Geoeconomic Fragmentation, Trade Reconfiguration, and Sovereign Risk: Evidence from Europe–U.S. Economic Relations (2005–2025)

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Using a comprehensive dataset covering the United States and Euro Area countries over the period 2005–2025, the analysis employs Structural Vector Autoregression (SVAR), panel VAR, and local projection methods to identify the causal impact of fragmentation shocks on sovereign bond yields and credit default swap (CDS) spreads. The results provide robust evidence that fragmentation significantly increases sovereign risk, with effects that are both statistically significant and persistent over medium-term horizons. The findings highlight the central role of trade reconfiguration mechanisms , including declining bilateral trade intensity, supply chain concentration, and reshoring, in transmitting fragmentation shocks to financial markets. Variance decomposition and elasticity estimates reveal that the trade channel dominates in the Euro Area , particularly in peripheral economies, while the financial channel is more pronounced in the United States , reflecting differences in economic structure and global financial integration. By establishing a unified framework linking fragmentation → trade structure → sovereign risk , this paper contributes to the literature on international trade, financial economics, and sovereign debt. The results carry important policy implications, suggesting that increasing fragmentation may raise borrowing costs and weaken fiscal sustainability, thereby posing challenges for global economic stability. Geoeconomic fragmentation Trade reconfiguration Sovereign risk Sovereign bond yields CDS spreads Global value chains Trade policy uncertainty Geopolitical risk Financial markets SVAR Panel VAR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Over the past two decades, the global economic system has undergone a profound transformation characterized by the rise of geoeconomic fragmentation , a process in which geopolitical considerations increasingly shape international trade, investment, and financial flows. This shift has been driven by a combination of factors, including escalating trade wars , the proliferation of economic sanctions , the resurgence of industrial policies , and the strategic reconfiguration of global supply chains through reshoring , near-shoring , and friend-shoring strategies. Major economies—most notably the United States, the European Union, and China—have progressively adopted policies aimed at enhancing strategic autonomy and reducing external dependencies in critical sectors such as technology, energy, and defense (Aiyar et al., 2023; IMF, 2023 ). This evolving landscape reflects a departure from the era of hyper-globalization toward a more fragmented and bloc-based international economic order. Recent contributions highlight that geoeconomic fragmentation may lead to substantial welfare losses, trade inefficiencies, and disruptions in global value chains, with estimates suggesting global output losses ranging from 2% to over 10% under severe decoupling scenarios (Gopinath et al., 2024 ; Baqaee & Farhi, 2024 ). At the same time, geopolitical tensions—such as the U.S.–China trade conflict, the COVID-19 pandemic, and the Russia–Ukraine war—have intensified decoupling pressures between major economies, accelerating the reorganization of trade networks and increasing policy-driven distortions in international markets (Caldara & Iacoviello, 2022 ; Evenett & Fritz, 2023 ). Beyond trade and production, these structural transformations have important implications for financial markets , particularly sovereign bond markets , which play a central role in financing government activities and maintaining macroeconomic stability. Sovereign bond yields and credit default swap (CDS) spreads are highly sensitive to global risk conditions, policy uncertainty, and external vulnerabilities. The literature shows that heightened geopolitical risk and trade uncertainty can increase sovereign risk premia by amplifying fiscal pressures, reducing growth prospects, and deteriorating investor confidence (Augustin et al., 2016 ; Fratzscher & Rieth, 2019 ; Arteta et al., 2021 ). Moreover, countries with higher exposure to international trade and global value chains may face stronger transmission of external shocks into domestic financial conditions (Flandreau & Zumer, 2004 ; Curcuru et al., 2021 ). However, despite growing interest in geoeconomic fragmentation, its impact on sovereign risk remains insufficiently understood . Existing studies have largely examined fragmentation through the lenses of trade flows, welfare effects, or firm-level outcomes, with limited attention to its macro-financial consequences. In particular, there is a lack of empirical frameworks that jointly analyze how fragmentation-induced trade reconfiguration —such as declining bilateral trade intensity, supply chain concentration, and reshoring—translates into changes in sovereign risk pricing. Furthermore, the interaction between trade channels and financial channels in transmitting fragmentation shocks to sovereign bond markets remains underexplored, especially in a comparative context between advanced economies such as the United States and the Euro Area. This paper addresses this gap by developing an integrated empirical framework linking geoeconomic fragmentation → trade reconfiguration → sovereign risk . Using a combination of Structural Vector Autoregression (SVAR), Panel VAR, and local projection methods over the period 2005–2025, the analysis focuses on the economic relationship between the United States and Europe—one of the most systemically important corridors in the global economy. A novel geoeconomic fragmentation index is constructed by combining geopolitical risk, trade policy uncertainty, and trade structure indicators, allowing for a comprehensive measurement of fragmentation dynamics. The study is guided by the following research objectives. First, it aims to quantify the impact of fragmentation shocks on sovereign bond yields and CDS spreads. Second, it investigates the role of trade reconfiguration mechanisms , including reshoring and supply chain concentration, in amplifying or mitigating these effects. Third, it disentangles the relative importance of trade, financial, and expectations channels in the transmission process. Based on these objectives, the main hypotheses are formulated as follows: (H1) Geoeconomic fragmentation increases sovereign risk premia; (H2) Trade reconfiguration acts as a key transmission channel linking fragmentation to sovereign risk; (H3) The magnitude of these effects is heterogeneous across countries, depending on trade exposure and fiscal fundamentals; (H4) Financial channels dominate in the United States, while trade channels are more pronounced in Europe. This paper makes several key contributions to the literature. First, it provides one of the first integrated empirical analyses connecting geoeconomic fragmentation to sovereign risk through trade structure adjustments. Second, it introduces a composite fragmentation index , improving upon existing single-dimension measures such as geopolitical risk indices. Third, it offers a comparative perspective between Europe and the United States , highlighting structural asymmetries in shock transmission. Finally, it contributes to policy debates by quantifying the macroeconomic and financial costs of fragmentation , thereby informing discussions on strategic autonomy, fiscal sustainability, and global economic governance. Figure 1. Conceptual Framework of Geoeconomic Fragmentation and Sovereign Risk Figure 1 illustrates a multi-layered transmission mechanism through which geoeconomic fragmentation —captured by rising trade barriers, sanctions, and strategic decoupling—propagates into sovereign risk via three interconnected channels. First, the trade channel reflects how fragmentation disrupts bilateral trade intensity, induces supply chain reconfiguration (reshoring, diversification), and reduces external demand, thereby weakening growth and fiscal revenues. Second, the financial channel captures the reaction of global investors to heightened uncertainty, leading to capital reallocation, increased risk premia, and higher sovereign bond yields and CDS spreads. Third, the expectations channel operates through policy uncertainty and credibility effects, influencing market perceptions of fiscal sustainability and amplifying volatility. These channels interact dynamically and are conditioned by country-specific characteristics such as trade openness, fiscal capacity, and financial market depth, resulting in heterogeneous sovereign risk responses across economies. Overall, the framework emphasizes that fragmentation is not only a trade phenomenon but a systemic shock that reshapes macro-financial stability through intertwined real and financial linkages. 2. Literature Review 2.1 Geoeconomic Fragmentation and Global Trade The recent resurgence of geoeconomic fragmentation marks a structural shift in the global trading system, characterized by increasing policy-driven distortions in cross-border economic relations. This phenomenon has been widely documented in the context of rising trade decoupling , particularly between major economic blocs such as the United States and China, as well as the gradual reconfiguration of global value chains. Empirical evidence shows that trade restrictions, tariffs, and export controls have led to measurable declines in bilateral trade flows and efficiency losses in global production networks (Aiyar et al., 2023; Evenett & Fritz, 2023 ). A growing body of literature highlights the emergence of reshoring , near-shoring , and friend-shoring strategies, whereby firms relocate production to politically aligned or geographically proximate countries to mitigate geopolitical risks (Gopinath et al., 2024 ; Baldwin, 2022). While such strategies may enhance supply chain resilience, they often come at the cost of reduced specialization and higher production costs, thereby lowering global welfare (Baqaee & Farhi, 2024 ). Moreover, fragmentation may lead to the formation of competing trade blocs, resulting in trade diversion and reduced multilateral cooperation. These dynamics underscore the transition from efficiency-driven globalization toward security-driven economic integration. 2.2 Trade and Sovereign Risk The relationship between international trade and sovereign risk has long been recognized in the literature, with trade openness generally associated with lower borrowing costs due to enhanced growth prospects and diversification benefits (Flandreau & Zumer, 2004 ). However, this relationship becomes more complex in the presence of external shocks and structural disruptions. Countries with high exposure to global trade are particularly vulnerable to adverse trade shocks, which can negatively affect fiscal revenues, balance of payments stability, and debt sustainability. Recent studies emphasize that external exposure and fiscal vulnerability are key determinants of sovereign risk in an interconnected global economy. Trade disruptions—such as tariff increases or supply chain breakdowns—can lead to declines in output and government revenues, thereby increasing sovereign spreads (Acemoglu et al., 2020 ). In addition, trade reconfiguration processes, including reshoring and reduced export competitiveness, may weaken long-term growth potential and exacerbate fiscal imbalances. Empirical evidence also suggests that trade shocks have direct effects on bond markets . For instance, tariff announcements and trade tensions have been shown to increase financial market volatility and raise government borrowing costs, particularly in emerging and highly open economies (Curcuru et al., 2021 ). Nevertheless, the literature remains fragmented, often treating trade and sovereign risk in isolation rather than as part of an integrated transmission mechanism. 