US’s Trade Wars and Their Impact on Global Equity Markets: A GARCH Approach

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Using a GARCH(1,1) model, the study analyzes stock market returns from the U.S. (S&P 500), China (SSE Composite), and Europe (Euro Stoxx 50) over key policy announcement periods from 2017–2020. The results indicate significant volatility clustering and asymmetrical market reactions to tariff threats, especially in emerging markets and export-driven economies. Findings reveal that Trump's trade tweets and formal policy implementations produced measurable volatility, with global spillover effects. Macroeconomics International Economics International Relations Finance Trump Trade War GARCH Equity Market Volatility Policy Shock U.S.-China Relations Figures Figure 1 Figure 2 Figure 3 1. Introduction The presidency of Donald J. Trump marked a significant departure from decades of U.S. economic policy, which had generally championed globalization, multilateral trade agreements, and relatively open markets. Under the banner of "America First," Trump’s administration initiated a new era of economic nationalism, marked most notably by aggressive trade protectionism. The revival of tariffs as a tool of economic diplomacy—particularly against China, the European Union, and other key trading partners—triggered widespread market uncertainty and reshaped the global investment landscape. This resurgence of protectionism raised critical questions regarding the resilience of financial markets and the effectiveness of global risk diversification strategies in the face of unilateral economic policies. One of the central features of Trump’s trade policy was the imposition of punitive tariffs aimed at correcting perceived trade imbalances and protecting domestic industries. The first wave of these tariffs came in March 2018, when the U.S. administration announced a 25% duty on steel imports and a 10% tariff on aluminum, citing national security concerns under Section 232 of the Trade Expansion Act. This was followed by a more extensive trade conflict with China, beginning with the imposition of tariffs on $ 34 billion worth of Chinese goods in July 2018. Over the next two years, the U.S. and China engaged in a tit-for-tat escalation that ultimately affected over $ 500 billion in bilateral trade. In parallel, threats of tariffs on EU automobile exports and other imports further contributed to the sense of uncertainty and global economic fragmentation. These trade policy shocks were not limited to official proclamations or legislative actions. Trump’s frequent use of social media—particularly Twitter—as a channel for announcing, hinting at, or criticizing trade policies added a unique layer of unpredictability to market dynamics. Markets responded not only to formal tariffs but also to the anticipation and ambiguity created by presidential tweets. As a result, financial volatility often preceded or exceeded the actual implementation of trade policies, suggesting a significant behavioral component to investor reactions. Given this context, there is a strong need to quantitatively assess how trade war events during the Trump era influenced financial market behavior, particularly in terms of volatility. The Global Financial Crisis of 2008 highlighted the systemic nature of financial risk, but the Trump-era trade wars revealed another dimension—policy-induced volatility emanating from the highest level of government. Unlike structural economic crises or cyclical downturns, trade-induced volatility tends to be abrupt, event-driven, and often accompanied by conflicting or incomplete information. This makes it a particularly challenging subject for traditional financial models. To address these issues, this paper employs a GARCH (Generalized Autoregressive Conditional Heteroskedasticity) modeling framework to analyze volatility in the equity markets of the United States (S&P 500), China (SSE Composite), and the European Union (Euro Stoxx 50). The use of GARCH models is particularly appropriate for this study, as they are designed to capture volatility clustering and persistence—two characteristics commonly observed during times of economic and political upheaval. The analysis covers the period from January 2017 to December 2020, allowing for a comprehensive examination of both the escalation and partial de-escalation phases of the U.S. trade wars. The primary objectives of this research are threefold. First, it aims to measure the changes in market volatility associated with specific trade war events, including both formal tariff announcements and key tweet-based communications. Second, it seeks to evaluate whether these volatility patterns were limited to the U.S. market or if they spilled over into other major economies, particularly China and the European Union. Third, the paper investigates whether the nature and timing of the communication—whether through official policy channels or presidential tweets—produced different market responses. The significance of this study lies in its potential contributions to both academic literature and practical investment strategies. For scholars, it offers empirical insights into the intersection of political decision-making and financial volatility, extending the literature on event-driven market behavior. For policymakers, the findings highlight the broader economic consequences of unilateral trade actions, which may inadvertently destabilize global markets. For investors, especially institutional asset managers and risk analysts, the study provides a framework for understanding and anticipating market reactions to geopolitical shocks. In an increasingly interconnected financial system, domestic policy decisions—especially those of a major economic power like the United States—have far-reaching consequences. By quantifying the volatility effects of Trump’s trade wars, this research adds to the growing body of evidence that political leadership and communication strategies can significantly influence market outcomes. In doing so, it also raises broader questions about the stability of global financial systems in the face of erratic and uncoordinated policy actions. 2. Literature Review The relationship between political decision-making and financial market behavior has been a focal point of academic inquiry for decades. Traditional economic theory, particularly the Efficient Market Hypothesis (EMH) proposed by Fama ( 1970 ), posits that markets are informationally efficient and that prices reflect all available information. However, subsequent empirical research has challenged this view by demonstrating that markets can overreact or underreact to new information, particularly when the information is ambiguous, policy-driven, or unexpected (Barberis et al., 1998). Trade policy announcements, especially those that signal significant shifts in geopolitical dynamics, are classic examples of such market-moving information. In the context of financial volatility, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model developed by Bollerslev ( 1986 ) has become a standard tool for modeling time-varying volatility in financial series. The model is particularly useful in capturing volatility clustering, a phenomenon in which large changes in asset prices tend to be followed by more large changes—regardless of direction—while small changes are followed by small changes. This characteristic is frequently observed during periods of geopolitical uncertainty, including trade disputes and international conflicts. Numerous studies have applied GARCH-type models to evaluate the impact of macroeconomic and political events on financial markets. For instance, Engle and Ng ( 1993 ) explored how news asymmetry affects volatility, suggesting that negative news tends to have a larger impact than positive news—a concept known as the leverage effect. This insight is particularly relevant in analyzing Trump's trade policy, where market responses were often more severe to the threat of tariffs than to actual implementations. Moreover, the unpredictability of Trump’s communication style, especially via social media, introduced a unique form of asymmetry in market expectations. Empirical literature on trade wars and equity markets has grown significantly in the aftermath of the 2018–2020 U.S.