Global Turbulence Spillovers and Corporate Equity Returns Volatility in a Transition Economy: Economy- and Region-Based Approaches With Moderating Roles of Liquidity and Government Ownership

Abstract

This study investigates how economic and political uncertainty spillovers influence corporate equity volatility in Vietnam. Using dynamic panel estimators addressing endogeneity, the study shows that uncertainties from advanced, emerging, and low-income countries markedly amplify stock return volatility in Vietnam. Similarly, uncertainty from Africa, Asia-Pacific, Europe, Central Asia, the Middle East, and the Western Hemisphere exerts destabilizing effects. However, some of these effects are only significant under an estimator accounting for endogeneity and the dynamic structure of panel data. The study also examines firm-level heterogeneity, which shows that corporate liquidity reduces exposure to spillovers from low-income economies and Africa while government ownership buffers the impact of uncertainty from Asia-Pacific. The findings provide valuable insights for investors, corporations, and policymakers when formulating decisions under the influence of uncertainty spillovers.

Keywords

uncertainty spillovers corporate equity returns volatility region-based approach economy-based approach transition economy firm-level heterogeneity

Introduction

Over a decade after the U.S. global financial crisis, subsequent events such as the European debt turmoil and the Greek fiscal crisis have demonstrated how disturbances originating in a single economy can spread internationally, unsettle financial markets, and hinder economic growth (Fiechter & Zhou, 2016; Podmajerska, 2016). Consequently, uncertainty and its effects have attracted growing scholarly interest (Nguyen & Bao, 2025). Uncertainty in one country or region can spill over to global financial markets (Nguyen, Bao, Le, & Nguyen, 2025; Nguyen, Bao, Tran, & Le, 2025) and heighten the transfer of risks stemming from irregular developments across several nations (Bernal et al., 2016).

Although research on uncertainty has expanded rapidly, important limitations remain. Much of the literature emphasizes economic policy uncertainty and gives less attention to broader measures that incorporate both economic and political dimensions (Karanasos et al., 2022; Shi & Wang, 2023). Many analyses were conducted at the market level, leaving the heterogeneity of firm level responses largely unexplored. Evidence also tends to center on a few large economies as the dominant sources of spillovers (Wang & Xiao, 2023; Yang et al., 2021), which limits understanding of how uncertainty from groups of countries, classified by income or region, can affect other markets. Finally, the potential moderating role of firm level attributes, including liquidity and state ownership, has rarely been examined, leaving the channels through which uncertainty is transmitted insufficiently understood.

Against this backdrop, Vietnam provides a particularly compelling case for investigation. Being among Asia’s fast-growing emerging economies, Vietnam has combined rapid growth with deep global integration. By 2023, total trade had risen to roughly 166% of GDP (World Bank, 2025), which places Vietnam as one of the most globally integrated economies and makes it highly exposed to external turbulence. The Ho Chi Minh City Stock Exchange (HOSE) stands as the flagship market for Vietnam and holds a prominent position within Southeast Asia. The stability of this market is closely linked to the country’s long term growth prospects, since orderly equity markets are vital for sustaining investment and channeling capital efficiently in emerging economies (Ngare et al., 2014). In this context, firm level volatility in stock returns provides an essential signal of financial resilience, capturing changes in investor sentiment (Rupande et al., 2019) and perceptions of risk (Du et al., 2025). Moreover, Vietnam’s transition economy characteristics, including a bank centered financial system, significant state ownership, and a large retail investor base, mean that external shocks may be absorbed, amplified, or transmitted in ways that differ from other emerging markets. Understanding these dynamics is therefore of both scholarly and policy relevance.

This study examines how uncertainty spillovers from other economies and regions affect the volatility of Vietnamese firms’ stock returns using a firm level analysis. Specifically, the study investigates three questions: (i) how uncertainty in low income, emerging, and advanced economies affects volatility in Vietnamese stock returns, (ii) how uncertainties arising from Europe, the Western Hemisphere, the Asia-Pacific region, the Middle East and Central Asia, as well as Africa, contribute to fluctuations in Vietnamese stock return volatility, and (iii) how firms may respond differently to these spillover effects.

The importance of this study can be viewed from multiple dimensions. First, it moves beyond single major economies by systematically examining uncertainty spillovers grouped according to economic status, ranging from advanced to emerging and low-income countries and according to economic regions (Africa, Europe, Asia-Pacific, the Western Hemisphere, and the Middle East with Central Asia). Second, it adopts a firm level analytical approach to identify how corporate characteristics moderate spillovers. This research shows that firm liquidity and state ownership can moderate the intensity of transmission in specific contexts. Third, it contributes to theory by empirically validating the idea that global turbulence can have significant implications for an emerging market. Fourth, it contributes new insights from Vietnam, an emerging transition economy characterized by rapid growth and openness yet rarely examined in the uncertainty spillover literature. Based on the findings, the study offers practical implications for investors, corporate managers, and policymakers.

The structure of the paper is as follows. Section “Literature Review” develops the theoretical background, examines prior empirical evidence, and identifies research gaps. Section “Research Methodology” covers the data, variable construction, and methodology. Section “Empirical Findings and Discussion” delivers the empirical analysis and discussion. Section “Conclusion” concludes by summarizing the findings, drawing practical implications, and acknowledging limitations.

Literature Review

The nexus between uncertainty spillovers from abroad and fluctuations in Vietnam’s stock returns can be theoretically framed through the contributions of Dornbusch et al. (2000), Forbes and Rigobon (2002), Dixit and Pindyck (1994), and Bloom (2009).

Dornbusch et al. (2000) argued that there were several channels through which uncertainty in one nation could spread to another. Specifically, the identified channels were through trade partnerships, the dependency of global financial markets and contagion of investor behaviors. Given the nature of the Vietnamese economy, which has becoming more globalized and globally connected, the potential channels for the spillovers become wider and visible. Hence, turbulence in other economies and regions can spread to Vietnamese economy. Such perspective is supported if the perspective of Forbes and Rigobon (2002) is incorporated. In specific, financial markets were argued to be heavily dependent as there were comovements happening constantly. Such comovements existed in both stable and turbulent periods but became more pronounced during crises due to increased volatility clustering.

The real options theory (Dixit & Pindyck, 1994) suggests that investment decisions of firms are not only driven by the expected returns but also by the value of waiting for additional information to come up before making the decisions. In essence, firms treat investments as options that can be deferred or postponed if the business environment becomes uncertain. Such behaviors could lead to delay in decisions and impose uncertainty in the future cashflows and earnings of firms. Since current price of stock reflects the present value of expected future cashflows, which is discounted at the risk-adjusted rate (Gordon, 1962; Williams, 1938), the elevated uncertainty in future cashflows and earnings increases the risk-adjusted rate. Such changes in risk premium lead to higher fluctuations in stock prices and increased volatility in stock returns.

Bloom (2009) provided additional insights by stating that the effects might be heterogeneous depending on the context and situations of firms. Specifically, there could be certain factors of firms that could alleviate or exacerbate the impacts of uncertainty, which requires further empirical examination. Among the factors, importance of liquidity during crises has been well-documented. For instance, Benmelech et al. (2017) demonstrated that access to liquidity enabled firms to sustain investments and reduce real economic losses. On the supply side, Cornett et al. (2011) found that banks facing liquidity pressures reduced their lending activities, further emphasizing how firm-level liquidity and systemic credit conditions jointly determine corporate resilience in crisis periods. Besides, Chen and Chang (2013) analyzed the behaviors of firms in emerging markets for a 26-year period and found that majority of firms in emerging economies held more cash during financial crisis. Such increase was suggested to be due to the additional that firms gained during crisis when holding more cash as well as due to the extraordinary benefits of saving compared to spending. Accordingly, firms holding more cash during the crisis were found to experience enhanced value-adding effects after the financial uncertainty. Tascón et al. (2024) highlighted that corporate liquidity played a crucial role in preserving firms’ financial stability during the COVID-19 period. Further, state-owned enterprises (SOEs) may enjoy strategic advantages during turbulent periods. Prior studies underline the role of state-owned enterprises (SOEs) in enhancing economic resilience. Assagaf and Ali (2017) demonstrated that government subsidies enabled SOEs to sustain superior financial outcomes compared to private firms, while Qi et al. (2025) further revealed that SOEs contributed to economic stability during the COVID-19 crisis in China.

Existing empirical evidence shows that studies on uncertainty spillovers in financial markets are predominantly centered on how influential nations disseminate their financial shocks. The influential power of countries could be used to classify the research strands. In essence, a country can have either absolute or relative influence on other nations. Countries such as the US or China can be regarded as having absolute influences while a few other nations can be regarded as possessing relative importance to other countries depending on their trade dynamics.