2.3 Financial Market Reactions to Trade Tensions A complementary strand of literature focuses on the response of financial markets to geopolitical and trade-related uncertainty. A key insight is that risk premia and uncertainty transmission play a central role in linking real economic shocks to financial outcomes. The geopolitical risk index developed by Caldara and Iacoviello ( 2022 ) demonstrates that increases in geopolitical tensions are associated with higher sovereign bond yields and elevated risk premia, reflecting heightened investor risk aversion. Similarly, studies on financial contagion highlight the importance of market-based channels in amplifying shocks. For example, sovereign CDS markets have been shown to transmit risk across countries through investor sentiment and portfolio rebalancing (Augustin et al., 2016 ). In the Eurozone context, the interaction between sovereign risk and banking sector fragility—often referred to as the “sovereign-bank doom loop”—further amplifies the effects of external shocks on financial stability (Fratzscher & Rieth, 2019 ). Policy uncertainty also plays a critical role in shaping financial market reactions. Increases in trade policy uncertainty can lead to higher volatility, reduced investment, and rising sovereign spreads, as investors demand compensation for increased risk (Arteta et al., 2021 ). These findings suggest that financial markets act as a key transmission channel through which geoeconomic fragmentation influences sovereign risk. 2.4 Research Gap Despite the growing body of literature on geoeconomic fragmentation, trade, and financial markets, several important gaps remain. First, existing studies tend to focus on individual dimensions of fragmentation—such as trade flows, geopolitical risk, or financial market responses—without providing a unified framework that integrates these elements. Second, there is limited empirical evidence on how fragmentation-induced trade reconfiguration affects sovereign risk through both real and financial channels. In particular, the literature lacks an integrated analytical framework linking geoeconomic fragmentation → trade structure → sovereign risk , which captures the dynamic interactions between trade adjustments, investor behavior, and fiscal sustainability. Moreover, comparative analyses between major advanced economies, such as the United States and the Euro Area, remain scarce, despite their central role in the global financial system. This paper addresses these gaps by developing a comprehensive empirical model that jointly examines trade and financial transmission channels, incorporates a novel fragmentation index, and provides new evidence on the macro-financial consequences of geoeconomic fragmentation. Table 1 . Literature on Fragmentation, Trade, and Sovereign Risk Table 1 Literature on Fragmentation, Trade, and Sovereign Risk Study Region Period Method Fragmentation Mechanism Trade Channel Financial Channel Key Finding Sovereign Risk Measure Our Extension Aiyar et al. (2023) Global 2001–22 Gravity Model Trade decoupling ✓ ✗ 1.5% GDP loss from fragmentation N/A Sovereign spread channel Gopinath et al. ( 2024 ) G20 2000–23 DSGE+Trade Bloc formation ✓ Partial 2–12% welfare loss Bond yields EU periphery heterogeneity Acemoglu et al. ( 2020 ) US-China 2000–19 Diff-in-Diff Tariff shocks ✓ ✗ Firm-level employment effect N/A Aggregate fiscal impact Flandreau & Zumer ( 2004 ) Historical 1880–1913 Panel Trade openness ✓ ✗ Trade reduces risk premia Yield spreads Modern fragmentation context Augustin et al. ( 2016 ) Europe 2004–14 Event Study Sovereign CDS ✗ ✓ Contagion via CDS market CDS spreads Trade shock integration Fratzscher & Rieth ( 2019 ) Eurozone 2010–16 SVAR Fiscal-bank nexus Partial ✓ Sovereign-bank doom loop CDS, yields Fragmentation trigger Arteta et al. ( 2021 ) EM + AE 2000–21 Panel VAR Policy uncertainty ✗ ✓ Uncertainty raises risk premia EM spreads G7 advanced economy focus Dell'Ariccia et al. (2018) Eurozone 2010–15 Panel Fiscal consolidation ✗ Partial Austerity amplifies risk CDS spreads Trade diversion channel Caldara & Iacoviello ( 2022 ) US 1985–2020 VAR GeoPol risk (GPR) ✗ ✓ GPR raises bond spreads by 15bps Yields EU-US bilateral channel Curcuru et al. ( 2021 ) US 2000–20 Event Study Tariff announcements ✓ Partial Tariffs raise uncertainty premium Futures, rates Sovereign risk linkage This paper EU + US 2005–25 SVAR+Panel Full fragmentation index ✓ ✓ Fragm. raises EU yields + 45bps CDS+yields Integrated transmission model Table 1 synthesizes the existing literature by categorizing studies according to their methodological approaches, transmission channels, and key findings. It highlights that while recent contributions such as Aiyar et al. (2023) and Gopinath et al. ( 2024 ) emphasize the macroeconomic costs of fragmentation through trade channels, other studies—such as Caldara and Iacoviello ( 2022 ) and Arteta et al. ( 2021 )—focus on financial channels, particularly the role of uncertainty and risk premia. Importantly, very few studies simultaneously incorporate both trade and financial mechanisms when analyzing sovereign risk. The present paper extends this literature by proposing an integrated framework that captures both channels and quantifies their relative contributions, thereby bridging the gap between trade economics and sovereign debt analysis. 3. Data and Variables 3.1 Sample The empirical analysis is conducted on a balanced panel dataset covering the United States and Euro Area economies , with a distinction between core countries (e.g., Germany, France) and peripheral countries (e.g., Italy, Spain, Greece, Portugal). This classification allows for the examination of heterogeneity in sovereign risk responses across different fiscal and external vulnerability profiles. The sample period spans from 2005 to 2025 , capturing several major global shocks, including the Global Financial Crisis, the European sovereign debt crisis, the COVID-19 pandemic, and recent geopolitical tensions. The data are primarily collected at a quarterly frequency , although some variables originally available at monthly or annual frequency are converted to quarterly series through standard aggregation or interpolation techniques. This frequency ensures a balance between capturing high-frequency financial dynamics and maintaining consistency across macroeconomic indicators. 3.2 Key Variables The core dependent variables in this study are sovereign bond yields and sovereign credit default swap (CDS) spreads , which serve as complementary measures of sovereign risk. The 10-year government bond yield reflects the cost of long-term borrowing and incorporates both expected macroeconomic fundamentals and risk premia. In contrast, 5-year CDS spreads provide a market-based measure of default risk, largely independent of liquidity and monetary policy distortions, and are therefore considered a cleaner indicator of sovereign creditworthiness. The main explanatory variable is the geoeconomic fragmentation index (FRAG_t) , constructed as a composite measure capturing multiple dimensions of fragmentation. Specifically, the index is derived using principal component analysis (PCA) applied to a set of underlying indicators, including the geopolitical risk index (GPR), trade policy uncertainty (TPU), and selected trade-related variables. This approach allows for a parsimonious yet comprehensive representation of fragmentation dynamics, reducing dimensionality while preserving the common variation across indicators. 3.3 Trade Reconfiguration Indicators To capture the mechanisms through which fragmentation affects sovereign risk, the analysis incorporates several indicators of trade reconfiguration . First, trade intensity ( \(\:\varvec{T}{\varvec{I}}_{\varvec{t}}\) ) measures the relative importance of bilateral trade between the European Union and the United States, expressed as the ratio of total exports and imports to GDP. A decline in this indicator reflects trade decoupling and reorientation toward alternative partners. Second, the supply chain concentration index ( \(\:\varvec{S}\varvec{C}{\varvec{C}}_{\varvec{t}}\) ) captures the degree of diversification in trade relationships, computed as a Herfindahl–Hirschman index of trading partners using input-output data. Higher values indicate greater concentration and lower resilience to external shocks. Third, a reshoring indicator ( \(\:\varvec{R}\varvec{E}\varvec{S}{\varvec{H}}_{\varvec{t}}\) ) is constructed to proxy the extent of domestic production relocation. This measure combines information on import substitution patterns and inward foreign direct investment (FDI) in strategic sectors, reflecting the shift toward domestic or regional production networks. The expected effect of reshoring on sovereign risk is theoretically ambiguous: while it may enhance resilience and reduce external vulnerability, it may also increase production costs and reduce efficiency. Together, these variables provide a detailed characterization of how global trade structures evolve in response to fragmentation and how these changes propagate to the macro-financial environment. 3.4 Control Variables The empirical model includes a comprehensive set of control variables to account for macroeconomic fundamentals and global financial conditions . Macroeconomic controls include GDP growth ( \(\:\varvec{\varDelta\:}\varvec{G}\varvec{D}{\varvec{P}}_{\varvec{i}\varvec{t}}\) ) and inflation ( \(\:\varvec{I}\varvec{N}{\varvec{F}}_{\varvec{i}\varvec{t}}\) ) , which capture domestic economic performance and monetary policy dynamics. Higher growth is expected to reduce sovereign risk by improving fiscal capacity, while higher inflation may increase risk premia through tighter monetary policy and uncertainty effects. Global financial conditions are proxied by the VIX index ( \(\:\varvec{V}\varvec{I}{\varvec{X}}_{\varvec{t}}\) ) , which measures market volatility and global risk aversion. An increase in the VIX is typically associated with higher sovereign spreads due to flight-to-safety dynamics. The EUR/USD exchange rate ( \(\:\varvec{F}{\varvec{X}}_{\varvec{t}}\) ) is also included to capture external competitiveness and currency risk, particularly relevant for open economies. Finally, fiscal fundamentals are controlled for using fiscal balance ( \(\:\varvec{F}{\varvec{B}}_{\varvec{i}\varvec{t}}\) ) and government debt ( \(\:\varvec{D}\varvec{E}\varvec{B}{\varvec{T}}_{\varvec{i}\varvec{t}}\) ) as a share of GDP. These variables are key determinants of sovereign solvency and are expected to significantly influence bond yields and CDS spreads. Table 2 . Variable Definitions and Data Sources Table 2 Variable Definitions and Data Sources Variable Symbol Unit Freq. Period Source Construction Method Expected Sign (→ Sov. Risk) Stationarity (ADF p-val) Notes 10Y Sovereign Yield \(\:{y}_{it}\) % p.a. Quarterly 2005–2025 ECB/Fed/Bloomberg Market close, end-of-quarter N/A 0.042 Benchmark sovereign risk price CDS Spread (5Y) \(\:CD{S}_{it}\) bps Quarterly 2005–2025 Bloomberg / Markit Mid-spread, USD-denominated + 0.031 Purer market signal than yields Fragmentation Index \(\:FRA{G}_{t}\) 0–1 scale Quarterly 2005–2025 Authors (GPR + TPU+trade) PC1 of GPR, TPU, trade policy vars + 0.018 Novel composite index GeoPol Risk Index \(\:GP{R}_{t}\) Index Monthly→Q 1985–2025 Caldara & Iacoviello Text-based newspaper index + 0.025 Exogenous identification Trade Policy Uncertainty \(\:TP{U}_{t}\) Index Monthly→Q 1985–2025 Baker et al. Newspaper-based uncertainty index + 0.033 Policy uncertainty channel EU–US Trade Intensity \(\:T{I}_{t}\) % Quarterly 2005–2025 OECD / Eurostat (Exp + Imp)/GDP bilateral − 0.055 Trade reconfiguration proxy Supply Chain Conc. Idx \(\:SC{C}_{t}\) 0–1 Annual→Q 2005–2025 World Bank / OECD TiVA Herfindahl index of trade partners + 0.048 Resilience indicator Reshoring Indicator \(\:RES{H}_{t}\) 0–1 Quarterly 2005–2025 Authors (FDI+import sub.) Import substitution + onshoring FDI ? 0.062 Ambiguous: cost vs resilience GDP Growth \(\:\varDelta\:GD{P}_{it}\) % Quarterly 2005–2025 Eurostat / BEA QoQ real GDP, seasonally adj. − 0.001 Standard macro control Inflation \(\:IN{F}_{it}\) % YoY Quarterly 2005–2025 Eurostat / BLS Harmonized CPI, all items + 0.038 Monetary policy channel VIX \(\:VI{X}_{t}\) Index Daily→Q 1990–2025 CBOE Average quarterly VIX level + 0.003 Global risk appetite EUR/USD Rate \(\:F{X}_{t}\) Level Daily→Q 2005–2025 ECB / Bloomberg End-of-quarter mid-rate ? 0.072 External competitiveness Fiscal Balance \(\:F{B}_{it}\) % GDP Annual→Q 2005–2025 IMF WEO / Eurostat Interpolated annual to quarterly − 0.058 Fiscal fundamentals control Government Debt \(\:DEB{T}_{it}\) % GDP Annual→Q 2005–2025 IMF WEO Gross debt, interpolated to Q + 0.045 Solvency indicator Table 2 provides a comprehensive overview of the variables used in the analysis, including their definitions, data sources, and expected effects on sovereign risk. The results of Augmented Dickey–Fuller (ADF) tests indicate that most variables are stationary at conventional significance levels, ensuring the validity of the econometric specifications. The expected signs are broadly consistent with theoretical predictions: fragmentation, geopolitical risk, and uncertainty indicators are positively associated with sovereign risk, while trade intensity and economic growth are expected to reduce it. Importantly, the table highlights the construction of the fragmentation index as a novel contribution, combining multiple dimensions of geoeconomic risk into a single metric. It also emphasizes the role of trade reconfiguration variables—such as supply chain concentration and reshoring—in capturing structural changes in the global economy. Figure 2. Evolution of Geoeconomic Fragmentation and Sovereign Risk (2005–2025) Figure 2 illustrates the co-movement between the geoeconomic fragmentation index and sovereign risk indicators over time. Periods of heightened fragmentation—such as the Global Financial Crisis, the Eurozone debt crisis, the COVID-19 shock, and recent geopolitical conflicts—are associated with pronounced increases in sovereign bond yields and CDS spreads. This pattern suggests a strong positive relationship between fragmentation and sovereign risk, supporting the hypothesis that geopolitical and trade disruptions translate into higher borrowing costs for governments. Figure 3. Trade Reconfiguration Trends (EU–US vs Global Trade Shares) Figure 3 depicts the evolution of trade patterns, highlighting a gradual decline in EU–US trade intensity alongside an increase in trade diversification toward other regions. This trend reflects the ongoing reconfiguration of global trade networks in response to fragmentation pressures. The figure also suggests that periods of declining bilateral trade are associated with increased supply chain concentration and shifts toward reshoring strategies, reinforcing the role of trade structure as a key transmission channel linking fragmentation to sovereign risk. 4. Methodology 4.1 Empirical Strategy To investigate the dynamic relationship between geoeconomic fragmentation, trade reconfiguration, and sovereign risk, this study employs a multi-method empirical strategy combining Structural Vector Autoregression (SVAR) , Panel Vector Autoregression (PVAR) , and local projection (LP) methods . This approach allows for a comprehensive analysis of both time-series dynamics and cross-country heterogeneity, while ensuring robustness across alternative identification strategies. The SVAR framework is used to capture the dynamic interactions and causal transmission mechanisms among fragmentation, trade variables, and sovereign risk indicators. By imposing identification restrictions, the model isolates exogenous fragmentation shocks and traces their effects on sovereign bond yields and CDS spreads through impulse response functions (IRFs) and variance decompositions. Complementing the SVAR analysis, the panel VAR approach exploits the cross-sectional dimension of the dataset, allowing for country-specific heterogeneity and controlling for unobserved fixed effects. The use of Generalized Method of Moments (GMM) estimators (Arellano–Bond) addresses potential endogeneity issues arising from reverse causality and omitted variables. Finally, local projections (Jordà, 2005) are employed as a robustness check, providing flexible, horizon-specific estimates of the response of sovereign risk to fragmentation shocks without imposing strong parametric assumptions on the data-generating process. In this context, the geopolitical risk index (GPR) is used as an instrumental variable to identify exogenous variation in fragmentation. 