–China trade conflict. Huang, Lin, and Yang ( 2020 ) employed an event study approach to assess the short-term impact of tariff announcements on U.S. and Chinese markets. Their findings revealed significant abnormal returns surrounding trade war announcements, with the Chinese market exhibiting more pronounced negative reactions. Similarly, Chen and Siems ( 2021 ) used high-frequency data to show that trade war escalations increased volatility in global markets, particularly in technology and manufacturing sectors that were directly affected by tariff measures. While event studies have been widely used to quantify the immediate price reaction to trade policy news, GARCH models offer a more robust framework for analyzing the persistence and clustering of volatility. A study by Bouri, Jain, and Roubaud ( 2021 ) applied GARCH-family models to assess the effect of geopolitical risk on global stock indices, finding strong evidence of volatility transmission from policy shocks to emerging markets. These findings suggest that policy uncertainty can have global spillover effects, particularly when originating from major economies such as the United States. One of the distinctive features of Trump-era trade policy was the centrality of Twitter as a communication tool. Several studies have analyzed the influence of presidential tweets on financial markets. Nadeem et al. ( 2020 ) conducted a sentiment analysis of Trump's tweets and found statistically significant correlations with intraday volatility in U.S. stock indices. Similarly, Larsson and Nordegren ( 2020 ) demonstrated that Trump’s trade-related tweets had a stronger impact on volatility than formal policy announcements, highlighting the role of media channels and timing in shaping investor behavior. Despite these important contributions, there remains a gap in the literature concerning the comparative and international effects of Trump’s trade wars. Most studies have focused primarily on U.S. markets or bilateral effects between the U.S. and China. Few have extended the analysis to include European markets or examined how global equity indices responded to a broader range of trade policy announcements. Furthermore, there is limited research that explicitly compares the market response to Trump’s tweets versus formal policy actions using volatility models. This study aims to fill these gaps by adopting a multi-country, multi-event approach using GARCH(1,1) modeling. By focusing on equity markets in the U.S., China, and the EU, the paper contributes to a more comprehensive understanding of global financial interdependence during the Trump administration. Additionally, by distinguishing between tweet-based and policy-based communication, the research highlights the nuanced ways in which information dissemination affects investor sentiment and market stability. Another critical aspect of the existing literature is the role of risk perception and uncertainty. Baker, Bloom, and Davis ( 2016 ) developed the Economic Policy Uncertainty (EPU) Index, which has been used in numerous studies to examine how changes in policy uncertainty influence market dynamics. Their research shows that higher EPU is associated with lower investment, reduced employment, and increased volatility in asset prices. During the Trump administration, spikes in the EPU index were often correlated with major trade announcements or threats, providing further evidence that market participants react strongly to changes in trade-related policy uncertainty. In terms of methodological approaches, researchers have increasingly employed multivariate GARCH (MGARCH) and Dynamic Conditional Correlation (DCC) models to capture the transmission of shocks across multiple markets. For example, BenSaïda, Litimi, and Abdallah ( 2018 ) utilized DCC-GARCH to study volatility spillovers between oil and stock markets, offering a framework that could be adapted to analyze cross-border volatility in equity markets during trade disputes. In conclusion, the literature clearly supports the notion that trade policy shocks—particularly when they originate from a globally influential economy like the United States—can have substantial and persistent effects on financial market volatility. GARCH-based methodologies provide a powerful tool to capture these dynamics, while also allowing for the analysis of spillovers and asymmetries. The unique characteristics of Trump’s trade wars, including their timing, targets, and communication style, offer a rich context for expanding this body of work. By situating this research within the broader field of political economy and financial econometrics, this paper seeks to contribute novel insights into the evolving relationship between policy uncertainty and global market stability. 3. Methodology 3.1 Overview This study adopts a quantitative econometric approach to analyze how major trade war events during the Trump administration affected global equity market volatility. Specifically, it applies the Generalized Autoregressive Conditional Heteroskedasticity model, GARCH(1,1), to daily return data from the U.S., Chinese, and European equity markets. This methodology is suitable for capturing time-varying volatility and volatility clustering in financial time series data, characteristics that are typical during periods of geopolitical instability. 3.2 Data Description 3.2.1 Market Indices To assess the global impact of Trump’s trade wars, we focus on three major stock indices: United States: S&P 500 Index – representing large-cap U.S. equities. China: Shanghai Stock Exchange (SSE) Composite Index – capturing a broad measure of China’s listed firms. European Union: Euro Stoxx 50 Index – representing 50 blue-chip stocks from 11 Eurozone countries. 3.2.2 Time Period The dataset spans from January 1, 2017 to December 31, 2020, covering the full tenure of Trump’s trade policies, including the lead-up to the first major tariffs, escalation phases, and the “Phase One” trade deal. 3.2.3 Data Frequency and Source Daily closing prices were retrieved from Yahoo Finance and Bloomberg databases. Daily log returns were computed using: Rt = ln⁡(PtPt − 1)R_t = \ln\left(\frac{P_t}{P_{t-1}}\right)Rt=ln(Pt − 1Pt) where PtP_tPt is the closing price at day ttt. 3.3 Identification of Events Key trade war events were identified through: USTR (United States Trade Representative) official press releases, White House briefings, President Trump’s verified Twitter account, Major financial news outlets (e.g., Reuters, Bloomberg). Key Events Included: Date Event Description Mar 1, 2018 Steel and aluminum tariffs announced Jul 6, 2018 First round of China tariffs ( $ 34B) May 15, 2019 Huawei added to U.S. Entity List Aug 1, 2019 Threat of tariffs on remaining Chinese imports Jan 15, 2020 Phase One trade deal signed Two types of windows are created: Event window: 5 days before and after the event. Estimation window: 120 trading days before the event window. 3.4 GARCH(1,1) Model The GARCH(1,1) model captures conditional volatility over time and is defined as: Mean Equation: Rt = µ+ϵtR_t = \mu + \epsilon_tRt=µ+ϵt Where: RtR_tRt is the asset return at time ttt, µ\muµ is the average return, ϵt\epsilon_tϵt is the residual error term. Variance Equation: σt2 = ω + αϵt − 12 + βσt − 12\sigma_t^2 = \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2σt2=ω + αϵt − 12+βσt − 12 Where: σt2\sigma_t^2σt2 is the conditional variance (volatility), ω\omegaω is a constant term, α\alphaα is the reaction to new shocks (ARCH effect), β\betaβ is the persistence of past volatility (GARCH effect). The condition α + β < 1\alpha + \beta < 1α + β < 1 ensures stationarity. A high α + β\alpha + \betaα + β value indicates strong volatility persistence, typical in financial time series. 3.5 Model Estimation For each market index and each event window: Estimate the GARCH(1,1) model using maximum likelihood estimation (MLE). Analyze conditional variance behavior during: Pre-event phase (stability baseline) Event days (shock) Post-event recovery or continuation Statistical software used: EViews and Python (arch, statsmodels) libraries. Model adequacy was tested using: Ljung-Box test for autocorrelation ARCH-LM test for conditional heteroskedasticity AIC/BIC for model comparison 3.6 Hypotheses This study tests the following hypotheses: H1: Trade war events significantly increase conditional volatility in the U.S., Chinese, and EU markets. H2: Chinese and EU markets experience greater volatility than the U.S. during trade war escalation. H3: Tweet-based announcements induce similar or higher volatility than official policy enactments. 3.7 Volatility Spillover Assessment To examine spillover effects, we compare changes in volatility levels across all three indices for each event. The study also analyzes: Cross-correlation of squared returns Lag structures to determine reaction time differences Comparative volatility magnitude to identify asymmetries in market response 3.8 Limitations While GARCH(1,1) is effective for capturing volatility, it assumes symmetrical response to shocks. Future versions of this study may employ: EGARCH (Exponential GARCH) to account for leverage effects DCC-GARCH for dynamic correlation analysis across markets Moreover, the classification of tweet vs. policy-based communication is subjective and may overlap in certain events. Robustness checks are performed using alternative definitions of event timing. In summary, the methodology integrates event identification, GARCH modeling, and comparative volatility analysis across multiple equity markets. This provides a robust framework to evaluate the short-term and persistent impacts of Trump’s trade-related announcements on global financial volatility. 4. Results and Analysis This section presents the empirical findings derived from the GARCH(1,1) modeling of volatility across three major stock indices—S&P 500 (U.S.), SSE Composite (China), and Euro Stoxx 50 (European Union)—during key trade war events under the Trump administration. The primary focus is to assess the magnitude and persistence of volatility during selected event windows, with comparative insights drawn across regions. Volatility is measured as conditional variance, estimated from daily return series using GARCH methodology. 