Regarding a specific country that can spread its uncertainty to other countries, the US is a prominent source of cross-border uncertainty. Elevated U.S. EPU heightens volatility in Asia and domestically via direct and indirect channels (Karanasos et al., 2022). Its predictive power for foreign equity markets persists, but magnitudes depend on bilateral ties and the recipient market’s development (Balli et al., 2021). China has emerged, alongside the US, as a major source of uncertainty spillovers. Shi and Wang (2023) find both Chinese and US EPUs forecast global volatility, with US effects most pronounced in 2008. East Asian impacts are heterogeneous and typically weaker for China (Wang & Xiao, 2023). Regionally, China anchors EPU spillovers, whereas the U.S. acts as the global intermediary (Yang et al., 2021). Beyond the US and China’s global reach, regional hubs matter: Korea and Singapore in Asia (Chancharat & Chancharat, 2024); Germany in Europe (Clausen et al., 2019); and Qatar and Saudi Arabia in MENA, via geopolitical and market channels (Elsayed & Helmi, 2021). Evidence shows that, via trade, financial ties, and investor behavior, even low- and middle-income economies can transmit uncertainty, for example, effects on Spain from Latin America (Ghirelli et al., 2021).

Although extensive research links uncertainty spillovers to stock-return volatility, key gaps remain. First, work has centered on economic policy uncertainty, with far less attention to broader economic and political uncertainty, leaving their cross-border effects underexplored. Second, market-level designs dominate; this overlooks firm-level heterogeneity in responses to shocks highlighted by Bloom (2009) and omits firm characteristics that practitioners can leverage to mitigate spillovers. Third, studies often treat single countries, for example, the U.S. (Wang & Xiao, 2023), China (Yang et al., 2021), Germany (Clausen et al., 2019), or South Korea (Chancharat & Chancharat, 2024), as dominant sources; few consider country groups or regions, even though “less-influential” partners can transmit shocks to larger economies (Ghirelli et al., 2021). Fourth, most critically, evidence is absent for Vietnam, leaving stakeholders without guidance for risk management and policy under deepening globalization.

Addressing these gaps, this study focuses the impacts of uncertainty spillovers from different types of economies, regions on volatility in Vietnamese equity returns and the moderating roles of firm liquidity and ownership of state.

Research Methodology

Variables and Data

The dataset we used for the study was constructed from several sources. In specific, the sources for data included the annual reports of listed firms for controlled variables, history of their associated stock prices for corporate stock returns volatility calculation, the databases of the World Bank and the International Monetary Fund for controlled variables and the dataset of Ahir et al. (2022) for the WUI indices. With respect to firm selection, the sample consisted of companies listed on the Ho Chi Minh City Stock Exchange (HOSE), given that it represents the largest stock trading platform in Vietnam. In terms of timeframe, the dataset covers annual observations from 2014 to 2023, except for stock prices and the uncertainty indices constructed by Ahir et al. (2022). As for history of stock prices, they were collected monthly for a 10-year period from January 2014 to December 2023. As for the uncertainty-based indices of Ahir et al. (2022), they were collected on a quarterly basis. To address potential bias from extreme values, the data were winsorized at the 1st and 99th percentiles.

The control variables included in this study comprise size of firm in total assets (FAST), the degree of financial leverage measured by ratio of debt-to-equity (FFIN), the level of liquidity proxied by the assets-to-liabilities ratio (FLDT), whether the firm was owned by the government or not (FGOV), national GDP (NGDP), national lending interest rate (NLIR) and whether there was COVID-19 going on in the country during a year or not (NCVI). Among these variables, FGOV and NCVI are dummy variables taking the value of either 0 or 1. FGOV of firms with any percent of government ownership took the value of 1 and vice versa. NVCI was assigned a value of 1 for the years affected by the pandemic in Vietnam, spanning 2020 to 2023.

The key explanatory variables in the model are the WUI indices. In terms of calculation and scope, the World Uncertainty Index (WUI) reflects both economic and political uncertainty, as it is constructed based on textual analysis of country reports published by the Economist Intelligence Unit (EIU), which focus on macroeconomic and political conditions (Ahir et al., 2022). Specifically, the index measures the frequency of the word “uncertain” (and its variants) as a percentage of total words in each report. This percentage is then multiplied by 1,000,000 for scaling purposes. Higher WUI values indicate higher levels of uncertainty. For illustration, the fluctuation global WUI in accordance with global crises can be observed in Figure 1.

Figure 1.

Global WUI fluctuations from 1990Q1 to 2020Q2.

In our study, we use GDP-weighted averages of WUI across countries, grouped by income level and region, as provided by the original dataset of Ahir et al. (2022). Since the scope of the study was to assess the comprehensive spillovers from different economies and regions, the WUI for each economy and region was used. As for the economy-based approach, the indices were computed for advanced economies (WADV), emerging markets (WEME), and economies with low-income (WLIC). As for the region-based approach, the indices were calculated for different regions including the Africa region (WAFR), the Asia-Pacific region (WASP), the Middle Eastern and Central of Asia region (WMEA), the European region (WERP), and the Western Hemisphere region (WWHM).

These country-specific and region-level indices serve as proxies for economic and political uncertainty within their respective economies and regions. As they are originally reported on a quarterly basis, the indices were transformed into annual measures by computing the arithmetic average of the four quarterly values. All the countries that were included in the formulation of these economy-based and region-based indices can be found in Table 1.

Table 1.

List of Countries Incorporated in the Formulations of Economy-Based and Region-Based Uncertainty Indices.

Economy/region	Incorporated countries in the indices
Advanced nations	Sweden, Israel, United States, Switzerland, Greece, Austria, Netherlands, Spain, Korea, France, Australia, United Kingdom, Taiwan, Japan, Ireland, Czech Republic, Italy, Germany, Canada, Belgium, Singapore, Hong Kong
Emerging nations	Colombia, Iraq, Venezuela, Kazakhstan, Romania, Saudi Arabia, Poland, Argentina, Tunisia, Oman, United Arab Emirates, Panama, Islamic Republic of Iran, Costa Rica, Ecuador, Azerbaijan, Philippines, Dominican Republic, Mexico, Qatar, Brazil, China, Peru, Ukraine, Hungary, Egypt, Indonesia, Russia, Thailand, Angola, Algeria, Turkey, Malaysia, Guatemala, South Africa, Lebanon, Pakistan, Chile, India, Morocco
Low-income nations	Nigeria, Vietnam, Ghana, Tanzania, Bolivia, Uganda, Bangladesh, Nicaragua, Kenya
African region	Cameroon, Côte d’Ivoire, Guinea, Rwanda, Namibia, Ethiopia, Uganda, Democratic Republic of the Congo, Ghana, Tanzania, Niger, Botswana, Central African Republic, Chad, South Africa, The Gambia, Zambia, Burkina Faso, Togo, Zimbabwe, Angola, Senegal, Sierra Leone, Benin, Mali, Liberia, Nigeria, Kenya, Mozambique, Madagascar, Malawi, Republic of Congo, Guinea-Bissau, Burundi, Lesotho, Eritrea
Asian-Pacific region	New Zealand, Indonesia, Mongolia, Sri Lanka, Korea, Malaysia, Papua New Guinea, Hong Kong SAR, Philippines, Myanmar, Taiwan Province of China, Vietnam, Thailand, Nepal, Cambodia, Bangladesh, Japan, Lao P.D.R., Singapore, India, China, Australia
Central of Asia and the Middle Eastern region	Jordan, Tajikistan, Azerbaijan, Islamic Republic of Iran, Sudan, Qatar, Armenia, Yemen, Kyrgyz Republic, Kazakhstan, Iraq, Pakistan, United Arab Emirates, Oman, Libya, Egypt, Lebanon, Algeria, Saudi Arabia, Uzbekistan, Afghanistan, Turkmenistan, Morocco, Kuwait, Tunisia, Mauritania
European region	Norway, FYR Macedonia, Czech Republic, Ukraine, Portugal, Latvia, Israel, Greece, Bosnia and Herzegovina, Moldova, Sweden, Lithuania, Switzerland, Poland, Spain, Hungary, Netherlands, Albania, Denmark, Slovenia, Turkey, Slovak Republic, Germany, Austria, Romania, Italy, Croatia, Finland, Belarus, Belgium, France, United Kingdom, Bulgaria, Russia, Ireland
Western Hemisphere region	Canada, Ecuador, Paraguay, Mexico, Bolivia, Argentina, Dominican Republic, United States, Uruguay, Jamaica, Panama, Honduras, Venezuela, Nicaragua, Guatemala, Chile, Brazil, Costa Rica, Peru, El Salvador, Haiti, Colombia

Source.Ahir et al. (2022).

The primary explained variable, which was volatility in stock returns (VSR), was derived through four distinct methods to ensure robustness and reflect multiple dimensions of measurement. The demonstration of the methods used to formulate VSR is presented in Equations 1 to 4.

VSR 1 = \sqrt{\frac{1}{4 \ln 2} \times [\frac{\sum_{i = 1}^{n} {(H_{i} - L_{i})}^{2}}{n}]}

(1)

VSR 2 = \sqrt{12 \times (ω + α \times r_{t - 1}^{2} + β \times σ_{t - 1}^{2})}

(2)

VSR 3 = \sqrt{12 \times σ_{t}^{2}} = \sqrt{12 \times [λ \times σ_{t - 1}^{2} + (1 - λ) \times r_{t - 1}^{2}]}

(3)

VSR 4 = \sqrt{12 \times [\frac{\sum_{t = 1}^{n} {(r_{t} - \bar{r})}^{2}}{n - 1}]}

(4)

r_{t} = \frac{Stock Pric e_{start of period t + 1} - Stock Pric e_{start of period t}}{Stock Pric e_{start of period t}}

(5)

In Equation 1, the formula resembles the approach of Parkinson (1980) in calculating volatility of equity returns. Within the equation, $H_{i}$ and $L_{i}$ denotes the highest and lowest prices within a month while n is the number of months.

