Equal Weighted) for a one lag, suggesting that changes in BRICS

volatility connectivity influence global ESG uncertainty in the very short term. This supports the idea that

regional financial turmoil can lead to an immediate reassessment of global ESG risks (market reactions, investor

repositioning, etc.). However, there is no causality from ESGUI to TCI for any lag, indicating that changes in

global ESG uncertainty do not seem to predict or directly affect volatility dynamics between BRICS markets, at

least not in the following days. Similarly, the Granger causality tests between the Global GDP Weighted and the

TCI (Tests 3and 4) show that TCI causes ESG uncertainty (GDP Weighted) only at lag 5, but no significant

causality from ESG uncertainty (GDP weighted) to TCI for all lags tested.

These findings suggest no strong evidence that ESG uncertainty indices cause spillover volatility (TCI). This

result doesn’t confirm Sun et al. (2025), who found that higher levels of ESG uncertainty, measured by ESG

divergence, significantly increase the risk of future stock price crashes, increasing their share volatility and

volatility spillover. However, TCI appears to influence ESGUI, indicating that the dynamics of interconnected

volatility in BRICS markets could affect sustainability uncertainty. This is consistent with the view that financial

markets, as barometers of the real economy and the political environment, can trigger a rapid reassessment of

extra-financial risks (He et al., 2023). Thus, a high volatility contagion reflects a climate of instability that fuels

the perception of uncertainty regarding sustainability.

We interpret these results as follows. The TCI reflects the intensity and spread of risks and uncertainties among

BRICS markets. When the interconnected volatility increases, this can create fear and uncertainty among BRICS

investors that are connected to global investors, and this reveals a deterioration or overall economic instability,

prompting stakeholders to reassess ESG risks and thus modify the ESGUI indices. Furthermore, ESGUI doesn’t

react to TCI can be explained by its slower reactivity. ESG uncertainty indices are often constructed from

indicators, reports, aggregated data, or surveys that do not react instantly. In contrast, financial markets are highly

sensitive and reactive to events, creating a time lag where market volatility precedes and statistically "causes"

the change in the ESGUI. Moreover, these results can be specific to BRICS markets. Knowing that the

sustainability concept in BRICS markets is being developed in comparison to developed markets, these emerging

markets are slowly integrating ESG information. Furthermore, BRICS markets are subject to more immediate

economic, political, and geopolitical influences than ESG concerns (Feng et al., 2022; Hao & He, 2022). This

can explain why ESG's effect on spillovers may be weak or delayed. These findings imply that BRICS markets

play the role of an ESG risk "barometer" and the need to improve ESG integration in BRICS asset prices as an

opportunity to improve transparency, disclosure, and the consideration of ESG factors in investment decisions.

This is consistent with Liu et al. (2023) and Boubaker et al. (2022b) that it is crucial to integrate ESG data into

financial decision-making to better anticipate and mitigate the spread of systemic risks.

To search: what is the Pairwise Directional Connectedness that causes ESG uncertainty? We apply in Table 6

the Granger causality tests between Global Equal Weighted and Net Pairwise Directional Connectedness for lags

from 01 to 05.

Table 5. Granger causality tests between ESGUI and TCI

Test 1: Global Equal Weighted does not Granger-cause BRICS Total Connectedness

Lag

1

F-Stat

0.5682

0.5080

0.3437

2

3

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4

5

0.2910

1.0033

Test 2: BRICS Total Connectedness does not Granger-cause Global Equal Weighted

Lag

1

F-Stat

5.4535^***

1.8199

1.0541

1.0442

1.6993

2

3

4

5

Test 3: Global GDP Weighted does not Granger-cause BRICS Total Connectedness

Lag

1

F-Stat

0.3770

1.1892

0.8911

1.1379

1.2513

2

3

4

5

Test 4: BRICS Total Connectedness does not Granger-cause Global GDP Weighted

Lag

1

F-Stat

0.9368

2.0147

1.6819

0.9978

3.6316^***

2

3

4

5

Note: Table 5 presents the Granger causality test results between ESGUI (Global Equal Weighted) and BRICS