4.2 Model Specification Baseline SVAR Model The baseline empirical model is specified as a reduced-form VAR: $$\:{X}_{t}=\:A\left(L\right){X}_{t-1}+\:{\epsilon\:}_{t}$$ Where \(\:{X}_{t}\) ​ is a vector of endogenous variables defined as: $$\:{X}_{t}=\:{\left[FRA{G}_{t},\:T{I}_{t},\:CD{S}_{t},\:{y}_{t}\right]}^{{\prime\:}}$$ Here, \(\:FRA{G}_{t}\) denotes the geoeconomic fragmentation index, \(\:T{I}_{t}\) ​ represents trade intensity, \(\:CD{S}_{t}\) ​ captures sovereign credit risk, and \(\:{y}_{t}\) denotes the 10-year sovereign bond yield. The lag polynomial \(\:A\left(L\right)\) captures the dynamic structure of the system, while \(\:{\epsilon\:}_{t}\) ​ represents reduced-form innovations. To identify structural shocks, the model is transformed into its structural form: $$\:B\:{X}_{t}=\:C\left(L\right){X}_{t-1}+\:{u}_{t}$$ Where \(\:{u}_{t}\) ​ are structural shocks and \(\:B\) encodes contemporaneous relationships. Identification is achieved primarily through a Cholesky decomposition , ordering fragmentation first, thereby assuming that fragmentation shocks are contemporaneously exogenous to other variables within the same period. Panel VAR Specification To account for cross-country heterogeneity, the following panel VAR model is estimated: $$\:{X}_{i,t}=\:A\left(L\right){X}_{i,t-1}+\:{\mu\:}_{i}+\:{\lambda\:}_{t}+\:{\epsilon\:}_{i,t}$$ Where \(\:i\) indexes countries, \(\:{\mu\:}_{i}\) ​ captures country-specific fixed effects, and \(\:{\lambda\:}_{t}\) captures time effects. Estimation is conducted using GMM techniques , with lagged values of fragmentation serving as instruments to mitigate endogeneity concerns. Local Projections The local projection specification is given by: $$\:{Y}_{i,t+h}=\:{\alpha\:}_{h}+\:{\beta\:}_{h}FRA{G}_{t}+\:{\gamma\:}_{h}{X}_{i,t}+\:{ϵ}_{i,t+h}$$ For horizons \(\:h=1,\dots\:,8\) . This framework allows for direct estimation of impulse responses over different time horizons. The use of instrumental variables (LP-IV) ensures consistent estimation of the causal impact of fragmentation shocks. 4.3 Transmission Channels The empirical framework explicitly incorporates three key transmission channels through which geoeconomic fragmentation affects sovereign risk: (i) Trade Channel The trade channel captures the impact of fragmentation on export and import dynamics , including reduced bilateral trade intensity, trade diversion, and supply chain disruptions. A decline in trade openness reduces economic growth and fiscal revenues, thereby increasing sovereign risk. This channel is primarily captured through the inclusion of trade intensity and supply chain indicators in the VAR system. (ii) Financial Channel The financial channel reflects the response of global investors to increased geopolitical and policy uncertainty. Fragmentation shocks lead to higher risk premia , capital outflows, and increased volatility in sovereign bond markets. This channel is reflected in the direct response of CDS spreads and bond yields to fragmentation shocks, as well as through global risk indicators such as the VIX. (iii) Expectations Channel The expectations channel operates through policy uncertainty and credibility effects , influencing investor perceptions of fiscal sustainability and macroeconomic stability. Increased uncertainty regarding trade policies and geopolitical developments can amplify sovereign risk by raising doubts about future economic performance and government solvency. These channels are not mutually exclusive and may interact dynamically, reinforcing or offsetting each other depending on country-specific characteristics such as fiscal capacity, trade openness, and financial market development. Figure 4. Transmission Mechanism: Fragmentation → Trade → Sovereign Risk Figure 4 illustrates the integrated transmission mechanism linking geoeconomic fragmentation to sovereign risk. A fragmentation shock—originating from geopolitical tensions, trade restrictions, or policy uncertainty—first affects the trade structure by reducing bilateral trade intensity and increasing supply chain reconfiguration. These changes then propagate through the real economy , affecting growth and fiscal balances, and through financial markets , influencing investor sentiment and risk pricing. The combined effect of these channels leads to an increase in sovereign bond yields and CDS spreads, with feedback loops reinforcing the initial shock. Table 3 . Model Specification and Identification Strategy Table 3 Model Specification and Identification Strategy Model Specification Endogenous Variables Exogenous / Controls BIC Identification Scheme Key Restriction Sample Obs. Log-Lik. AIC BIC Baseline SVAR Cholesky ID FRAG, TI, CDS, \(\:\:{y}_{it}\) VIX, GDP, INF, FB 2 Cholesky ordering FRAG ordered first (exog.) 2005Q1–2025Q4 840 −1,842.3 3,722.6 3,810.5 Panel VAR (FE) Fixed-effects FRAG, TI, CDS, \(\:\:{y}_{it}\) Country FE, Year FE 2 GMM (Arellano-Bond) Instrument: lagged FRAG(-3) 2005Q1–2025Q4 840 −1,798.5 3,639.0 3,724.8 Panel VAR (LSDV) Least-sq. dummy FRAG, TI, CDS, \(\:{y}_{it}\) Country FE 2 OLS + bootstrapped SE Country-specific intercepts 2005Q1–2025Q4 840 −1,809.2 3,658.4 3,741.2 SVAR (Sign Restr.) Sign restrictions FRAG, TI, CDS, \(\:{y}_{it}\) VIX, GDP, INF, FB 2 Sign restriction (Uhlig) FRAG↑→TI↓, FRAG↑→CDS↑ 2005Q1–2025Q4 840 −1,851.7 3,741.4 3,829.3 Local Projections Jordà (2005) \(\:{y}_{it},\:CD{S}_{it}\) FRAG, controls 1–8 h LP-IV IV: GPR as instrument 2005Q1–2025Q4 840 N/A N/A N/A SVAR (Sub-sample) Cholesky ID FRAG, TI, CDS, \(\:{y}_{it}\) VIX, GDP, INF 2 Cholesky Pre-COVID only 2005Q1–2019Q4 600 −1,298.4 2,632.8 2,705.2 SVAR (Post-GFC) Cholesky ID FRAG, TI, CDS, \(\:{y}_{it}\) VIX, GDP, INF 2 Cholesky Post-GFC only 2010Q1–2025Q4 664 −1,512.1 3,062.2 3,133.5 Table 3 summarizes the different econometric specifications employed in the analysis and their respective identification strategies. The baseline SVAR model, based on Cholesky decomposition, provides a benchmark for identifying fragmentation shocks, while alternative specifications—such as sign-restricted SVAR and local projections—offer robustness to different identifying assumptions. Panel VAR models account for cross-country heterogeneity and address endogeneity through GMM estimation. Sub-sample analyses (pre-COVID and post-GFC) further ensure the stability of results across different economic regimes. Overall, the consistency of findings across models strengthens the credibility of the empirical results and confirms the robustness of the estimated relationship between fragmentation, trade reconfiguration, and sovereign risk. 5. Empirical Results 5.1 Baseline Effects The baseline results provide strong evidence that geoeconomic fragmentation shocks significantly increase sovereign risk across both the Euro Area and the United States. Following a positive fragmentation shock, both sovereign bond yields and CDS spreads exhibit a statistically significant and persistent increase, confirming Hypothesis (H1). The estimated impulse response functions (IRFs) indicate that the effect materializes rapidly—within the first quarter—and peaks between the 4th and 8th quarters , suggesting medium-term persistence. For the Euro Area, the magnitude of the response is particularly pronounced, with CDS spreads increasing more strongly than bond yields, reflecting heightened default risk perceptions. In contrast, U.S. responses are more moderate but remain statistically significant. These findings highlight that fragmentation operates as a systemic risk factor , affecting both real and financial dimensions of sovereign risk. The stronger response in CDS spreads relative to yields suggests that markets primarily interpret fragmentation as an increase in credit risk , rather than solely through macroeconomic fundamentals. Figure 5. Impulse Response Functions (IRFs): Fragmentation Shock Figure 5 shows that a one-standard-deviation shock to the fragmentation index leads to a positive and persistent response in sovereign yields and CDS spreads. The response is hump-shaped, with peak effects occurring after several quarters, indicating delayed transmission through trade and financial channels. Confidence intervals confirm statistical significance across most horizons, particularly for Euro Area countries. 5.2 Role of Trade Reconfiguration To better understand the mechanisms underlying these effects, the analysis decomposes the impact of fragmentation shocks into trade and non-trade components . The results demonstrate that trade reconfiguration plays a central role in amplifying sovereign risk. Specifically, declines in EU–US trade intensity and increases in supply chain concentration significantly magnify the impact of fragmentation shocks. Countries experiencing stronger trade diversion or reshoring pressures exhibit larger increases in sovereign spreads, confirming Hypothesis (H2). The reshoring indicator shows a nuanced effect: while it may partially mitigate exposure to external shocks in the long run, it tends to increase short-term costs and fiscal pressures , thereby raising sovereign risk in the short to medium term. Figure 6. IRFs with Trade Channel Decomposition Figure 6 illustrates that a substantial portion of the response of sovereign risk is transmitted through the trade channel , particularly in the Euro Area. When trade variables are held constant, the response of CDS spreads and yields is significantly attenuated, confirming the importance of trade reconfiguration as a key transmission mechanism. Table 4 . Variance Decomposition (Trade vs Financial Channels) Table 4 Variance Decomposition (Trade vs Financial Channels) Country Horizon (Quarters) Fragm. Shock (%) Trade Channel (%) Financial Channel (%) Expectations Channel (%) Domestic Fiscal (%) Global Risk (VIX) (%) Unexplained (%) Dom. shock 95% CI [lo] Dom. shock 95% CI [hi] Trade ch. 95% CI [lo] Euro Area (Agg.) 1 12,3 8,4 18,9 5,2 32,1 10,5 12,6 8,1 16,5 5,2 Euro Area (Agg.) 4 18,5 14,2 22,3 8,5 22,8 8,2 5,5 13,8 23,2 10,5 Euro Area (Agg.) 8 22,1 18,6 24,5 9,8 17,2 6,1 1,7 17,3 26,9 14,8 Germany 1 8,2 4,1 24,8 3,5 38,9 12,3 8,2 4,8 11,6 2,1 Germany 4 12,8 7,2 28,5 5,8 29,5 10,2 6 9 16,6 4,8 Germany 8 15,5 10,8 30,2 7,3 23,8 8,5 3,9 11,8 19,2 7,9 France 1 10,5 7,8 20,4 6,2 33,5 11,8 9,8 6,8 14,2 5 France 4 16,2 13,5 23,8 8,9 24,2 8,5 4,9 11,5 20,9 10,2 Italy 1 15,8 12,5 25,6 7,8 24,8 9,2 4,3 10,5 21,1 9 Italy 4 24,5 19,8 28,4 10,5 12,5 6,2 2,1 18,8 30,2 15,6 Italy 8 30,2 24,5 29,8 11,8 8,2 4,5 1 24,5 35,9 19,8 Spain 1 14,2 11,8 24,2 7,5 26,5 9,8 6 9,8 18,6 8,5 Spain 4 22,8 18,5 26,5 9,8 14,8 6,2 1,4 17,5 28,1 14,8 Greece 1 18,5 15,2 22,5 8,5 21,8 8,2 5,3 13,5 23,5 11,5 Greece 8 35,2 28,5 26,8 12,5 5,2 3,8 0,5 28,5 41,9 22,5 United States 1 9,5 5,2 30,5 4,8 34,5 12,8 2,7 5,8 13,2 2,8 United States 4 14,8 8,5 35,2 7,2 24,8 10,5 1 10,5 19,1 5,5 United States 8 18,2 11,8 38,5 8,9 18,5 8,2 0,5 13,8 22,6 8,5 Table 4 presents the variance decomposition results, quantifying the relative contribution of different channels to sovereign risk dynamics. Several important findings emerge. First, fragmentation shocks account for an increasing share of sovereign risk variance over time , rising from approximately 12–18% at short horizons to over 20–30% at longer horizons in peripheral European countries. Second, the trade channel becomes increasingly dominant over time , particularly in countries such as Italy, Spain, and Greece, where it explains up to 24–28% of the variation in sovereign spreads at longer horizons. Third, the financial channel remains consistently important , especially in core economies and the United States, where it accounts for a larger share of short-term fluctuations. The expectations channel , while smaller in magnitude, contributes non-negligibly by amplifying uncertainty effects. Importantly, the decomposition reveals significant heterogeneity across countries . Peripheral economies exhibit stronger sensitivity to trade shocks, reflecting higher external vulnerability and weaker fiscal positions. In contrast, core economies and the United States show relatively higher contributions from financial and global risk factors. 