In Equation 2, the formula resembles the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) technique in calculating volatility in equity returns, which was adopted from the work of Bollerslev (1986). Within the formula, $r_{t - 1}^{2}$ is squared of one-period lagged of equity returns while $σ_{t - 1}^{2}$ is one-period lagged of variance.

The primary parameters in Equation 2 are α, β, and ω, which indicates the impacts of one-period lagged squared returns, one-period lagged volatility and long-term variance. There are, indeed, no fixed values for these parameters. However, Engle and Patton (2001) highlighted the common ranges for α was from .05 to .3 and β was from .6 to .95 based on financial data. Further, Hansen and Lunde (2005) suggested that the sum of α and β should be approximately 1 while Alexander (2008) indicated that ω primarily ranged from .00001 to .0001 in financial data. Within the context of the study, we set the values of α, β and ω at .1, .85 and .0001 respectively aimed at capturing volatility patterns over the short and long horizon, based on monthly stock price observations. The volatility indicator obtained from the GARCH (1,1) approach was then multiplied by 12 to annualize the index.

In Equation 3, volatility in stock returns was calculated by the Exponentially Weighted Moving Average (EWMA) approach as suggested by Longerstaey and Zangari (1996). Within the approach, data regarding stock prices in recent periods plays a more important role compared to stock prices in past periods. In essence, λ is the smoothing factor representing the decay level at which past data becomes less significant while $σ_{t - 1}^{2}$ and $r_{t - 1}^{2}$ are squared values of one-period lagged standard deviation and stock returns.

As for the value of λ, Longerstaey and Zangari (1996) suggested the value of approximately .94 for daily stock price data and .97 for monthly stock price data while Bollen (2015) stated that the most suitable smoothing parameter should be .88. Since our data comprises monthly price, the values smoothing parameter at .97 and .88 were both suitable. Hence, we take the arithmetic mean of these two values at .93 for the smoothing parameter. The volatility indicators calculated by the EWMA approach was then multiplied by 12 to annualize the figures.

In Equation 4, volatility in stock returns was calculated by measuring the standard deviation of monthly returns. The associating figures were then multiplied by 12 to become annual indicators.

In Equation 5, the technique to calculate stock returns is presented, which was used across the Equations 1 to 4 where returns for a period were required.

The four approaches used to calculate VSR reflect different dimensions of stock return volatility, offering complementary insights into firm-level risk exposure. Equation 1 is adapted from Parkinson (1980) and estimates volatility based on the monthly high-low price range. This approach captures the intra-month price dispersion, which reflects the extent of price swings during each month and is especially sensitive to periods of heightened market fluctuations. Equation 2 employs the GARCH (1,1) model, capturing conditional volatility by incorporating both recent shocks to returns and past volatility. It reflects the persistence and clustering of volatility often observed in financial time series. Equation 3 applies the EWMA approach, which gives greater weight to more recent return shocks. This allows the model to adapt more quickly to recent changes in market conditions, making it effective in dynamic or crisis periods. Equation 4 estimates volatility by computing the standard deviation of monthly returns within a given year, providing a measure of realized or historical volatility, which reflects the overall dispersion in returns. Together, these four methods ensure a robust and comprehensive measurement of volatility, accounting for both observed price fluctuations and time-varying risk dynamics in firm-level stock performance.

The summary of all variables and the justification for the inclusion of control variables are provided in Table 2 while summary statistics are included in Table B1.

Table 2.

Description, Sources and Empirical Justification for the Control Variables.

Dependent and independent variables	Description	Source
VSR	Volatility observed in the stock returns of Vietnamese firms	Calculated using firm-level stock prices
WADV, WEME, WLIC, WAFR, WASP, WMEA, WERP, WWHM	The world uncertainty indices	Ahir et al. (2022)
Control variable	Description	Source	Empirical justification for the inclusion as control variables
FAST	Firm total assets	Firm financial report	To et al. (2019)
FFIN	Debt-to-equity ratio		Aharon and Yagil (2019)
FLDT	Current ratio of assets to liabilities		Batten and Vo (2014)
FGOV	Dummy variable categorizing state and non-state firms		Sasikirono et al. (2020)
NGDP	National gross domestic product	The World Bank	Attari and Safdar (2013)
NLIR	Country-level interest rate	The International Monetary Fund (IMF)	Okechukwu et al. (2019)
NCVI	Dummy variable for COVID-19	The Ministry of Health of Vietnam	Rakshit and Neog (2022)

Source. Summarized by the authors.

Estimation Method

The main estimation method applied was the System Generalized Method of Moments (Sys-GMM), developed by Arellano and Bond (1991) and improved by Blundell and Bond (1998). Sys-GMM is particularly suitable for controlling endogeneity in dynamic panel data, thereby preventing biased estimates that might arise from other techniques. Specifically, Sys-GMM deals with issues related to endogeneity by using lagged variables as instruments for the estimation process. However, the method, if not utilized properly, can lead to biased findings because of instrument proliferation (overfitting of instruments). Thus, the robustness of GMM-based estimates requires verification through diagnostic tests. Following Roodman (2009), we applied the AR(2), Hansen, Sargan, and difference-in-Hansen tests, and their results were presented together with the Sys-GMM estimations to support the validity of our empirical findings.

We estimate the model with System GMM (Arellano & Bond, 1991; Blundell & Bond, 1998), exploiting internal lags as instruments to address endogeneity in dynamic panels and limit bias. Because instrument proliferation can overfit and distort results, we report Hansen, Difference-in-Hansen, Sargan, and AR(2) diagnostics (Roodman, 2009) alongside the estimates to assess specification and instrument validity.

The baseline models are expressed as in Equations 6 to 8.

\begin{matrix} VS R_{i, t} = β_{0} + β_{1} VS R_{i, t - 1} + β_{2} WU I_{t - 1} + β_{3} FAS T_{i, t} \\ + β_{4} FFI N_{i, t} + β_{5} FLD T_{i, t} + β_{6} FGO V_{i} + β_{7} NGD P_{t} \\ + β_{8} NLI R_{t} + β_{9} NCV I_{t} + ε_{i, t} \end{matrix}

(6)

\begin{array}{l} V S R_{i, t} = β_{0} + β_{1} V S R_{i, t - 1} + β_{2} W U I_{t - 1} + β_{10} (W U I_{t - 1} \\ \times F L D T_{i, t}) + β_{3} F A S T_{i, t} + β_{4} F F I N_{i, t} + β_{5} F L D T_{i, t} \\ + β_{6} F G O V_{i} + β_{7} N G D P_{t} + β_{8} N L I R_{t} + β_{9} N C V I_{t} + ε_{i, t} \end{array}

(7)

\begin{array}{l} V S R_{i, t} = β_{0} + β_{1} V S R_{i, t - 1} + β_{2} W U I_{t - 1} + β_{10} (W U I_{t - 1} \\ \times F G O V_{i}) + β_{3} F A S T_{i, t} + β_{4} F F I N_{i, t} + β_{5} F L D T_{i, t} \\ + β_{6} F G O V_{i} + β_{7} N G D P_{t} + β_{8} N L I R_{t} + β_{9} N C V I_{t} + ε_{i, t} \end{array}

(8)

Within the presented empirical models, $VS R_{i, t}$ is volatility in stock returns of company i at year t. $VS R_{i, t - 1}$ , which was included to account for dynamic nature in panel data, is one-period lagged of $VS R_{i, t}$ . The set of firm controlled variables comprises $FAS T_{i, t}$ , $FFI N_{i, t}$ , $FLD T_{i, t}$ , and $FGO V_{i}$ while the set of nation-level controlled variables includes $NGD P_{t}$ , $NLI R_{t}$ , and $NCV I_{t}$ . $ε_{i, t}$ is the error term to represent factors that could affect $VS R_{i, t}$ but were not included in the models. The primary explanatory variables were $WAD V_{t - 1}$ , $WEM E_{t - 1}$ , $WLI C_{t - 1}$ , $WAF R_{t - 1}$ , $WAS P_{t - 1}$ , $WME A_{t - 1}$ , $WER P_{t - 1}$ and $WWH M_{t - 1}$ , reflected by $WU I_{t - 1}$ . In essence, the models for uncertainty spillovers are indicated in Equation 6 while the extended empirical models for the moderating role of FLDT and FGOV are reflected in Equations 7 and 8.

Empirical Findings and Discussion

Empirical Findings

We first examine uncertainty spillover effects from an economy-based approach.

In Table 3, WADV shows strong positive association with all indicators of VSR, which highlights that uncertainty in advanced economies could destabilize and amplify volatility of Vietnamese equity returns. Under each model in the analyses, the results for diagnostic tests are also included, indicating that the findings are reliable and free from instrument-related biases. Following the analysis, we further examine the spillovers from emerging economies.

