Global Uncertainty and BRICS+ Equity Markets: Spillovers from VIX, Geopolitical Risk, and U.S. Macro-Financial Shocks

Abstract

This paper investigates how global uncertainty and macro-financial shocks transmitted to BRICS+ equity markets between April 2016 and July 2025. A vector autoregressive (VAR) framework, complemented by Granger-causality tests, variance decompositions, and impulse response functions, is employed to examine four key drivers: U.S. financial market volatility (VIX), geopolitical risk (GPRD), U.S. inflation expectations (T5YIE), and the U.S. term spread (T10Y3M). The findings show that the VIX functions both as a recipient and a transmitter of shocks, amplifying volatility across BRICS+ markets, with India, Brazil, and the Gulf states acting as important nodes in the global contagion network. By contrast, geopolitical risk shocks have only short-lived effects on both U.S. yields and emerging equity markets. Shocks to U.S. inflation expectations and yield-curve dynamics transmit quickly to BRICS+ markets but dissipate within a few days, underscoring efficient market adjustment. Overall, the evidence points to a multipolar structure of global contagion in which BRICS+ markets exert growing influence alongside the United States. These results offer important implications for risk management, portfolio diversification, and policy coordination under heightened uncertainty.

1. Introduction

Financial globalization has significantly amplified the interconnectedness of markets, thereby making financial systems more vulnerable to cross-market contagion during episodes of heightened uncertainty. Early research emphasized global spillovers as a natural byproduct of integration (Kaminsky and Reinhart 2000; Forbes and Rigobon 2002), but more recent evidence shows that systemic risk in emerging markets is often heterogeneous, time-varying, and regionally compartmentalized rather than uniformly tied to U.S. dynamics (Das et al. 2022). At the same time, the channels through which crises are transmitted have become increasingly complex, with the 2008 global financial crisis, the COVID-19 epidemic, the 2022 Russia–Ukraine war, and U.S.–China tensions illustrating that upper-tail and lower-tail contagion may propagate through different mechanisms, such as portfolio rebalancing, wealth effects, credit spreads, and investor sentiment (Yuan et al. 2023). These insights highlight that contemporary contagion dynamics cannot be explained solely through traditional models of financial integration but require a broader analytical approach that accounts for regional structures and crisis specific transmission mechanisms.

Within this complex financial architecture, the BRICS countries, with their extension to BRICS+, have consolidated their position as major actors with a growing influence on global capital dynamics. These economies are not limited to absorbing shocks from developed markets; they also act as net transmitters of volatility and investor sentiment under conditions of heightened economic policy uncertainty, thereby reshaping international financial connectedness (Luong 2025). Recent evidence further confirms that global factors exert significant and asymmetric effects on BRICS equity markets, underscoring their dual role as both recipients and sources of financial shocks (Wang and You 2023). Their interdependence extends beyond the traditional domains of equities and bonds, encompassing linkages through energy, currency markets, and digital assets. Evidence shows that Bitcoin can transmit risk to financial markets through foreign-exchange (FX) channels, with contagion effects more pronounced in FX markets than in equity indices (Soepriyanto et al. 2025). In addition, interactions among geopolitical risk, energy prices, and cryptocurrencies shape co-movements within BRICS economies, confirming the role of crypto-assets as an additional channel of global financial spillovers (Li et al. 2021; Lau et al. 2024). These findings highlight that BRICS+ simultaneously act as both transmitters and recipients of shocks, reinforcing their systemic relevance and adding to the complexity of risk assessment and global financial stability.

In the analysis of contagion mechanisms, global uncertainty indicators occupy a pivotal role. The VIX, widely recognized as the “fear gauge,” encapsulates investors’ perceptions of volatility and has consistently emerged as a key driver of international financial spillovers (Whaley 2000; Kang et al. 2019). Complementing this, the geopolitical risk index (GPRD) reflects global tensions and conflicts that have the potential to disrupt capital flows and undermine macro-financial stability (Caldara and Iacoviello 2022a). Equally important are U.S. macro-financial variables, notably five-year inflation expectations (T5YIE) and the term spread between long- and short-term Treasury yields (T10Y3M), which serve as central indicators of monetary policy orientation and business cycle conditions, exerting systemic influence on international markets (Bayaa and Qadan 2024; Kamal et al. 2025). More recently, empirical evidence highlights the bidirectional nature of these relationships. Geopolitical risks in BRICS markets significantly affect domestic volatility and can spill over globally. Their responses to monetary policy uncertainty also contribute to the transmission of risk across the international markets, although with lower volatility than in G7 economies (Wen et al. 2022). However, most existing studies focus primarily on advanced economies. These contrasting findings highlight the need to reassess the role of global uncertainty indicators in the BRICS+ context, where financial integration has deepened but structural asymmetries remain significant.

Nevertheless, the existing literature reveals several critical limitations that justify the need for deeper analysis. First, the research has focused almost exclusively on developed markets and on the VIX as the main determinant of international contagion, addressing BRICS+ dynamics only marginally and overlooking their potentially asymmetric mechanisms of shock transmission (Su et al. 2019). This approach constrains understanding of how emerging economies, through their institutional structures and dependence on trade and energy flows, may generate impulses that propagate globally. Second, although geopolitical risk has increasingly been incorporated into empirical models, analyses have been confined mainly to advanced economies or to narrow groups of emerging markets, underestimating the role of regions included in BRICS+. Yet recent evidence suggests that these economies can amplify shock transmission through energy and trade channels, and their omission leaves an incomplete picture of global contagion (Jiang et al. 2023; Kayani et al. 2024). Third, the bidirectional dynamics of shocks are often treated unilaterally: while most studies document how uncertainties from the U.S. affect emerging markets, relatively little empirical evidence addresses reverse transmission, from emerging markets to developed economies. This lack of consensus on the directionality of spillovers constrains policymakers’ and investors’ ability to design truly effective risk-management strategies. This gap is particularly relevant for the BRICS+ structure, where large economies such as China, India, and Brazil are increasingly integrated into global capital markets and have the potential to generate feedback effects on U.S. financial conditions and global uncertainty indices. Failing to consider these reverse spillovers may therefore bias empirical findings and limit the development of effective risk management and policy coordination strategies

These limitations underscore the need for analytical frameworks that not only revisit contagion across global markets, a theme extensively studied in the literature but also integrate less explored U.S. macro-financial indicators such as T5YIE and T10Y3M. These measures are critical because they capture the forward-looking dimension of monetary policies and the predictive power of the yield curve for economic activity, yet they have rarely been considered in spillover studies involving BRICS+ markets, despite evidence that inflation dynamics play a central role in shaping growth outcomes in these economies (Manamperi 2014). In this context, the use of a VAR framework is particularly justified, as it allows the detection of dynamic and bidirectional interactions, while impulse response functions trace how shocks to these indicators are transmitted and absorbed across markets. Accordingly, this study contributes to the literature by offering an integrated perspective that goes beyond the conventional focus on VIX and GPRD, empirically testing whether BRICS+ markets are not only recipients, but also potential transmitters of shocks driven by T5YIE and T10Y3M. In addition, by extending the analysis to the broader BRICS+ structure, this study responds directly to recent calls in the literature for the examination of emerging markets as active participants in global risk transmission. To the best of our knowledge, this is the first study to jointly investigate the role of T5YIE and T10Y3M in shaping contagion dynamics between BRICS+ equity markets and global uncertainty indicators.

The next sections present the main contributions of the related literature, followed by the description of the collected data and methodology employed. The empirical section highlights the mechanisms of shock transmission, and the concluding section synthesizes the practical and theoretical implications of the study.

2. Literature Review

2.1. Global Financial Interconnectedness and BRICS+ Dynamics

The interconnectedness of financial markets has been extensively documented, initially through cointegration theories and VAR models (Engle and Granger 1987; Johansen 1995), and more recently through quantitative connectedness frameworks (Diebold and Yilmaz 2014). Empirical evidence shows that shocks propagate rapidly across equities, bonds, energy, and commodities, thereby amplifying global vulnerability (Adeosun et al. 2023; Ren et al. 2022). Within this setting, BRICS and the extended BRICS+ have emerged as pivotal actors, not only as recipients of shocks from advanced economies but also as transmitters through channels of trade, cross-border investment, and energy markets. This dual role underscores their growing systemic importance in shaping global financial dynamics. Their influence extends beyond conventional equity linkages, shaping global stability through energy, commodity, and increasingly digital-asset markets. Recent evidence further indicates that BRICS+ spillovers intensify during episodes of elevated global uncertainty and monetary tightening, underscoring their evolution from primarily shock recipients to active transmitters within the international risk network. Consistent with this view, Mensi et al. (2025) show that emerging economies such as South Africa and Mexico act as persistent net transmitters of volatility, reinforcing the systemic role of large emerging markets in global contagion dynamics.