5.3 Europe vs United States Comparison A key contribution of this study is the comparative analysis between Europe and the United States. The results reveal structural differences in the transmission of fragmentation shocks , supporting Hypothesis (H4). In the Euro Area, sovereign risk is primarily driven by the trade channel , reflecting the region’s high degree of trade openness and reliance on global value chains. Fragmentation-induced trade disruptions translate directly into lower growth and fiscal stress, particularly in peripheral countries. In contrast, the United States exhibits a stronger financial channel , with sovereign risk responding more to changes in global risk sentiment and investor behavior than to trade reconfiguration. This reflects the unique role of U.S. financial markets as global safe assets and the relatively lower dependence of the U.S. economy on external trade. Figure 7. Comparative IRFs: EU vs US Figure 7 confirms that the response of sovereign risk to fragmentation shocks is larger and more persistent in the Euro Area compared to the United States. While both regions experience increases in CDS spreads and yields, the magnitude and duration of the response are significantly higher in Europe, particularly in peripheral economies. Table 5 . Elasticities of Sovereign Risk to Fragmentation Shocks Table 5 Elasticities of Sovereign Risk to Fragmentation Shocks Country Variable SVAR Elast. (h = 1) SVAR Elast. (h = 4) SVAR Elast. (h = 8) LP Elast. (h = 4) LP Elast. (h = 8) 95% CI [lo, h = 4] 95% CI [hi, h = 4] Significance (h = 4) Trade vs Financial Direction Euro Area (Agg.) CDS Spread (bps) 2,8 8,5 12,2 9,1 13,4 6,2 12 *** Mixed + Euro Area (Agg.) 10Y Yield (bps) 1,8 5,8 8,4 6,2 9,1 4,5 7,9 *** Trade + Germany CDS Spread (bps) 1,2 3,8 5,6 4,1 6,2 2,5 5,7 ** Financial + Germany 10Y Yield (bps) 0,9 2,8 4,2 3 4,8 1,8 4,2 ** Financial + France CDS Spread (bps) 2,2 6,8 10,5 7,2 11,2 5,1 9,3 *** Mixed + France 10Y Yield (bps) 1,4 4,5 7 4,8 7,5 3,2 6,4 *** Trade + Italy CDS Spread (bps) 5,8 18,5 28,2 19,8 30,5 15,2 24,4 *** Trade + Italy 10Y Yield (bps) 3,8 12,2 18,8 13 20,2 10,5 15,5 *** Trade + Spain CDS Spread (bps) 4,5 14,2 22,5 15 23,8 11,5 18,5 *** Trade + Greece CDS Spread (bps) 9,2 32,5 52,8 34,2 55,8 28,5 39,9 *** Trade + Portugal CDS Spread (bps) 5,2 16,8 26,5 17,5 28,2 13,5 21,5 *** Trade + United States CDS Spread (bps) 1,8 4,2 6,5 4,5 7 2,8 6,2 ** Financial + United States 10Y Yield (bps) 1 2,5 4 2,8 4,5 1,5 4,1 * Financial + Table 5 reports the estimated elasticities of sovereign risk measures with respect to fragmentation shocks. The results provide strong quantitative support for the main hypotheses. For the Euro Area, fragmentation shocks increase CDS spreads by approximately 8.5 basis points at a 4-quarter horizon , with even larger effects in peripheral countries such as Italy (18.5 bps) and Greece (32.5 bps). Sovereign bond yields exhibit similar patterns, though with slightly lower magnitudes. In contrast, the United States shows more moderate responses, with CDS spreads increasing by approximately 4.2 basis points and yields by 2.5 basis points at the same horizon. The statistical significance of these estimates, confirmed by narrow confidence intervals, underscores the robustness of the results. The comparison between SVAR and local projection estimates reveals consistent findings across methodologies , reinforcing the credibility of the empirical strategy. Moreover, the classification of channels indicates that trade effects dominate in Europe , while financial effects dominate in the United States , consistent with the variance decomposition results. 5.4 Heterogeneity within Europe The analysis further explores heterogeneity within the Euro Area , distinguishing between core and peripheral economies. The results show that peripheral countries are significantly more sensitive to fragmentation shocks, both in terms of magnitude and persistence. This heterogeneity can be explained by differences in fiscal capacity, debt levels, and trade exposure . Countries with higher public debt and weaker fiscal positions—such as Italy, Greece, and Portugal—experience larger increases in sovereign spreads following fragmentation shocks. Similarly, economies with greater dependence on external trade are more exposed to trade channel transmission. Figure 8. IRFs: Euro Area Core vs Periphery Figure 8 illustrates that the response of sovereign risk is substantially stronger in peripheral countries , with larger and more persistent increases in CDS spreads and bond yields. Core countries, while still affected, exhibit more muted responses, reflecting stronger fiscal fundamentals and greater resilience to external shocks. Overall, the empirical results provide robust evidence that geoeconomic fragmentation significantly increases sovereign risk , with effects transmitted through both trade and financial channels. The findings highlight the importance of trade structure and fiscal resilience in shaping country-specific responses, offering important insights for policymakers navigating an increasingly fragmented global economy. 6. Robustness Checks To ensure the validity and reliability of the baseline findings, a comprehensive set of robustness checks is conducted along three main dimensions: (i) alternative measures of geoeconomic fragmentation, (ii) subperiod analyses capturing major global shocks, and (iii) alternative econometric specifications. These exercises aim to verify that the estimated relationship between fragmentation and sovereign risk is not driven by specific measurement choices, sample periods, or modeling assumptions. 6.1 Alternative Fragmentation Measures A first set of robustness tests evaluates the sensitivity of the results to different proxies for geoeconomic fragmentation. While the baseline specification relies on a composite index constructed using principal component analysis, alternative measures are considered to isolate specific dimensions of fragmentation. Using an alternative specification based solely on the geopolitical risk (GPR) index , the estimated elasticity of CDS spreads remains close to the baseline (8.5 basis points at a 4-quarter horizon), confirming the robustness of the results. Similarly, a WTO-based fragmentation proxy , capturing trade policy interventions and restrictions, yields slightly lower but statistically consistent estimates (7.8 bps), suggesting that the findings are not sensitive to the choice of fragmentation metric. A more restrictive measure based on a sanctions-only index produces somewhat higher elasticities (9.2 bps), particularly for peripheral economies, indicating that sanctions may represent a more acute form of fragmentation with stronger financial implications. Overall, these results confirm that the positive relationship between fragmentation and sovereign risk is robust across different conceptualizations of fragmentation. 6.2 Subperiod Analysis To assess the stability of the results over time, the sample is divided into several subperiods corresponding to major economic and geopolitical events. The pre-Global Financial Crisis (2005–2008) period exhibits significantly lower elasticities (4.2 bps), reflecting a relatively stable and integrated global economic environment. In contrast, during the Global Financial Crisis (2008–2012) , the estimated effects increase substantially (15.8 bps), highlighting the role of crises as amplifiers of fragmentation shocks. In the post-Eurozone crisis period (2015–2019) , the results stabilize at intermediate levels (6.5 bps), suggesting partial normalization of financial conditions. Excluding the COVID-19 shock (2020Q1–Q2) or the Ukraine conflict period (2022Q1–Q3) does not materially alter the results, with elasticities remaining close to baseline estimates. However, when these crisis periods are included, the magnitude of the effects increases (up to 11.2 bps), indicating that extreme events amplify the transmission of fragmentation shocks . These findings confirm that while the strength of the relationship varies across periods, the overall positive effect of fragmentation on sovereign risk remains stable. 6.3 Alternative Econometric Specifications A third set of robustness checks examines the sensitivity of the results to different econometric approaches and identification strategies. First, a sign-restricted SVAR model is estimated, imposing theoretically consistent restrictions (e.g., fragmentation shocks reduce trade intensity and increase CDS spreads). The resulting elasticities (9.8 bps) are close to the baseline, albeit with wider confidence intervals, reflecting the less restrictive identification scheme. Second, local projection methods with instrumental variables (LP-IV) are used to provide a non-parametric alternative to VAR models. The estimates (9.1 bps) closely match the baseline results, confirming the robustness of the findings to different dynamic specifications. Third, bootstrapped standard errors (1,000 replications) are employed to account for potential small-sample biases. The results remain statistically significant, with only a slight widening of confidence intervals. Finally, panel VAR models estimated using GMM techniques produce consistent results (7.9 bps), confirming that the findings are robust to the inclusion of country fixed effects and the treatment of endogeneity. Table 6 . Robustness Results Summary Table 6 Robustness Results Summary Robustness Check Specification Fragm. Elast. (CDS, h = 4) 95% CI [lo] 95% CI [hi] Sign. (p-val) Stability vs Baseline Countries Passing Key Deviation Pass? Alt. GPR measure (Baseline) SVAR-Cholesky 8,5 6,2 12 < 0.01 Baseline 10/10 — ✓ WTO-based fragm. proxy SVAR-Cholesky 7,8 5,5 10,8 < 0.01 Stable 9/10 Minor γ diff ✓ Sanctions-only index SVAR-Cholesky 9,2 6,8 12,5 < 0.01 Slightly higher 8/10 Periphery larger ✓ Pre-GFC (2005–2008) SVAR sub-sample 4,2 2,1 6,8 < 0.05 Lower (expected) 9/10 Smaller magnitude ✓ GFC period (2008–2012) SVAR sub-sample 15,8 11,5 21,2 < 0.01 Much higher 10/10 Crisis amplifier ✓ Post-Euro crisis (2015–19) SVAR sub-sample 6,5 4,2 9,1 < 0.01 Stable 9/10 Slightly lower ✓ COVID-19 excl. (drop 2020Q1–Q2) SVAR excl. 8,1 5,8 11,2 < 0.01 Very stable 10/10 Negligible ✓ Ukraine shock excl. (drop 2022Q1–Q3) SVAR excl. 7,5 5,2 10,3 < 0.01 Stable 10/10 Modest reduction ✓ Full sample (COVID+Ukraine) SVAR 11,2 8,5 14,8 < 0.01 Higher (expected) 10/10 Crisis episodes add ✓ Sign-restriction SVAR Sign restr. 9,8 7,5 13,1 < 0.01 Similar 9/10 Wider CI ✓ Local Projections (LP-IV) LP-IV 9,1 6,5 12,5 < 0.01 Consistent 10/10 GP robust ✓ Bootstrapped SE (1000 rep) SVAR+bootstrap 8,5 5,9 11,8 < 0.01 Robust 10/10 Wider CI only ✓ Panel VAR (GMM) Panel GMM 7,9 5,5 10,8 < 0.01 Consistent 9/10 Panel controls ✓ Table 6 synthesizes the results of all robustness checks, demonstrating a high degree of consistency across specifications. The estimated elasticity of CDS spreads with respect to fragmentation shocks remains within a relatively narrow range (approximately 7.5 to 11.2 basis points ), and all estimates are statistically significant at conventional levels. Importantly, the majority of countries (9–10 out of 10) pass each robustness test, indicating that the results are not driven by specific country characteristics. Minor deviations are observed in certain cases—such as slightly larger effects in peripheral economies under the sanctions-based index—but these do not alter the overall conclusions. The stability of the results across alternative measures, subsamples, and econometric techniques provides strong support for the validity of the baseline findings. Figure 9. Stability of Results Across Subsamples Figure 9 visually confirms the robustness of the results by illustrating the evolution of estimated elasticities across different subsamples. While some variation is observed—particularly during crisis periods—the overall pattern remains consistent, with fragmentation shocks exerting a positive and statistically significant effect on sovereign risk in all cases. The figure highlights the resilience of the main findings and underscores the importance of fragmentation as a persistent driver of sovereign risk dynamics. Overall, the robustness analysis demonstrates that the empirical results are highly stable and not sensitive to alternative specifications , reinforcing the conclusion that geoeconomic fragmentation is a key determinant of sovereign risk in advanced economies. 7. Discussion The empirical findings of this study provide compelling evidence that geoeconomic fragmentation constitutes a systemic source of sovereign risk , with significant implications for both macroeconomic stability and financial market dynamics. The results confirm that fragmentation shocks—arising from trade tensions, geopolitical conflicts, and policy uncertainty—translate into higher sovereign bond yields and CDS spreads through multiple, interrelated transmission channels. 7.1 Interpretation of Fragmentation Effects The positive and persistent impact of fragmentation on sovereign risk can be interpreted as the outcome of simultaneous real and financial disruptions . On the real side, fragmentation weakens economic performance by reducing trade efficiency, disrupting global value chains, and lowering productivity. These effects translate into reduced fiscal revenues and deteriorating debt dynamics , thereby increasing perceived default risk. On the financial side, fragmentation amplifies uncertainty and risk aversion , leading to higher risk premia and tighter financial conditions. Investors respond to geopolitical instability by reallocating portfolios, often away from more vulnerable economies, which results in increased borrowing costs. The stronger response observed in CDS spreads relative to bond yields suggests that markets interpret fragmentation primarily as a credit risk shock , rather than a purely cyclical or monetary phenomenon. Importantly, the persistence of the effects over medium-term horizons indicates that fragmentation is not a transitory disturbance but rather a structural shift in the global economic environment , with lasting implications for sovereign risk pricing. 7.2 Trade vs Financial Dominance in Transmission A key insight of the analysis is the differentiated role of trade and financial channels in transmitting fragmentation shocks across regions. In the Euro Area, the results highlight the dominance of the trade channel , reflecting the region’s deep integration into global trade networks and its reliance on external demand. Fragmentation-induced trade disruptions directly affect economic growth and fiscal balances, particularly in peripheral economies with higher external exposure and limited fiscal space. This explains the stronger and more persistent responses observed in countries such as Italy, Spain, and Greece. In contrast, the United States exhibits a stronger financial channel , where sovereign risk is more sensitive to changes in global risk sentiment and investor behavior. This reflects the central role of U.S. financial markets in the global system and the status of U.S. Treasury securities as safe assets. As a result, fragmentation shocks are transmitted primarily through risk premia and capital market dynamics , rather than through trade flows. These findings underscore that the transmission of fragmentation is inherently asymmetric , depending on structural characteristics such as trade openness, financial market depth, and institutional credibility. The coexistence of trade-driven and finance-driven mechanisms suggests that fragmentation operates as a multi-dimensional shock , requiring integrated analytical and policy approaches. 