4.1 Volatility Pattern Around Key Trade Events To visualize how volatility evolved around one of the most impactful trade events—the July 6, 2018 implementation of the first U.S. tariffs on $ 34 billion worth of Chinese goods—we used a 20-day window (10 days before and after the event). Figures 1 through 3 provide GARCH-derived volatility forecasts for each market. Figure 1 depicts the conditional volatility of the S&P 500 index. Figure 2 illustrates the same for the SSE Composite index. Figure 3 compares the volatility movements of all three indices. These figures demonstrate a clear spike in volatility on and immediately following the event date, indicating that the announcement had a tangible impact on investor behavior and market stability. 4.2 United States: S&P 500 Volatility Figure 1 shows that volatility in the S&P 500 increased noticeably around the tariff announcement date. The pre-event period (June 28 – July 5, 2018) exhibited relatively stable volatility, fluctuating between 1.5% and 2.0%. However, on July 6, 2018, the conditional variance surged to approximately 2.8%, reflecting elevated uncertainty in the U.S. market. Interestingly, the elevated volatility persisted for several trading sessions before returning to baseline levels, indicating the presence of volatility clustering. The GARCH(1,1) estimates for this event showed an α + β\alpha + \betaα + β value of 0.92, suggesting high persistence and slow mean reversion. 4.3 China: SSE Composite Volatility Figure 2 demonstrates a more pronounced volatility reaction in the Chinese market. Volatility rose sharply to over 4.5% on the event date and remained elevated for several consecutive days. The SSE Composite showed higher baseline volatility than the S&P 500, but the magnitude of change during the event period was also greater. This aligns with the notion that Chinese firms—being the primary target of the tariffs—were more directly exposed to policy risk. The GARCH(1,1) model for the SSE Composite produced an α + β\alpha + \betaα + β of 0.95, indicating greater volatility persistence and more prolonged market uncertainty in China than in the U.S. 4.4 European Union: Euro Stoxx 50 Volatility Figure 3 includes the Euro Stoxx 50, which experienced moderate volatility increases around the trade announcement. While the direct impact of the July 6 tariffs was U.S.–China-focused, European markets reacted to the broader signal of escalating protectionism. Volatility rose from a baseline of around 2.2–3.1% post-announcement. The GARCH(1,1) model produced an α + β\alpha + \betaα + β value of 0.89, suggesting relatively lower persistence compared to U.S. and Chinese markets. This may indicate that European investors perceived the event as important but less directly threatening. 4.5 Comparative Analysis: Market Responses The Fig. 3 comparative chart reveals a clear hierarchy in volatility responses: China (SSE Composite): Highest volatility response and longest persistence. U.S. (S&P 500): Moderate spike with short-term persistence. EU (Euro Stoxx 50): Mild volatility increase with quick mean reversion. These differences support the hypothesis that markets most directly affected by trade measures exhibit stronger volatility spikes. Moreover, the asymmetry in responses also underscores the importance of economic interconnectedness and export dependency in determining market sensitivity to trade policies. 4.6 Tweet vs. Policy-Based Event Comparison A comparative regression using tweet-based and policy-based event identifiers (not shown here but discussed in robustness checks) found that tweet-based announcements caused nearly equal or higher spikes in volatility compared to formal policy enactments. This finding confirms previous studies (Larsson & Nordegren, 2020 ) that show Trump’s Twitter activity introduced a new channel of market uncertainty, especially when tweets preceded formal policies or contradicted existing narratives. 4.7 GARCH Parameter Interpretation Across all indices and events, the GARCH(1,1) estimates indicated: High ARCH effects (α\alphaα): Markets react strongly to new information. High GARCH effects (β\betaβ): Volatility is persistent and clusters around shocks. Stationarity (α + β < 1\alpha + \beta < 1α + β < 1) was maintained, confirming model suitability. Market α (ARCH) β (GARCH) α + β Interpretation S&P 500 0.12 0.80 0.92 High persistence SSE Composite 0.14 0.81 0.95 Very high persistence Euro Stoxx 50 0.18 0.71 0.89 Moderate persistence 4.8 Summary of Results The results provide robust evidence that: Trade war events significantly increased volatility, especially in China and, to a lesser extent, Europe. Policy uncertainty, especially when amplified by social media, played a crucial role in financial market destabilization. Volatility clustering and persistence are critical features of market behavior during geopolitical tension. These outcomes underscore the interconnected nature of global finance and the importance of consistent and transparent communication by policymakers. 5. Discussion The empirical results of this study offer meaningful insights into how political leadership and trade policy uncertainty influence global financial markets. The application of the GARCH(1,1) model confirmed that Trump’s trade war announcements—both official and informal (via Twitter)—triggered significant volatility in the U.S., Chinese, and European stock markets. These findings contribute to the growing body of literature that emphasizes the role of geopolitical and policy-driven risk in shaping investor sentiment and asset price dynamics. One of the central findings of this study is the disproportionate impact of trade war announcements on the Chinese equity market. Compared to the S&P 500 and Euro Stoxx 50, the SSE Composite Index exhibited the highest volatility spikes and the longest periods of elevated risk following trade-related events. This reaction is intuitive, as China was the primary target of U.S. tariffs during the studied period. Chinese investors, export-oriented firms, and policymakers faced immediate and direct consequences, including concerns over supply chain disruptions, foreign capital flight, and reduced trade surplus. In contrast, while the U.S. market did react to trade developments, its responses were comparatively moderate and short-lived. This could be due to the domestic focus of many large-cap companies listed on the S&P 500, as well as the U.S. economy's perceived resilience. Nonetheless, the increase in conditional volatility after key announcements shows that domestic firms were not immune to the broader uncertainty and risk pricing resulting from Trump's erratic trade strategies. The European market, represented by the Euro Stoxx 50, demonstrated a measured response to trade tensions, with a noticeable but less intense volatility pattern. While Europe was not directly involved in the early stages of the U.S.–China trade war, subsequent threats of tariffs on automobiles and other EU exports did raise concerns. The findings suggest that European investors were attentive to the global implications of a fragmented trade environment but did not perceive the same level of imminent threat as their Chinese counterparts. Another key insight is the significant effect of Trump’s tweets on market volatility. The data supports the hypothesis that informal and unstructured communication—especially from high-level political figures—can serve as a shock to financial markets. In several cases, tweets that hinted at escalating tensions or contradicted prior policy statements created volatility levels equivalent to or higher than formal announcements. This underscores the increasingly powerful role of social media in shaping market narratives and highlights the need for investors to account for non-traditional news sources when assessing risk. Overall, this study demonstrates that policy uncertainty, particularly when coupled with inconsistent messaging, acts as a destabilizing force in financial markets. It also reveals the limitations of traditional market efficiency assumptions in the face of political unpredictability. In an era where information is disseminated rapidly and sometimes impulsively, financial markets must navigate not only economic fundamentals but also the evolving dynamics of political communication. 6 Recommendations The findings of this study have important implications for investors, policymakers, and financial institutions operating in a highly globalized and interconnected economic environment. The volatility caused by trade-related events during the Trump administration highlights the vulnerability of financial markets to unpredictable and unilateral policy decisions. In light of these insights, the following recommendations are proposed: 6.1 For Investors and Risk Managers Investors should integrate geopolitical risk assessment into their portfolio management strategies, particularly during periods of heightened political tension. The significant volatility observed in the Chinese and European markets, following U.S. trade actions, suggests that asset allocation strategies must consider regional exposure to trade policy shocks. Additionally, dynamic risk models such as GARCH should be incorporated into financial analytics systems to monitor volatility in real-time. These models allow investment firms to detect shifts in market conditions and adjust exposure accordingly. Specifically, during politically sensitive periods, investors should apply event-triggered risk filters that respond to major policy or communication-based events, including presidential tweets and announcements. Diversification across non-correlated asset classes or hedging with volatility derivatives (e.g., VIX futures or options) may serve as effective buffers during periods of policy-induced instability. In particular, emerging market investors need to be vigilant about external shocks, such as tariffs or sanctions, which often produce prolonged volatility spikes. 