Recent contributions have begun to address this gap by illustrating that spillovers in BRICS markets are not confined to equities. For instance, Shao et al. (2025) report strong contemporaneous linkages between BRICS staple grain futures and U.S. markets, with Brazil and the U.S. acting as major net contributors of shocks, while South Africa functions primarily as a receiver. Similarly, Ouyang et al. (2024) construct systemic risk indices for global commodities and show that while G7 economies are more exposed to extreme price fluctuations, BRICS countries exhibit heterogeneous vulnerabilities depending on their commodity structures. In addition, behavioral dynamics, particularly herding effects, have been shown to amplify spillovers across G7 and BRICS markets (Gouta and BenMabrouk 2024). These findings highlight that BRICS economies are not only recipients of global shocks but also significant transmitters, both through conventional financial channels and through commodity and sentiment contagion mechanisms.

2.2. Volatility, Uncertainty, and Geopolitical Risk

The VIX index has become one of the most widely used measures of expected volatility and has been linked to the equity risk premium. Numerous studies confirm that volatility captured by the VIX affects both emerging and developed markets. Altinkeski et al. (2024) show that while the VIX exerts a strong and lasting influence globally, developed markets tend to absorb shocks more efficiently, whereas emerging markets remain more vulnerable to prolonged volatility transmission. Focusing specifically on BRICS, Sharma et al. (2019) analyses volatility spillovers among BRICS equity volatility indices and identify a long-run equilibrium relationship across several countries. Using an MGARCH framework, they show strong intertemporal linkages and heterogeneous spillover intensities over time, underscoring that VIX dynamics are not purely local but also reflect regional and global interconnectedness. However, most empirical studies have focused on spillovers to advanced economies, neglecting emerging blocs such as BRICS+. For instance, Papathanasiou and Koutsokostas (2024) find that VIX shocks predominantly transmit volatility to developed European markets, suggesting that their propagation mechanisms may differ significantly across emerging financial systems, which represents a gap that this study addresses.

In parallel, geopolitical risk has gained prominence with the development of the GPRD index, which has been widely applied to evaluate the impact of conflict and uncertainty on financial flows. The evidence is mixed: some studies highlight transitory effects (Pástor and Veronesi 2013), while others emphasize persistent and structural impacts, especially during crisis periods (Bouri et al. 2018). Recent analyses of the Russia–Ukraine war demonstrate that geopolitical shocks exert asymmetric effects on emerging versus advanced markets (Gopal et al. 2025; Gökgöz et al. 2024). Extending this direction, Zhang et al. (2025) show that volatility transmission between the BRICS and US equity markets varies with the nature of the shock: contagion was persistent and strong during the COVID-19 crisis, but short-lived and pulse-like during the Russo–Ukrainian conflict. However, most of these studies have focused either on global markets or on bilateral linkages, without examining the extended BRICS+ structure. Moreover, Ouyang et al. (2025) report that geopolitical risk exerts a strong and persistent influence on advanced economies, suggesting that its effects may differ across market groups. This highlights the need for further empirical assessment of whether geopolitical shocks in BRICS+ markets propagate differently compared to those in developed economies. These findings highlight that BRICS markets’ vulnerability to global uncertainty is context-dependent, shaped by both the origin and the type of the crisis.

2.3. U.S. Macro-Financial Variables and Global Spillovers

The role of U.S. macro-financial indicators in driving global spillovers has been extensively documented. The slope of the U.S. Treasury yield curve, measured by the spread between long- and short-term maturities, is widely regarded as a robust predictor of recessions (Estrella and Hardouvelis 1991) and as a proxy for market expectations of future economic conditions (Gürkaynak et al. 2010). Empirical evidence shows that changes in the term spread influence international capital flows and asset pricing in both advanced and emerging economies (Sarno et al. 2016). Likewise, inflation expectations extracted from Treasury Inflation-Protected Securities (TIPS) and breakeven rates (T5YIE) provide forward-looking indicators that are increasingly employed to evaluate risk–return trade-offs in global financial markets (Christensen et al. 2010). Recent evidence confirms that U.S. inflation expectations play a central role in transmitting monetary shocks internationally and shaping investor sentiment in global equity markets (Chiang and Chen 2023).

These macro-financial variables serve as channels through which fiscal policy and U.S. monetary shocks are transmitted internationally. Evidence shows that movements in U.S. yield spreads and inflation expectations generate volatility spillovers to emerging-market equities, bonds, and currencies, with TIPS-based measures providing useful insights into these dynamics (Papathanasiou et al. 2023). More recent studies demonstrate that the magnitude of these spillovers differs across countries and over time, with emerging markets often displaying heightened sensitivity (Castello and Resta 2022). However, while much of the literature focuses on the transmission of U.S. variables to international markets, relatively little is known about potential feedback effects from BRICS+ to the U.S., leaving bidirectional dynamics underexplored.

2.4. Research Gaps, Hypotheses, and Contribution

The review highlights three main shortcomings in the existing literature. First, prior research remains fragmented, often analyzing global uncertainty indices, geopolitical risk, or U.S. macro-financial variables separately rather than within an integrated framework (Antonakakis et al. 2014). Recent evidence confirms that uncertainty indicators exert joint and nonlinear effects across global financial systems, yet most studies still treat them separately rather than simultaneously (Chen 2023). Second, BRICS+ markets have received limited attention, despite their growing influence through both traditional and alternative contagion channels (Salisu et al. 2022). However, much of the existing work focuses on volatility connectedness alone, overlooking broader transmission mechanisms and cross-market interdependencies within BRICS financial systems (Ijaz et al. 2025). Third, most studies emphasize unidirectional transmission from the U.S. to emerging markets, with limited exploration of possible feedback effects, even though some evidence suggests their existence (Su et al. 2019). This leaves a major research gap concerning whether BRICS+ markets act solely as shock absorbers or also as net transmitters of volatility back to the United States and global uncertainty indicators. To address these gaps, this study applies a VAR framework with impulse response functions, enabling a simultaneous investigation of interactions between BRICS+, the VIX, GPRD, and U.S. macro-financial variables. Studies such as Li et al. (2024) highlight the impact of geopolitical risks and economic policy uncertainty on stock market spillovers within BRICS, yet the literature still lacks a unified approach that incorporates both global uncertainty indices and U.S. macro-financial indicators, leaving bidirectional spillovers underexplored.

Accordingly, the study formulates the following testable hypotheses:

H1: 

Shocks originating from U.S. macro-financial indicators are transmitted to BRICS+ equity markets, generating significant volatility spillovers.

H2: 

BRICS+ markets are not only recipients but also potential transmitters of shocks, producing bidirectional spillovers toward U.S. financial indicators and global uncertainty indices.

3. Data and Methodology

3.1. Data

This study employs daily data spanning the period from April 1, 2016, to July 2, 2025. The selected period is justified by two considerations: first, it captures major episodes of global financial and geopolitical uncertainty (Brexit aftermath, U.S.–China trade tensions, COVID-19 crisis, Russia–Ukraine conflict, and post-pandemic monetary tightening cycles), and second, consistent data availability for all BRICS+ members begins after 2016, ensuring sample comparability and reliability. Equity market indices for BRICS+ economies were retrieved from Refinitiv Datastream (Refinitiv 2025) and Bloomberg (Bloomberg L.P. 2025), covering SSE (China), RTSI (Russia), BSE30 (India), BVSP (Brazil), JTOPI (South Africa), TASI (Saudi Arabia), ADX (Abu Dhabi), and EGX30 (Egypt). Iran, Indonesia and Ethiopia, despite being full BRICS+ members, were excluded due to the lack of consistent and continuous daily equity data for the full sample period, as well as limited market liquidity and accessibility in global databases. Moreover, their recent accession to BRICS+ does not allow for a sufficiently long time series required for reliable VAR estimation and impulse response analysis. VIX was sourced from CBOE (CBOE Volatility Index), while GPRD was obtained from the Caldara and Iacoviello database (Caldara and Iacoviello 2022b). To capture U.S. macro-financial dynamics, T5YIE and T10Y3M were collected from FRED (Federal Reserve Bank of St. Louis 2024) and serve as forward-looking indicators of monetary policy expectations and yield-curve behavior.

The choice of national benchmark indices is motivated by their ability to reflect overall market performance in each country. These indices are widely recognized as standard barometers of national equity markets and are extensively employed in the literature on international spillovers and financial integration. Focusing on country-level benchmarks allows the analysis to capture broad market dynamics, avoiding sectoral or firm-specific idiosyncrasies, and is consistent with the objective of assessing systemic cross-market volatility transmission across BRICS+ economies during periods of heightened uncertainty. All equity indices were analyzed in their domestic currency denominations, following established practices in spillover literature, to preserve country-specific market dynamics (Diebold and Yilmaz 2014). This approach prevents distortion of equity return behavior that would arise from currency conversion and ensures that spillover effects reflect genuine equity market interdependence rather than exchange rate volatility.