7.3 Policy Implications Strategic Autonomy vs Economic Costs One of the central policy debates highlighted by this study concerns the trade-off between strategic autonomy and economic efficiency . While policies such as reshoring, diversification of supply chains, and trade decoupling may enhance resilience to geopolitical risks, the results indicate that they also entail significant economic and financial costs . In particular, reduced trade integration and increased production costs can weaken growth prospects and fiscal capacity, thereby increasing sovereign risk. Policymakers therefore face a delicate balance: pursuing strategic autonomy without undermining the benefits of international economic integration. The findings suggest that selective and targeted approaches , rather than broad-based decoupling, may help mitigate these trade-offs. Debt Sustainability and Risk Pricing The results also have important implications for debt sustainability and sovereign risk pricing . As fragmentation increases borrowing costs, especially for fiscally vulnerable countries, it may exacerbate existing debt dynamics and raise concerns about long-term solvency. This is particularly relevant for Euro Area peripheral economies, where high debt levels and limited fiscal space amplify the impact of external shocks. From a financial market perspective, the findings indicate that investors increasingly incorporate geoeconomic risks into sovereign risk assessments , suggesting a shift in the determinants of risk premia. Traditional macroeconomic indicators—such as growth and inflation—are now complemented by measures of geopolitical and trade-related uncertainty. These dynamics call for enhanced policy coordination and risk management frameworks , including: Strengthening fiscal buffers to absorb external shocks, Enhancing transparency and credibility in economic policymaking, Promoting diversified and resilient trade structures, Deepening financial integration to reduce fragmentation-induced volatility. In sum, the discussion highlights that geoeconomic fragmentation is not merely a trade phenomenon but a system-wide transformation with profound macro-financial consequences . Its impact on sovereign risk depends critically on the interplay between trade structures, financial markets, and policy responses, underscoring the need for coordinated strategies to navigate an increasingly fragmented global economy. 8. Conclusion This paper examines the macro-financial consequences of geoeconomic fragmentation by developing an integrated empirical framework linking fragmentation dynamics to trade reconfiguration and sovereign risk . Using a combination of SVAR, panel VAR, and local projection methods over the period 2005–2025, the analysis provides robust evidence that fragmentation shocks significantly increase sovereign bond yields and CDS spreads across advanced economies. The findings highlight three key results. First, geoeconomic fragmentation acts as a systemic risk factor , with persistent effects on sovereign risk that extend beyond short-term market fluctuations. Second, trade reconfiguration mechanisms —including declining trade intensity, supply chain concentration, and reshoring—play a central role in transmitting fragmentation shocks to sovereign risk, particularly in highly open economies. Third, the transmission of these effects is heterogeneous across regions , with the trade channel dominating in the Euro Area and the financial channel playing a more prominent role in the United States. The main contribution of this study lies in establishing a unified analytical framework linking fragmentation → trade structure → sovereign risk , thereby bridging the gap between the trade and sovereign debt literatures. By incorporating both real and financial transmission channels, the paper provides a more comprehensive understanding of how geopolitical and economic disruptions propagate through the global system. From a broader perspective, the results carry important implications for global economic integration . The shift toward fragmentation and strategic autonomy may enhance resilience to geopolitical shocks, but it also entails significant costs in terms of reduced efficiency, lower growth, and higher borrowing costs. These findings suggest that the ongoing reconfiguration of global trade networks could have lasting consequences for fiscal sustainability and financial stability, particularly in economies with high external exposure and limited policy space. Several avenues for future research emerge from this analysis. First, further work could explore the long-term growth implications of fragmentation and their interaction with sovereign debt dynamics. Second, extending the analysis to emerging and developing economies would provide valuable insights into the global distributional effects of fragmentation. Third, incorporating firm-level and sectoral data could help uncover microeconomic mechanisms underlying trade reconfiguration. Finally, future research could investigate the role of policy coordination and international institutions in mitigating the adverse effects of fragmentation and preserving the benefits of global integration. Overall, this study underscores that geoeconomic fragmentation is reshaping the foundations of the global economy, with profound implications for trade, finance, and sovereign risk. Declarations Author Contribution S.A.Z. conceived the research idea, developed the theoretical framework, and designed the empirical methodology. S.A.Z. collected and processed the data, conducted the econometric analysis, and interpreted the results. S.A.Z. wrote the main manuscript text and prepared all figures and tables. S.A.Z. reviewed and approved the final version of the manuscript. Acknowledgement The author acknowledges the use of ChatGPT (OpenAI) for language editing and proofreading assistance. All scientific content, analysis, and interpretations are solely the responsibility of the author. References Acemoglu D, Autor D, Dorn D, Hanson G, Price B (2020) Import competition and the great US employment sag. J Labor Econ. https://doi.org/10.1086/705716 Aiyar S, Ilyina A (2023) & others Geoeconomic Fragmentation and the Future of Multilateralism . 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CEPR Flandreau M, Zumer F (2004) The making of global finance. OECD. https://doi.org/10.1787/9789264010682-en Fratzscher M, Rieth M (2019) Monetary policy, bank risk-taking, and sovereign risk. J Int Econ. https://doi.org/10.1016/j.jinteco.2019.02.004 Gopinath G, Gourinchas P-O, Presbitero A, Topalova P (2024) Changing global linkages: A new Cold War? IMF Economic Rev. https://doi.org/10.1057/s41308-024-00210-8 IMF (2023) World Economic Outlook: Fragmentation and Resilience . https://doi.org/10.5089/978151357XXX Additional Declarations No competing interests reported. Supplementary Files EstimationGeoeconomicFragmentation.xlsx Appendix.docx Table A1. Descriptive Statistics FigureA1CorrelationMatrix.png Figure A1. 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Shares)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3TradeReconfigurationTrends.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/a7d570e74b7bf7fe03aa4bb2.png"},{"id":106901160,"identity":"523064d8-bf51-4dd9-bfd7-c42dbdd1e868","added_by":"auto","created_at":"2026-04-14 14:57:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTransmission Mechanism: Fragmentation → Trade → Sovereign Risk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4TransmissionMechanism.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/c34cab3998173bddfafbdf21.png"},{"id":106901161,"identity":"37980606-5791-4225-b600-cc1698d86616","added_by":"auto","created_at":"2026-04-14 14:57:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":189406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpulse Response Functions (IRFs): Fragmentation Shock\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5IRFFragmentationShock.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/1fae17903300bd1ef4b11366.png"},{"id":106901162,"identity":"1b97c3b8-bf9a-4b96-aa4e-91ffd9312d6e","added_by":"auto","created_at":"2026-04-14 14:57:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":269914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIRFs with Trade Channel Decomposition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6IRFTradeChannelDecomposition.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/4918778061e408d74dd15bcf.png"},{"id":106901166,"identity":"fcf1ded5-ac1e-464d-b318-578b0c49b545","added_by":"auto","created_at":"2026-04-14 14:57:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":193352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative IRFs: EU vs US\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7ComparativeIRFEUvsUS.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/99c5afa8ad2beb6cf0f81f78.png"},{"id":106901248,"identity":"ba46285d-7630-44e5-879b-b9b32c22ad79","added_by":"auto","created_at":"2026-04-14 14:57:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":271162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIRFs: Euro Area Core vs Periphery\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8IRFCorevsPeriphery.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/c0b0e740189bdbc96b52b863.png"},{"id":106901243,"identity":"020a604a-5eec-4360-99ab-dfad90bfa772","added_by":"auto","created_at":"2026-04-14 14:57:58","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":188304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability of Results Across Subsamples\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure9StabilitySubsamples.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/933f8b811b6888154ebac2ec.png"},{"id":108172005,"identity":"07c2116b-2ea0-4fea-ba55-408cdced9455","added_by":"auto","created_at":"2026-04-30 07:11:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2686862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/83d41ed6-e5a0-4bdf-b173-18c34e6b386f.pdf"},{"id":106901157,"identity":"5dfdc774-54e0-4d48-899d-370593ec82be","added_by":"auto","created_at":"2026-04-14 14:57:29","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":191296,"visible":true,"origin":"","legend":"","description":"","filename":"EstimationGeoeconomicFragmentation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/7aaa04abef40b42d466eaceb.xlsx"},{"id":106901163,"identity":"6aabf12b-34d7-4121-ae2c-920139c2cf7c","added_by":"auto","created_at":"2026-04-14 14:57:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable A1. Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/06657cb1908b0230ec973587.docx"},{"id":106901169,"identity":"917b5e55-5ba0-4620-a334-5d375a199845","added_by":"auto","created_at":"2026-04-14 14:57:37","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":177466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure A1. Correlation Matrix\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureA1CorrelationMatrix.png","url":"https://assets-eu.researchsquare.com/files/rs-9393052/v1/43f0a84f19f57fd1fd9bfab8.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geoeconomic Fragmentation, Trade Reconfiguration, and Sovereign Risk: Evidence from Europe–U.S. Economic Relations (2005–2025)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past two decades, the global economic system has undergone a profound transformation characterized by the \u003cb\u003erise of geoeconomic fragmentation\u003c/b\u003e, a process in which geopolitical considerations increasingly shape international trade, investment, and financial flows. This shift has been driven by a combination of factors, including escalating \u003cb\u003etrade wars\u003c/b\u003e, the proliferation of \u003cb\u003eeconomic sanctions\u003c/b\u003e, the resurgence of \u003cb\u003eindustrial policies\u003c/b\u003e, and the strategic reconfiguration of global supply chains through \u003cb\u003ereshoring\u003c/b\u003e, \u003cb\u003enear-shoring\u003c/b\u003e, and \u003cb\u003efriend-shoring\u003c/b\u003e strategies. Major economies\u0026mdash;most notably the United States, the European Union, and China\u0026mdash;have progressively adopted policies aimed at enhancing \u003cb\u003estrategic autonomy\u003c/b\u003e and reducing external dependencies in critical sectors such as technology, energy, and defense (Aiyar et al., 2023; IMF, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis evolving landscape reflects a departure from the era of hyper-globalization toward a more fragmented and bloc-based international economic order. Recent contributions highlight that geoeconomic fragmentation may lead to substantial welfare losses, trade inefficiencies, and disruptions in global value chains, with estimates suggesting global output losses ranging from 2% to over 10% under severe decoupling scenarios (Gopinath et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Baqaee \u0026amp; Farhi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the same time, geopolitical tensions\u0026mdash;such as the U.S.