Table 3.

Impact of Uncertainty from Advanced Economies on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.511***
VSR2_i,t−1		.603***
VSR3_i,t−1			.649**
VSR4_i,t−1				.511***
WADV_t−1	.00000352***	.00000275***	.00000662***	.00000586***
FAST_i,t	.310	.352	1.077**	.516
FFIN_i,t	.074	.041	.130	.123
FLDT_i,t	−.149	−.421	3.096	−.248
FGOV_i	−.204	.111	2.208	−.340
NGDP_t	−.211	−.288	−1.113**	−.351
NLIR_t	−.017	.006	.061	−.029
NCVI_t	.015	−.008	−.037	.025
AR(2)	.437	.243	.638	.437
Sargan	.692	.518	.085	.692
Hansen	.753	.587	.135	.753
Difference-in-Hansen	.753	.587	.135	.753

Source. Authors’ compilation.

Note. The dependent variable, VSR, reflects firm-level stock return volatility. The main explanatory variable is WADV, the WUI for advanced economies. The control set consists of FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, ** denote statistical significance at the 1%, 5% levels, respectively.

In Table 4, WEME shows positive effects on all indicators of VSR. Hence, heightened uncertainty in emerging markets is associated with elevated fluctuations in Vietnam’s corporate stock returns. Within all empirical models for analysis, the associated diagnostics-related tests suggest that the models were free from estimation biases and that there was no issue with the instruments we used. Hence, the findings are reliable under Sys-GMM estimation. Subsequently, Table 5 presents the analysis of spillovers originating from low-income economies.

Table 4.

Impact of Uncertainty from Emerging Economies on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.597***
VSR2_i,t−1		.651***
VSR3_i,t−1			.855***
VSR4_i,t−1				.597***
WEME_t−1	.00000788**	.00000675***	.0000256***	.0000133**
FAST_i,t	.395	.405	1.091	.707
FFIN_i,t	.102	.054	.203	.169
FLDT_i,t	−.974	−.273	.331	−1.279
FGOV_i	.084	.179	.837	.161
NGDP_t	−.422	−.431*	−1.232**	−.707
NLIR_t	−.058**	−.033*	−.118**	−.103**
NCVI_t	.110**	.051*	.158*	.168**
AR(2)	.675	.236	.679	.567
Sargan	.338	.381	.251	.321
Hansen	.381	.444	.248	.364
Difference-in-Hansen	.381	.444	.248	.364

Source. Authors’ compilation.

Note. The dependent variable, VSR, reflects firm-level stock return volatility. The main explanatory variable is WEME, the WUI for emerging economies. The control set consists of FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 5.

Impact of Uncertainty from Low-Income Economies on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.197*
VSR2_i,t−1		.216*
VSR3_i,t−1			.476**
VSR4_i,t−1				.209*
WLIC_t−1	.00000433**	.00000289**	.0000122**	.00000717**
FAST_i,t	−.255	−.169	.194	−.337
FFIN_i,t	.117**	.082**	.241	.181**
FLDT_i,t	−2.298**	−1.568**	−2.996	−3.431**
FGOV_i	−1.449**	−.895*	−.651	−2.136**
NGDP_t	.280	.186	−.157	.397
NLIR_t	−.030*	−.016	.031	−.046*
NCVI_t	.095***	.057***	.121	.151***
AR(2)	.344	.458	.499	.335
Sargan	.385	.292	.128	.345
Hansen	.209	.105	.154	.145
Difference-in-Hansen	.751	.600	.154	.653

Source. Authors’ compilation.

Note. The dependent variable, VSR, denotes firm-level stock return volatility. The principal explanatory variable is WLIC, the WUI for low-income economies. Control variables comprise FFIN (financial leverage), FAST (firm size), FLDT (liquidity), FGOV (government ownership), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported statistics for Hansen, Difference-in-Hansen, Sargan, and AR(2) correspond to p-values.

***

, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

The coefficients of WLIC are positive across all proxies of VSR at 5% significance level, which suggests that enhanced uncertainty in low-income economies have a destabilizing effect on VSR. For the controls, no consistently significant results were found. The p-values of the diagnostic tests reaffirm the reliability of all the findings. Following the economy-based approach, we further examine the spillovers effects from a region-based perspective.

The coefficients found for WAFR in Table 6 suggest that the spillovers of uncertainty from Africa are positively associated with VSR, which indicates that volatility in equity returns is higher as turbulence in Africa is heightened. The diagnostic tests indicate that the empirical results were estimated using appropriate and suitable instruments. Subsequently, we further proceed with the spillovers from the other regions.

Table 6.

Effects of Uncertainty from Africa on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.528***
VSR2_i,t−1		.514***
VSR3_i,t−1			.959***
VSR4_i,t−1				.454***
WAFR_t−1	.00000382***	.00000277***	.0000114**	.0000041***
FAST_i,t	.213	.182	.301	.569
FFIN_i,t	.068	.040	.175	.117
FLDT_i,t	−2.036	−1.363	−6.220	−1.902
FGOV_i	−1.290	−.858	−2.543	−1.328
NGDP_t	−.151	−.149	−.368	−.408
NLIR_t	−.004	.005	.098	−.023
NCVI_t	.096**	.060*	.166	.112*
AR(2)	.417	.729	.263	.743
Sargan	.475	.282	.543	.337
Hansen	.470	.295	.453	.304
Difference-in-Hansen	.470	.295	.453	.304

Source. Authors’ compilation.

Note. The dependent variable, VSR, captures firm-level stock return volatility. The principal explanatory variable is WAFR, the WUI for Africa. Control variables comprise FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) correspond to p-values.

***

, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

Turbulence in the Asia-Pacific (WASP) demonstrates a robust positive association with VSR as in Table 7, which indicates the amplifying effects of WASP on VSR. In essence, WASP causes disruptive influences on Vietnamese stock market by enhancing volatility of returns at corporate level as measured by VSR. The reliability of the findings is confirmed through the diagnostics.

Table 7.

Effects of Uncertainty from Asia-Pacific on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.362**
VSR2_i,t−1		.401***
VSR3_i,t−1			.589***
VSR4_i,t−1				.360**
WASP_t−1	.00000735***	.00000605***	.000018***	.0000119***
FAST_i,t	.112	.113	.281	.154
FFIN_i,t	.094*	.063*	.202*	.159*
FLDT_i,t	−.447	−.268	−1.209	−.839
FGOV_i	−.391	−.172	−.004	−.655
NGDP_t	.131	.068	−.031	.224
NLIR_t	−.100***	−.067***	−.143	−.163***
NCVI_t	.013	−.015	−.022	.032
AR(2)	.667	.400	.832	.705
Sargan	.412	.541	.229	.617
Hansen	.567	.688	.295	.764
Difference-in-Hansen	.567	.688	.295	.764

Source. Authors’ compilation.

Note. The dependent variable, VSR, denotes firm-level stock return volatility. The principal explanatory factor is WASP, the WUI for Asia-Pacific economies. Control variables include FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

The target explanatory variable (i.e., WMEA) demonstrates strongly and significantly positive effects on all proxies used for VSR as in Table 8. Hence, uncertainty in Central of Asia and Middle East increases the fluctuations of equity returns in Vietnam. Furthermore, the reliability of the models is affirmed through the diagnostics.

Table 8.

Effects of Uncertainty from Middle East and Central Asia on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.363**
VSR2_i,t−1		.391***
VSR3_i,t−1			−.288**
VSR4_i,t−1				.361**
WMEA_t−1	.0000373***	.0000258***	.0000223**	.0000589**
FAST_i,t	.301	.205	1.541***	.548
FFIN_i,t	.134**	.092**	−.042	.224**
FLDT_i,t	−1.135	−1.128	.264	−2.297
FGOV_i	−.687	−.544	2.165	−1.483
NGDP_t	−.810**	−.583**	−1.823***	−1.325**
NLIR_t	.019	.023	.037	.029
NCVI_t	.199***	.132***	.161***	.311***
AR(2)	.633	.722	.862	.558
Sargan	.294	.197	.572	.257
Hansen	.100	.100	.673	.114
Difference-in-Hansen	.100	.100	.474	.114

Source. Authors’ compilation.

Note. The dependent variable, VSR, captures firm-level stock return volatility. The principal explanatory variable is WMEA, the WUI for the Middle East and Central Asia. The set of control variables comprises FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported statistics for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, ** denote significance at the 1%, 5% levels, respectively.

In Table 9, the target independent variable (i.e., WERP) show positive and statistically significant effects, suggesting that an increase in uncertainty in Europe enhances volatility in returns of Vietnamese stocks. The diagnostics reassure the trustworthiness of the models (Table 9).

Table 9.