All price series were converted into daily log returns to ensure stationarity, in line with prior studies (Antonakakis et al. 2014). The macro-financial indicators (T5YIE, T10Y3M) and GPRD were employed in first differences when required, based on standard unit root tests (ADF and PP). Data reliability was ensured by cross-checking values across alternative sources and by trimming the data at the 1% and 99% levels to mitigate the influence of extreme outliers.

3.2. Descriptive Statistics

Table 1. Descriptive statistics.

Variable	Mean	Variance	Skewness	Kurtosis	Jarque–Bera	ERS	Q(20)	Q2(20)
VIX	0.000	0.006	1.365 ***	9.027 ***	9079.079 ***	−21.753 ***	29.161 ***	149.459 ***
	(0.953)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
SSE	0.000	0	−0.596 ***	8.336 ***	7239.386 ***	−22.377 ***	37.812 ***	256.549 ***
	(0.931)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
RTSI	0.000	0.005	0.088 *	1014.613 ***	105,088,582.902 ***	−29.865 ***	526.912 ***	611.303 ***
	(0.904)		(0.075)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
BSE.30	0.000 **	0	−1.588 ***	23.269 ***	56,299.866 ***	−20.146 ***	56.998 ***	1197.153 ***
	(0.019)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
BVSP	0.000 *	0	−1.189 ***	18.753 ***	36,477.877 ***	−18.027 ***	83.800 ***	2779.189 ***
	(0.099)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
JTOPI	0.000	0	−0.512 ***	7.312 ***	5564.267 ***	−22.180 ***	27.564 ***	1769.483 ***
	(0.223)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
TASI	0.000	0	−1.156 ***	10.933 ***	12,748.277 ***	−3.509 ***	77.542 ***	745.889 ***
	(0.330)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
ADX	0.000 *	0	−0.335 ***	19.170 ***	37,560.939 ***	−6.158 ***	90.914 ***	2742.648 ***
	(0.060)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
EGX.30	0.001 **	0	−0.368 ***	6.074 ***	3821.802 ***	−7.854 ***	76.255 ***	484.591 ***
	(0.020)		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
T5YIE	0.000	0.002	−0.466 ***	5.922 ***	3668.308 ***	−19.449 ***	23.139 ***	541.638 ***
	(0.593)		(0.000)	(0.000)	(0.000)	(0.004)	(0.004)	(0.000)
T10Y3M	−0.001	0.003	−0.143 ***	3.344 ***	1149.565 ***	−19.720 ***	15.729 *	541.677 ***
	(0.425)		(0.004)	(0.000)	(0.000)	(0.094)	(0.094)	(0.000)
GPRD	0.000	0.19	−0.023	2.094 ***	436.279 ***	−5.454 ***	492.601 ***	316.776 ***
	(0.999)		(0.648)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Values in parentheses represent p-values.

reports the descriptive statistics. Equity indices display near-zero mean returns, consistent with the stylized facts of financial time series. Volatility varies markedly across markets, with BVSP and EGX30 exhibiting higher standard deviations, whereas Gulf markets (TASI, ADX) appear comparatively more stable. RTSI records the widest return range (±2.3), reflecting the impact of financial and geopolitical turbulence. VIX shows substantially higher variability relative to equity indices, capturing episodes of global uncertainty. All return series exhibit negative skewness, indicating a higher probability of extreme negative returns, a common feature in emerging markets. Kurtosis values exceed 3 for all series, confirming leptokurtic distributions with fat tails, which justify the use of econometric techniques robust to non-normality. Among macro-financial indicators, T5YIE remains relatively stable yet responds to inflation expectations and monetary policy shocks, while T10Y3M fluctuates more strongly and frequently turns negative during recessionary phases as the yield curve inverts. GPRD displays the greatest dispersion, in line with its construction as a measure of tail geopolitical risk episodes rather than normal market fluctuations. The return distributions exhibit significant skewness, with some indices showing pronounced asymmetry indicating a tendency toward extreme positive or negative returns. Additionally, the excess kurtosis values are markedly high across the indices, reflecting heavy-tailed distributions and a higher likelihood of extreme market events than suggested by normal distributions.

3.3. Unit Root Tests

To ensure the suitability of the data for VAR modeling, the stationarity of all series was examined using the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests. The results, reported in

Table 2. Unit root test results.

Variable	ADF	PP	1%	5%	10%	p-Value
VIX	$-$26.955	$-$49.028	$-$3.960	$-$3.410	$-$3.120	0.0000
SSE	$-$26.128	$-$49.019	$-$3.960	$-$3.410	$-$3.120	0.0000
RTSI	$-$44.339	$-$76.480	$-$3.960	$-$3.410	$-$3.120	0.0000
BSE30	$-$27.810	$-$49.602	$-$3.960	$-$3.410	$-$3.120	0.0000
BVSP	$-$27.077	$-$49.953	$-$3.960	$-$3.410	$-$3.120	0.0000
JTOPI	$-$26.632	$-$43.928	$-$3.960	$-$3.410	$-$3.120	0.0000
TASI	$-$18.566	$-$37.397	$-$3.960	$-$3.410	$-$3.120	0.0000
ADX	$-$30.296	$-$43.786	$-$3.960	$-$3.410	$-$3.120	0.0000
EGX30	$-$21.855	$-$37.144	$-$3.960	$-$3.410	$-$3.120	0.0000
T5YIE	$-$26.164	$-$41.990	$-$3.960	$-$3.410	$-$3.120	0.0000
T10Y3M	$-$27.117	$-$44.839	$-$3.960	$-$3.410	$-$3.120	0.0000
GPRD	$-$40.571	$-$72.972	$-$3.960	$-$3.410	$-$3.120	0.0000

, indicate that the null hypothesis of a unit root is rejected at the 1% significance level for all variables. This confirms that the series are stationary in their employed form, thereby eliminating concerns about spurious regression and validating their use in the subsequent econometric analysis.

3.4. Methodology

To investigate the dynamic interdependencies among BRICS+ equity indices, U.S. macro-financial indicators, and global risk measures, this study employs a vector autoregressive (VAR) framework. Originally introduced by Sims (1980), VAR models are well suited for multivariate time series in which each variable is a linear function of its own past values and those of other variables. Their flexibility and ability to capture complex dynamic interactions have established them as standard tools in applied macro-financial research (Lütkepohl 2005; Becketti 2020). Compared with alternative approaches such as structural equation models, GARCH-type frameworks, or quantile regressions, the VAR methodology offers a more parsimonious way to capture both short- and medium-term interdependencies without imposing restrictive assumptions on the transmission channels.

The general form of a VAR(p) can be written as:

$${Y_t=A}_1{Y}_{t-1}+A_2{Y}_{t-2}+\cdots +A_p{Y}_{t-p}+B{X}_t+u_t,$$

(1)

where Y_t is an n × 1 vector of endogenous variables (BRICS+ equity returns, VIX, GPRD, T5YIE, T10Y3M), X_t is an m × 1 vector of exogenous controls, D_t contains deterministic terms, A_i, and B, are parameter matrices, and u_t is a vector of innovations assumed to be white noise.

In this study, two alternative VAR specifications are estimated. Both models include the BRICS+ benchmark indices (SSE, RTSI, BSE30, BVSP, JTOPI, TASI, ADX, EGX30) together with the U.S. macro-financial variables T5YIE and T10Y3M. The distinction lies in the choice of the global uncertainty proxy: the first specification incorporates VIX, whereas the second replaces it with GPRD to assess whether the transmission of shocks differs between financial market volatility and geopolitical risk.

Formally, the first specification is:

$$\left[\begin{array}{c}{SSE}_t\\ {RTSI}_t\\ {BSE30}_t\\ {BVSP}_t\\ {JTOPI}_t\\ {TASI}_t\\ {ADX}_t\\ {EGX30}_t\\ {T5YIE}_t\\ {T10Y3M}_t\\ {VIX}_t\end{array}\right]={\alpha }_0+A_1\left[\begin{array}{c}{SSE}_{t-1}\\ {RTSI}_{t-1}\\ \dots \\ {VIX}_{t-1}\end{array}\right]+\cdots +A_k\left[\begin{array}{c}{SSE}_{t-k}\\ {RTSI}_{t-k}\\ \dots \\ {VIX}_{t-k}\end{array}\right]+\left[\begin{array}{c}{\epsilon }_{1,t}\\ {\epsilon }_{2,t}\\ \dots \\ {\epsilon }_{11,t}\end{array}\right]$$

(2)

The second specification replaces VIX with GPRD:

$$\left[\begin{array}{c}{SSE}_t\\ {RTSI}_t\\ {BSE30}_t\\ {BVSP}_t\\ {JTOPI}_t\\ {TASI}_t\\ {ADX}_t\\ {EGX30}_t\\ {T5YIE}_t\\ {T10Y3M}_t\\ {GPRD}_t\end{array}\right]={\alpha }_0+A_1\left[\begin{array}{c}{SSE}_{t-1}\\ {RTSI}_{t-1}\\ \dots \\ {GPRD}_{t-1}\end{array}\right]+\cdots +A_k\left[\begin{array}{c}{SSE}_{t-k}\\ {RTSI}_{t-k}\\ \dots \\ {GPRD}_{t-k}\end{array}\right]+\left[\begin{array}{c}{\epsilon }_{1,t}\\ {\epsilon }_{2,t}\\ \dots \\ {\epsilon }_{11,t}\end{array}\right]$$

(3)

This dual specification enables disentangling the effects of VIX from GPRD, thereby providing a richer perspective on the sources of uncertainty affecting BRICS+ markets. Following the estimation of the VAR, impulse response functions (IRFs) are employed to trace the time-path impacts of shocks originating in one variable on all others in the system. The forecast error variance decomposition (FEVD) measures the extent to which the forecast uncertainty of a given variable can be attributed to innovations in other variables. These tools capture both the magnitude and persistence of spillovers across financial, macroeconomic, and uncertainty channels. This methodological framework offers a comprehensive basis for assessing the role of BRICS+ markets as both recipients and transmitters of global shocks.

