The impact of COVID-19 on global stock markets: Comparative insights from developed, developing, and regionally integrated markets

Zonon, Babatounde Ifred Paterne; Bouraima, Mouhamed Bayane; Chen, Chuang; Dumor, Koffi Article The impact of COVID-19 on global stock markets: Comparative insights from developed, developing, and regionally integrated markets Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Zonon, Babatounde Ifred Paterne; Bouraima, Mouhamed Bayane; Chen, Chuang; Dumor, Koffi (2025) : The impact of COVID-19 on global stock markets: Comparative insights from developed, developing, and regionally integrated markets, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 2, pp. 1-23, https://doi.org/10.3390/economies13020039 This Version is available at: https://hdl.handle.net/10419/329319 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Robert Czudaj Received: 18 January 2025 Revised: 31 January 2025 Accepted: 1 February 2025 Published: 6 February 2025 Citation: Zonon, B. I. P., Bouraima, M. B., Chen, C., & Dumor, K. (2025). The Impact of COVID-19 on Global Stock Markets: Comparative Insights from Developed, Developing, and Regionally Integrated Markets. Economies,13(2), 39. https://doi.org/ 10.3390/economies13020039 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article The Impact of COVID-19 on Global Stock Markets: Comparative Insights from Developed, Developing, and Regionally Integrated Markets Babatounde Ifred Paterne Zonon 1,* , Mouhamed Bayane Bouraima 2, Chuang Chen 3and Koffi Dumor 4 1School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China 2 Sichuan College of Architectural Technology, Deyang 618000, China; [email protected] 3School of Business, University of New South Wales, Sydney, NSW 2052, Australia; [email protected] 4School of Management and Economics, University of Electronic Sciences and Technology of China, Chengdu 611731, China; [email protected] *Correspondence: [email protected] Abstract: This study examines the impact of the COVID-19 pandemic on global stock markets by comparing developed and developing economies, while highlighting regional differences. Using dynamic panel regression models, this study explores the role of pandemic-related variables, fiscal policies, and investor sentiment in shaping market performance. Developed markets, although highly sensitive to infections, benefited from robust fiscal interventions and institutional resilience. Developing markets face greater volatility owing to stringent measures, structural vulnerabilities, and limited fiscal capacities. Regionally, Europe demonstrated resilience through coordinated policies, whereas the Americas experienced significant volatility from fragmented responses. Africa and parts of Asia encountered fewer initial shocks but struggled with prolonged recovery due to limited financial and institutional resources. The findings underscore the importance of economic integration, coordinated fiscal and monetary policies, and investor sentiment management to stabilize markets during crises. These insights guide policymakers in enhancing resilience and fostering sustainable economic growth amid future global disruptions. Keywords: COVID-19; pandemic; developed markets; developing markets; stock returns JEL Classification: F15; F42; G01; G15 1. Introduction Throughout history, pandemics have left deep economic and social scars that disrupt livelihoods, trade, and financial stability. From the Spanish Flu to SARS and Ebola, these crises have reshaped economies and exposed vulnerabilities in the global markets (Sampath et al.,2021). The COVID-19 pandemic, which emerged in late 2019, triggered unprecedented global economic challenges (Goldstein et al.,2021). When the World Health Organization declared it a global pandemic on 11 March 2020, governments responded with severe restrictions to control the outbreak, stalling economic activities and cross-border trade in the process. For example, the World Trade Organization reported a 5.3% contraction in global trade volumes in 2020, and the International Labor Organization estimated a loss equivalent to 255 million full-time jobs, illustrating the pandemic’s disruptive economic impact. The interconnectedness of modern economies, with tightly linked supply chains and financial systems, meant that the economic fallout from COVID-19 was widespread. Many regional blocs such as the European Union (EU) and the Association of Southeast Asian Economies 2025,13, 39 https://doi.org/10.3390/economies13020039 Economies 2025,13, 39 2 of 23 Nations (ASEAN) have experienced disruptions in economic cooperation and trade flows (del Maria et al.,2020). Fiscal and monetary responses varied significantly across regions, highlighting structural differences in policy flexibility. Developed economies, such as the United States and the European Union, implemented expansive fiscal interventions, which included stimulus packages totaling trillions of dollars, stabilizing markets, and reinforcing investor confidence (Baker et al.,2020;Zaremba et al.,2021). However, in developing markets such as those in ASEAN and Africa, limited fiscal capacity and weaker institutional frameworks restrict the effectiveness of policy responses, exacerbate economic vulnerabilities, and prolong recovery (Ozili & Arun,2020). A critical factor that amplifies economic challenges is the inflexibility of fiscal and monetary policies in many markets. Developing economies, constrained by structural inefficiencies and external dependencies, have struggled to adapt swiftly to the evolving crisis. For example, Indonesia’s stock market dynamics highlight the challenges faced by developing economies when integrating into global financial systems, where limited economic freedom and weaker institutional frameworks hinder resilience (Robiyanto,2018; Endri et al.,2024). This contrasts sharply with developed markets, where coordinated and flexible policy frameworks mitigate market volatility and accelerate recovery (Chevallier et al.,2018). The differences in fiscal and monetary policies across regions underscore the critical importance of policy adaptability during crises, as markets with inflexible policies face extended periods of instability (Bekaert & Harvey,1995). Psychological factors have also played a pivotal role during the pandemic, driving significant market instability. Heightened levels of fear and uncertainty, captured through indices such as the Global Fear Index (GFI) and Volatility Index (VIX), underscored the outsized impact of sentiment on market behavior. For