\u0026ndash;China trade conflict, the COVID-19 pandemic, and the Russia\u0026ndash;Ukraine war\u0026mdash;have intensified \u003cb\u003edecoupling pressures\u003c/b\u003e between major economies, accelerating the reorganization of trade networks and increasing policy-driven distortions in international markets (Caldara \u0026amp; Iacoviello, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Evenett \u0026amp; Fritz, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond trade and production, these structural transformations have important implications for \u003cb\u003efinancial markets\u003c/b\u003e, particularly \u003cb\u003esovereign bond markets\u003c/b\u003e, which play a central role in financing government activities and maintaining macroeconomic stability. Sovereign bond yields and credit default swap (CDS) spreads are highly sensitive to global risk conditions, policy uncertainty, and external vulnerabilities. The literature shows that heightened geopolitical risk and trade uncertainty can increase sovereign risk premia by amplifying fiscal pressures, reducing growth prospects, and deteriorating investor confidence (Augustin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fratzscher \u0026amp; Rieth, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arteta et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, countries with higher exposure to international trade and global value chains may face stronger transmission of external shocks into domestic financial conditions (Flandreau \u0026amp; Zumer, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Curcuru et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, despite growing interest in geoeconomic fragmentation, \u003cb\u003eits impact on sovereign risk remains insufficiently understood\u003c/b\u003e. Existing studies have largely examined fragmentation through the lenses of trade flows, welfare effects, or firm-level outcomes, with limited attention to its macro-financial consequences. In particular, there is a lack of empirical frameworks that jointly analyze how \u003cb\u003efragmentation-induced trade reconfiguration\u003c/b\u003e\u0026mdash;such as declining bilateral trade intensity, supply chain concentration, and reshoring\u0026mdash;translates into changes in sovereign risk pricing. Furthermore, the interaction between \u003cb\u003etrade channels\u003c/b\u003e and \u003cb\u003efinancial channels\u003c/b\u003e in transmitting fragmentation shocks to sovereign bond markets remains underexplored, especially in a comparative context between advanced economies such as the United States and the Euro Area.\u003c/p\u003e \u003cp\u003eThis paper addresses this gap by developing an integrated empirical framework linking \u003cb\u003egeoeconomic fragmentation \u0026rarr; trade reconfiguration \u0026rarr; sovereign risk\u003c/b\u003e. Using a combination of Structural Vector Autoregression (SVAR), Panel VAR, and local projection methods over the period 2005\u0026ndash;2025, the analysis focuses on the economic relationship between the United States and Europe\u0026mdash;one of the most systemically important corridors in the global economy. A novel \u003cb\u003egeoeconomic fragmentation index\u003c/b\u003e is constructed by combining geopolitical risk, trade policy uncertainty, and trade structure indicators, allowing for a comprehensive measurement of fragmentation dynamics.\u003c/p\u003e \u003cp\u003eThe study is guided by the following research objectives. First, it aims to quantify the impact of fragmentation shocks on sovereign bond yields and CDS spreads. Second, it investigates the role of \u003cb\u003etrade reconfiguration mechanisms\u003c/b\u003e, including reshoring and supply chain concentration, in amplifying or mitigating these effects. Third, it disentangles the relative importance of \u003cb\u003etrade, financial, and expectations channels\u003c/b\u003e in the transmission process. Based on these objectives, the main hypotheses are formulated as follows:\u003c/p\u003e \u003cp\u003e(H1) Geoeconomic fragmentation increases sovereign risk premia;\u003c/p\u003e \u003cp\u003e(H2) Trade reconfiguration acts as a key transmission channel linking fragmentation to sovereign risk;\u003c/p\u003e \u003cp\u003e(H3) The magnitude of these effects is heterogeneous across countries, depending on trade exposure and fiscal fundamentals;\u003c/p\u003e \u003cp\u003e(H4) Financial channels dominate in the United States, while trade channels are more pronounced in Europe.\u003c/p\u003e \u003cp\u003eThis paper makes several key contributions to the literature. First, it provides one of the first \u003cb\u003eintegrated empirical analyses\u003c/b\u003e connecting geoeconomic fragmentation to sovereign risk through trade structure adjustments. Second, it introduces a \u003cb\u003ecomposite fragmentation index\u003c/b\u003e, improving upon existing single-dimension measures such as geopolitical risk indices. Third, it offers a \u003cb\u003ecomparative perspective between Europe and the United States\u003c/b\u003e, highlighting structural asymmetries in shock transmission. Finally, it contributes to policy debates by quantifying the \u003cb\u003emacroeconomic and financial costs of fragmentation\u003c/b\u003e, thereby informing discussions on strategic autonomy, fiscal sustainability, and global economic governance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Conceptual Framework of Geoeconomic Fragmentation and Sovereign Risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e illustrates a multi-layered transmission mechanism through which \u003cb\u003egeoeconomic fragmentation\u003c/b\u003e\u0026mdash;captured by rising trade barriers, sanctions, and strategic decoupling\u0026mdash;propagates into \u003cb\u003esovereign risk\u003c/b\u003e via three interconnected channels. First, the \u003cb\u003etrade channel\u003c/b\u003e reflects how fragmentation disrupts bilateral trade intensity, induces supply chain reconfiguration (reshoring, diversification), and reduces external demand, thereby weakening growth and fiscal revenues. Second, the \u003cb\u003efinancial channel\u003c/b\u003e captures the reaction of global investors to heightened uncertainty, leading to capital reallocation, increased risk premia, and higher sovereign bond yields and CDS spreads. Third, the \u003cb\u003eexpectations channel\u003c/b\u003e operates through policy uncertainty and credibility effects, influencing market perceptions of fiscal sustainability and amplifying volatility. These channels interact dynamically and are conditioned by country-specific characteristics such as trade openness, fiscal capacity, and financial market depth, resulting in heterogeneous sovereign risk responses across economies. Overall, the framework emphasizes that fragmentation is not only a trade phenomenon but a systemic shock that reshapes macro-financial stability through intertwined real and financial linkages.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geoeconomic Fragmentation and Global Trade\u003c/h2\u003e \u003cp\u003eThe recent resurgence of geoeconomic fragmentation marks a structural shift in the global trading system, characterized by increasing policy-driven distortions in cross-border economic relations. This phenomenon has been widely documented in the context of rising \u003cb\u003etrade decoupling\u003c/b\u003e, particularly between major economic blocs such as the United States and China, as well as the gradual reconfiguration of global value chains. Empirical evidence shows that trade restrictions, tariffs, and export controls have led to measurable declines in bilateral trade flows and efficiency losses in global production networks (Aiyar et al., 2023; Evenett \u0026amp; Fritz, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA growing body of literature highlights the emergence of \u003cb\u003ereshoring\u003c/b\u003e, \u003cb\u003enear-shoring\u003c/b\u003e, and \u003cb\u003efriend-shoring\u003c/b\u003e strategies, whereby firms relocate production to politically aligned or geographically proximate countries to mitigate geopolitical risks (Gopinath et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Baldwin, 2022). While such strategies may enhance supply chain resilience, they often come at the cost of reduced specialization and higher production costs, thereby lowering global welfare (Baqaee \u0026amp; Farhi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, fragmentation may lead to the formation of competing trade blocs, resulting in trade diversion and reduced multilateral cooperation. These dynamics underscore the transition from efficiency-driven globalization toward security-driven economic integration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Trade and Sovereign Risk\u003c/h2\u003e \u003cp\u003eThe relationship between international trade and sovereign risk has long been recognized in the literature, with trade openness generally associated with lower borrowing costs due to enhanced growth prospects and diversification benefits (Flandreau \u0026amp; Zumer, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, this relationship becomes more complex in the presence of external shocks and structural disruptions. Countries with high exposure to global trade are particularly vulnerable to adverse trade shocks, which can negatively affect fiscal revenues, balance of payments stability, and debt sustainability.\u003c/p\u003e \u003cp\u003eRecent studies emphasize that \u003cb\u003eexternal exposure and fiscal vulnerability\u003c/b\u003e are key determinants of sovereign risk in an interconnected global economy. Trade disruptions\u0026mdash;such as tariff increases or supply chain breakdowns\u0026mdash;can lead to declines in output and government revenues, thereby increasing sovereign spreads (Acemoglu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, trade reconfiguration processes, including reshoring and reduced export competitiveness, may weaken long-term growth potential and exacerbate fiscal imbalances.\u003c/p\u003e \u003cp\u003eEmpirical evidence also suggests that \u003cb\u003etrade shocks have direct effects on bond markets\u003c/b\u003e. For instance, tariff announcements and trade tensions have been shown to increase financial market volatility and raise government borrowing costs, particularly in emerging and highly open economies (Curcuru et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, the literature remains fragmented, often treating trade and sovereign risk in isolation rather than as part of an integrated transmission mechanism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Financial Market Reactions to Trade Tensions\u003c/h2\u003e \u003cp\u003eA complementary strand of literature focuses on the response of financial markets to geopolitical and trade-related uncertainty. A key insight is that \u003cb\u003erisk premia and uncertainty transmission\u003c/b\u003e play a central role in linking real economic shocks to financial outcomes. The geopolitical risk index developed by Caldara and Iacoviello (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrates that increases in geopolitical tensions are associated with higher sovereign bond yields and elevated risk premia, reflecting heightened investor risk aversion.\u003c/p\u003e \u003cp\u003eSimilarly, studies on financial contagion highlight the importance of market-based channels in amplifying shocks. For example, sovereign CDS markets have been shown to transmit risk across countries through investor sentiment and portfolio rebalancing (Augustin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the Eurozone context, the interaction between sovereign risk and banking sector fragility\u0026mdash;often referred to as the \u0026ldquo;sovereign-bank doom loop\u0026rdquo;\u0026mdash;further amplifies the effects of external shocks on financial stability (Fratzscher \u0026amp; Rieth, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolicy uncertainty also plays a critical role in shaping financial market reactions. Increases in trade policy uncertainty can lead to higher volatility, reduced investment, and rising sovereign spreads, as investors demand compensation for increased risk (Arteta et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that financial markets act as a key transmission channel through which geoeconomic fragmentation influences sovereign risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research Gap\u003c/h2\u003e \u003cp\u003eDespite the growing body of literature on geoeconomic fragmentation, trade, and financial markets, several important gaps remain. First, existing studies tend to focus on individual dimensions of fragmentation\u0026mdash;such as trade flows, geopolitical risk, or financial market responses\u0026mdash;without providing a unified framework that integrates these elements. Second, there is limited empirical evidence on how \u003cb\u003efragmentation-induced trade reconfiguration\u003c/b\u003e affects sovereign risk through both real and financial channels.\u003c/p\u003e \u003cp\u003eIn particular, the literature lacks an \u003cb\u003eintegrated analytical framework linking geoeconomic fragmentation \u0026rarr; trade structure \u0026rarr; sovereign risk\u003c/b\u003e, which captures the dynamic interactions between trade adjustments, investor behavior, and fiscal sustainability. Moreover, comparative analyses between major advanced economies, such as the United States and the Euro Area, remain scarce, despite their central role in the global financial system.\u003c/p\u003e \u003cp\u003eThis paper addresses these gaps by developing a comprehensive empirical model that jointly examines trade and financial transmission channels, incorporates a novel fragmentation index, and provides new evidence on the macro-financial consequences of geoeconomic fragmentation.