4. Results and Discussion

4.1. VIX Shocks and Spillovers to BRICS+ Equity Markets

The Granger-causality results (

Table A1. VAR Granger test results.

Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$
VIX	SSE	0.646	1	0.422	RTSI	T10Y3M	0.786	1	0.375	JTOPI	EGX30	0.389	1	0.533	EGX30	JTOPI	6.762	1	0.009
VIX	RTSI	0.502	1	0.479	RTSI	ALL	28.63	10	0.001	JTOPI	T5YIE	3.417	1	0.065	EGX30	TASI	0.467	1	0.494
VIX	BSE30	3.978	1	0.046	BSE30	VIX	48.49	1	0.000	JTOPI	T10Y3M	11.79	1	0.001	EGX30	ADX	2.865	1	0.091
VIX	BVSP	4.162	1	0.041	BSE30	SSE	4.238	1	0.040	JTOPI	ALL	184.9	10	0.000	EGX30	T5YIE	4.904	1	0.027
VIX	JTOPI	0.176	1	0.675	BSE30	RTSI	0.247	1	0.619	TASI	VIX	29.28	1	0.000	EGX30	T10Y3M	1.871	1	0.171
VIX	TASI	10.33	1	0.001	BSE30	BVSP	21.16	1	0.000	TASI	SSE	3.161	1	0.075	EGX30	ALL	43.67	10	0.000
VIX	ADX	2.361	1	0.124	BSE30	JTOPI	3.496	1	0.062	TASI	RTSI	0.657	1	0.418	T5YIE	VIX	1.765	1	0.184
VIX	EGX30	0.854	1	0.355	BSE30	TASI	1.285	1	0.257	TASI	BSE30	13.00	1	0.000	T5YIE	SSE	0.011	1	0.918
VIX	T5YIE	0.008	1	0.929	BSE30	ADX	25.48	1	0.000	TASI	BVSP	0.692	1	0.406	T5YIE	RTSI	0.206	1	0.650
VIX	T10Y3M	0.156	1	0.693	BSE30	EGX30	1.275	1	0.259	TASI	JTOPI	6.333	1	0.012	T5YIE	BSE30	12.22	1	0.000
VIX	ALL	21.88	10	0.016	BSE30	T5YIE	5.154	1	0.023	TASI	ADX	13.00	1	0.000	T5YIE	BVSP	1.494	1	0.222
SSE	VIX	43.62	1	0.000	BSE30	T10Y3M	6.774	1	0.009	TASI	EGX30	0.099	1	0.753	T5YIE	JTOPI	9.219	1	0.002
SSE	RTSI	0.016	1	0.898	BSE30	ALL	206.4	10	0.000	TASI	T5YIE	3.485	1	0.062	T5YIE	TASI	5.580	1	0.018
SSE	BSE30	11.23	1	0.001	BVSP	VIX	0.065	1	0.798	TASI	T10Y3M	0.244	1	0.621	T5YIE	ADX	7.035	1	0.008
SSE	BVSP	2.826	1	0.093	BVSP	SSE	0.372	1	0.542	TASI	ALL	81.13	10	0.000	T5YIE	EGX30	5.995	1	0.014
SSE	JTOPI	15.38	1	0.000	BVSP	RTSI	0.000	1	0.986	ADX	VIX	8.277	1	0.004	T5YIE	T10Y3M	2.463	1	0.117
SSE	TASI	4.939	1	0.026	BVSP	BSE30	5.852	1	0.016	ADX	SSE	4.698	1	0.030	T5YIE	ALL	63.68	10	0.000
SSE	ADX	0.470	1	0.493	BVSP	JTOPI	0.623	1	0.430	ADX	RTSI	1.028	1	0.311	T10Y3M	VIX	3.047	1	0.081
SSE	EGX30	0.643	1	0.423	BVSP	TASI	6.247	1	0.012	ADX	BSE30	15.53	1	0.000	T10Y3M	SSE	0.116	1	0.733
SSE	T5YIE	1.762	1	0.184	BVSP	ADX	1.941	1	0.164	ADX	BVSP	4.987	1	0.026	T10Y3M	RTSI	0.470	1	0.493
SSE	T10Y3M	0.536	1	0.464	BVSP	EGX30	1.491	1	0.222	ADX	JTOPI	2.578	1	0.108	T10Y3M	BSE30	16.26	1	0.000
SSE	ALL	113.2	10	0.000	BVSP	T5YIE	4.899	1	0.027	ADX	TASI	21.99	1	0.000	T10Y3M	BVSP	0.118	1	0.731
RTSI	VIX	1.896	1	0.169	BVSP	T10Y3M	0.125	1	0.724	ADX	EGX30	0.005	1	0.942	T10Y3M	JTOPI	4.383	1	0.036
RTSI	SSE	0.213	1	0.645	BVSP	ALL	23.51	10	0.009	ADX	T5YIE	6.908	1	0.009	T10Y3M	TASI	4.742	1	0.029
RTSI	BSE30	1.465	1	0.226	JTOPI	VIX	83.43	1	0.000	ADX	T10Y3M	0.772	1	0.380	T10Y3M	ADX	9.916	1	0.002
RTSI	BVSP	5.523	1	0.019	JTOPI	SSE	3.481	1	0.062	ADX	ALL	81.05	10	0.000	T10Y3M	EGX30	2.118	1	0.146
RTSI	JTOPI	0.449	1	0.503	JTOPI	RTSI	0.304	1	0.582	EGX30	VIX	10.25	1	0.001	T10Y3M	T5YIE	0.414	1	0.520
RTSI	TASI	1.322	1	0.250	JTOPI	BSE30	32.17	1	0.000	EGX30	SSE	0.494	1	0.482	T10Y3M	ALL	42.49	10	0.000
RTSI	ADX	1.851	1	0.174	JTOPI	BVSP	4.892	1	0.027	EGX30	RTSI	0.005	1	0.944
RTSI	EGX30	0.054	1	0.816	JTOPI	TASI	0.188	1	0.665	EGX30	BSE30	0.222	1	0.637
RTSI	T5YIE	1.661	1	0.197	JTOPI	ADX	24.61	1	0.000	EGX30	BVSP	0.731	1	0.393

Note: “Eq” denotes the dependent equation, “Excl” the excluded variable, χ² the test statistic, “df” the degrees of freedom, and p(χ²) the p-value.

) reveal a highly interconnected global financial network in which shocks propagate rapidly across emerging-market equities, regional indices, and U.S. interest-rate indicators. At the center of this network lies VIX, functioning both as a barometer and a generator of global risk. Notably, BSE30, BVSP, and TASI exert significant influence on VIX, indicating that risk transmission is not unidirectional from advanced to emerging markets and that volatility can also originate within major emerging economies. Once activated, VIX becomes an active transmitter of shocks: it significantly affects SSE, BSE30, JTOPI, TASI, ADX, EGX30, and T10Y3M, confirming its dual role as both a receiver and an amplifier of global risk within the U.S.—BRICS+ interconnected system.

Cross-market equity spillovers reinforce this view of deep integration. As shown by the Granger-causality results, SSE responds not only to VIX but also to shocks from BSE30, BVSP, JTOPI, and TASI, underscoring China’s exposure to both regional neighbors and distant emerging markets. BVSP, in turn, transmits significant impulses to RTSI, highlighting a South–South transmission channel within the BRICS sphere. This pattern is in line with findings by Gnagne et al. (2024), who show that BRICS markets increasingly act as transmitters of systemic risk. BSE30 stands out as both vulnerable and influential: it is affected by VIX, SSE, BVSP, ADX, T5YIE, and T10Y3M, yet also propagates shocks to BVSP and TASI. This dual role reflects India’s growing integration in global capital markets and its sensitivity to both equity and macro-financial shocks, consistent with the evidence presented by Kumar and Dua (2024), who document how U.S. yield dynamics interact with capital flows to BRICS and reinforce cross-market dependence.