instance, the VIX surged to record highs of over 80 in March 2020, reflecting the unprecedented investor anxiety that surpassed the 2008 global financial crisis (Zhang et al.,2020). These sentiment-driven fluctuations are particularly pronounced in less integrated financial markets, where weak institutional frameworks amplify the adverse effects of fear-induced volatility (Bouri et al.,2018;Makun, 2021). This highlights the need for comprehensive approaches to manage psychological factors during crises. This study explores how COVID-19 impacted stock market returns in developed and emerging markets, focusing on regions central to global trade and financial networks. As indicators of economic health, stock markets provide insights into the broader effects of the pandemic on cross-border trade and investment flows (Ramelli & Wagner,2020). This study examines the extent to which fiscal responses, government restrictions, and psychological factors helped stabilize markets or exacerbated volatility across Europe, Asia, and Latin America, regions with distinct economic structures and levels of policy integration. For instance, West Africa’s BRVM provides a compelling case study of how structural factors such as a fixed exchange rate regime amplify financial vulnerabilities during global crises (Zaremba et al.,2021). To analyze these dynamics, this study employs a dynamic panel regression model using stock market indices for both developed and developing economies. The data set spans October 2019 to December 2021 and integrates multiple variables, including COVID-19 infection rates, government response stringency, fiscal policies, and investor sentiment indicators, such as the Volatility Index (VIX) and the Global Fear Index (GFI). By incorporating epidemiological, economic, and psychological variables, this study provides a comprehensive framework for assessing how financial markets respond to pandemics across diverse economic structures. This study’s findings have several important implications. First, they underscore the importance of aligning fiscal and monetary policies within regional trade networks to Economies 2025,13, 39 3 of 23 absorb economic shocks more effectively. Second, the results highlight the need for flexible and adaptive policies that account for the structural differences between the developed and emerging markets. Finally, the research demonstrates the critical role of psychological factors, such as fear and uncertainty, in stabilizing financial markets during crises. By providing a comparative, cross-regional analysis of stock market behavior during the COVID-19 pandemic, this study contributes to the growing literature on pandemic economics. This underscores the importance of coordinated policy responses, resilient financial systems, and sentiment management for mitigating the effects of global disruptions. The following sections review the relevant literature, present the methodology and data, discuss empirical findings, and provide practical recommendations for policymakers and avenues for future research. 2. Literature Review Pandemics have historically disrupted economic systems, exposing weaknesses in financial markets and revealing disparities in resilience among countries. The COVID-19 pandemic, unlike previous crises, simultaneously induced supply and demand shocks, posing unique challenges to economies and financial networks (del Maria et al.,2020). These shocks included disruptions to global supply chains, reduced labor force participation, and sharp declines in consumer demand, as documented by Baldwin and Mauro (2020) and McKibbin and Fernando (2023). The compounded nature of these shocks magnified their impact on stock markets, which served as critical indicators of economic health and policy effectiveness within regional blocs. Economic integration, in which countries align policies to promote trade, financial cooperation, and investment, plays a significant role in determining how well regions navigate such disruptions. 2.1. Economic Integration and Financial Market Performance The interconnectedness of economies through trade, capital flows, and financial markets has deepened with globalization, making them more susceptible to external shocks. Economic integration frameworks, such as the European Union (EU), ASEAN, and Mercosur, aim to reduce barriers to trade and capital movement, but these frameworks have also been tested during global crises. Ramelli and Wagner (2020) showed that financial markets react swiftly to disruptions, reflecting investor sentiment and economic fundamentals. Regions with stronger integration tend to stabilize faster because of coordinated policy responses such as the EU’s synchronized fiscal actions during COVID-19, which mitigated market volatility (Zaremba et al.,2021). These policies included stimulus packages and measures to stabilize financial institutions, which provided immediate relief to markets and reinforced confidence among investors. However, global financial integration also increases systemic risk because market shocks in one region can spread rapidly to others through contagion effects. A key driver of this phenomenon is herding behavior, in which investors make decisions based on market trends rather than on fundamental analysis (Bikhchandani & Sharma,2001). Herding amplifies volatility, especially during crises, as panic-driven sell-offs spread across markets through a domino effect. The COVID-19 pandemic saw a sharp increase in market correlations, with stock indices across developed and emerging economies experiencing synchronized declines owing to global risk aversion and liquidity shortages. This highlights how interconnected financial markets react not only to domestic economic conditions, but also to broader shifts in investor sentiment and risk perception. The contagion effect was particularly visible in emerging markets, where foreign institutional investors exited en masse, exacerbating local currency depreciation and stock market declines. Studies have shown that, during global crises, emerging markets with Economies 2025,13, 39 4 of 23 higher foreign investor participation experience larger capital outflows and prolonged recovery periods (Forbes & Rigobon,2002). Conversely, markets with lower foreign exposure, such as some segments of China’s stock market, exhibit more insulation from external shocks. The pandemic has also exposed gaps in regional coordination, particularly in the developing regions. ASEAN’s financial cooperation buffered economic fallout through mechanisms