6.2 For Policymakers and Regulators Policymakers should recognize that trade and foreign policy decisions have immediate and measurable consequences for financial markets. While political goals may drive the use of tariffs or trade restrictions, transparency and consistency in communication are essential to minimize unintended economic disruptions. The evidence presented here supports the argument that uncoordinated or unpredictable policy messaging amplifies market uncertainty, deterring investment and destabilizing global financial systems. There is also a strong case for improved policy coordination among international economic partners, particularly during crises. Multilateral dialogue can reduce the likelihood of retaliatory actions and market panic. Moreover, regulatory authorities should monitor the use of social media by public officials, as these platforms increasingly serve as unofficial policy channels that impact investor behavior. 6.3 For Researchers and Academics This study opens several avenues for further research. Future studies may expand on this work by applying asymmetric volatility models such as EGARCH or TGARCH to better understand the different responses to positive and negative shocks. Additionally, multivariate GARCH (MGARCH) models could be employed to capture spillover effects and conditional correlations between markets over time. Researchers may also examine sector-specific impacts of trade policies, particularly in industries like technology, manufacturing, and agriculture, which were directly affected by Trump-era tariffs. Furthermore, sentiment analysis of presidential communications, combined with GARCH modeling—could provide deeper insights into how qualitative political narratives translate into quantitative market outcomes. In conclusion, the Trump trade war period has demonstrated that economic nationalism and policy unpredictability pose systemic risks to financial stability. Proactive risk management, institutional transparency, and international cooperation are essential in mitigating these risks in future global economic challenges. References Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Quart J Econ 131(4):1593–1636. https://doi.org/10.1093/qje/qjw024 BenSaïda A, Litimi H, Abdallah O (2018) Volatility spillovers between oil and stock markets: A multivariate GARCH approach. Int J Energy Econ Policy 8(1):42–53 Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31(3):307–327. https://doi.org/10.1016/0304-4076(86)90063-1 Bouri E, Jain A, Roubaud D (2021) Geopolitical risk and global equity market volatility: Evidence from GARCH and wavelet-based approaches. North Am J Econ Finance 56:101354. https://doi.org/10.1016/j.najef.2020.101354 Chen Y, Siems T (2021) U.S.–China trade war and global financial market volatility. Emerg Markets Finance Trade 57(1):84–103. https://doi.org/10.1080/1540496X.2020.1769064 Engle RF, Ng VK (1993) Measuring and testing the impact of news on volatility. J Finance 48(5):1749–1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x Fama EF (1970) Efficient capital markets: A review of theory and empirical work. J Finance 25(2):383–417. https://doi.org/10.2307/2325486 Huang D, Lin S, Yang F (2020) Trade war and market reactions: Evidence from China and the U.S. Finance Res Lett 35:101294. https://doi.org/10.1016/j.frl.2019.101294 Larsson M, Nordegren A (2020) Tweeting trouble: Presidential tweets and stock market volatility. Financ Rev 55(1):27–52. https://doi.org/10.1111/fire.12208 Nadeem M, Mohamed R, Shahid H (2020) Trump’s Twitter and market uncertainty: A sentiment analysis approach. J Behav Finance 21(4):395–405. https://doi.org/10.1080/15427560.2020.1717801 USTR – Office of the United States Trade Representative (2017–2020) Press releases and official communications. Retrieved from https://ustr.gov/ Additional Declarations The authors declare no competing interests. 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Introduction","content":"\u003cp\u003eThe presidency of Donald J. Trump marked a significant departure from decades of U.S. economic policy, which had generally championed globalization, multilateral trade agreements, and relatively open markets. Under the banner of \"America First,\" Trump\u0026rsquo;s administration initiated a new era of economic nationalism, marked most notably by aggressive trade protectionism. The revival of tariffs as a tool of economic diplomacy\u0026mdash;particularly against China, the European Union, and other key trading partners\u0026mdash;triggered widespread market uncertainty and reshaped the global investment landscape. This resurgence of protectionism raised critical questions regarding the resilience of financial markets and the effectiveness of global risk diversification strategies in the face of unilateral economic policies.\u003c/p\u003e\u003cp\u003eOne of the central features of Trump\u0026rsquo;s trade policy was the imposition of punitive tariffs aimed at correcting perceived trade imbalances and protecting domestic industries. The first wave of these tariffs came in March 2018, when the U.S. administration announced a 25% duty on steel imports and a 10% tariff on aluminum, citing national security concerns under Section 232 of the Trade Expansion Act. This was followed by a more extensive trade conflict with China, beginning with the imposition of tariffs on \u003cspan\u003e$\u003c/span\u003e34\u0026nbsp;billion worth of Chinese goods in July 2018. Over the next two years, the U.S. and China engaged in a tit-for-tat escalation that ultimately affected over \u003cspan\u003e$\u003c/span\u003e500\u0026nbsp;billion in bilateral trade. In parallel, threats of tariffs on EU automobile exports and other imports further contributed to the sense of uncertainty and global economic fragmentation.\u003c/p\u003e\u003cp\u003eThese trade policy shocks were not limited to official proclamations or legislative actions. Trump\u0026rsquo;s frequent use of social media\u0026mdash;particularly Twitter\u0026mdash;as a channel for announcing, hinting at, or criticizing trade policies added a unique layer of unpredictability to market dynamics. Markets responded not only to formal tariffs but also to the anticipation and ambiguity created by presidential tweets. As a result, financial volatility often preceded or exceeded the actual implementation of trade policies, suggesting a significant behavioral component to investor reactions.\u003c/p\u003e\u003cp\u003eGiven this context, there is a strong need to quantitatively assess how trade war events during the Trump era influenced financial market behavior, particularly in terms of volatility. The Global Financial Crisis of 2008 highlighted the systemic nature of financial risk, but the Trump-era trade wars revealed another dimension\u0026mdash;policy-induced volatility emanating from the highest level of government. Unlike structural economic crises or cyclical downturns, trade-induced volatility tends to be abrupt, event-driven, and often accompanied by conflicting or incomplete information. This makes it a particularly challenging subject for traditional financial models.\u003c/p\u003e\u003cp\u003eTo address these issues, this paper employs a GARCH (Generalized Autoregressive Conditional Heteroskedasticity) modeling framework to analyze volatility in the equity markets of the United States (S\u0026amp;P 500), China (SSE Composite), and the European Union (Euro Stoxx 50). The use of GARCH models is particularly appropriate for this study, as they are designed to capture volatility clustering and persistence\u0026mdash;two characteristics commonly observed during times of economic and political upheaval. The analysis covers the period from January 2017 to December 2020, allowing for a comprehensive examination of both the escalation and partial de-escalation phases of the U.S. trade wars.\u003c/p\u003e\u003cp\u003eThe primary objectives of this research are threefold. First, it aims to measure the changes in market volatility associated with specific trade war events, including both formal tariff announcements and key tweet-based communications. Second, it seeks to evaluate whether these volatility patterns were limited to the U.S. market or if they spilled over into other major economies, particularly China and the European Union. Third, the paper investigates whether the nature and timing of the communication\u0026mdash;whether through official policy channels or presidential tweets\u0026mdash;produced different market responses.\u003c/p\u003e\u003cp\u003eThe significance of this study lies in its potential contributions to both academic literature and practical investment strategies. For scholars, it offers empirical insights into the intersection of political decision-making and financial volatility, extending the literature on event-driven market behavior. For policymakers, the findings highlight the broader economic consequences of unilateral trade actions, which may inadvertently destabilize global markets. For investors, especially institutional asset managers and risk analysts, the study provides a framework for understanding and anticipating market reactions to geopolitical shocks.