Regional dynamics in Asia, Africa, and the Middle East further demonstrate the global reach of these spillovers. South Africa’s JTOPI is affected by VIX, SSE, BSE30, BVSP, ADX, T5YIE, and T10Y3M, which reflects strong financial exposure to both global volatility conditions and U.S. monetary policy signals. Saudi Arabia’s TASI responds to volatility shocks from VIX, SSE, and BSE30, while Abu Dhabi’s ADX is influenced by both global and regional factors, including VIX, T5YIE, and TASI, as well as BRICS indices such as BVSP and BSE30. North Africa is integrated as well, with Egypt’s EGX30 shaped by shocks from VIX, JTOPI, ADX, and T5YIE. Importantly, the results confirm the existence of reverse spillovers: together with BSE30, JTOPI, TASI, and ADX, EGX30 feeds directly into U.S. five-year inflation expectations, confirming a feedback channel from emerging markets to U.S. financial conditions rather than a purely unidirectional dependence.

Finally, the U.S. yield curve is far from insulated. T10Y3M is significantly influenced by VIX, BSE30, JTOPI, TASI, and ADX, indicating that global equity dynamics and Middle Eastern markets shape U.S. monetary policy expectations. Similarly, T5YIE reacts to spillovers from BRICS+ markets, suggesting that inflation expectations in the United States embed external risk factors transmitted through financial globalization. Taken together, T5YIE and T10Y3M function not only as conduits of domestic fundamentals but also as receivers of international equity shocks, confirming the existence of bidirectional shock transmission between the United States and BRICS+ financial systems.

The IRFs provide further insights into the short-run dynamics of VIX shocks (Figure 1). SSE exhibits an immediate and sharp decline, followed by a rapid reversal within one to two periods, which aligns with the view that Chinese markets absorb external volatility swiftly through state-driven liquidity injections and targeted policy interventions. RTSI shows the most volatile response, with a sluggish return to equilibrium over three periods, reflecting its structural vulnerability to global risk sentiment and commodity dependence. BSE30 also reacts negatively but with a smaller magnitude, stabilizing more quickly than RTSI, indicating that India, while financially integrated, maintains a relatively stronger market shock-absorption capacity due to diversified capital flows and macroprudential buffers.

BVSP and JTOPI both register sharp negative responses to VIX shocks, though the effects dissipate rapidly. For BVSP, the shock is absorbed within a few periods, while JTOPI’s smaller amplitude suggests greater resilience, possibly due to a higher share of domestic institutional investors and lower exposure to speculative capital flows. Similar short-lived declines are also evident for TASI, ADX, and EGX30, confirming that Gulf and North African markets are exposed to volatility spillovers, although the effects remain transitory and do not generate persistent contagion dynamics within the regional markets.

Turning to U.S. financial indicators, T5YIE exhibits a modest increase following a VIX shock, whereas T10Y3M steepens sharply, reflecting flight-to-safety flows into long-term Treasuries. Both effects dissipate within two to three periods, highlighting the resilience of U.S. markets and the anchoring of inflation expectations even under heightened global uncertainty. Overall, these results lend strong support to H1, confirming that global volatility shocks captured by the VIX are transmitted across BRICS+ equity markets and U.S. yields, albeit with heterogeneous intensity and short-lived persistence. The time-path behavior of these dynamics underscores the dominance of short-horizon contagion mechanisms over longer-term structural spillovers. The detailed VAR regression results underlying these responses are presented in Supplementary Table S1.

4.2. GPRD Shocks and Spillovers to BRICS+ Equity Markets

The Granger-causality results (

Table A2. Granger test results.

Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$
SSE	RTSI	0.002	1	0.963	BSE30	T10Y3M	10.73	1	0.001	TASI	T5YIE	2.305	1	0.129	T5YIE	TASI	0.038	1	0.845
SSE	BSE30	8.578	1	0.003	BSE30	ALL	199.7	10	0.000	TASI	GPRD	0.456	1	0.499	T5YIE	ADX	55.94	1	0.000
SSE	BVSP	12.75	1	0.000	BVSP	SSE	0.000	1	1.000	TASI	T10Y3M	0.777	1	0.378	T5YIE	EGX30	20.28	1	0.000
SSE	JTOPI	29.25	1	0.000	BVSP	RTSI	0.000	1	0.992	TASI	ALL	65.82	10	0.000	T5YIE	GPRD	0.954	1	0.329
SSE	TASI	0.454	1	0.501	BVSP	BSE30	1.734	1	0.188	ADX	SSE	4.535	1	0.033	T5YIE	T10Y3M	0.188	1	0.664
SSE	ADX	0.461	1	0.497	BVSP	JTOPI	0.075	1	0.784	ADX	RTSI	0.864	1	0.353	T5YIE	ALL	147.9	10	0.000
SSE	EGX30	1.673	1	0.196	BVSP	TASI	0.025	1	0.875	ADX	BSE30	6.847	1	0.009	GPRD	SSE	0.012	1	0.912
SSE	T5YIE	0.232	1	0.630	BVSP	ADX	2.791	1	0.095	ADX	BVSP	22.24	1	0.000	GPRD	RTSI	0.096	1	0.757
SSE	GPRD	0.375	1	0.540	BVSP	EGX30	3.191	1	0.074	ADX	JTOPI	1.168	1	0.280	GPRD	BSE30	0.566	1	0.452
SSE	T10Y3M	1.399	1	0.237	BVSP	T5YIE	36.82	1	0.000	ADX	TASI	2.440	1	0.118	GPRD	BVSP	0.154	1	0.695
SSE	ALL	71.88	10	0.000	BVSP	GPRD	0.149	1	0.699	ADX	EGX30	1.000	1	0.317	GPRD	JTOPI	1.140	1	0.286
RTSI	SSE	0.431	1	0.512	BVSP	T10Y3M	0.808	1	0.369	ADX	T5YIE	22.01	1	0.000	GPRD	TASI	1.344	1	0.246
RTSI	BSE30	1.019	1	0.313	BVSP	ALL	42.80	10	0.000	ADX	GPRD	0.864	1	0.353	GPRD	ADX	0.219	1	0.640
RTSI	BVSP	5.692	1	0.017	JTOPI	SSE	4.626	1	0.031	ADX	T10Y3M	0.194	1	0.659	GPRD	EGX30	2.725	1	0.099
RTSI	JTOPI	0.079	1	0.779	JTOPI	RTSI	0.309	1	0.578	ADX	ALL	73.83	10	0.000	GPRD	T5YIE	1.455	1	0.228
RTSI	TASI	0.497	1	0.481	JTOPI	BSE30	21.94	1	0.000	EGX30	SSE	0.762	1	0.383	GPRD	T10Y3M	0.353	1	0.552
RTSI	ADX	0.311	1	0.577	JTOPI	BVSP	39.47	1	0.000	EGX30	RTSI	0.015	1	0.903	GPRD	ALL	8.817	10	0.550
RTSI	EGX30	0.012	1	0.914	JTOPI	TASI	9.188	1	0.002	EGX30	BSE30	0.199	1	0.655	T10Y3M	SSE	1.474	1	0.225
RTSI	T5YIE	5.496	1	0.019	JTOPI	ADX	7.420	1	0.006	EGX30	BVSP	0.468	1	0.494	T10Y3M	RTSI	0.486	1	0.486
RTSI	GPRD	0.039	1	0.842	JTOPI	EGX30	0.141	1	0.707	EGX30	JTOPI	4.949	1	0.026	T10Y3M	BSE30	9.900	1	0.002
RTSI	T10Y3M	0.793	1	0.373	JTOPI	T5YIE	25.13	1	0.000	EGX30	TASI	0.069	1	0.792	T10Y3M	BVSP	0.989	1	0.320
RTSI	ALL	21.39	10	0.019	JTOPI	GPRD	1.596	1	0.206	EGX30	ADX	2.233	1	0.135	T10Y3M	JTOPI	3.293	1	0.070
BSE30	SSE	2.339	1	0.126	JTOPI	T10Y3M	8.979	1	0.003	EGX30	T5YIE	4.836	1	0.028	T10Y3M	TASI	3.378	1	0.066
BSE30	RTSI	0.244	1	0.622	JTOPI	ALL	129.9	10	0.000	EGX30	GPRD	0.805	1	0.370	T10Y3M	ADX	6.664	1	0.010
BSE30	BVSP	46.13	1	0.000	TASI	SSE	2.099	1	0.147	EGX30	T10Y3M	0.081	1	0.775	T10Y3M	EGX30	2.652	1	0.103
BSE30	JTOPI	10.87	1	0.001	TASI	RTSI	0.922	1	0.337	EGX30	ALL	25.94	10	0.004	T10Y3M	T5YIE	22.14	1	0.000
BSE30	TASI	2.430	1	0.119	TASI	BSE30	12.35	1	0.000	T5YIE	SSE	1.939	1	0.164	T10Y3M	GPRD	0.163	1	0.686
BSE30	ADX	5.069	1	0.024	TASI	BVSP	13.97	1	0.000	T5YIE	RTSI	0.881	1	0.348	T10Y3M	ALL	62.32	10	0.000
BSE30	EGX30	0.571	1	0.450	TASI	JTOPI	13.34	1	0.000	T5YIE	BSE30	35.91	1	0.000
BSE30	T5YIE	51.81	1	0.000	TASI	ADX	23.26	1	0.000	T5YIE	BVSP	22.04	1	0.000
BSE30	GPRD	1.753	1	0.185	TASI	EGX30	0.009	1	0.923	T5YIE	JTOPI	2.448	1	0.118

Note: ”Eq” denotes the dependent equation, “Excl” the excluded variable, χ² the test statistic, “df” the degrees of freedom, and p(χ²) the p-value.