such as the Chiang Mai Initiative and shared monetary policies (Wang et al., 2021). This was in stark contrast to Africa and Latin America, where limited policy alignment and weaker market integration exacerbate vulnerability (Ozili & Arun,2020). These disparities underscore the importance of deeper financial integration, which enhances market stability and strengthens investor confidence in times of uncertainty. Recent findings by Endri et al. (2024) emphasize that low long-term integration levels in developing markets, such as Indonesia, provide diversification benefits, but also hinder swift recovery during global crises. This finding highlights the trade-off between financial integration and portfolio diversification opportunities. While stronger financial integration can improve liquidity and risk-sharing mechanisms, it also increases exposure to global shocks, as seen during COVID-19-induced market sell-offs. 2.2. Disparities Between Developed and Developing Markets The impact of the pandemic on stock markets varies significantly between developed and developing economies, revealing structural differences. Developed markets in Europe and North America have benefited from robust fiscal measures and institutional support, which have helped stabilize markets (Baker et al.,2020). These economies exhibit faster recovery and mean reversion of stock returns, reflecting their stronger institutional frameworks (Gormsen & Koijen,2020). Conversely, developing economies experience prolonged volatility, driven by weaker institutions and limited fiscal capacity (Goodell,2020). Ozili and Arun (2020) highlight that government restrictions exacerbate market instability in these regions, as stringent lockdowns disrupt economic activities and expose structural weaknesses. In Africa, the limited depth of financial markets and weak cross-border cooperation magnify the economic challenges (Del et al.,2021). Similarly, Latin American markets were particularly vulnerable to capital outflows, reflecting the region’s dependence on external investments and limited ability to mobilize domestic resources. The unique dynamics of financial integration during crises are evident in studies by Robiyanto (2018) and Yuliadi et al. (2024), who find that while developing markets exhibit lower integration with global markets, this segmentation can shield them from the full extent of global shocks. However, these benefits are often outweighed by the structural vulnerabilities that limit recovery. 2.3. Psychological Factors and Regional Market Responses Investor sentiment, measured through the Global Fear Index (GFI) and VIX, plays a pivotal role in shaping market behavior during the pandemic. In developing regions, where investor confidence is more sensitive to global events, markets experience heightened volatility because of elevated fear levels (Zhang et al.,2020). This fear-induced volatility disproportionately affects emerging markets, where weaker institutional frameworks, lower liquidity, and higher dependence on foreign investors exacerbate uncertainty (Afees et al.,2021). For instance, the VIX surged to an all-time high in March 2020, reflecting widespread panic among global investors and leading to capital outflows, particularly from less integrated financial markets (Zhang et al.,2020). Economies 2025,13, 39 5 of 23 A crucial factor amplifying this volatility is the behavior of foreign institutional investors in emerging stock markets. Unlike domestic investors, foreign investors often react strongly to global risk perceptions, leading to capital flights during crises (Alfaro et al.,2020). This phenomenon, commonly referred to as the “hot money” effect, intensifies stock market downturns, as large withdrawals of foreign capital trigger liquidity shortages and force downward price spirals (Bekaert & Harvey,1995). Moreover, exchange rate fluctuations compound instability, as depreciating local currencies increase the cost of foreign-denominated debt, further weakening investor confidence. Emerging markets with significant exposure to foreign portfolio investments, such as Indonesia, Brazil, and South Africa, are particularly vulnerable and have experienced sharp currency depreciation and stock market sell-offs during the early phases of the pandemic (IMF,2021; Ozili & Arun,2020). In contrast, markets in regions with deeper integration frameworks, such as the Eurozone, benefit from coordinated policy announcements that temper investor anxiety and stabilize markets (Izzeldin et al.,2021). These efforts, including quantitative easing programs and fiscal stimulus packages, signaled stability and helped retain investors’ confidence. However, regions with weaker integration, particularly those dependent on foreign capital inflows, struggle to align market expectations with policy actions, leading to more prolonged disruptions (Mert & Omer,2020). The segmentation of some financial markets, as observed in Indonesia and China, offers opportunities for diversification but also exposes emerging markets to sentimentdriven volatility and exchange rate shocks (Endri et al.,2024). Managing psychological factors requires a dual approach: (1) targeted communication strategies that reinforce policy credibility and market stability and (2) regulatory measures to mitigate excessive foreign capital dependency and strengthen domestic investor participation. Additionally, mechanisms such as foreign exchange interventions and capital flow management tools can help stabilize markets during periods of excessive volatility. These findings underscore the importance of addressing both structural and psychological factors when managing financial crises. Effective policy coordination within regional blocs can mitigate the impact of fear-driven volatility, whereas stronger financial integration enhances resilience to external shocks. Furthermore, harmonizing fiscal and monetary policies across regions strengthens economic recovery, reduces capital flight risks, and supports long-term market stability. 