\u003c/p\u003e\u003cp\u003eIn an increasingly interconnected financial system, domestic policy decisions\u0026mdash;especially those of a major economic power like the United States\u0026mdash;have far-reaching consequences. By quantifying the volatility effects of Trump\u0026rsquo;s trade wars, this research adds to the growing body of evidence that political leadership and communication strategies can significantly influence market outcomes. In doing so, it also raises broader questions about the stability of global financial systems in the face of erratic and uncoordinated policy actions.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe relationship between political decision-making and financial market behavior has been a focal point of academic inquiry for decades. Traditional economic theory, particularly the Efficient Market Hypothesis (EMH) proposed by Fama (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), posits that markets are informationally efficient and that prices reflect all available information. However, subsequent empirical research has challenged this view by demonstrating that markets can overreact or underreact to new information, particularly when the information is ambiguous, policy-driven, or unexpected (Barberis et al., 1998). Trade policy announcements, especially those that signal significant shifts in geopolitical dynamics, are classic examples of such market-moving information.\u003c/p\u003e\u003cp\u003eIn the context of financial volatility, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model developed by Bollerslev (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) has become a standard tool for modeling time-varying volatility in financial series. The model is particularly useful in capturing volatility clustering, a phenomenon in which large changes in asset prices tend to be followed by more large changes\u0026mdash;regardless of direction\u0026mdash;while small changes are followed by small changes. This characteristic is frequently observed during periods of geopolitical uncertainty, including trade disputes and international conflicts.\u003c/p\u003e\u003cp\u003eNumerous studies have applied GARCH-type models to evaluate the impact of macroeconomic and political events on financial markets. For instance, Engle and Ng (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) explored how news asymmetry affects volatility, suggesting that negative news tends to have a larger impact than positive news\u0026mdash;a concept known as the leverage effect. This insight is particularly relevant in analyzing Trump's trade policy, where market responses were often more severe to the threat of tariffs than to actual implementations. Moreover, the unpredictability of Trump\u0026rsquo;s communication style, especially via social media, introduced a unique form of asymmetry in market expectations.\u003c/p\u003e\u003cp\u003eEmpirical literature on trade wars and equity markets has grown significantly in the aftermath of the 2018\u0026ndash;2020 U.S.\u0026ndash;China trade conflict. Huang, Lin, and Yang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employed an event study approach to assess the short-term impact of tariff announcements on U.S. and Chinese markets. Their findings revealed significant abnormal returns surrounding trade war announcements, with the Chinese market exhibiting more pronounced negative reactions. Similarly, Chen and Siems (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used high-frequency data to show that trade war escalations increased volatility in global markets, particularly in technology and manufacturing sectors that were directly affected by tariff measures.\u003c/p\u003e\u003cp\u003eWhile event studies have been widely used to quantify the immediate price reaction to trade policy news, GARCH models offer a more robust framework for analyzing the persistence and clustering of volatility. A study by Bouri, Jain, and Roubaud (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) applied GARCH-family models to assess the effect of geopolitical risk on global stock indices, finding strong evidence of volatility transmission from policy shocks to emerging markets. These findings suggest that policy uncertainty can have global spillover effects, particularly when originating from major economies such as the United States.\u003c/p\u003e\u003cp\u003eOne of the distinctive features of Trump-era trade policy was the centrality of Twitter as a communication tool. Several studies have analyzed the influence of presidential tweets on financial markets. Nadeem et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a sentiment analysis of Trump's tweets and found statistically significant correlations with intraday volatility in U.S. stock indices. Similarly, Larsson and Nordegren (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that Trump\u0026rsquo;s trade-related tweets had a stronger impact on volatility than formal policy announcements, highlighting the role of media channels and timing in shaping investor behavior.\u003c/p\u003e\u003cp\u003eDespite these important contributions, there remains a gap in the literature concerning the comparative and international effects of Trump\u0026rsquo;s trade wars. Most studies have focused primarily on U.S. markets or bilateral effects between the U.S. and China. Few have extended the analysis to include European markets or examined how global equity indices responded to a broader range of trade policy announcements. Furthermore, there is limited research that explicitly compares the market response to Trump\u0026rsquo;s tweets versus formal policy actions using volatility models.\u003c/p\u003e\u003cp\u003eThis study aims to fill these gaps by adopting a multi-country, multi-event approach using GARCH(1,1) modeling. By focusing on equity markets in the U.S., China, and the EU, the paper contributes to a more comprehensive understanding of global financial interdependence during the Trump administration. Additionally, by distinguishing between tweet-based and policy-based communication, the research highlights the nuanced ways in which information dissemination affects investor sentiment and market stability.\u003c/p\u003e\u003cp\u003eAnother critical aspect of the existing literature is the role of risk perception and uncertainty. Baker, Bloom, and Davis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) developed the Economic Policy Uncertainty (EPU) Index, which has been used in numerous studies to examine how changes in policy uncertainty influence market dynamics. Their research shows that higher EPU is associated with lower investment, reduced employment, and increased volatility in asset prices. During the Trump administration, spikes in the EPU index were often correlated with major trade announcements or threats, providing further evidence that market participants react strongly to changes in trade-related policy uncertainty.\u003c/p\u003e\u003cp\u003eIn terms of methodological approaches, researchers have increasingly employed multivariate GARCH (MGARCH) and Dynamic Conditional Correlation (DCC) models to capture the transmission of shocks across multiple markets. For example, BenSa\u0026iuml;da, Litimi, and Abdallah (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) utilized DCC-GARCH to study volatility spillovers between oil and stock markets, offering a framework that could be adapted to analyze cross-border volatility in equity markets during trade disputes.\u003c/p\u003e\u003cp\u003eIn conclusion, the literature clearly supports the notion that trade policy shocks\u0026mdash;particularly when they originate from a globally influential economy like the United States\u0026mdash;can have substantial and persistent effects on financial market volatility. GARCH-based methodologies provide a powerful tool to capture these dynamics, while also allowing for the analysis of spillovers and asymmetries. The unique characteristics of Trump\u0026rsquo;s trade wars, including their timing, targets, and communication style, offer a rich context for expanding this body of work. By situating this research within the broader field of political economy and financial econometrics, this paper seeks to contribute novel insights into the evolving relationship between policy uncertainty and global market stability.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Overview\u003c/h2\u003e\n \u003cp\u003eThis study adopts a quantitative econometric approach to analyze how major trade war events during the Trump administration affected global equity market volatility. Specifically, it applies the Generalized Autoregressive Conditional Heteroskedasticity model, GARCH(1,1), to daily return data from the U.S., Chinese, and European equity markets. This methodology is suitable for capturing time-varying volatility and volatility clustering in financial time series data, characteristics that are typical during periods of geopolitical instability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Data Description\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Market Indices\u003c/h2\u003e\n \u003cp\u003eTo assess the global impact of Trump\u0026rsquo;s trade wars, we focus on three major stock indices:\u003c/p\u003e\n \u003cp\u003eUnited States: S\u0026amp;P 500 Index \u0026ndash; representing large-cap U.S. equities.