) point to a densely interconnected global financial system in which equity markets across Asia, Latin America, the Middle East, and North Africa transmit shocks both among themselves and toward U.S. interest-rate expectations. By contrast, GPRD occupies a largely peripheral position, consistent with evidence from Zhang and Hamori (2022), who find that geopolitical risk plays a secondary role compared to financial indicators. However, its effects should not be underestimated, as Çepni et al. (2023) show that BRICS carry trades are highly responsive to geopolitical uncertainty, suggesting that such shocks may influence capital flows and risk premia through specific channels, even if their aggregate role remains limited. Within this framework, SSE emerges as a key receiver of shocks from BSE30, BVSP, and JTOPI, underscoring its sensitivity to both regional and global equity conditions, a result aligned with Wen et al. (2022), who highlight heterogeneous but persistent transmission patterns in BRICS. Additionally, the role of geopolitical risk may operate indirectly through exchange rate movements and trade balances, as documented by Ekanayake and Dissanayake (2022) document that exchange rate volatility shapes U.S. trade with BRICS, reinforcing the broader view that uncertainty can transmit through multiple economic channels

RTSI responds significantly to impulses from BVSP and T5YIE, highlighting the combined influence of Latin American markets and U.S. rate expectations. BSE30 is affected by BVSP, JTOPI, ADX, T5YIE, and T10Y3M, reflecting its dual exposure to emerging-market peers and U.S. monetary policy signals. BVSP, in turn, is shaped by ADX, EGX30, and T5YIE, revealing strong transmission channels from the Middle East and North Africa as well as sensitivity to U.S. real yields.

JTOPI exhibits the broadest global integration, reacting to SSE, BSE30, BVSP, TASI, ADX, T5YIE, and T10Y3M, thereby acting as a conduit that links major emerging equity markets with U.S. bond dynamics. Within the Gulf, TASI is influenced by BSE30, BVSP, JTOPI, and ADX, while ADX itself responds to SSE, BSE30, BVSP, and T5YIE. EGX30 is affected by JTOPI and T5YIE, anchoring North Africa to both South African equity conditions and U.S. real yields.

Feedback effects into U.S. rates are pronounced. T5YIE is driven by BSE30, BVSP, ADX, and EGX30, while T10Y3M responds to BSE30, JTOPI, TASI, ADX, and T5YIE. These findings indicate that U.S. yield expectations incorporate external financial conditions transmitted through BRICS+ equity markets, demonstrating that U.S. yield expectations are shaped not only by domestic fundamentals but also by international equity markets.

The IRFs provide further insight into the short-run dynamics of GPRD shocks (Figure 2). For T5YIE, a positive shock to geopolitical risk generates an immediate increase of about 0.02, heightened risk aversion and a temporary repricing of inflation expectations. The effect, however, quickly reverses, falling below zero by the second day and returning to baseline within five days, consistent with the transitory nature of geopolitical shocks. Similarly, T10Y3M jumps to about 0.04 on impact, steepening the yield curve, but reverts to baseline within three days as markets reassess growth and monetary policy expectations. These patterns indicate that U.S. bond markets internalize geopolitical shocks rapidly and efficiently, preventing the emergence of prolonged contagion.

Across equity markets, RTSI, BVSP, JTOPI, EGX30, TASI, and BSE30 exhibit immediate negative responses to a GPRD shock, consistent with the view that heightened geopolitical tensions depress short-term performance by increasing uncertainty and risk premia. These effects are transitory, with most indices reverting to equilibrium within a few days. BVSP and JTOPI, in particular, rebound rapidly, suggesting that although these markets are sensitive to geopolitical risk, they do not experience persistent dislocations and are able to absorb external shocks through rapid price adjustments and liquidity recovery.

For ADX and SSE, the IRFs reveal a short-lived increase followed by a sharp decline below zero, with both indices stabilizing around baseline within four days. The response is consistent with expectations, as geopolitical shocks tend to dampen equity valuations. However, the limited magnitude and duration of these responses indicate that these markets possess effective absorption mechanisms, likely supported by domestic policy interventions, market regulation frameworks, and liquidity buffers that prevent volatility amplification.

The evidences indicates that GPRD shocks generate short-term volatility in U.S. yields and BRICS+ equity markets, but the effects are rapidly absorbed and lack persistence. For investors, this suggests that while geopolitical events may temporarily affect bond pricing and equity valuations, long-term portfolio strategies should remain anchored in fundamentals rather than transient risk episodes. For policymakers, the results highlight the importance of robust institutional frameworks and adequate liquidity buffers that enable financial systems to absorb geopolitical shocks without major disruptions. Overall, the results support H2 by confirming that BRICS+ markets participate in global shock transmission, although geopolitical risk generates weaker and less persistent spillovers compared to VIX. The detailed VAR regression results underlying these responses are presented in Supplementary Table S2.

4.3. T5YIE Shocks and Spillovers to BRICS+ Equity Markets

The Granger-causality results point to a complex and tightly interconnected global financial system in which emerging-market equities and U.S. interest-rate measures are strongly interlinked (

Table A3. Granger VAR test results.

Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$
SSE	RTSI	0.003	1	0.958	BVSP	TASI	0.041	1	0.840	ADX	ALL	72.75	8	0.000
SSE	BSE30	8.397	1	0.004	BVSP	ADX	2.622	1	0.105	EGX30	SSE	0.817	1	0.366
SSE	BVSP	12.77	1	0.000	BVSP	EGX30	3.114	1	0.078	EGX30	RTSI	0.015	1	0.901
SSE	JTOPI	28.99	1	0.000	BVSP	T5YIE	35.92	1	0.000	EGX30	BSE30	0.195	1	0.659
SSE	TASI	0.534	1	0.465	BVSP	ALL	41.81	8	0.000	EGX30	BVSP	0.445	1	0.505
SSE	ADX	0.565	1	0.452	JTOPI	SSE	5.277	1	0.022	EGX30	JTOPI	5.008	1	0.025
SSE	EGX30	1.597	1	0.206	JTOPI	RTSI	0.294	1	0.588	EGX30	TASI	0.069	1	0.793
SSE	T5YIE	0.456	1	0.500	JTOPI	BSE30	22.67	1	0.000	EGX30	ADX	2.326	1	0.127
SSE	ALL	70.01	8	0.000	JTOPI	BVSP	39.26	1	0.000	EGX30	T5YIE	4.690	1	0.030
RTSI	SSE	0.487	1	0.485	JTOPI	TASI	10.08	1	0.002	EGX30	ALL	25.03	8	0.002
RTSI	BSE30	1.079	1	0.299	JTOPI	ADX	8.401	1	0.004	T5YIE	SSE	1.936	1	0.164
RTSI	BVSP	5.710	1	0.017	JTOPI	EGX30	0.20	1	0.655	T5YIE	RTSI	0.880	1	0.348
RTSI	JTOPI	0.087	1	0.768	JTOPI	T5YIE	21.02	1	0.000	T5YIE	BSE30	36.35	1	0.000
RTSI	TASI	0.435	1	0.509	JTOPI	ALL	118.4	8	0.000	T5YIE	BVSP	22.27	1	0.000
RTSI	ADX	0.371	1	0.543	TASI	SSE	2.038	1	0.153	T5YIE	JTOPI	2.434	1	0.119
RTSI	EGX30	0.017	1	0.897	TASI	RTSI	0.925	1	0.336	T5YIE	TASI	0.056	1	0.813
RTSI	T5YIE	4.986	1	0.026	TASI	BSE30	12.05	1	0.001	T5YIE	ADX	56.20	1	0.000
RTSI	ALL	20.54	8	0.008	TASI	BVSP	13.80	1	0.000	T5YIE	EGX30	20.29	1	0.000
BSE30	SSE	2.852	1	0.091	TASI	JTOPI	13.30	1	0.000	T5YIE	ALL	146.7	8	0.000
BSE30	RTSI	0.256	1	0.613	TASI	ADX	23.67	1	0.000
BSE30	BVSP	45.85	1	0.000	TASI	EGX30	0.012	1	0.912
BSE30	JTOPI	11.20	1	0.001	TASI	T5YIE	2.743	1	0.098
BSE30	TASI	2.949	1	0.086	TASI	ALL	64.57	8	0.000
BSE30	ADX	5.959	1	0.015	ADX	SSE	4.537	1	0.033
BSE30	EGX30	0.455	1	0.500	ADX	RTSI	0.862	1	0.353
BSE30	T5YIE	45.47	1	0.000	ADX	BSE30	7.026	1	0.008
BSE30	ALL	185.8	8	0.000	ADX	BVSP	22.46	1	0.000
BVSP	SSE	0.002	1	0.964	ADX	JTOPI	1.160	1	0.282
BVSP	RTSI	0.000	1	0.995	ADX	TASI	2.323	1	0.127
BVSP	BSE30	1.805	1	0.179	ADX	EGX30	0.996	1	0.318
BVSP	JTOPI	0.067	1	0.796	ADX	T5YIE	22.02	1	0.000