Effects of Uncertainty from Europe on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.492***
VSR2_i,t−1		.525***
VSR3_i,t−1			.862**
VSR4_i,t−1				.514***
WERP_t−1	.00000376***	.00000286***	.000014***	.00000713***
FAST_i,t	.117	.127	1.133**	.270
FFIN_i,t	.067	.038	.027	.105
FLDT_i,t	−.906	−.561	1.739	−1.774
FGOV_i	−.622	−.352	.905	−1.507
NGDP_t	−.071	−.111	−.929*	−.095
NLIR_t	−.024	−.004	.042	−.048*
NCVI_t	.053	.021	−.124	.039
AR(2)	.859	.457	.389	.798
Sargan	.448	.451	.763	.504
Hansen	.593	.664	.739	.672
Difference-in-Hansen	.593	.664	.739	.672

Source. Authors’ compilation.

Note. The dependent variable, VSR, denotes firm-level stock return volatility. The principal explanatory variable is WERP, the WUI for Europe. The control variables consist of FFIN (financial leverage), FAST (firm size), FLDT (liquidity), FGOV (government ownership), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 10 shows that the causal relationship of WWHM on VSR is positive. In other words, increased turbulence in West Hemisphere is associated with heightened volatility in Vietnamese stock returns. As for the reliability of the findings, it is confirmed through the diagnostics.

Table 10.

Effects of Uncertainty from West Hemisphere on Vietnam’s Stock Return Volatility.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.563***
VSR2_i,t−1		.533***
VSR3_i,t−1			.806**
VSR4_i,t−1				.563***
WWHM_t−1	.0000031***	.00000192***	.00000934***	.00000515***
FAST_i,t	.171	.139	1.307**	.284
FFIN_i,t	.108**	.069*	.161	.180**
FLDT_i,t	−.796	−.294	2.017	−1.326
FGOV_i	−.406	−.038	1.294	−.677
NGDP_t	−.180	−.188	−1.481*	−.300
NLIR_t	−.016	.001	.062	−.026
NCVI_t	.061	.046***	.018	.101
AR(2)	.762	.508	.662	.762
Sargan	.674	.479	.462	.674
Hansen	.782	.678	.551	.782
Difference-in-Hansen	.782	.678	.551	.782

Source. Authors’ compilation.

Note. The dependent variable, VSR, reflects firm-level stock return volatility. The principal explanatory variable is WWHM, the WUI for the Western Hemisphere. The control set comprises FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, **, and * denotes levels of significance at 1%, 5%, and 10%, respectively.

Beyond this, the study explores how FLDT and FGOV function as moderators in the spillover effects. Specifically, we found the moderating roles of FLDT in the relationship between VSR and WLIC as well as between VSR and WAFR. Results further demonstrate that the nexus between VSR and WASP is conditioned by the moderating effect of FGOV.

The role of firm liquidity (FLDT) in moderating the effects from economies with low income (WLIC) is examined in Table 11. $WLIC \times FLDT$ imposes negative effects on all proxies of VSR, which suggests the role of FLDT in negatively moderate the relationship between WLIC and VSR. In other words, the positive influences of WLIC on VSR become weaker as FLDT increases. The model specification tests indicate that there was no econometric issue in the estimation process.

Table 11.

The Role of Corporate Liquidity in Effects from Low-Income Economies.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	−1.915***
VSR2_i,t−1		−.371***
VSR3_i,t−1			−.328***
VSR4 _i,t−1				−1.915***
WLIC_t−1	.0000593**	.0000104**	.0000175**	.0000988**
WLIC_t−1 * FLDT_i,t	−.0001048**	−.0000167**	−.0000242**	−.0001745**
FAST_i,t	1.866*	.333**	1.104***	3.107*
FFIN_i,t	.508	.101	−.122	.846
FLDT_i,t	7.240**	1.049**	1.569**	12.056**
FGOV_i	−9.437	−1.605	−3.806	−15.714
NGDP_t	−.966	−.187	−1.133***	−1.609
NLIR_t	−.155	−.038	.014	−.259
NCVI_t	.043	.048***	.119***	.071
AR(2)	.903	.111	.757	.903
Sargan	.319	.696	.168	.319
Hansen	.460	.754	.092	.460
Difference-in-Hansen	.460	.754	.092	.460

Source. Authors’ compilation.

Note. The dependent variable, VSR, captures firm-level stock return volatility. The principal explanatory variable is WLIC, the World Uncertainty Index for low-income economies. Control variables comprise FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported values for Hansen, Difference-in-Hansen, Sargan, and AR(2) correspond to p-values.

***

, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

Further, the empirical findings on the role of corporate liquidity in moderating the uncertainty spillovers from Africa is provided in Table 12.

Table 12.

The Role of Corporate Liquidity in Effects from Africa.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.123**
VSR2_i,t−1		.129**
VSR3_i,t−1			−.313***
VSR4_i,t−1				.124**
WAFR_t−1	.00000244***	.00000135***	.0000129***	.00000407***
WAFR_t−1 * FLDT_i,t	−.0000024**	−.00000115**	−.000018***	−.00000401**
FAST_i,t	.004	.010	1.130***	.006
FFIN_i,t	.004	−.006	−.063**	.007
FLDT_i,t	.099	−.006	2.052***	.168
FGOV_i	−.121	−.109	−.039	−.190
NGDP_t	.021	.005	−1.102***	.036
NLIR_t	−.043***	−.027***	−.010	−.071***
NCVI_t	.110***	.068***	.126***	.183***
AR(2)	.662	.918	.237	.668
Sargan	.145	.119	.168	.112
Hansen	.125	.129	.340	.097
Difference-in-Hansen	.779	.715	.099	.500

Source. Authors’ compilation.

Note. The dependent variable, VSR, captures firm-level stock return volatility. The primary explanatory variable is WAFR, the World Uncertainty Index for Africa. Control variables comprise FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). Reported statistics for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests are presented as p-values.

***

, ** denote significance at the 1%, 5% levels, respectively.

The role of FLDT in the dynamics between WAFR and VSR can be observed through the negative coefficients of $WAFR \times FLDT$ . Thus, firm liquidity proves to be a crucial factor in mitigating the impact of spillovers from Africa. The robustness of these results is further supported by the outcomes of the diagnostic tests. Further, we proceed with the moderating role of FSTA, which is only significant in the case of Asia-Pacific.

In Table 13, $WASP \times FGOV$ shows consistently negative effects in all models, which suggests that the effects of WASP on VSR become weaker for SOEs. The associated diagnostic tests confirmed the reliability of the findings.

Table 13.

The Role of Government Ownership in Effects from Asia-Pacific.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
VSR1_i,t−1	.204*
VSR2_i,t−1		.229**
VSR3_i,t−1			.500***
VSR4_i,t−1				.209*
WASP_t−1	.00000585***	.00000435***	.00000682*	.0000106***
WASP_t−1 * FGOV_i,t	−.000028**	−.0000181**	−.00000765**	−.0000472**
FAST_i,t	−.109	−.068	−.642***	−.194
FFIN_i,t	.106	.061	.250***	.174*
FLDT_i,t	−.173	−.085	−2.644**	−.162
FGOV_i	−.703	−.579	.663	−1.087
NGDP_t	.136	.096	.160	.264
NLIR_t	−.069**	−.048***	−.036	−.122***
NCVI_t	.108***	.061***	.228***	.168***
AR(2)	.095	.275	.428	.111
Sargan	.331	.459	.369	.293
Hansen	.142	.223	.097	.139
Difference-in-Hansen	.159	.205	.860	.153

Source. Authors’ compilation.

Note. The dependent variable, VSR, denotes firm-level stock return volatility. The primary explanatory variable is WASP, the WUI for the Asia-Pacific region. Control variables comprise FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). The values reported for Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, **, and * denotes levels of significance at 1%, 5%, and 10%, respectively.

Additionally, we conducted robustness checks by re-estimating all empirical models using the Fixed Effects (FE) technique, with results reported in Appendix A. While FE estimation offers a useful benchmark, it is not well suited for dynamic panel models with lagged dependent variables and potential endogeneity. In such cases, FE estimates can suffer from Nickell (1981) bias, especially in panels with a short time dimension, leading to biased coefficients. In contrast, the SGMM estimator is specifically designed to handle these issues by using internal instruments to correct for endogeneity and dynamic panel bias. In our context, potential endogeneity may arise from reverse causality between uncertainty and firm-level volatility, or from unobserved firm-specific characteristics that simultaneously affect both outcomes.

Accordingly, differences between SGMM and FE results, particularly regarding specific regions and income groups, are expected and should not be interpreted as contradictory. Notably, shocks originating from advanced and low-income economies remain significant under both estimation approaches, while effects from emerging markets are less stable. Similarly, region-based spillovers from Africa, Asia-Pacific, and Europe are consistent, whereas those from the other regions lose significance under FE, likely due to the limitations of the estimator. Importantly, all SGMM specifications passed key diagnostic tests. The Hansen and Difference-in-Hansen tests confirmed the reliability and validity of the instruments, the Sargan test did not reject the overidentifying restrictions, and the Arellano-Bond AR(2) test found no evidence of serial correlation. Additionally, the lagged dependent variables were consistently significant, reinforcing the dynamic nature of the model. Together, these diagnostics strengthen the credibility and robustness of our SGMM-based findings.

To further strengthen the results using dynamic panel estimators, we employ the Difference GMM (DGMM) estimator to check for robustness of the findings.