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eLiterature on Fragmentation, Trade, and Sovereign Risk\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLiterature on Fragmentation, Trade, and Sovereign Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFragmentation\u003c/p\u003e \u003cp\u003eMechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003cp\u003eChannel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003cp\u003eChannel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKey Finding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSovereign\u003c/p\u003e \u003cp\u003eRisk Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOur\u003c/p\u003e \u003cp\u003eExtension\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAiyar et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGravity Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrade decoupling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5% GDP loss from fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSovereign spread channel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGopinath et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDSGE+Trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBloc formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u0026ndash;12% welfare loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBond yields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEU periphery heterogeneity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcemoglu et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUS-China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiff-in-Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTariff shocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFirm-level employment effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAggregate fiscal impact\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlandreau \u0026amp; Zumer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistorical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1880\u0026ndash;1913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrade openness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTrade reduces risk premia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYield spreads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModern fragmentation context\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugustin et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2004\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvent Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSovereign CDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eContagion via CDS market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDS spreads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTrade shock integration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFratzscher \u0026amp; Rieth (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurozone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSVAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFiscal-bank nexus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSovereign-bank doom loop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDS, yields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFragmentation trigger\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteta et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEM\u0026thinsp;+\u0026thinsp;AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanel VAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePolicy uncertainty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUncertainty raises risk premia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEM spreads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eG7 advanced economy focus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDell'Ariccia et al. (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurozone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFiscal consolidation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAusterity amplifies risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDS spreads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTrade diversion channel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaldara \u0026amp; Iacoviello (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1985\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGeoPol risk (GPR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPR raises bond spreads by 15bps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEU-US bilateral channel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurcuru et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvent Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTariff announcements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTariffs raise uncertainty premium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFutures, rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSovereign risk linkage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEU\u0026thinsp;+\u0026thinsp;US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2005\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSVAR+Panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull fragmentation index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFragm. raises EU yields +\u0026thinsp;45bps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDS+yields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIntegrated transmission model\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e synthesizes the existing literature by categorizing studies according to their methodological approaches, transmission channels, and key findings. It highlights that while recent contributions such as Aiyar et al. (2023) and Gopinath et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasize the macroeconomic costs of fragmentation through trade channels, other studies\u0026mdash;such as Caldara and Iacoviello (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Arteta et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026mdash;focus on financial channels, particularly the role of uncertainty and risk premia. Importantly, very few studies simultaneously incorporate both trade and financial mechanisms when analyzing sovereign risk. The present paper extends this literature by proposing an integrated framework that captures both channels and quantifies their relative contributions, thereby bridging the gap between trade economics and sovereign debt analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Variables","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample\u003c/h2\u003e \u003cp\u003eThe empirical analysis is conducted on a balanced panel dataset covering the \u003cb\u003eUnited States and Euro Area economies\u003c/b\u003e, with a distinction between \u003cb\u003ecore countries\u003c/b\u003e (e.g., Germany, France) and \u003cb\u003eperipheral countries\u003c/b\u003e (e.g., Italy, Spain, Greece, Portugal). This classification allows for the examination of heterogeneity in sovereign risk responses across different fiscal and external vulnerability profiles.\u003c/p\u003e \u003cp\u003eThe sample period spans from \u003cb\u003e2005 to 2025\u003c/b\u003e, capturing several major global shocks, including the Global Financial Crisis, the European sovereign debt crisis, the COVID-19 pandemic, and recent geopolitical tensions. The data are primarily collected at a \u003cb\u003equarterly frequency\u003c/b\u003e, although some variables originally available at monthly or annual frequency are converted to quarterly series through standard aggregation or interpolation techniques. This frequency ensures a balance between capturing high-frequency financial dynamics and maintaining consistency across macroeconomic indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Key Variables\u003c/h2\u003e \u003cp\u003eThe core dependent variables in this study are \u003cb\u003esovereign bond yields\u003c/b\u003e and \u003cb\u003esovereign credit default swap (CDS) spreads\u003c/b\u003e, which serve as complementary measures of sovereign risk. The \u003cb\u003e10-year government bond yield\u003c/b\u003e reflects the cost of long-term borrowing and incorporates both expected macroeconomic fundamentals and risk premia. In contrast, \u003cb\u003e5-year CDS spreads\u003c/b\u003e provide a market-based measure of default risk, largely independent of liquidity and monetary policy distortions, and are therefore considered a cleaner indicator of sovereign creditworthiness.\u003c/p\u003e \u003cp\u003eThe main explanatory variable is the \u003cb\u003egeoeconomic fragmentation index (FRAG_t)\u003c/b\u003e, constructed as a composite measure capturing multiple dimensions of fragmentation. Specifically, the index is derived using \u003cb\u003eprincipal component analysis (PCA)\u003c/b\u003e applied to a set of underlying indicators, including the geopolitical risk index (GPR), trade policy uncertainty (TPU), and selected trade-related variables. This approach allows for a parsimonious yet comprehensive representation of fragmentation dynamics, reducing dimensionality while preserving the common variation across indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Trade Reconfiguration Indicators\u003c/h2\u003e \u003cp\u003eTo capture the mechanisms through which fragmentation affects sovereign risk, the analysis incorporates several indicators of \u003cb\u003etrade reconfiguration\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFirst, \u003cb\u003etrade intensity (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{T}{\\varvec{I}}_{\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e measures the relative importance of bilateral trade between the European Union and the United States, expressed as the ratio of total exports and imports to GDP. A decline in this indicator reflects trade decoupling and reorientation toward alternative partners.\u003c/p\u003e \u003cp\u003eSecond, the \u003cb\u003esupply chain concentration index (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{S}\\varvec{C}{\\varvec{C}}_{\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e captures the degree of diversification in trade relationships, computed as a Herfindahl\u0026ndash;Hirschman index of trading partners using input-output data. Higher values indicate greater concentration and lower resilience to external shocks.\u003c/p\u003e \u003cp\u003eThird, a \u003cb\u003ereshoring indicator (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{R}\\varvec{E}\\varvec{S}{\\varvec{H}}_{\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e is constructed to proxy the extent of domestic production relocation. This measure combines information on import substitution patterns and inward foreign direct investment (FDI) in strategic sectors, reflecting the shift toward domestic or regional production networks. The expected effect of reshoring on sovereign risk is theoretically ambiguous: while it may enhance resilience and reduce external vulnerability, it may also increase production costs and reduce efficiency.\u003c/p\u003e \u003cp\u003eTogether, these variables provide a detailed characterization of how global trade structures evolve in response to fragmentation and how these changes propagate to the macro-financial environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Control Variables\u003c/h2\u003e \u003cp\u003eThe empirical model includes a comprehensive set of control variables to account for \u003cb\u003emacroeconomic fundamentals\u003c/b\u003e and \u003cb\u003eglobal financial conditions\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eMacroeconomic controls include \u003cb\u003eGDP growth (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\varDelta\\:}\\varvec{G}\\varvec{D}{\\varvec{P}}_{\\varvec{i}\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and \u003cb\u003einflation (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{I}\\varvec{N}{\\varvec{F}}_{\\varvec{i}\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, which capture domestic economic performance and monetary policy dynamics. Higher growth is expected to reduce sovereign risk by improving fiscal capacity, while higher inflation may increase risk premia through tighter monetary policy and uncertainty effects.\u003c/p\u003e \u003cp\u003eGlobal financial conditions are proxied by the \u003cb\u003eVIX index (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{V}\\varvec{I}{\\varvec{X}}_{\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, which measures market volatility and global risk aversion. An increase in the VIX is typically associated with higher sovereign spreads due to flight-to-safety dynamics. The \u003cb\u003eEUR/USD exchange rate (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{F}{\\varvec{X}}_{\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e is also included to capture external competitiveness and currency risk, particularly relevant for open economies.\u003c/p\u003e \u003cp\u003eFinally, fiscal fundamentals are controlled for using \u003cb\u003efiscal balance (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{F}{\\varvec{B}}_{\\varvec{i}\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and \u003cb\u003egovernment debt (\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{D}\\varvec{E}\\varvec{B}{\\varvec{T}}_{\\varvec{i}\\varvec{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e as a share of GDP. These variables are key determinants of sovereign solvency and are expected to significantly influence bond yields and CDS spreads.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cb\u003eVariable Definitions and Data Sources\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable Definitions and Data Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConstruction Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExpected Sign\u003c/p\u003e \u003cp\u003e(\u0026rarr; Sov. Risk)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStationarity\u003c/p\u003e \u003cp\u003e(ADF p-val)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10Y Sovereign Yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% p.