Note: ”Eq” denotes the dependent equation, “Excl” the excluded variable, χ² the test statistic, “df” the degrees of freedom, and p(χ²) the p-value.

). The SSE is significantly affected by shocks from BSE30, BVSP, and JTOPI, underscoring that developments in major emerging markets propagate swiftly across regions. The RTSI is shaped by impulses from BVSP and T5YIE, suggesting that both Latin American equity dynamics and T5YIE influence Russian equities, consistent with the evidence that commodity-linked economies display heightened sensitivity to U.S. macro-financial shocks.

BSE30 responds to a wide set of drivers, including SSE, BVSP, JTOPI, TASI, ADX, and T5YIE, highlighting its high degree of global financial integration and vulnerability to shifts in U.S. monetary policy. BVSP is shaped primarily by EGX30 and T5YIE, reflecting both South–South linkages and the centrality of U.S. yield expectations for Latin American equities, in line with Gao et al. (2024). JTOPI emerges as a major transmission hub, absorbing shocks from SSE, BSE30, BVSP, TASI, ADX, and T5YIE, thereby connecting African equities with both Asian markets and U.S. interest-rate conditions.

In the Gulf, TASI is significantly affected by T5YIE, BSE30, BVSP, JTOPI, and ADX, underscoring the strong interdependence of Gulf markets with both emerging equities and T5YIE. Balli et al. (2021) show that U.S. uncertainties drive spillovers into international markets; our findings extend this evidence by demonstrating that T5YIE acts as a critical transmission channel for Gulf markets. Similarly, ADX responds to SSE, BSE30, BVSP, and T5YIE, illustrating the influence of both U.S. yields and Asian markets in shaping regional dynamics. EGX30 reacts to JTOPI and T5YIE, confirming North Africa’s exposure to African equity shocks and U.S. real yields through integrated financial and trade linkages.

These results are consistent with Panda et al. (2023), who show that BRICS equity markets are highly sensitive to both inflation dynamics and broader macroeconomic fundamentals. By incorporating T5YIE as a forward-looking measure of T5YIE, our analysis extends this literature and demonstrates that BRICS+ markets are not only driven by domestic inflation but also by international monetary policy signals. Kumar and Dua (2024) argue that higher U.S. Treasury yields reduce foreign portfolio investment inflows into BRICS economies. However, our findings reveal heterogeneous responses across BRICS+ markets: some markets, such as BVSP and JTOPI, display quick adjustments, indicating partial resilience to U.S. monetary tightening through regional capital reallocation and market depth effects.

The IRFs provide further insight into the transmission of T5YIE shocks (Figure 3). JTOPI exhibits the strongest immediate response: following a positive T5YIE shock, the index rises sharply on impact, indicating that higher T5YIE can temporarily boost returns in highly integrated markets through anticipations of stronger nominal growth and capital inflows. The effect is statistically significant yet short-lived, dissipating within three days, a pattern consistent with rapid arbitrage activity and efficient adjustment to global macro-financial signals.

Other BRICS+ markets exhibit heterogeneous responses. SSE and TASI display small and statistically insignificant reactions, suggesting that domestic policy frameworks and regional conditions buffer them from global inflation shocks. In contrast, BSE30, BVSP, and ADX record more pronounced but temporary positive responses, all converging to baseline within three days. This pattern indicates that although equity valuations across emerging economies are sensitive to T5YIE, the adjustment remains swift and non-persistent, reflecting underlying market resilience.

From an economic perspective, these results suggest that T5YIE shocks function primarily as short-term signals for equity markets rather than as persistent drivers of valuation. Highly integrated economies such as South Africa, India, and Brazil display more immediate and pronounced reactions, whereas markets with stronger domestic anchors such as China and the Gulf states exhibit more muted responses. For investors, this implies that opportunities arising from inflation expectation surprises are transitory. For policymakers, the findings underscore the importance of credible monetary policy frameworks and well-developed bond markets in mitigating volatility spillovers, consistent with recent evidence on the stabilizing role of TIPS during periods of heightened uncertainty. Overall, the evidence lends support to H2 by demonstrating that U.S. macro financial indicators, in particular inflation expectations, constitute significant channels of shock transmission to BRICS+ markets, even though the associated effects remain short lived and exhibit limited persistence. The detailed VAR regression results underlying these responses are presented in Supplementary Table S3.

4.4. T10Y3M Shocks and Spillovers to BRICS+ Equity Markets

The Granger-causality analysis (

Table A4. Granger causality.

Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$	Eq	Excl	${\mathit{\chi }}^2$	df	$\mathbf{p}\left({\mathit{\chi }}^2\right)$
SSE	RTSI	0.002	1	0.961	BVSP	TASI	0.007	1	0.934	ADX	ALL	50.24	8	0.000
SSE	BSE30	8.654	1	0.003	BVSP	ADX	0.068	1	0.794	EGX30	SSE	0.743	1	0.389
SSE	BVSP	13.78	1	0.000	BVSP	EGX30	2.717	1	0.099	EGX30	RTSI	0.014	1	0.905
SSE	JTOPI	30.09	1	0.000	BVSP	T10Y3M	0.000	1	0.995	EGX30	BSE30	0.190	1	0.663
SSE	TASI	0.446	1	0.504	BVSP	ALL	5.784	8	0.671	EGX30	BVSP	1.060	1	0.303
SSE	ADX	0.348	1	0.555	JTOPI	SSE	4.407	1	0.036	EGX30	JTOPI	6.143	1	0.013
SSE	EGX30	1.688	1	0.194	JTOPI	RTSI	0.311	1	0.577	EGX30	TASI	0.092	1	0.762
SSE	T10Y3M	1.648	1	0.199	JTOPI	BSE30	21.58	1	0.000	EGX30	ADX	4.281	1	0.039
SSE	ALL	71.24	8	0.000	JTOPI	BVSP	50.53	1	0.000	EGX30	T10Y3M	0.000	1	0.984
RTSI	SSE	0.397	1	0.529	JTOPI	TASI	8.608	1	0.003	EGX30	ALL	20.29	8	0.009
RTSI	BSE30	1.023	1	0.312	JTOPI	ADX	15.89	1	0.000	T10Y3M	SSE	1.624	1	0.203
RTSI	BVSP	7.799	1	0.005	JTOPI	EGX30	0.231	1	0.631	T10Y3M	RTSI	0.468	1	0.494
RTSI	JTOPI	0.286	1	0.593	JTOPI	T10Y3M	5.256	1	0.022	T10Y3M	BSE30	9.654	1	0.002
RTSI	TASI	0.538	1	0.463	JTOPI	ALL	101.9	8	0.000	T10Y3M	BVSP	0.052	1	0.820
RTSI	ADX	1.287	1	0.257	TASI	SSE	2.081	1	0.149	T10Y3M	JTOPI	1.730	1	0.188
RTSI	EGX30	0.024	1	0.876	TASI	RTSI	0.923	1	0.337	T10Y3M	TASI	3.180	1	0.075
RTSI	T10Y3M	0.305	1	0.581	TASI	BSE30	12.28	1	0.000	T10Y3M	ADX	14.08	1	0.000
RTSI	ALL	15.82	8	0.045	TASI	BVSP	16.14	1	0.000	T10Y3M	EGX30	2.923	1	0.087
BSE30	SSE	2.091	1	0.148	TASI	JTOPI	14.75	1	0.000	T10Y3M	ALL	39.51	8	0.000
BSE30	RTSI	0.228	1	0.633	TASI	ADX	21.02	1	0.000
BSE30	BVSP	62.73	1	0.000	TASI	EGX30	0.004	1	0.951
BSE30	JTOPI	16.38	1	0.000	TASI	T10Y3M	1.215	1	0.270
BSE30	TASI	2.064	1	0.151	TASI	ALL	62.99	8	0.000
BSE30	ADX	15.97	1	0.000	ADX	SSE	4.163	1	0.041
BSE30	EGX30	0.356	1	0.551	ADX	RTSI	0.836	1	0.361
BSE30	T10Y3M	4.983	1	0.026	ADX	BSE30	6.934	1	0.008
BSE30	ALL	142.5	8	0.000	ADX	BVSP	30.64	1	0.000
BVSP	SSE	0.005	1	0.946	ADX	JTOPI	2.477	1	0.116
BVSP	RTSI	0.000	1	0.984	ADX	TASI	2.523	1	0.112
BVSP	BSE30	1.732	1	0.188	ADX	EGX30	0.825	1	0.364
BVSP	JTOPI	0.141	1	0.707	ADX	T10Y3M	0.079	1	0.779

Note: “Eq” denotes the dependent equation, “Excl” the excluded variable, χ² the test statistic, “df” the degrees of freedom, and p(χ²) the p-value.