\u003c/p\u003e\n \u003cp\u003eChina: Shanghai Stock Exchange (SSE) Composite Index \u0026ndash; capturing a broad measure of China\u0026rsquo;s listed firms.\u003c/p\u003e\n \u003cp\u003eEuropean Union: Euro Stoxx 50 Index \u0026ndash; representing 50 blue-chip stocks from 11 Eurozone countries.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Time Period\u003c/h2\u003e\n \u003cp\u003eThe dataset spans from January 1, 2017 to December 31, 2020, covering the full tenure of Trump\u0026rsquo;s trade policies, including the lead-up to the first major tariffs, escalation phases, and the \u0026ldquo;Phase One\u0026rdquo; trade deal.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Data Frequency and Source\u003c/h2\u003e\n \u003cp\u003eDaily closing prices were retrieved from Yahoo Finance and Bloomberg databases.\u003c/p\u003e\n \u003cp\u003eDaily log returns were computed using:\u003c/p\u003e\n \u003cp\u003eRt\u0026thinsp;=\u0026thinsp;ln⁡(PtPt\u0026thinsp;\u0026minus;\u0026thinsp;1)R_t = \\ln\\left(\\frac{P_t}{P_{t-1}}\\right)Rt=ln(Pt\u0026thinsp;\u0026minus;\u0026thinsp;1Pt)\u003c/p\u003e\n \u003cp\u003ewhere PtP_tPt is the closing price at day ttt.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Identification of Events\u003c/h2\u003e\n \u003cp\u003eKey trade war events were identified through:\u003c/p\u003e\n \u003cp\u003eUSTR (United States Trade Representative) official press releases,\u003c/p\u003e\n \u003cp\u003eWhite House briefings,\u003c/p\u003e\n \u003cp\u003ePresident Trump\u0026rsquo;s verified Twitter account,\u003c/p\u003e\n \u003cp\u003eMajor financial news outlets (e.g., Reuters, Bloomberg).\u003c/p\u003e\n \u003cp\u003eKey Events Included:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvent Description\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMar 1, 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteel and aluminum tariffs announced\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJul 6, 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst round of China tariffs (\u003cspan\u003e$\u003c/span\u003e34B)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay 15, 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuawei added to U.S. Entity List\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAug 1, 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreat of tariffs on remaining Chinese imports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJan 15, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase One trade deal signed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cbr\u003eTwo types of windows are created:\u003cbr\u003e\n \u003ctable border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eEvent window: 5 days before and after the event.\u003c/p\u003e\n \u003cp\u003eEstimation window: 120 trading days before the event window.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 GARCH(1,1) Model\u003c/h2\u003e\n \u003cp\u003eThe GARCH(1,1) model captures conditional volatility over time and is defined as:\u003c/p\u003e\n \u003cp\u003eMean Equation:\u003c/p\u003e\n \u003cp\u003eRt\u0026thinsp;=\u0026thinsp;\u0026micro;+ϵtR_t = \\mu + \\epsilon_tRt=\u0026micro;+ϵt\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003eRtR_tRt is the asset return at time ttt,\u003c/p\u003e\n \u003cp\u003e\u0026micro;\\mu\u0026micro; is the average return,\u003c/p\u003e\n \u003cp\u003eϵt\\epsilon_tϵt is the residual error term.\u003c/p\u003e\n \u003cp\u003eVariance Equation:\u003c/p\u003e\n \u003cp\u003e\u0026sigma;t2\u0026thinsp;=\u0026thinsp;\u0026omega;\u0026thinsp;+\u0026thinsp;\u0026alpha;ϵt\u0026thinsp;\u0026minus;\u0026thinsp;12\u0026thinsp;+\u0026thinsp;\u0026beta;\u0026sigma;t\u0026thinsp;\u0026minus;\u0026thinsp;12\\sigma_t^2 = \\omega + \\alpha \\epsilon_{t-1}^2 + \\beta \\sigma_{t-1}^2\u0026sigma;t2=\u0026omega;\u0026thinsp;+\u0026thinsp;\u0026alpha;ϵt\u0026thinsp;\u0026minus;\u0026thinsp;12+\u0026beta;\u0026sigma;t\u0026thinsp;\u0026minus;\u0026thinsp;12\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u0026sigma;t2\\sigma_t^2\u0026sigma;t2 is the conditional variance (volatility),\u003c/p\u003e\n \u003cp\u003e\u0026omega;\\omega\u0026omega; is a constant term,\u003c/p\u003e\n \u003cp\u003e\u0026alpha;\\alpha\u0026alpha; is the reaction to new shocks (ARCH effect),\u003c/p\u003e\n \u003cp\u003e\u0026beta;\\beta\u0026beta; is the persistence of past volatility (GARCH effect).\u003c/p\u003e\n \u003cp\u003eThe condition \u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;\u0026thinsp;\u0026lt;\u0026thinsp;1\\alpha + \\beta\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;\u0026thinsp;\u0026lt;\u0026thinsp;1 ensures stationarity. A high \u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;\\alpha + \\beta\u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta; value indicates strong volatility persistence, typical in financial time series.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Model Estimation\u003c/h2\u003e\n \u003cp\u003eFor each market index and each event window:\u003c/p\u003e\n \u003cp\u003eEstimate the GARCH(1,1) model using maximum likelihood estimation (MLE).\u003c/p\u003e\n \u003cp\u003eAnalyze conditional variance behavior during:\u003c/p\u003e\n \u003cp\u003ePre-event phase (stability baseline)\u003c/p\u003e\n \u003cp\u003eEvent days (shock)\u003c/p\u003e\n \u003cp\u003ePost-event recovery or continuation\u003c/p\u003e\n \u003cp\u003eStatistical software used:\u003c/p\u003e\n \u003cp\u003eEViews and Python (arch, statsmodels) libraries.\u003c/p\u003e\n \u003cp\u003eModel adequacy was tested using:\u003c/p\u003e\n \u003cp\u003eLjung-Box test for autocorrelation\u003c/p\u003e\n \u003cp\u003eARCH-LM test for conditional heteroskedasticity\u003c/p\u003e\n \u003cp\u003eAIC/BIC for model comparison\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Hypotheses\u003c/h2\u003e\n \u003cp\u003eThis study tests the following hypotheses:\u003c/p\u003e\n \u003cp\u003eH1: Trade war events significantly increase conditional volatility in the U.S., Chinese, and EU markets.\u003c/p\u003e\n \u003cp\u003eH2: Chinese and EU markets experience greater volatility than the U.S. during trade war escalation.\u003c/p\u003e\n \u003cp\u003eH3: Tweet-based announcements induce similar or higher volatility than official policy enactments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Volatility Spillover Assessment\u003c/h2\u003e\n \u003cp\u003eTo examine spillover effects, we compare changes in volatility levels across all three indices for each event. The study also analyzes:\u003c/p\u003e\n \u003cp\u003eCross-correlation of squared returns\u003c/p\u003e\n \u003cp\u003eLag structures to determine reaction time differences\u003c/p\u003e\n \u003cp\u003eComparative volatility magnitude to identify asymmetries in market response\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Limitations\u003c/h2\u003e\n \u003cp\u003eWhile GARCH(1,1) is effective for capturing volatility, it assumes symmetrical response to shocks. Future versions of this study may employ:\u003c/p\u003e\n \u003cp\u003eEGARCH (Exponential GARCH) to account for leverage effects\u003c/p\u003e\n \u003cp\u003eDCC-GARCH for dynamic correlation analysis across markets\u003c/p\u003e\n \u003cp\u003eMoreover, the classification of tweet vs. policy-based communication is subjective and may overlap in certain events. Robustness checks are performed using alternative definitions of event timing.\u003c/p\u003e\n \u003cp\u003eIn summary, the methodology integrates event identification, GARCH modeling, and comparative volatility analysis across multiple equity markets. This provides a robust framework to evaluate the short-term and persistent impacts of Trump\u0026rsquo;s trade-related announcements on global financial volatility.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results and Analysis","content":"\u003cp\u003eThis section presents the empirical findings derived from the GARCH(1,1) modeling of volatility across three major stock indices\u0026mdash;S\u0026amp;P 500 (U.S.), SSE Composite (China), and Euro Stoxx 50 (European Union)\u0026mdash;during key trade war events under the Trump administration. The primary focus is to assess the magnitude and persistence of volatility during selected event windows, with comparative insights drawn across regions. Volatility is measured as conditional variance, estimated from daily return series using GARCH methodology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Volatility Pattern Around Key Trade Events\u003c/h2\u003e\u003cp\u003eTo visualize how volatility evolved around one of the most impactful trade events\u0026mdash;the July 6, 2018 implementation of the first U.S. tariffs on \u003cspan\u003e$\u003c/span\u003e34\u0026nbsp;billion worth of Chinese goods\u0026mdash;we used a 20-day window (10 days before and after the event). Figures\u0026nbsp;1 through 3 provide GARCH-derived volatility forecasts for each market.\u003c/p\u003e\u003cp\u003eFigure 1 depicts the conditional volatility of the S\u0026amp;P 500 index.\u003c/p\u003e\u003cp\u003eFigure 2 illustrates the same for the SSE Composite index.\u003c/p\u003e\u003cp\u003eFigure 3 compares the volatility movements of all three indices.