3. Methodology, Data, and Variables 3.1. Data This study uses a comprehensive data set that combines stock market indices from both developed and developing economies to ensure regional diversity and varying levels of economic integration. Countries are classified using recognized financial groupings, such as MSCI, Investing.com (accessed on 26 January 2024), S&P, and STOXX (see Appendices Aand B). The classification is based on the yearly evaluation and ranking of equity markets around the world as well as a review of market accessibility. This classification provides a clear and objective distinction between developed and developing nations and ensures a meaningful comparison of stock market behavior across diverse economic contexts. The data set covers October 2019 to December 2021, and is segmented into three distinct phases: • The pre-outbreak period (October to December 2019): represents a stable baseline period before the emergence of COVID-19. Economies 2025,13, 39 6 of 23 • Outbreak (January–March 2020): captures the initial shock as the pandemic spreads globally. • Pandemic (April 2020 to December 2021): reflects sustained impacts and policy responses during the prolonged crisis. Data on stock market prices and daily returns were sourced from Investing.com and ADVFN, which provide reliable historical financial data for the global markets. Information on COVID-19 confirmed cases, deaths, and government response stringency measures was obtained from Our World in Data and Worldometer Statistics. The Government Response Stringency Index (SI) captured the strictness of policy interventions on a scale from 0 to 100. Market sentiment data were collected from the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) and Global Fear Index (GFI) proposed by Afees and Lateef (2020). Fiscal policy data, including government spending and forgone revenues, were sourced from the International Monetary Fund (IMF) COVID-19 Fiscal Monitor Database. This data set enables a robust analysis of financial market dynamics during the COVID-19 pandemic, emphasizing the differences between developed and developing markets. By integrating epidemiological, fiscal, and psychological variables, this study captures the multifaceted nature of the impact of the pandemic on global markets. 3.2. Methodology This study employs a dynamic panel regression model to analyze the impact of COVID-19 on stock market returns in developed and developing economies. This approach is well-suited to capturing temporal dynamics and cross-sectional heterogeneity, enabling insights into how market responses vary across regions and over time. Three models were estimated to assess the different aspects of market behavior. • Base Model: Examines the direct effects of COVID-19 variables (confirmed cases, deaths, and stringency measures) on stock market returns. • Control Model: Incorporates additional variables, such as exchange rate fluctuations, fiscal policies, and sentiment indices (VIX and GFI). • Interaction Model: This model introduces interaction terms between the COVID-19 variables and government stringency measures to assess how policy interventions influence market dynamics. The lagged dependent variable is included to account for return persistence, reflecting the momentum and mean reversion tendencies in stock markets. Diagnostic tests were conducted before estimation: • Panel Unit Root Tests: Levin–Lin–Chu and Fisher tests ensure the stationarity of variables. • Hausman Test: Determines the suitability between the random effects and fixed effects models. The interaction terms allow for an in-depth examination of how government responses mitigate or amplify the economic effects of the pandemic. For example, stringent lockdowns in developing countries may exacerbate market instability due to weaker economic structures, whereas in developed markets, such measures may enhance investor confidence. This methodological framework provides a nuanced understanding of the effects of the pandemic, accounting for both the structural and temporal dimensions of market responses. 3.3. Variables The dependent variable is Index Return (Indexret), calculated as the daily log returns of stock market indices. Independent Variables: Economies 2025,13, 39 7 of 23 COVID-19 Variables: • Confirmed Cases and Deaths: Log-transformed to account for exponential growth and normalize the distribution. • Stringency Index (SI): A composite measure based on nine response indicators, including school closures, workplace closures, and travel bans. Pandemic Severity: Ratio of confirmed cases to population size, reflecting the intensity of the pandemic’s impact on each country. To analyze the direct effect of COVID-19-related variables on stock market returns while accounting for return persistence, the study estimates the following base model: Indexretit =α0+α1Indexreti,t−1+α2ccasesit +α3cdeathsit +α4SIit +ϵit (1) This base model includes the lagged dependent variable, Indexret i,t−1 , to capture the persistence of stock returns, indicating whether returns exhibit mean reversion (i.e., whether gains or losses are corrected over time). The coefficients α (2–4) estimate the direct effects of COVID-19 cases, deaths, and government stringency measures on stock returns. The control variables: Fiscal Policy Measures: Quantified as additional government spending and forgone revenues (in billions of USD). These data were not available for all countries, such as the Palestinian Territory and Venezuela. Exchange Rate Returns (ex): Daily changes in exchange rates to account for currency effects on market returns. CBOE Volatility Index (VIX): Captures market sentiment and reflects global risk aversion. Global Fear Index (GFI): Broader measure of investor sentiment that accounts for global cases and deaths over a 14-day horizon. The GFI is calculated as the weighted average of the Reported Cases Index (RCI) and the Reported Deaths Index (RDI). The RCI measures to which extent expectations from reported cases in a period of 14 days ahead swerved from the present reported case, as most estimates of the COVID-19 incubation period range from 1–14 days (WHO,2020). RCIt= ∑N iCi,t ΣN i(Ci,t +Ci,t−14)!×100 (2) where the numerator is the total number of COVID-19 pandemic reported cases at time tfor all the countries globally, i = 1, 2, . . . , N. N stands for the total number of captured cross-sections in the index; C i,t−14, the number of COVID-19 pandemic reported cases for each cross-section at the incubation period beginning, is represented as the preceding 14th day. The whole equation is then multiplied by 100 to provide the index on a scale of 0 to 100. The highest value represents the highest level of fear in the pandemic period. The fear level decreases as the index tends toward 0. The reported cases are mirrored by the Reported Death Index by connecting the number of daily reported deaths to the expected number of deaths reported in a 14-day period ahead, which is in line with the assumption for RCI. RDIt= ∑N iDi,t ΣN i(Di,t +Di,t−14)!