Total Connectedness Index from 1 to 5 lags (Test 1 and Test 2), and the Granger causality test results between

ESGUI (Global GDP Weighted) and BRICS Total Connectedness Index from 1 to 5 lags (Test 3 and Test 4). ^***

indicates significance of F-statistic at 1% level and reject H₀that Global Equal Weighted does not Granger-cause

Net Pairwise Directional Connectedness

Source: Author’s work

Table 6 presents the results of Granger causality tests between Global Equal Weighted and Net Pairwise

Directional Connectedness for lags from 01 to 05. Table 5 shows several significant causal relationships (p <

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0.05) between ESGUI and certain pairwise directional connectedness. Bidirectional volatility spillovers between

Brazil-China, Russia-India, Russia-China, and China-India significantly cause ESG uncertainty. This reflects

the dominant influence of major economies, principally China and Russia, and to a lesser extent India and Brazil,

on sustainability concerns. Furthermore, the significance of Granger causality tests is bidirectional, suggesting

a strong interdependence in ESG dynamics and financial risks. This result echoes the work of Liu et al. (2023)

and Zhang et al. (2025), who emphasize the importance of understanding the complex interactions between

financial volatility and ESG risks, particularly in emerging markets where sustainability is still under institutional

construction.

These findings help investors to better anticipate ESG risk transmissions between markets, thus guiding

diversification and risk management. Understanding which countries are major sources of ESG risk can guide

financial and environmental oversight policies, particularly in the context of sustainable transition.

Table 6. Granger causality tests between ESGUI and Net Pairwise Directional Connectedness

Test 5: Global Equal Weighted does not Granger-cause Net Pairwise Directional Connectedness

FROM

TO

F-Stat

BRAZIL

RUSSIA

0.7628

BRAZIL

INDIA

1.2988

BRAZIL

CHINA

18.4527^***

0.8070

SOUTH AFRICA

BRAZIL

RUSSIA

0.7628

RUSSIA

INDIA

5.2152^***

12.8702^***

0.0008

RUSSIA

CHINA

RUSSIA

SOUTH AFRICA

BRAZIL

INDIA

1.2988

INDIA

RUSSIA

CHINA

5.2152^***

8.0164^***

0.0576

INDIA

SOUTH AFRICA

BRAZIL

RUSSIA

INDIA

CHINA

18.4527^***

12.8702^***

8.0164^***

0.4317

CHINA

SOUTH AFRICA

BRAZIL

SOUTH AFRICA

0.8070

RUSSIA

0.0008

INDIA

0.0576

CHINA

0.4317

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Note: Table 6 presents the Granger causality test result between ESGUI (Global Equal Weighted) and Net

Pairwise Directional Connectedness of BRICS markets from 1 to 5 lags (Test 5). ^***indicates significance of F-

statistic at 1% level and reject H₀that Global Equal Weighted does not Granger-cause Net Pairwise Directional

Connectedness.

Source: Author’s work

CONCLUSIONS, IMPLICATIONS, AND LIMITATIONS

Conclusions

This study examines the bidirectional relationship between ESG-based sustainability uncertainty (ESGUI) and

dynamic volatility spillovers among BRICS stock markets from November 2002 to March 2025. To forecast

BRICS' conditional volatilities, we utilize the E-GARCH model. We also explore a Time-Varying Parameter

Vector Autoregression (TVP-VAR) framework to analyze the dynamic volatility spillovers across BRICS

markets, and conduct Granger causality tests to investigate the relationship between ESG uncertainty indices of

Ongan, Gocer, and Isik (2025) and the dynamic volatility spillover. The goal is to provide new evidence on how

sustainability-related uncertainty and financial market interconnectedness interact in emerging economies.

Our empirical results reveal several key findings:

 Volatility spillovers in BRICS markets: There are substantial but asymmetric spillovers. Brazil, Russia,

and South Africa generally act as net transmitters of volatility, while China and India tend to be net

receivers. These roles, however, fluctuate over time.