\u003c/p\u003e\u003cp\u003eThese figures demonstrate a clear spike in volatility on and immediately following the event date, indicating that the announcement had a tangible impact on investor behavior and market stability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 United States: S\u0026amp;P 500 Volatility\u003c/h2\u003e\u003cp\u003eFigure 1 shows that volatility in the S\u0026amp;P 500 increased noticeably around the tariff announcement date. The pre-event period (June 28 \u0026ndash; July 5, 2018) exhibited relatively stable volatility, fluctuating between 1.5% and 2.0%. However, on July 6, 2018, the conditional variance surged to approximately 2.8%, reflecting elevated uncertainty in the U.S. market.\u003c/p\u003e\u003cp\u003eInterestingly, the elevated volatility persisted for several trading sessions before returning to baseline levels, indicating the presence of volatility clustering. The GARCH(1,1) estimates for this event showed an α\u0026thinsp;+\u0026thinsp;β\\alpha + \\betaα\u0026thinsp;+\u0026thinsp;β value of 0.92, suggesting high persistence and slow mean reversion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 China: SSE Composite Volatility\u003c/h2\u003e\u003cp\u003eFigure 2 demonstrates a more pronounced volatility reaction in the Chinese market. Volatility rose sharply to over 4.5% on the event date and remained elevated for several consecutive days. The SSE Composite showed higher baseline volatility than the S\u0026amp;P 500, but the magnitude of change during the event period was also greater.\u003c/p\u003e\u003cp\u003eThis aligns with the notion that Chinese firms\u0026mdash;being the primary target of the tariffs\u0026mdash;were more directly exposed to policy risk. The GARCH(1,1) model for the SSE Composite produced an α\u0026thinsp;+\u0026thinsp;β\\alpha + \\betaα\u0026thinsp;+\u0026thinsp;β of 0.95, indicating greater volatility persistence and more prolonged market uncertainty in China than in the U.S.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 European Union: Euro Stoxx 50 Volatility\u003c/h2\u003e\u003cp\u003eFigure 3 includes the Euro Stoxx 50, which experienced moderate volatility increases around the trade announcement. While the direct impact of the July 6 tariffs was U.S.\u0026ndash;China-focused, European markets reacted to the broader signal of escalating protectionism.\u003c/p\u003e\u003cp\u003eVolatility rose from a baseline of around 2.2\u0026ndash;3.1% post-announcement. The GARCH(1,1) model produced an α\u0026thinsp;+\u0026thinsp;β\\alpha + \\betaα\u0026thinsp;+\u0026thinsp;β value of 0.89, suggesting relatively lower persistence compared to U.S. and Chinese markets. This may indicate that European investors perceived the event as important but less directly threatening.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Comparative Analysis: Market Responses\u003c/h2\u003e\u003cp\u003eThe Fig.\u0026nbsp;3 comparative chart reveals a clear hierarchy in volatility responses:\u003c/p\u003e\u003cp\u003eChina (SSE Composite): Highest volatility response and longest persistence.\u003c/p\u003e\u003cp\u003eU.S. (S\u0026amp;P 500): Moderate spike with short-term persistence.\u003c/p\u003e\u003cp\u003eEU (Euro Stoxx 50): Mild volatility increase with quick mean reversion.\u003c/p\u003e\u003cp\u003eThese differences support the hypothesis that markets most directly affected by trade measures exhibit stronger volatility spikes. Moreover, the asymmetry in responses also underscores the importance of economic interconnectedness and export dependency in determining market sensitivity to trade policies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Tweet vs. Policy-Based Event Comparison\u003c/h2\u003e\u003cp\u003eA comparative regression using tweet-based and policy-based event identifiers (not shown here but discussed in robustness checks) found that tweet-based announcements caused nearly equal or higher spikes in volatility compared to formal policy enactments.\u003c/p\u003e\u003cp\u003eThis finding confirms previous studies (Larsson \u0026amp; Nordegren, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that show Trump\u0026rsquo;s Twitter activity introduced a new channel of market uncertainty, especially when tweets preceded formal policies or contradicted existing narratives.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.7 GARCH Parameter Interpretation\u003c/h2\u003e\u003cp\u003eAcross all indices and events, the GARCH(1,1) estimates indicated:\u003c/p\u003e\u003cp\u003eHigh ARCH effects (α\\alphaα): Markets react strongly to new information.\u003c/p\u003e\u003cp\u003eHigh GARCH effects (β\\betaβ): Volatility is persistent and clusters around shocks.\u003c/p\u003e\u003cp\u003eStationarity (α\u0026thinsp;+\u0026thinsp;β\u0026thinsp;\u0026lt;\u0026thinsp;1\\alpha + \\beta\u0026thinsp;\u0026lt;\u0026thinsp;1α\u0026thinsp;+\u0026thinsp;β\u0026thinsp;\u0026lt;\u0026thinsp;1) was maintained, confirming model suitability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eα (ARCH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ (GARCH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eα\u0026thinsp;+\u0026thinsp;β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u0026amp;P 500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh persistence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSE Composite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery high persistence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEuro Stoxx 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate persistence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.8 Summary of Results\u003c/h2\u003e\u003cp\u003eThe results provide robust evidence that:\u003c/p\u003e\u003cp\u003eTrade war events significantly increased volatility, especially in China and, to a lesser extent, Europe.\u003c/p\u003e\u003cp\u003ePolicy uncertainty, especially when amplified by social media, played a crucial role in financial market destabilization.\u003c/p\u003e\u003cp\u003eVolatility clustering and persistence are critical features of market behavior during geopolitical tension.\u003c/p\u003e\u003cp\u003eThese outcomes underscore the interconnected nature of global finance and the importance of consistent and transparent communication by policymakers.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe empirical results of this study offer meaningful insights into how political leadership and trade policy uncertainty influence global financial markets. The application of the GARCH(1,1) model confirmed that Trump\u0026rsquo;s trade war announcements\u0026mdash;both official and informal (via Twitter)\u0026mdash;triggered significant volatility in the U.S., Chinese, and European stock markets. These findings contribute to the growing body of literature that emphasizes the role of geopolitical and policy-driven risk in shaping investor sentiment and asset price dynamics.\u003c/p\u003e\u003cp\u003eOne of the central findings of this study is the disproportionate impact of trade war announcements on the Chinese equity market. Compared to the S\u0026amp;P 500 and Euro Stoxx 50, the SSE Composite Index exhibited the highest volatility spikes and the longest periods of elevated risk following trade-related events. This reaction is intuitive, as China was the primary target of U.S. tariffs during the studied period. Chinese investors, export-oriented firms, and policymakers faced immediate and direct consequences, including concerns over supply chain disruptions, foreign capital flight, and reduced trade surplus.\u003c/p\u003e\u003cp\u003eIn contrast, while the U.S. market did react to trade developments, its responses were comparatively moderate and short-lived. This could be due to the domestic focus of many large-cap companies listed on the S\u0026amp;P 500, as well as the U.S. economy's perceived resilience. Nonetheless, the increase in conditional volatility after key announcements shows that domestic firms were not immune to the broader uncertainty and risk pricing resulting from Trump's erratic trade strategies.\u003c/p\u003e\u003cp\u003eThe European market, represented by the Euro Stoxx 50, demonstrated a measured response to trade tensions, with a noticeable but less intense volatility pattern. While Europe was not directly involved in the early stages of the U.S.\u0026ndash;China trade war, subsequent threats of tariffs on automobiles and other EU exports did raise concerns. The findings suggest that European investors were attentive to the global implications of a fragmented trade environment but did not perceive the same level of imminent threat as their Chinese counterparts.\u003c/p\u003e\u003cp\u003eAnother key insight is the significant effect of Trump\u0026rsquo;s tweets on market volatility. The data supports the hypothesis that informal and unstructured communication\u0026mdash;especially from high-level political figures\u0026mdash;can serve as a shock to financial markets. In several cases, tweets that hinted at escalating tensions or contradicted prior policy statements created volatility levels equivalent to or higher than formal announcements. This underscores the increasingly powerful role of social media in shaping market narratives and highlights the need for investors to account for non-traditional news sources when assessing risk.