×100 (3) where the numerator is the total number of COVID-19 reported deaths at time tfor all the countries in the world. D i,t−14 is the number of COVID-19 reported deaths at the beginning of the incubation period, t −14. Economies 2025,13, 39 8 of 23 From the above two equations, the Global fear Index is computed as: GFIt=[0.5(RCIt+RDIt)] (4) Following the development of the GFI, Makun (2021) applied to examine the effect of COVID-19 on stock returns of nine major Asia–Pacific countries. He used the COVID-19 GFI to estimate the COVID-19’s effect on stock returns empirically. The results showed that the global fear index negatively and significantly affects stock returns in the long and short run. To control for additional factors that could influence stock market returns, such as exchange rates, fiscal policies, and market sentiment, the model is extended as follows: Indexretit =α0+α1IndexretI,t−1+α2ccasesit +α3cdeathsit +α4SIit +α5psevit +α6fiscpit +α7VIXit +α8exit +ϵit (5) The interaction Terms: The model includes interaction terms (e.g., COVID-19 Variables × Stringency Index) to assess how policy measures influenced the relationship between pandemic developments and stock market performance. These terms capture the differential effects of policy interventions across regions and market types. Indexretit =α0+α1Indexreti,t−1+α2ccasesit +α3cdeathsit +α4SIit +α5(ccases ×SI)it +α6(cdeaths ×SI)it +ϵit (6) This model includes the interaction terms between COVID-19 cases and stringency measures, allowing us to examine how government restrictions modified the impact of the pandemic on stock returns. The lagged dependent variable remains important for capturing momentum or mean-reversion tendencies. This interaction model provides a comprehensive understanding of the impact of the pandemic, ensuring robust and policyrelevant insights into the market dynamics. By combining epidemiological, fiscal, and psychological factors, this study offers a multidimensional perspective on financial market resilience during global crises. 4. Empirical Analysis The empirical analysis was designed to systematically evaluate the effects of the COVID-19 pandemic on global stock markets using a strategic segmentation of the data set into distinct time frames. This segmentation facilitates nuanced risk assessment by capturing temporal and regional variations in market behavior. The calm period (1 October 2019 to 30 November 2019) serves as a baseline for market stability, free from pandemic-related disruptions. This period allows for the control of seasonal effects and provides a reference point against which subsequent volatility can be measured. By establishing a stable benchmark, this phase contextualizes the shifts observed in the later periods. The outbreak period (1 December 2019 to 10 March 2020) marks the emergence of COVID-19, characterized by uncertainty surrounding the virus’s transmission and economic implications. This phase captures the initial responses of global markets, reflecting the heightened uncertainty and early impact of government interventions aimed at curbing the spread of the virus. During this period, stock markets were influenced by speculation and information asymmetry, as investors reacted to unfolding developments. The pandemic began on 11 March 2020, when the World Health Organization officially declared COVID-19 a global pandemic. To capture evolving market responses, this period is further divided into the early pandemic phase (11 March to 31 May 2020) and the extended Economies 2025,13, 39 15 of 23 related disruptions. These findings highlight the region-specific sensitivities of markets to pandemic-related shocks, driven by their economic structures and sectoral dependencies. Table 7. Matrix of The Regional Ranking of the COVID-19 Impact on Stock Returns. Ccases Cdeaths SI Africa 3 Asia 2 2 Europe 1 Americas 1 1 Middle East 2 3 Note: This table ranks the COVID-19 variables affecting each region’s stock returns. The ranking goes from 1 to 3, with 1 being the region most negatively impacted by the variables and 3 the lowest. The ranking is based on the coefficients retrieved from Equations (2) and (3) from the regression analysis. Government stringency measures negatively impacted stock returns in Asia, Europe, and the Americas, with the most severe effects being recorded in the Americas. These results corroborate those of Zaremba et al. (2021), who noted that, while stringent measures were essential for controlling public health risks, they intensified market volatility, especially in interconnected regions. In contrast, Africa experienced a milder impact from stringency measures, reflecting its limited integration into global trade and financial networks. This relative insulation aligns with Makun (2021), who observed that less interconnected financial markets such as those in Africa were partially shielded from the economic turbulence of global crises. The lagged dependent variable confirms significant mean reversion tendencies in the Americas and Europe, where stock markets stabilized more effectively after the initial shocks. This stability is supported by deeper market liquidity and robust institutional frameworks, particularly in Europe, where the Union’s coordinated fiscal and monetary interventions mitigate volatility and foster investor confidence. Conversely, Africa and the Middle East showed weaker mean reversion tendencies, reflecting market inefficiencies and slower recovery trajectories due to limited policy coordination and weaker institutional support. Asia also demonstrated mixed patterns of mean reversion, with stabilization observed primarily during periods of heightened government intervention. This underscores the critical role of policy measures in stabilizing investor sentiment in the region. Fear indices, including the Global Fear Index (GFI) and the CBOE Volatility Index (VIX), have the strongest influence on stock returns in Asia and the Americas, contributing to sustained volatility. These regions have experienced heightened uncertainty due to varying policy responses and disparities in healthcare infrastructure. Surprisingly, Europe exhibits lower levels of fear-driven volatility, likely due to stronger investor confidence in its financial systems and the efficacy of coordinated policy measures. These observations align with those of Broadstock and Zhang (2019), who emphasized the role of effective governance in mitigating the psychological drivers of market instability. This integrated analysis highlights the interplay among public health measures, fiscal interventions, and