) highlights a highly interconnected global financial system in which shocks from emerging-market equities, Gulf markets, and U.S. interest-rate measures propagate rapidly across regions, consistent with the view that yield-curve dynamics function as global risk transmission mechanisms. SSE is significantly influenced by BSE30, BVSP, and JTOPI, confirming its exposure to both Asian and Latin American developments. RTSI is primarily driven by BVSP, pointing to a South–South transmission channel, while BSE30 absorbs impulses from BVSP, JTOPI, ADX, and T10Y3M itself, underscoring its dual sensitivity to emerging-market peers and U.S. bond-market dynamics. BVSP, in turn, is shaped by EGX30, reflecting strong cross-regional financial linkages between Latin America and North Africa.

JTOPI emerges as a central hub, responding to SSE, BSE30, BVSP, TASI, ADX, and T10Y3M, which underscores Africa’s position as an intermediary between Asian, Middle Eastern, and U.S. markets. In the Gulf, TASI is driven by BSE30, BVSP, JTOPI, and ADX, while ADX itself reacts to SSE, BSE30, and BVSP. EGX30 receives impulses from JTOPI and ADX, reinforcing the role of Africa–Gulf linkages in transmitting financial shocks across regions. Finally, T10Y3M is influenced by BSE30, TASI, ADX, and EGX30, confirming that global equity developments feed directly back into U.S. interest-rate expectations. These results are consistent with evidence that yield-curve movements embed international spillovers from equity markets and commodity markets alike. In particular, Umar et al. (2022) demonstrate that oil price shocks significantly affect the level, slope, and curvature of the U.S. yield curve across different time horizons, highlighting T10Y3M as both a receiver and transmitter of global shocks.

The IRFs (Figure 4) provide further insights into how BRICS+ markets absorb T10Y3M shocks. For SSE and TASI, the responses are positive on impact, peaking within the first day before quickly reverting to zero. This short-lived reaction suggests temporary portfolio rebalancing toward emerging markets, possibly driven by yield differentials or delayed equity repricing in response to shifts in the U.S. term structure. By contrast, BSE30, RTSI, and JTOPI register immediate and sharp negative reactions. Similar to the quantile-based network analysis of Rehman et al. (2025), these findings suggest that tighter U.S. financial conditions tend to trigger equity outflows and raise risk premia in emerging markets. However, the declines dissipate within a few days across all markets, indicating rapid adjustment without long-term dislocation.

For ADX, EGX30, and BVSP, the responses are statistically insignificant, indicating that U.S. term-spread shocks do not exert persistent influence across all emerging markets equally. This heterogeneous impact reflects differences in market openness, depth of financial integration, macroeconomic stability, and the role of domestic institutional investors, supporting the view that sensitivity to U.S. yield-curve movements varies significantly across BRICS+ economies.

Taken together, these results suggest that U.S. term-spread shocks generate short-term volatility across BRICS+ equities, but the effects are neither uniform nor persistent. Highly integrated economies such as India, South Africa, and Russia are most vulnerable, while Gulf and North African markets exhibit greater resilience. For investors, this implies that yield-curve movements create tactical but temporary risks for equity portfolios. For policymakers, the findings highlight the importance of monitoring spillovers from U.S. bond markets, as domestic monetary conditions can be influenced by external equity dynamics. Overall, the evidence reinforces H2 by showing that U.S. yield-curve dynamics represent a key channel of macro-financial transmission to BRICS+ markets, although the resulting spillovers are predominantly short lived. The detailed VAR regression results underlying these responses are presented in Supplementary Table S4.

5. Conclusions, Limitations, and Future Research Directions

This study examined the transmission of global risk factors, captured by VIX, GPRD, T5YIE, and T10Y3M, to BRICS+ equity markets and T5YIE. Using Granger-causality tests and impulse response functions, the analysis revealed a tightly interconnected global financial system in which shocks propagate rapidly across regions and asset classes, although with heterogeneous intensity and persistence. The results highlight that while spillovers from global volatility (VIX) and U.S. macro-financial indicators (T5YIE and T10Y3M) are strong and immediate, those originating from GPRD are weaker and short lived.

The results highlight several key insights. First, VIX functions as both a transmitter and a receiver of volatility shocks, confirming its central role within the global risk network. Second, GPRD, although theoretically relevant, plays only a peripheral role during the sample period, generating short-lived and statistically weak effects on equities and U.S. yields. Third, shocks to U.S. macro-financial variables show that T5YIE and yield-curve dynamics exert significant influence on BRICS+ markets, although the effects are generally transitory. The most pronounced responses are observed in highly integrated economies such as India, South Africa, and Brazil, whereas Gulf and North African markets display greater resilience. These findings indicate that financial fundamentals and U.S. monetary policy expectations outweigh geopolitical shocks as primary drivers of cross-market contagion during the sample period.

From a policy perspective, the results call for targeted rather than generic monitoring responses from regulators and central banks in BRICS+ economies. Specifically, policy action should be linked to measurable external shock signals: (i) sharp increases in VIX should trigger short-horizon liquidity backstops and temporary macroprudential tightening, (ii) persistent T10Y3M inversions should activate yield-curve stress diagnostics in domestic bond markets, and (iii) unexpected rises in T5YIE should prompt supervisory scrutiny of duration risk and FX-mismatch exposure in the banking sector. Strengthening market resilience requires operational measures such as developing local currency bond markets, expanding central clearing to reduce margin stress, and enhancing swap line arrangements to stabilize cross-border funding.

For investors, the empirical evidence implies that passive diversification across regions is insufficient during global stress episodes. Instead, active risk management must account for the documented (1–3) day spillover window identified in the IRFs. This means dynamically reducing portfolio beta during VIX-driven shocks and reallocating toward resilient BRICS+ markets (TASI, ADX, EGX30) when contagion originates from macro-financial channels (T5YIE, T10Y3M). Even geographically distant markets can be strongly influenced by shocks originating in the U.S. or from major BRICS+ hubs such as India and South Africa, but the rapid dissipation of spillovers also creates tactical trading opportunities for short-horizon strategies.

Despite its contributions, this study is subject to several limitations. First, the analysis focuses exclusively on BRICS+ economies, which, although highly relevant, do not fully capture the complexity of global spillover dynamics. Second, the sample period does not encompass extreme geopolitical or financial episodes, such as global wars or systemic crises, during which the role of GPRD might become more pronounced and potentially shift from a peripheral to a dominant risk factor. Third, the empirical framework, which relies on VAR-based Granger-causality tests and linear IRFs, may not fully account for nonlinearities, structural breaks, or regime-dependent dynamics that often characterize financial contagion, particularly during high-volatility phases or liquidity freezes.

Future research could address these limitations in several directions. Expanding the sample to include advanced economies or other emerging regions would yield a more comprehensive picture of global interconnectedness and help determine whether the bidirectional spillovers identified here persist beyond the BRICS+ network. Using high-frequency data or alternative indicators of geopolitical risk and uncertainty may uncover more nuanced patterns of short-term contagion. From a methodological perspective, adopting nonlinear frameworks such as quantile VAR, Markov-switching models, or network-based approaches would allow researchers to capture asymmetric effects and regime shifts more effectively and to distinguish between shock amplification and shock absorption phases. Finally, extending the analysis to sectoral or firm-level data within BRICS+ could help identify which segments of the economy are most vulnerable to global shocks.

In conclusion, the global financial system is characterized by strong interdependencies across equity and bond markets, yet the effects of shocks are largely transitory and display substantial heterogeneity across countries. These findings highlight both the challenges and opportunities faced by policymakers and investors in a world where financial risks are increasingly global and rapidly transmitted. Strengthening resilience while leveraging short-lived market adjustments thus remains a central task for ensuring stability and sustaining growth in an interconnected financial landscape, as well as clarifying whether transmission originates primarily from financial, energy, or technology sectors.