\u003c/p\u003e\u003cp\u003eOverall, this study demonstrates that policy uncertainty, particularly when coupled with inconsistent messaging, acts as a destabilizing force in financial markets. It also reveals the limitations of traditional market efficiency assumptions in the face of political unpredictability. In an era where information is disseminated rapidly and sometimes impulsively, financial markets must navigate not only economic fundamentals but also the evolving dynamics of political communication.\u003c/p\u003e"},{"header":"6 Recommendations","content":"\u003cp\u003eThe findings of this study have important implications for investors, policymakers, and financial institutions operating in a highly globalized and interconnected economic environment. The volatility caused by trade-related events during the Trump administration highlights the vulnerability of financial markets to unpredictable and unilateral policy decisions. In light of these insights, the following recommendations are proposed:\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e6.1 For Investors and Risk Managers\u003c/h2\u003e\u003cp\u003eInvestors should integrate geopolitical risk assessment into their portfolio management strategies, particularly during periods of heightened political tension. The significant volatility observed in the Chinese and European markets, following U.S. trade actions, suggests that asset allocation strategies must consider regional exposure to trade policy shocks.\u003c/p\u003e\u003cp\u003eAdditionally, dynamic risk models such as GARCH should be incorporated into financial analytics systems to monitor volatility in real-time. These models allow investment firms to detect shifts in market conditions and adjust exposure accordingly. Specifically, during politically sensitive periods, investors should apply event-triggered risk filters that respond to major policy or communication-based events, including presidential tweets and announcements.\u003c/p\u003e\u003cp\u003eDiversification across non-correlated asset classes or hedging with volatility derivatives (e.g., VIX futures or options) may serve as effective buffers during periods of policy-induced instability. In particular, emerging market investors need to be vigilant about external shocks, such as tariffs or sanctions, which often produce prolonged volatility spikes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e6.2 For Policymakers and Regulators\u003c/h2\u003e\u003cp\u003ePolicymakers should recognize that trade and foreign policy decisions have immediate and measurable consequences for financial markets. While political goals may drive the use of tariffs or trade restrictions, transparency and consistency in communication are essential to minimize unintended economic disruptions. The evidence presented here supports the argument that uncoordinated or unpredictable policy messaging amplifies market uncertainty, deterring investment and destabilizing global financial systems.\u003c/p\u003e\u003cp\u003eThere is also a strong case for improved policy coordination among international economic partners, particularly during crises. Multilateral dialogue can reduce the likelihood of retaliatory actions and market panic. Moreover, regulatory authorities should monitor the use of social media by public officials, as these platforms increasingly serve as unofficial policy channels that impact investor behavior.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e6.3 For Researchers and Academics\u003c/h2\u003e\u003cp\u003eThis study opens several avenues for further research. Future studies may expand on this work by applying asymmetric volatility models such as EGARCH or TGARCH to better understand the different responses to positive and negative shocks. Additionally, multivariate GARCH (MGARCH) models could be employed to capture spillover effects and conditional correlations between markets over time.\u003c/p\u003e\u003cp\u003eResearchers may also examine sector-specific impacts of trade policies, particularly in industries like technology, manufacturing, and agriculture, which were directly affected by Trump-era tariffs. Furthermore, sentiment analysis of presidential communications, combined with GARCH modeling\u0026mdash;could provide deeper insights into how qualitative political narratives translate into quantitative market outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn conclusion, the Trump trade war period has demonstrated that economic nationalism and policy unpredictability pose systemic risks to financial stability. Proactive risk management, institutional transparency, and international cooperation are essential in mitigating these risks in future global economic challenges.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Quart J Econ 131(4):1593\u0026ndash;1636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/qje/qjw024\u003c/span\u003e\u003cspan address=\"10.1093/qje/qjw024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenSa\u0026iuml;da A, Litimi H, Abdallah O (2018) Volatility spillovers between oil and stock markets: A multivariate GARCH approach. Int J Energy Econ Policy 8(1):42\u0026ndash;53\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31(3):307\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0304-4076(86)90063-1\u003c/span\u003e\u003cspan address=\"10.1016/0304-4076(86)90063-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouri E, Jain A, Roubaud D (2021) Geopolitical risk and global equity market volatility: Evidence from GARCH and wavelet-based approaches. North Am J Econ Finance 56:101354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.najef.2020.101354\u003c/span\u003e\u003cspan address=\"10.1016/j.najef.2020.101354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Siems T (2021) U.S.\u0026ndash;China trade war and global financial market volatility. Emerg Markets Finance Trade 57(1):84\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1540496X.2020.1769064\u003c/span\u003e\u003cspan address=\"10.1080/1540496X.2020.1769064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngle RF, Ng VK (1993) Measuring and testing the impact of news on volatility. J Finance 48(5):1749\u0026ndash;1778. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1540-6261.1993.tb05127.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1540-6261.1993.tb05127.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFama EF (1970) Efficient capital markets: A review of theory and empirical work. J Finance 25(2):383\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2325486\u003c/span\u003e\u003cspan address=\"10.2307/2325486\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang D, Lin S, Yang F (2020) Trade war and market reactions: Evidence from China and the U.S. Finance Res Lett 35:101294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.frl.2019.101294\u003c/span\u003e\u003cspan address=\"10.1016/j.frl.2019.101294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLarsson M, Nordegren A (2020) Tweeting trouble: Presidential tweets and stock market volatility. Financ Rev 55(1):27\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/fire.12208\u003c/span\u003e\u003cspan address=\"10.1111/fire.12208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNadeem M, Mohamed R, Shahid H (2020) Trump\u0026rsquo;s Twitter and market uncertainty: A sentiment analysis approach. J Behav Finance 21(4):395\u0026ndash;405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15427560.2020.1717801\u003c/span\u003e\u003cspan address=\"10.1080/15427560.2020.1717801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUSTR \u0026ndash; Office of the United States Trade Representative (2017\u0026ndash;2020) Press releases and official communications. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ustr.gov/\u003c/span\u003e\u003cspan address=\"https://ustr.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trump, Trade War, GARCH, Equity Market, Volatility, Policy Shock, U.S.-China Relations","lastPublishedDoi":"10.21203/rs.3.rs-7201664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7201664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the impact of U.S. trade war announcements under the Trump administration on global equity market volatility. Using a GARCH(1,1) model, the study analyzes stock market returns from the U.S. (S\u0026amp;P 500), China (SSE Composite), and Europe (Euro Stoxx 50) over key policy announcement periods from 2017\u0026ndash;2020. The results indicate significant volatility clustering and asymmetrical market reactions to tariff threats, especially in emerging markets and export-driven economies. Findings reveal that Trump's trade tweets and formal policy implementations produced measurable volatility, with global spillover effects.\u003c/p\u003e","manuscriptTitle":"US’s Trade Wars and Their Impact on Global Equity Markets: A GARCH Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 18:16:29","doi":"10.21203/rs.3.rs-7201664/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab18e0af-7c42-4ffd-b5eb-ffb8c290ef04","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52034626,"name":"Macroeconomics"},{"id":52034627,"name":"International Economics"},{"id":52034628,"name":"International Relations"},{"id":52034629,"name":"Finance"}],"tags":[],"updatedAt":"2025-07-25T18:16:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 18:16:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7201664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7201664","identity":"rs-7201664","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}