stock market responses during the COVID-19 pandemic. While global markets are universally affected, the magnitude and nature of the impact vary significantly across regions, reflecting differences in structural, institutional, and psychological factors. Regions with coordinated policy frameworks, such as the European Union, demonstrated greater resilience and faster recovery, providing a blueprint for enhancing market stability during future crises. Conversely, regions with weaker integration, such as Africa and parts of the Middle East, have experienced prolonged instability, underscoring the need for strengthened institutional frameworks and regional cooperation. Economies 2025,13, 39 16 of 23 The regional analysis underscores the critical importance of tailored interventions and regional cooperation in managing market stability during crises. Policymakers can draw valuable lessons from the relative success of highly integrated regions, emphasizing the role of fiscal coordination, robust governance, and effective communication in mitigating volatility and fostering investor confidence. Simultaneously, addressing the structural vulnerabilities of less integrated markets remains paramount for enhancing resilience and achieving sustainable financial stability in the face of future global disruptions. 5. Conclusions This study provides a comprehensive analysis of the impact of the COVID-19 pandemic on global stock markets, offering nuanced insights into the divergent responses of developed and developing economies and highlighting critical regional variations. Using a dynamic panel model, this study integrates pandemic-related variables, fiscal policies, and investor sentiment to explore the drivers of market performance during a global crisis. These findings emphasize the importance of economic integration, coordinated policy frameworks, and institutional robustness in mitigating volatility and promoting market stability during disruptions. Key insights emerged from this analysis. In developed markets, heightened sensitivity to confirmed cases exacerbates volatility. However, robust fiscal interventions effectively stabilize investor sentiment and facilitate recovery. These findings underscore the stabilizing influence of institutional resilience and policy flexibility in advanced economies. In contrast, developing markets face heightened volatility due to stringent government interventions, structural constraints, and weaker institutional frameworks. These disparities highlight the pressing need for deeper economic integration and institutional strengthening in less developed regions to enhance their resilience against external shocks. Psychological factors, including fear and uncertainty, significantly shaped market dynamics, as evidenced by indices such as the Global Fear Index (GFI) and Volatility Index (VIX). Fear-induced volatility disproportionately affects regions with weaker integration and institutional fragility, underscoring the necessity of managing investor sentiment alongside economic policies. Effective communication, transparent governance, and targeted measures to bolster confidence are crucial for stabilizing markets, particularly in economies with less diversified financial systems. From a regional perspective, this study reveals stark differences in the market responses to the pandemic. The Americas have experienced profound disruptions driven by high infection rates and fragmented policy responses. Europe demonstrated greater resilience by leveraging coordinated fiscal and monetary policies within the European Union to stabilize markets and accelerate recovery. Conversely, Africa and parts of Asia, while less integrated into global financial systems, experienced milder immediate disruptions, but faced prolonged recovery challenges due to limited access to international financial support and weaker institutional capacities. These regional disparities highlight the critical importance of developing robust cooperative frameworks and enhancing economic integration to mitigate the adverse effects of global crises. The findings further reveal that stock markets in more integrated regions, such as Europe, display mean reversion tendencies, reflecting their ability to absorb shocks and recover. Conversely, regions with weaker integration, such as Africa and parts of Asia, face prolonged instability, reflecting the persistent challenges in restoring investor confidence. Strengthening regional cooperation and fostering deeper financial integration can bolster resilience and support a faster recovery in the face of future crises. Economies 2025,13, 39 17 of 23 A key takeaway from this study is the importance of regional financial cooperation mechanisms in stabilizing stock markets during crises. Economic alliances such as the Association of Southeast Asian Nations Free Trade Area (AFTA), Asia-Pacific Economic Cooperation (APEC), the G20, and the BRICS coalition offer platforms for coordinated policy responses that mitigate economic shocks. For instance, ASEAN’s Chiang Mai Initiative provides a regional liquidity safety net that enhances financial stability during a crisis. Similarly, the G20’s collective fiscal interventions during the pandemic demonstrated the effectiveness of synchronized economic policies in cushioning global financial disruptions. In developing economies, leveraging regional economic cooperation can help counteract capital outflows, stabilize exchange rates, and restore investor confidence. For policymakers, this study underscores the value of coordinated fiscal and monetary policies within integrated economic frameworks to reduce volatility and foster stability. The lessons drawn from Europe’s resilience during the pandemic can serve as models for other regions. Investments in healthcare infrastructure, transparent communication strategies, and effective management of investor sentiment are pivotal for enhancing resilience and promoting financial stability. For investors and business owners, understanding regional integration levels and institutional robustness is critical to navigating uncertainty and making informed decisions during global disruptions. While this study provides important contributions, it is not without its limitations. The analysis is constrained by the data set’s timeframe, which primarily captures shortto medium-term pandemic impacts. Additionally, this study does not account for the nuanced interactions between fiscal and monetary policies or potential outliers that may skew the findings. The role of