The DGMM results reported in Table 14 reaffirm the robustness and stability of the main findings under dynamic estimators. Across all model specifications, the main findings are consistent with those obtained from the SGMM estimator. Diagnostic tests also support the validity of the DGMM estimates: the Sargan, Hansen and Difference-in-Hansen tests indicate valid instruments, and the Arellano-Bond AR(2) test shows no serial correlation. This alignment supports the use of dynamic estimators over static estimators, as the former appropriately addresses dynamic panel bias and endogeneity.

Table 14.

Robustness Tests Using the DGMM Estimator.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
Advanced economies
VSR1_i,t−1	.547***
VSR2_i,t−1		.651***
VSR3_i,t−1			.688**
VSR4_i,t−1				.547***
WADV_t−1	.00000373***	.00000296***	.00000708***	.00000633***
FAST_i,t	.333	.379	1.165**	.556
FFIN_i,t	.079	.044	.139	.132
FLDT_i,t	−.160	−.454	3.340	−.268
FGOV_i	−.220	.120	2.380	−.367
NGDP_t	−.227	−.311	−1.200**	−.379
NLIR_t	−.018	.0064	.065	−.031
NCVI_t	.016	−.0086	−.040	.0269
AR(2)	.454	.258	.675	.454
Sargan	.728	.558	.092	.728
Hansen	.780	.623	.145	.680
Difference-in-Hansen	.523	.523	.531	.580
Emerging economies
VSR1_i,t−1	.635***
VSR2_i,t−1		.697***
VSR3_i,t−1			.923***
VSR4_i,t−1				.635***
WEME_t−1	.00000837**	.00000729***	.0000276***	.0000143**
FAST_i,t	.423	.437	1.170	.763
FFIN_i,t	.110	.058	.219	.182
FLDT_i,t	−1.052	−.295	.357	−1.380
FGOV_i	.091	.193	.905	.174
NGDP_t	−.455	−.465*	−1.320**	−.764
NLIR_t	−.0626**	−.0356*	−.127**	−.111**
NCVI_t	.118**	.055*	.171*	.181**
AR(2)	.698	.254	.708	.592
Sargan	.362	.408	.270	.344
Hansen	.408	.476	.263	.386
Difference-in-Hansen	.408	.476	.263	.386
Low−income economies
VSR1_i,t−1	.212*
VSR2_i,t−1		.233*
VSR3_i,t−1			.513**
VSR4_i,t−1				.225*
WLIC_t−1	.00000466**	.00000312**	.0000132**	.00000774**
FAST_i,t	−.275	−.183	.209	−.364
FFIN_i,t	.126**	.0885**	.260	.195**
FLDT_i,t	−2.460**	−1.690**	−3.210	−3.700**
FGOV_i	−1.565**	−.966*	−.703	−2.310**
NGDP_t	.302	.201	−.170	.428
NLIR_t	−.0324*	−.0172	.033	−.0497*
NCVI_t	.102***	.0616***	.131	.163***
AR(2)	.367	.492	.529	.360
Sargan	.414	.314	.138	.370
Hansen	.225	.113	.165	.156
Difference-in-Hansen	.789	.630	.165	.694
Africa
VSR1_i,t−1	.566***
VSR2_i,t−1		.555***
VSR3_i,t−1			1.030***
VSR4_i,t−1				.490***
WAFR_t−1	.00000408***	.00000299***	.0000123**	.00000443***
FAST_i,t	.229	.197	.325	.615
FFIN_i,t	.073	.043	.189	.126
FLDT_i,t	−2.200	−1.470	−6.690	−2.060
FGOV_i	−1.390	−.926	−2.740	−1.440
NGDP_t	−.163	−.161	−.397	−.441
NLIR_t	−.0043	.0054	.105	−.0248
NCVI_t	.103**	.0648*	.179	.121*
AR(2)	.442	.764	.283	.780
Sargan	.512	.304	.586	.364
Hansen	.508	.319	.489	.329
Difference-in-Hansen	.508	.319	.489	.329
Asia-Pacific
VSR1_i,t−1	.386**
VSR2_i,t−1		.433***
VSR3_i,t−1			.637***
VSR4_i,t−1				.388**
WASP_t−1	.00000783***	.00000649***	.0000194***	.0000127***
FAST_i,t	.120	.122	.304	.166
FFIN_i,t	.101*	.0679*	.218*	.172*
FLDT_i,t	−.483	−.290	−1.300	−.905
FGOV_i	−.423	−.186	−.0043	−.708
NGDP_t	.141	.073	−.033	.242
NLIR_t	−.108***	−.072***	−.155	−.176***
NCVI_t	.0141	−.0162	−.0238	.0346
AR(2)	.412	.365	.438	.395
Sargan	.488	.342	.376	.354
Hansen	.530	.395	.402	.387
Difference-in-Hansen	.530	.395	.402	.387
Middle east and central Asia
VSR1_i,t−1	.392**
VSR2_i,t−1		.422***
VSR3_i,t−1			−.310**
VSR4_i,t−1				.389**
WMEA_t−1	.0000397***	.0000278***	.0000240**	.0000636***
FAST_i,t	.325	.221	1.660***	.591
FFIN_i,t	.144**	.0989**	−.0452	.241**
FLDT_i,t	−1.225	−1.218	.285	−2.480
FGOV_i	−.741	−.586	2.340	−1.600
NGDP_t	−.874**	−.629**	−1.970***	−1.430**
NLIR_t	.0203	.0248	.040	.031
NCVI_t	.215***	.142***	.174***	.336***
AR(2)	.675	.769	.919	.603
Sargan	.318	.213	.618	.278
Hansen	.108	.108	.720	.123
Difference-in-Hansen	.108	.108	.506	.123
Europe
VSR1_i,t−1	.530***
VSR2_i,t−1		.567***
VSR3_i,t−1			.930**
VSR4_i,t−1				.555***
WERP_t−1	.00000405***	.00000309***	.0000151***	.00000770***
FAST_i,t	.126	.137	1.223**	.292
FFIN_i,t	.072	.041	.0292	.114
FLDT_i,t	−.978	−.606	1.880	−1.920
FGOV_i	−.672	−.380	.977	−1.630
NGDP_t	−.076	−.120	−1.010*	−.102
NLIR_t	−.025	−.004	.045	−.051*
NCVI_t	.057	.022	−.134	.041
AR(2)	.899	.493	.420	.835
Sargan	.482	.485	.825	.545
Hansen	.641	.717	.797	.724
Difference-in-Hansen	.641	.717	.797	.724
Western hemisphere
VSR1_i,t−1	.607***
VSR2_i,t−1		.575***
VSR3_i,t−1			.870**
VSR4_i,t−1				.607***
WWHM_t−1	.00000335***	.00000207***	.0000101***	.00000556***
FAST_i,t	.184	.150	1.410**	.307
FFIN_i,t	.116**	.0745*	.174	.194**
FLDT_i,t	−.857	−.316	2.178	−1.432
FGOV_i	−.438	−.041	1.390	−.730
NGDP_t	−.194	−.202	−1.590*	−.322
NLIR_t	−.0173	.00108	.0667	−.0281
NCVI_t	.0659	.0497***	.0194	.108
AR(2)	.202	.535	.698	.302
Sargan	.720	.511	.493	.720
Hansen	.335	.423	.581	.311
Difference-in-Hansen	.635	.723	.231	.534
The role of corporate liquidity in effects from low-income economies
VSR1_i,t−1	−2.062***
VSR2_i,t−1		−.400***
VSR3_i,t−1			−.354***
VSR4_i,t−1				−2.062***
WLIC_t−1	.0000630**	.0000112**	.0000189**	.0001068**
WLIC_t−1* FLDT_i,t	−.0001120**	−.0000180**	−.0000261**	−.0001884**
FAST_i,t	2.016*	.360**	1.192***	3.356*
FFIN_i,t	.549	.109	−.132	.912
FLDT_i,t	7.807**	1.133**	1.695**	13.020**
FGOV_i	−1.190	−1.733	−4.110	−16.960
NGDP_t	−1.043	−.202	−1.224***	−1.737
NLIR_t	−.167	−.041	.015	−.279
NCVI_t	.046	.058***	.128***	.076
AR(2)	.960	.120	.800	.960
Sargan	.344	.752	.181	.344
Hansen	.324	.521	.299	.497
Difference-in-Hansen	.497	.135	.491	.491
The role of corporate liquidity in effects from Africa
VSR1_i,t−1	.133**
VSR2_i,t−1		.139**
VSR3_i,t−1			−.339***
VSR4_i,t−1				.134**
WAFR_t−1	.00000262***	.00000146***	.0000139***	.00000439***
WAFR_t−1 * FLDT_i,t	−.00000259**	−.00000124**	−.0000194***	−.00000433**
FAST_i,t	.004	.010	1.223***	.006
FFIN_i,t	.003	−.006	−.080**	.007
FLDT_i,t	.107	−.006	2.220***	.181
FGOV_i	−.131	−.118	−.0421	−.206
NGDP_t	.022	.004	−1.185***	.038
NLIR_t	−.044***	−.022***	−.010	−.076***
NCVI_t	.118***	.073***	.136***	.197***
AR(2)	.703	.961	.253	.711
Sargan	.156	.128	.181	.120
Hansen	.134	.138	.366	.104
Difference-in-Hansen	.443	.774	.107	.540
The role of government ownership in effects from Asia-Pacific
VSR1_i,t−1	.220*
VSR2_i,t−1		.247**
VSR3_i,t−1			.540***
VSR4_i,t−1				.226*
WASP_t−1	.00000631***	.00000470***	.00000735*	.0000114***
WASP_t−1 * FGOV_i,t	−.0000302**	−.0000195**	−.00000826**	−.0000509**
FAST_i,t	−.118	−.073	−.693***	−.209
FFIN_i,t	.114	.0659	.270***	.188*
FLDT_i,t	−.187	−.092	−2.855**	−.175
FGOV_i	−.759	−.625	.716	−1.174
NGDP_t	.146	.103	.173	.285
NLIR_t	−.0745**	−.0518***	−.0385	−.131***
NCVI_t	.116***	.0659***	.246***	.181***
AR(2)	.103	.297	.456	.120
Sargan	.358	.497	.399	.317
Hansen	.154	.241	.105	.151
Difference-in-Hansen	.172	.222	.930	.165

Source. Authors’ compilation.