 Impact of ESG uncertainty: Volatility interconnectedness peaks during periods of heightened ESG

uncertainty, coinciding with major global crises, such as the 2002–2003 corporate governance scandals,

the 2008–2009 global financial crisis, the 2010–2011 Eurozone debt crisis and Fukushima disaster, the

2015–2016 Paris Agreement period, and the COVID-19 pandemic.

 Causality patterns: Granger causality tests indicate that volatility spillovers (TCI) significantly

influence ESG uncertainty in the short term, whereas the reverse effect is generally absent. This suggests

that financial market turbulence in BRICS can act as a leading indicator of sustainability risk perceptions,

while ESG uncertainty reacts more slowly due to reliance on aggregated reports and surveys.

 Bilateral relationships: At the pairwise level, bidirectional causality between ESGUI and specific

volatility transmissions (notably Brazil–China, Russia–India, Russia–China, and China–India) highlights

the dominant role of larger BRICS economies in shaping ESG risk dynamics.

Implications

Our findings expand the literature on the interaction between ESG factors and financial contagion, particularly

in emerging markets. The asymmetric volatility spillovers indicate that market size, resource dependence, and

exposure to geopolitical risks shape risk transmission. Moreover, the causal link from market interconnectedness

to ESG uncertainty suggests that financial markets can act as early indicators of sustainability risks.

Regulatory implications: Volatility spillovers intensify during periods of high ESG uncertainty, highlighting

the need for macroprudential monitoring. Regulators should integrate ESG-based uncertainty indicators into

early warning systems, promote harmonized ESG reporting standards, enhance transparency, and encourage

cross-border data sharing among BRICS countries. These measures can reduce contagion risks driven by ESG

uncertainty.

Implications for investors and portfolio managers: Identifying net transmitters and receivers of volatility

provides actionable insights for risk diversification. Investors can dynamically adjust portfolio allocations based

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on spillover intensity and ESG uncertainty trends. Understanding that volatility often precedes changes in ESG

uncertainty enables portfolio managers to anticipate ESG shocks and optimize sustainable investment strategies.

Implications for policymakers and corporates: ESG uncertainty and financial volatility are interlinked, calling

for integrated sustainability and financial stability policies. Cooperation among major BRICS markets (e.g.,

China, Brazil, India) and integration of sustainability metrics into financial supervision can strengthen resilience

against global ESG shocks.

Consequently, ESG uncertainty is not only an environmental or social issue—it is a financial stability concern.

By monitoring ESG uncertainty and volatility spillovers, regulators and investors can make informed decisions

to manage risk and enhance market stability.

Limitations

Despite the theoretical and practical implications, several limitations should be acknowledged.

Limitations of the ESGUI index: The ESGUI, as developed by Ongan et al. (2025), has inherent

methodological constraints. First, it relies on English-language EIU reports, which introduces a language bias

and may reduce accuracy for non-English-speaking countries, including some BRICS members. Second, the

index covers a limited set of 25 countries, potentially underrepresenting smaller or less-documented emerging

economies, creating a geographical bias. Third, the equal weighting of the three ESG pillars (Environmental,

Social, Governance) may oversimplify their relative importance in different national contexts. Fourth, the

keyword-based text-mining approach imposes contextual and semantic limitations: it may miss nuances such as

tone, irony, negation, or emerging sector-specific ESG issues. Finally, institutional and cultural heterogeneity

implies that ESG-related uncertainty may manifest differently across regulatory and market environments.

Future research could address these limitations by incorporating multilingual sources, sentiment-aware or

contextual NLP models, and disaggregated ESGUI measures at sectoral or regional levels.

Limitations of empirical methods: While the TVP-VAR and Granger causality frameworks are effective for

detecting dynamic relationships, they do not fully account for potential non-linearities, structural breaks, or

higher-order network effects that may further influence the ESG-volatility relationship.

Declarations

Funding: The author received no financial support for this research.

Acknowledgements: Not applicable.

Conflict of Interest: The author declares no conflicts of interest.

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