financial and trade linkages as well as systemic contagion effects remains an area for further exploration. Future research could incorporate network analysis, detailed trade and financial flow data, or advanced contagion models to better quantify the interconnectedness and its influence on market dynamics during crises. This study advances the understanding of pandemic economics by providing a comparative perspective on global stock market responses to COVID-19. This underscores the pivotal role of economic integration, coordinated policy responses, and regional cooperation in promoting resilience and ensuring sustainable recovery. Future studies could explore the evolution of regional frameworks in a post-pandemic world and delve deeper into the interconnectedness of global economies through trade, finance, and systemic linkages to enrich our understanding of global market dynamics in the face of external shocks. Author Contributions: Conceptualization, B.I.P.Z. and M.B.B.; methodology, B.I.P.Z. and C.C.; software, B.I.P.Z. and C.C.; validation, B.I.P.Z., M.B.B., C.C. and K.D.; formal analysis, B.I.P.Z., M.B.B., C.C. and K.D.; data curation, C.C. and K.D.; writing—original draft preparation, M.B.B.; writing— review and editing, B.I.P.Z.; supervision, K.D.; project administration, M.B.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author due to ongoing project. Conflicts of Interest: The authors declare no conflict of interest. Economies 2025,13, 39 18 of 23 Appendix A. Selected Countries Developed Markets Developing Markets Africa - Botswana, Egypt, Kenya, Mauritius, Morocco, Namibia, Nigeria, Rwanda, South Africa, Tanzania, Tunisia, Uganda, Zambia, Zimbabwe. Asia Hong-Kong (China SAR), Japan, Singapore. Bangladesh, China, India, Indonesia, Iraq, Kazakhstan, Malaysia, Mongolia, Pakistan, Philippines, South Korea, Sri Lanka, Taiwan, Thailand, Vietnam. Europe Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Norway, Portugal, Slovakia, Spain, Sweden, Switzerland, the UK. Bulgaria, Croatia, Czech Republic, Greece, Hungary, Iceland, Malta, Montenegro, Poland, Romania, Russia, Serbia, Slovenia, Ukraine. Americas Canada, the United States of America (USA). Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Jamaica, Mexico, Peru. Middle East/Oceania and Pacific Australia, Israel, New Zealand. Bahrain, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Turkey, United Arab Emirates (UAE). Source: Authors’ computation based on MSCI, Investing.com, S&P, and STOXX’s classification. Notes: Australia and New Zealand will be later removed from the regional analysis to avoid biases due to short sample. Thus, only the Middle East Countries will be kept. Therefore, the final data contains observations for 86 countries. Also, the WAEMU (West African Economic and Monetary Union) is an African developing market not included in the present study, as it is composed of eight countries sharing and represented by a unique stock exchange, while they each had a different number of COVID-19 confirmed cases and deaths and at different periods. Further, countries such as Ghana, Malawi, Latvia, Luxembourg, Bosnia-Herzegovina, Estonia, and Lithuania had no updated stock market data available at the time of the study. Appendix B. Details for Selected Countries Country Name Selected Indices 1st COVID Case Country Name Selected Indices 1st COVID Case Argentina S&P Merral (MERV) 3-Mar-20 Mongolia MNE Top 20 (MNE TOP20) 10-Mar-20 Australia S&P/ASX 200 (AXJ0) 25-Jan-20 Montenegro MNSE 10 17-Mar-20 Austria ATX (ATX) 25-Feb-20 Morocco Moroccan All Shares (MASI) 2-Mar-20 Bahrain Bahrain All Share (BAX) 24-Feb-20 Namibia FTSE NSX Overall (FTN098) 14-Mar-20 Bangladesh Dhaka Stock Exchange 30 (DS30) 8-Mar-20 Netherlands AEX (AEX) 27-Feb-20 Belgium BEL 20 (BFX) 3-Feb-20 New Zealand NZX 50 (NZ50) 28-Feb-20 Botswana BSE Domestic Company (DCIBT) 30-Mar-20 Nigeria NSE 30 (NGSE 30) 27-Feb-20 Brazil Bovespa (BVSP) 25-Feb-20 Norway OSE Benchmark (MSX 30) 26-Feb-20 Bulgaria BSE SOFIX (SOFIX) 8-Mar-20 Oman MSM 30 (MSX 30) 24-Feb-20 Canada S&P/TSX Composite (CSPTSE) 25-Jan-20 Pakistan Karachi 100 (KSE) 26-Feb-20 Economies 2025,13, 39 19 of 23 Country Name Selected Indices 1st COVID Case Country Name Selected Indices 1st COVID Case Chile S&P CLX IPSA (SPIPSA) 3-Mar-20 Palestinian Territory Al-Quds (PLE) 5-Mar-20 China CSI 1000 (CSI 1000I) 1-Dec-19 Peru S&P Lima General (SPBLPGPT) 6-Mar-20 Colombia COLCAP (COLCAP) 6-Mar-20 Philippines Philippines PSEi Composite (PSI) 30-Jan-20 Costa Rica Costa Rica Indice Accionario (IACR) 6-Mar-20 Poland WIG20 (WIG20) 4-Mar-20 Croatia CROBEC (CR BEX) 25-Feb-20 Portugal PSI 20 (PSI20) 2-Mar-20 Cyprus Cyprus Main Market (CYMAIN) 9-Mar-20 Qatar QE General (QSI) 29-Feb-20 Czech Republic PX (PX) 1-Mar-20 Romania BET (BETI) 26-Feb-20 Denmark OMX Copenhagen 20 (OMXC20) 27-Feb-20 Russia MOEX Russia (IMOEX) 31-Jan-20 Ecuador Guayaquil Select (BVG) 29-Feb-20 Rwanda Rwanda All Sahres (ALSIRW) 14-Mar-20 Egypt EGX 30 (EGX30) 14-Feb-20 Saudi Arabia MSCI TADAWUL 30 Price Return (MISAT0002PSA) 2-Mar-20 Finland OMX Helsinki 25 (OMXH 25) 29-Jan-20 Serbia Belex 15 (BELEXIS) 6-Mar-20 France CAC 40 (FCHI) 24-Jan-20 Singapore MSCI Singapore (MISG0000FPSG) 23-Jan-20 Germany DAX (GDAXI) 27-Jan-20 Slovakia SAX (SAX) 6-Mar-20 Greece Athens General Composite (ATG) 26-Feb-20 Slovenia Slovenia Blue-Chip SBITOP (SBITOP) 4-Mar-20 Hong Kong Hang Seng (HIS) 22-Jan-20 South Africa South Africa Top 40 (JTOPI) 5-Mar-20 Hungary Budapest SE (BUX) 4-Mar-20 South Korea KOSPI (KSII) 20-Jan-20 Iceland ICEX Main (OMXIPI) 28-Feb-20 Spain IBEX 35 (IBEX) 31-Jan-20 India BSE Sensex 30 (BSEN) 30-Jan-20 Sri Lanka CSE All-share (CSE) 27-Jan-20 Indonesia Jakarta Stock Exchange Composite Index (JKSE) 2-Mar-20 Sweden OMX Stockholm 30 (OMXS30) 31-Jan-20 Iraq ISX Main 60 (ISX60) 24-Feb-20 Switzerland SMI (SSMI) 25-Feb-20 Ireland ISEQ Overall (ISEQ) 29-Feb-20 Taiwan Taiwan Weighted (TWII) 21-Jan-20 Israel TA 35 (TA35) 21-Feb-20 Tanzania Tanzania All Share (DSEI) 16-Mar-20 Italy Investing.com Italy 40 (invit40) 30-Jan-20 Thailand SET Index (SETI) 13-Jan-20 Jamaica JSE Market (JSEMI) 10-Mar-20 Tunisia Tunindex (TUNINDEX) 2-Mar-20 Japan Nikkei 225 (N225) 16-Jan-20 Turkey BIST 100 (XU100) 11-Mar-20 Jordan Amman SE General (AMGNRLX) 2-Mar-20 Uganda Uganda All Share (ALSIUG) 20-Mar-20 Kazakhstan Kase (KASE) 13-Mar-20 Ukraine PFTS (PFTSI) 3-Mar-20 Kenya Kenya NSE 20 (NSE20) 13-Mar-20 United Arab Emirates DFM General (DFMGI) 29-Jan-20 Kuwait Kuwait Main Market 50 (BKM50) 24-Feb-20 United Kingdom Investing.com United Kingdom 100 (invuk100) 31-Jan-20 