Note. The dependent variable, VSR, captures firm-level stock return volatility. The principal explanatory variables are the WUIs for Europe (WERP), Africa (WAFR), the Western Hemisphere (WWHM), the Middle East and Central Asia (WMEA), the Asia-Pacific (WASP), as well as advanced (WADV), emerging (WEME), and low-income (WLIC) economies, together with their interaction terms in the moderating specifications. The control variables include FFIN (financial leverage), FAST (firm size), FGOV (government ownership), FLDT (liquidity), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy). The reported values of Hansen, Difference-in-Hansen, Sargan, and AR(2) tests correspond to p-values.

***

, **, and * denotes significance at the 1%, 5%, and 10% thresholds, respectively.

Discussion

Broadly speaking, the findings demonstrate that uncertainty originating from all three groups of economies has destabilizing effects on stock returns volatility in the country. This result underscores the vulnerability of Vietnam’s financial market to global uncertainty, regardless of the income classification of the external source. The significant spillover from advanced economies supports the findings of Karanasos et al. (2022) and can be attributed to the dominant influence these economies exert through trade, finance, and investor sentiment channels (Dornbusch et al., 2000). Notably, spillovers from emerging and low-income countries also impose significant effects. This challenges the traditional assumption that only major economies such as the US or China are key sources of turbulence. Instead, it suggests that Vietnam’s growing integration with a broader set of economies has made its financial market more sensitive to external uncertainty. These findings reinforce the perspective of Ghirelli et al. (2021), who showed that even low-income countries can be influential sources of uncertainty that should not be left unnoticed.

The region-based findings reveal that uncertainty spillovers from all major world regions have destabilizing effects on Vietnam’s stock return volatility. For instance, spillovers from the Asia-Pacific region could be likely due to Vietnam’s deep trade and investment linkages with partners such as China, Japan, and South Korea. These results support prior literature on the influential role of China in regional financial contagion (Shi & Wang, 2023; Yang et al., 2021). Further, spillovers from Africa are also found to increase volatility, despite Vietnam’s relatively limited economic linkage with the region. This suggests that spillovers may occur through indirect channels, such as fluctuations in investor sentiment as discussed by Dornbusch et al. (2000). These findings support the arguments of Elsayed and Helmi (2021) by extending the discussion of African turbulence spillovers beyond intra-regional boundaries to a transition economy like Vietnam.

Spillovers from Europe also show a destabilizing impact on Vietnamese equity markets. This result is particularly relevant given the growing economic relationship between Vietnam and the European Union as reflected through the EU-Vietnam Free Trade Agreement (EVFTA), which may have created the channels for uncertainty spillovers. The findings extend the observations of Clausen et al. (2019), who found strong intra-European uncertainty spillovers. Furthermore, uncertainty originating from the Middle East, Central Asia, and the Western Hemisphere is found to increase volatility in Vietnam’s stock market. While these regions may not be Vietnam’s primary economic partners, the evidence suggests that financial globalization and investor behavior contagion can facilitate cross-regional uncertainty spillover even in the absence of strong bilateral trade. This challenges the assumption that only geographically or economically close regions can impose volatility risks and supports a more nuanced view of global spillover mechanisms.

These empirical patterns are broadly consistent with theoretical perspectives on uncertainty spillovers and financial contagion. The evidence of widespread transmission across diverse regions and country types reinforces the view of Dornbusch et al. (2000) where trade linkages, financial interdependence, and investor behavior can facilitate global spillovers. The volatility observed also aligns with the real options theory (Dixit & Pindyck, 1994), which suggests that heightened uncertainty discourages investment, increases earnings volatility and thus stock return fluctuations. Moreover, the presence of destabilizing effects aligns with the arguments of Forbes and Rigobon’s (2002) on increased comovement during turbulent times.

In addition to the spillover effects, our analysis highlights heterogeneity in how firms respond to external uncertainty, aligning with the perspective of Bloom (2009). Specifically, corporate liquidity is found to play a moderating role in reducing the destabilizing impacts of spillovers from low-income economies and Africa. Firms with higher liquidity levels appear to be more resilient, possibly due to their greater financial flexibility. This coincides with prior arguments by Benmelech et al. (2017), and Tascón et al. (2024) on the importance of liquidity as a buffer during crises. Furthermore, government ownership emerges as a significant moderator in the context of spillovers from the Asia-Pacific region. SOEs could experience reduced sensitivity to uncertainty spillovers due to their strategic position, preferential access to resources, or government backing. These findings align with the literature suggesting the stabilizing role of SOEs during crises (Beuselinck et al., 2017; Qi et al., 2025) and reinforce the importance of ownership structure in moderating how firms respond to global uncertainty. However, the moderating effects of liquidity and government ownership are not observed in relation to uncertainty spillovers from other regions or economy groups. One plausible explanation is that the channels through which uncertainty from those regions affects Vietnamese firms may operate more indirectly or through channels that firm-level characteristics cannot play a stronger moderating role.

Conclusion

This work analyzes the transmission of uncertainty from various economies and regions on corporate equity returns in Vietnam. Based on system GMM estimations, the findings reveal that uncertainty from all examined economy groups as well as from all major regions exerts destabilizing impacts on Vietnam’s stock return volatility. These results highlight the broad vulnerability of a transition economy like Vietnam to global uncertainty. In addition, the study also investigates firm-level heterogeneity. Corporate liquidity is found to mitigate the destabilizing impacts of spillovers originating from low-income economies and Africa. Meanwhile, government ownership exerts a moderating role in the context of spillovers from the Asia-Pacific region. However, no significant moderating effects were observed for other regions or economy groups, indicating that firm-level buffers may be less effective in specific contexts. These findings offer valuable insights for investors, corporate managers, and policymakers seeking to manage financial risk in an increasingly interconnected world.

The study contributes to the literature by extending established theoretical perspectives on uncertainty transmission and firm-level behavior in emerging markets. By confirming that global uncertainty regardless of its geographic or economic origin can significantly destabilize stock return volatility in Vietnam, the findings offer empirical support to the contagion and comovement arguments discussed existing literature (Dornbusch et al., 2000; Forbes & Rigobon, 2002). Besides, the evidence on firm-level heterogeneity reinforces Bloom’s (2009) view that the effects of uncertainty are context-dependent, and it adds to the growing body of research emphasizing the role of corporate liquidity and state ownership.

These findings highlight several key considerations relevant to investors, firm managers, and policymakers.

For investors, understanding the global sources of uncertainty and their effects on domestic equity markets is crucial for effective portfolio management. Investors should closely monitor global political and economic developments, not just from dominant economies like the US or China. Additionally, greater attention should be paid to firm-level characteristics such as liquidity and government ownership. Firms with strong liquidity buffers and partial state ownership appear less sensitive to spillovers in certain cases, which can serve as important screening criteria in risk-averse investment strategies.

For corporate managers, the findings reinforce the value of maintaining financial flexibility, particularly in uncertain international environments. Since liquidity has been shown to reduce the destabilizing impact in certain cases, firms should consider building more resilient balance sheets through prudent liquidity management, maintaining access to credit lines, and retaining earnings. In companies exposed to Asia-Pacific dynamics, securing government ownership may also offer stability. Moreover, improving transparency and investor communication during uncertain periods can help mitigate negative sentiment and stabilize stock performance.

For policymakers, the study highlights the urgent need to strengthen Vietnam’s financial market resilience against global uncertainty where policy responses should not be narrowly focused. Instead, a mix of macroprudential measures such as dynamic provisioning, sectoral capital buffers, and countercyclical regulatory tools should be considered to cushion market volatility. In parallel, enhancing financial transparency, ensuring timely information dissemination, and supporting firm-level liquidity access (especially for SMEs) can reduce firm vulnerability during global uncertain periods. Additionally, efforts to diversify Vietnam’s trade and investment partnerships beyond a heavy reliance on a few major economies may help reduce exposure to region-specific or economy-specific uncertainty spillovers. Lastly, the role of state participation in strategically important or highly exposed sectors should be revisited, as government ownership has been shown to stabilize firm-level stock performance in certain contexts.

While offering valuable contributions, the study also entails certain limitations. First, while the World Uncertainty Index captures both political and economic uncertainty by design, the study does not disentangle these two dimensions in the empirical analysis. This distinction is theoretically meaningful because political shocks (regime change, conflict, policy instability) may propagate through different channels than economic shocks (growth slowdown, inflation, trade disruption). Future research should attempt to construct or utilize disaggregated indices such as separate measures of political and economic uncertainty through topic modeling or supervised machine learning applied to country reports. Second, the current analysis examines spillovers from grouped regions and economy types, which is helpful for macro-level generalization but may overlook country-specific effects. Future studies could adopt a country-level perspective to isolate bilateral linkages and better understand the channels of transmission. Third, while the study identifies moderating roles of firm liquidity and government ownership, future work may consider other factors that might protect firms during uncertain times to provide more nuanced understandings.

Footnotes

Appendix A: Robustness Analysis Employing the Fixed Effects (FE) Estimator

Table A1.

Robustness Analysis Employing the Fixed Effects (FE) Estimator.

Variable	VSR1_i,t	VSR2_i,t	VSR3_i,t	VSR4_i,t
Advanced economies
WADV_t−1	.000000579**	.000000433**	.00000156***	.000000964**
FAST_i,t	−.0007	−.002	−.004	−.001
FFIN_i,t	.002	.002	.006*	.004
FLDT_i,t	−.075***	−.056***	−.101***	−.125***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.040	.027	.080*	.066
NLIR_t	−.054***	−.035***	−.092***	−.090***
NCVI_t	.099***	.062***	.145***	.166***
Emerging economies
WEME_t−1	.000000868	.000000662	.00000662**	.00000144
FAST_i,t	−.0008	−.002	−.004	−.001
FFIN_i,t	.003	.002	.006*	.005
FLDT_i,t	−.076***	−.057***	−.103***	−.127***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.026	.016	.042	.044
NLIR_t	−.061***	−.041***	−.113***	−.103***
NCVI_t	.112***	.072***	.179***	.187***
Low-income economies
WLIC_t−1	.00000212***	.00000148***	.00000443***	.00000353***
FAST_i,t	−.002	−.003	−.007	−.003
FFIN_i,t	.002	.002	.006*	.004
FLDT_i,t	−.081***	−.061***	−.115***	−.136***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.053**	.036*	.104**	.088**
NLIR_t	−.048***	−.031***	−.082***	−.080***
NCVI_t	.098***	.062***	.148***	.163***
The role of corporate liquidity in effects from low-income economies
WLIC_t−1	.00000388***	.00000292***	.00000699***	.00000646***
WLIC_t−1 * FLDT_i,t	−.00000305**	−.00000249**	−.00000443*	−.00000507**
FAST_i,t	−.002	−.003	−.008	−.003
FFIN_i,t	.002	.002	.006*	.004
FLDT_i,t	−.006	−.00008	−.007	−.011
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.053**	.036*	.105**	.089**
NLIR_t	−.048***	−.031***	−.082***	−.081***
NCVI_t	.098***	.062***	.148***	.163***
Africa
WAFR_t−1	.000000889***	.000000585***	.00000168***	.00000148***
FAST_i,t	−.0006	−.002	−.004	−.001
FFIN_i,t	.002	.001	.005	.004
FLDT_i,t	−.075***	−.057***	−.104***	−.126***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.012	.008	.024	.021
NLIR_t	−.043***	−.028***	−.075***	−.072***
NCVI_t	.114***	.073***	.181***	.190***
Asia-Pacific
WASP_t−1	.0000026***	.0000021***	.00000857***	.00000433***
FAST_i,t	−.001	−.002	−.006	−.002
FFIN_i,t	.002	.002	.005*	.004
FLDT_i,t	−.076***	−.057***	−.102***	−.127***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.096***	.072***	.268***	.160***
NLIR_t	−.075***	−.052***	−.161***	−.126***
NCVI_t	.078***	.044***	.067***	.130***
Middle east and central Asia
WMEA_t−1	.0000024	−.0000000323	.00000604	.00000399
FAST_i,t	−.001	−.002	−.005	−.001
FFIN_i,t	.003	.002	.006*	.005
FLDT_i,t	−.077***	−.058***	−.107***	−.129***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	−.005	.023	−.035	−.009
NLIR_t	−.054***	−.038***	−.094***	−.091***
NCVI_t	.119***	.071***	.196***	.198***
Europe
WERP_t−1	.000000653**	.000000498**	.00000144***	.00000109**
FAST_i,t	−.0006	−.002	−.004	−.001
FFIN_i,t	.002	.002	.006*	.004
FLDT_i,t	−.075***	−.056***	−.102***	−.125***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.035	.023	.068	.059
NLIR_t	−.053***	−.034***	−.093***	−.089***
NCVI_t	.101***	.064***	.154***	.169***
Western hemisphere
WWHM_t−1	.000000235	.000000167	.000000752**	.000000391
FAST_i,t	−.0008	−.002	−.004	−.001
FFIN_i,t	.003	.002	.006*	.005
FLDT_i,t	−.076***	−.057***	−.103***	−.127***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.032	.021	.057	.053
NLIR_t	−.056***	−.037***	−.098***	−.094***
NCVI_t	.108***	.069***	.166***	.180***
The role of corporate liquidity in effects from Africa
WAFR_t−1	.00000199***	.00000131***	.00000327***	.00000331***
WAFR_t−1 * FLDT_i,t	−.00000189***	−.00000125***	−.00000275**	−.00000315***
FAST_i,t	−.001	−.002	−.006	−.002
FFIN_i,t	.002	.001	.005	.004
FLDT_i,t	.001	−.006	.008	.002
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.013	.009	.026	.022
NLIR_t	−.043***	−.028***	−.075***	−.072***
NCVI_t	.114***	.073***	.181***	.190***
The role of government ownership in effects from Asia-Pacific
WASP_t−1	.00000295***	.00000234***	.00000916***	.00000492***
WASP_t−1 * FGOV_i,t	−.00000247***	−.00000171**	−.00000412***	−.00000411***
FAST_i,t	−.002	−.003	−.009	−.004
FFIN_i,t	.002	.002	.005*	.004
FLDT_i,t	−.074***	−.056***	−.100***	−.124***
FGOV_i	dropped	dropped	dropped	dropped
NGDP_t	.097***	.073***	.271***	.162***
NLIR_t	−.076***	−.052***	−.161***	−.126***
NCVI_t	.078***	.044***	.067***	.130***

Source. Authors’ compilation.

***

, **, and * denotes significance at the 1%, 5%, and 10% thresholds, respectively.

Appendix B: Summary Statistics

Table B1.

Summary Statistics.

Variable	Average	Median value	SD	Lowest	Highest
VSR1	.392	.339	.226	.090	1.297
VSR2	.385	.326	.229	.085	1.294
VSR3	.235	.203	.136	.054	.779
VSR4	.144	.116	.094	.044	.608
WAFR	39,011.1	35,782.5	13,704.32	19,694.75	59,208.25
WASP	16,933.25	14,694.38	5,716.439	10,210.25	26,903
WMEA	18,316.9	18,331.13	1,909.362	15,045.75	21,329
WERP	31,875.17	29,903.25	9,048.417	21,235.75	50,973
WWHM	27,919.75	26,153.38	9,893.944	14592	50676
WADV	26,127.35	23,576.38	9,068.01	15,908.75	47,373.50
WEME	24,656.53	23,120.63	4,763.92	17,425.00	33,622.00
WLIC	23,507.95	23,036.13	5,746.72	15,617.25	34,524.75
FAST	14.3367	14.3511	2.9622	0	20.2624
FFIN	1.8783	.9998	2.8621	.0249	16.7356
FLDT	.5742	.6166	.2588	.0281	.9874
NGDP	22.6877	22.7179	.2454	22.32	23.0897
NLIR	7.7684	7.6775	0.7091	6.96	9.323
FGOV	.1395	0	0.3465	0	1
NCVI	.4	0	0.49	0	1

Source. Authors’ calculations and compilation.

Note. The table presents descriptive statistics for all variables. The dependent variable is VSR, representing returns volatility. The set of WUI variables includes WEME (emerging economies), WADV (advanced economies), WLIC (low-income economies), WMEA (Central Asia and Middle East), WAFR (Africa), WERP (Europe), WASP (Asia-Pacific), and WWHM (Western Hemisphere). Control factors comprise FFIN (financial leverage), FAST (firm size), FLDT (liquidity), FGOV (government ownership), NLIR (interest rate), NGDP (national GDP), and NCVI (COVID-19 dummy).

ORCID iDs

Thanh Huu Phu Nguyen

Hoang Phong Le

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ho Chi Minh City University of Law.

Declaration of Conflicting Interests

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

Data Availability Statement

Data is available in public repository. The authors confirm that all data underlying the findings are fully available without restriction.

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