Lebanon BLOM Stock (BLSI) 21-Feb-20 United States S&P 500 (SPX) 20-Jan-20 Economies 2025,13, 39 20 of 23 Country Name Selected Indices 1st COVID Case Country Name Selected Indices 1st COVID Case Malaysia FTSE Malaysia KLCI (KLSE) 25-Jan-20 Venezuela Bursatil (IBC) 13-Mar-20 Malta MSE (MSE) 7-Mar-20 Vietnam HNX 30 (HNX30) 23-Jan-20 Mauritius Sandex (MDEX) 18-Mar-20 Zambia LSE All Share (LASILZ) 18-Mar-20 Mexico S&P/BMV IPC (MXX) 28-Feb-20 Zimbabwe ZSE All Share (ALSZI) 20-Mar-20 Source: Authors’ computation. Appendix C. Africa Region Equation (1) Equation (2) Equation (3) Variable Coef. p-Values Variable Coef. p-Values Variable Coef. p-Values indexret −0.0005 0.9570 indexret −0.0057 *** 0.0000 indexret −0.0002 0.8780 ccases −0.0220 * 0.0939 ccases −0.0190 * 0.0945 ccasesxSI −0.0370 * 0.0890 cdeaths 0.1640 * 0.0543 cdeaths 0.1330 * 0.0621 cdeathsxSI 0.1450 ** 0.0586 SI 0.0520 * 0.0882 SI 0.0060 * 0.0986 psev 1661.5660 *** 0.0070 psev 1664.5770 ** 0.0070 fiscp 0.1420 ** 0.0378 fiscp 0.1390 ** 0.0385 GFI −1.3400 ** 0.0309 GFI −1.3440 ** 0.0307 VIX −0.0020 * 0.0976 VIX −0.0020 * 0.0974 ex −13.3390 *** 0.0000 ex −13.3390 *** 0.0000 Constant −0.9030 0.6080 Constant −0.6570 0.7050 Constant −0.9420 0.5150 R-square 0.1000 R-square 0.3600 R-square 0.3600 N 8960 N 8960 N 8960 Notes: This table presents the dynamic panel regression with random effects on indices daily returns in Africa. Equation (1) is the base regression for the effect of the COVID-19 pandemic on stock returns. Equation (2) repeats the process in the presence of other independent variables. Equation (3) uses the interaction term controlling for the effect of the COVID-19 pandemic during the enforcement of stringency measures. ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. Appendix D. Asia Region Equation (1) Equation (2) Equation (3) Variable Coef. p-Values Variable Coef. p-Values Variable Coef. p-Values indexret −0.3395 *** 0.0000 indexret −0.0015 0.2440 indexret −0.0005 0.7400 ccases −0.0700 * 0.0634 ccases −0.0600 * 0.0694 ccasesxSI −0.0620 * 0.0686 cdeaths 0.0570 * 0.0666 cdeaths 0.0460 * 0.0735 cdeathsxSI 0.0390 * 0.0773 SI −0.2590 * 0.0510 SI −0.2560 * 0.0514 psev −315.1760 0.7600 psev −308.9040 0.7640 fiscp 0.0120 * 0.0841 fiscp 0.0140 * 0.0809 GFI −2.5420 ** 0.0133 GFI −2.5510 ** 0.0131 VIX −0.0360 * 0.0588 VIX −0.0360 * 0.0590 ex 1.8460 *** 0.0070 ex 1.844 *** 0.0070 Constant 1.2870 0.4760 Constant 1.3140 0.4750 Constant 0.4190 0.6960 R-square 0.1460 R-square 0.1470 R-square 0.1470 N 11,520 N 11,520 N 11,520 Notes: This table presents the dynamic panel regression with random effects in Asia. Equation (1) is the base regression for the effect of the COVID-19 pandemic on stock returns. Equation (2) repeats the process in the presence of other independent variables. Equation (3) uses the interaction term controlling for the effect of the COVID-19 pandemic during the enforcement of stringency measures. ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. Economies 2025,13, 39 21 of 23 Appendix E. Europe Region Equation (1) Equation (2) Equation (3) Variable Coef. p-Values Variable Coef. p-Values Variable Coef. p-Values indexret 0.0012 0.8610 indexret −0.0006 0.4040 indexret 0.0007 0.3130 ccases 0.0600 * 0.0662 ccases 0.2260 ** 0.0109 ccasesxSI 0.2260 ** 0.0109 cdeaths −0.0450 * 0.0737 cdeaths −0.2410 * 0.0860 cdeathsxSI −0.2400 * 0.0860 SI −0.1720 ** 0.0189 SI −0.0120 * 0.0932 psev −462.5910 ** 0.0290 psev −463.0270 ** 0.0280 fiscp 0.1840 ** 0.0150 fiscp 0.1840 ** 0.0120 GFI −0.0230 0.4260 GFI −0.0230 0.4260 VIX 1.1470 * 0.0790 VIX 1.1470 * 0.0790 ex 5.9480 *** 0.0000 ex 5.9480 *** 0.0000 Constant −1.1640 0.1840 Constant −1.2850 0.1480 Constant −1.2800 0.1230 R-square 0.1000 R-square 0.1200 R-square 0.1200 N 19,200 N 19,200 N 19,200 Notes: This table presents the dynamic panel regression with random effects on indices daily returns in Europe. Equation (1) is the base regression for the effect of the COVID-19 pandemic on stock returns. Equation (2) repeats the process in the presence of other independent variables. Equation (3) uses the interaction term controlling for the effect of the COVID-19 pandemic during the enforcement of stringency measures. ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. Appendix F. Americas Region Equation (1) Equation (2) Equation (3) Variable Coef. p-Values Variable Coef. p-Values Variable Coef. p-Values indexret −0.0467 *** 0.0000 indexret 0.0019 0.4010 indexret −0.0035 0.1300 ccases −0.0310 * 0.0933 ccases −0.4190 ** 0.0293 ccasesxSI −0.3410 ** 0.0394 cdeaths 0.0380 * 0.0914 cdeaths 0.2910 ** 0.0417 cdeathsxSI 0.2480 ** 0.0490 SI −2.1810 *** 0.0000 SI −2.3760 *** 0.0000 psev 4696.4290 *** 0.0000 psev 4374.0580 *** 0.0010 fiscp −0.0050 0.9670 fiscp −0.0250 0.8340 GFI −2.3940 ** 0.0182 GFI −2.4360 ** 0.0175 VIX −0.0050 * 0.0955 VIX −0.0010 * 0.0991 ex 8.4740 *** 0.0000 ex 8.5090 *** 0.0000 Constant 9.406 *** 0.0040 Constant 12.3670 *** 0.0010 Constant 2.3270 0.3880 R-square 0.1600 R-square 0.2500 R-square 0.2200 N 7029 N 7029 N 7029 Notes: This table presents the dynamic panel regression with random effects on indices daily returns in Americas. Equation (1) is the base regression for the effect of the COVID-19 pandemic on stock returns. Equation (2) repeats the process in the presence of other independent variables. Equation (3) uses the interaction term controlling for the effect of the COVID-19 pandemic during the enforcement of stringency measures. ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. Appendix G. Middle East Region Equation (1) Equation (2) Equation (3) Variable Coef. p-Values Variable Coef. p-Values Variable Coef. p-Values indexret −0.0017 0.8910 indexret −0.0014 0.3350 indexret 0.0008 0.5570 ccases 0.1400 * 0.0616 ccases 0.1810 * 0.0523 ccasesxSI 0.1420 * 0.0615 cdeaths −0.1640 * 0.0520 cdeaths −0.2190 ** 0.0395 cdeathsxSI −0.2330 ** 0.0367 SI 1.1350 * 0.0570 SI 1.1850 ** 0.0500 psev −440.7490 0.3830 psev −339.3150 0.5000 fiscp 0.0760 * 0.0517 fiscp 0.0860 ** 0.0462 GFI −0.4760 * 0.0684 GFI −0.4620 * 0.0693 Economies 2025,13, 39 22 of 23 Equation (1) Equation (2) Equation (3) Variable Coef. p-Values Variable Coef. p-Values Variable Coef. p-Values VIX −0.0260 * 0.0642 VIX −0.0270 * 0.0632 ex −1.1690 * 0.0710 ex −1.1260 * 0.0820 Constant −5.3770 0.1090 Constant −5.6420 0.1010 Constant 0.1240 0.9540 R-square 0.1500 R-square 0.1000 R-square 0.1000 N 7040 N 7040 N 7040 Notes: This table presents the dynamic panel regression with random effects on indices daily returns in the Middle East. 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