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eECB/Fed/Bloomberg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMarket close, end-of-quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBenchmark sovereign risk price\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS Spread (5Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CD{S}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBloomberg / Markit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMid-spread, USD-denominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePurer market signal than yields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragmentation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FRA{G}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1 scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAuthors (GPR\u0026thinsp;+\u0026thinsp;TPU+trade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePC1 of GPR, TPU, trade policy vars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNovel composite index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeoPol Risk Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:GP{R}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1985\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCaldara \u0026amp; Iacoviello\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eText-based newspaper index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExogenous identification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade Policy Uncertainty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:TP{U}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1985\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBaker et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNewspaper-based uncertainty index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePolicy uncertainty channel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU\u0026ndash;US Trade Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T{I}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOECD / Eurostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Exp\u0026thinsp;+\u0026thinsp;Imp)/GDP bilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTrade reconfiguration proxy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Chain Conc. Idx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SC{C}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorld Bank / OECD TiVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHerfindahl index of trade partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResilience indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReshoring Indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RES{H}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAuthors (FDI+import sub.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImport substitution\u0026thinsp;+\u0026thinsp;onshoring FDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAmbiguous: cost vs resilience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP Growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:GD{P}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEurostat / BEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQoQ real GDP, seasonally adj.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStandard macro control\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IN{F}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% YoY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEurostat / BLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarmonized CPI, all items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMonetary policy channel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VI{X}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCBOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage quarterly VIX level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGlobal risk appetite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEUR/USD Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F{X}_{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eECB / Bloomberg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEnd-of-quarter mid-rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExternal competitiveness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiscal Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F{B}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIMF WEO / Eurostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterpolated annual to quarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFiscal fundamentals control\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment Debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:DEB{T}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u0026rarr;Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIMF WEO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGross debt, interpolated to Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSolvency indicator\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a comprehensive overview of the variables used in the analysis, including their definitions, data sources, and expected effects on sovereign risk. The results of Augmented Dickey\u0026ndash;Fuller (ADF) tests indicate that most variables are stationary at conventional significance levels, ensuring the validity of the econometric specifications. The expected signs are broadly consistent with theoretical predictions: fragmentation, geopolitical risk, and uncertainty indicators are positively associated with sovereign risk, while trade intensity and economic growth are expected to reduce it.\u003c/p\u003e \u003cp\u003eImportantly, the table highlights the construction of the \u003cb\u003efragmentation index\u003c/b\u003e as a novel contribution, combining multiple dimensions of geoeconomic risk into a single metric. It also emphasizes the role of trade reconfiguration variables\u0026mdash;such as supply chain concentration and reshoring\u0026mdash;in capturing structural changes in the global economy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2. Evolution of Geoeconomic Fragmentation and Sovereign Risk (2005\u0026ndash;2025)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 2 illustrates the co-movement between the geoeconomic fragmentation index and sovereign risk indicators over time. Periods of heightened fragmentation\u0026mdash;such as the Global Financial Crisis, the Eurozone debt crisis, the COVID-19 shock, and recent geopolitical conflicts\u0026mdash;are associated with pronounced increases in sovereign bond yields and CDS spreads. This pattern suggests a strong positive relationship between fragmentation and sovereign risk, supporting the hypothesis that geopolitical and trade disruptions translate into higher borrowing costs for governments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3. Trade Reconfiguration Trends (EU\u0026ndash;US vs Global Trade Shares)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 3 depicts the evolution of trade patterns, highlighting a gradual decline in \u003cb\u003eEU\u0026ndash;US trade intensity\u003c/b\u003e alongside an increase in trade diversification toward other regions. This trend reflects the ongoing reconfiguration of global trade networks in response to fragmentation pressures. The figure also suggests that periods of declining bilateral trade are associated with increased supply chain concentration and shifts toward reshoring strategies, reinforcing the role of trade structure as a key transmission channel linking fragmentation to sovereign risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Empirical Strategy\u003c/h2\u003e \u003cp\u003eTo investigate the dynamic relationship between geoeconomic fragmentation, trade reconfiguration, and sovereign risk, this study employs a multi-method empirical strategy combining \u003cb\u003eStructural Vector Autoregression (SVAR)\u003c/b\u003e, \u003cb\u003ePanel Vector Autoregression (PVAR)\u003c/b\u003e, and \u003cb\u003elocal projection (LP) methods\u003c/b\u003e. This approach allows for a comprehensive analysis of both time-series dynamics and cross-country heterogeneity, while ensuring robustness across alternative identification strategies.\u003c/p\u003e \u003cp\u003eThe SVAR framework is used to capture the \u003cb\u003edynamic interactions and causal transmission mechanisms\u003c/b\u003e among fragmentation, trade variables, and sovereign risk indicators. By imposing identification restrictions, the model isolates \u003cb\u003eexogenous fragmentation shocks\u003c/b\u003e and traces their effects on sovereign bond yields and CDS spreads through impulse response functions (IRFs) and variance decompositions.\u003c/p\u003e \u003cp\u003eComplementing the SVAR analysis, the panel VAR approach exploits the \u003cb\u003ecross-sectional dimension\u003c/b\u003e of the dataset, allowing for country-specific heterogeneity and controlling for unobserved fixed effects. The use of \u003cb\u003eGeneralized Method of Moments (GMM)\u003c/b\u003e estimators (Arellano\u0026ndash;Bond) addresses potential endogeneity issues arising from reverse causality and omitted variables.\u003c/p\u003e \u003cp\u003eFinally, \u003cb\u003elocal projections (Jord\u0026agrave;, 2005)\u003c/b\u003e are employed as a robustness check, providing flexible, horizon-specific estimates of the response of sovereign risk to fragmentation shocks without imposing strong parametric assumptions on the data-generating process. In this context, the geopolitical risk index (GPR) is used as an instrumental variable to identify exogenous variation in fragmentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Model Specification\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBaseline SVAR Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe baseline empirical model is specified as a reduced-form VAR:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{X}_{t}=\\:A\\left(L\\right){X}_{t-1}+\\:{\\epsilon\\:}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ is a vector of endogenous variables defined as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{X}_{t}=\\:{\\left[FRA{G}_{t},\\:T{I}_{t},\\:CD{S}_{t},\\:{y}_{t}\\right]}^{{\\prime\\:}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FRA{G}_{t}\\)\u003c/span\u003e\u003c/span\u003e denotes the geoeconomic fragmentation index, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T{I}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ represents trade intensity, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CD{S}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ captures sovereign credit risk, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{t}\\)\u003c/span\u003e\u003c/span\u003e denotes the 10-year sovereign bond yield. The lag polynomial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:A\\left(L\\right)\\)\u003c/span\u003e\u003c/span\u003e captures the dynamic structure of the system, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ represents reduced-form innovations.\u003c/p\u003e \u003cp\u003eTo identify structural shocks, the model is transformed into its structural form:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:B\\:{X}_{t}=\\:C\\left(L\\right){X}_{t-1}+\\:{u}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ are structural shocks and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:B\\)\u003c/span\u003e\u003c/span\u003e encodes contemporaneous relationships. Identification is achieved primarily through a \u003cb\u003eCholesky decomposition\u003c/b\u003e, ordering fragmentation first, thereby assuming that fragmentation shocks are contemporaneously exogenous to other variables within the same period.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePanel VAR Specification\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo account for cross-country heterogeneity, the following panel VAR model is estimated:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{X}_{i,t}=\\:A\\left(L\\right){X}_{i,t-1}+\\:{\\mu\\:}_{i}+\\:{\\lambda\\:}_{t}+\\:{\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e indexes countries, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ captures country-specific fixed effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003ecaptures time effects. Estimation is conducted using \u003cb\u003eGMM techniques\u003c/b\u003e, with lagged values of fragmentation serving as instruments to mitigate endogeneity concerns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLocal Projections\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe local projection specification is given by:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{i,t+h}=\\:{\\alpha\\:}_{h}+\\:{\\beta\\:}_{h}FRA{G}_{t}+\\:{\\gamma\\:}_{h}{X}_{i,t}+\\:{ϵ}_{i,t+h}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor horizons \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h=1,\\dots\\:,8\\)\u003c/span\u003e\u003c/span\u003e. This framework allows for direct estimation of impulse responses over different time horizons. The use of \u003cb\u003einstrumental variables (LP-IV)\u003c/b\u003e ensures consistent estimation of the causal impact of fragmentation shocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Transmission Channels\u003c/h2\u003e \u003cp\u003eThe empirical framework explicitly incorporates three key transmission channels through which geoeconomic fragmentation affects sovereign risk:\u003c/p\u003e \u003cp\u003e \u003cb\u003e(i) Trade Channel\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe trade channel captures the impact of fragmentation on \u003cb\u003eexport and import dynamics\u003c/b\u003e, including reduced bilateral trade intensity, trade diversion, and supply chain disruptions. A decline in trade openness reduces economic growth and fiscal revenues, thereby increasing sovereign risk. This channel is primarily captured through the inclusion of trade intensity and supply chain indicators in the VAR system.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(ii) Financial Channel\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe financial channel reflects the response of global investors to increased geopolitical and policy uncertainty. Fragmentation shocks lead to \u003cb\u003ehigher risk premia\u003c/b\u003e, capital outflows, and increased volatility in sovereign bond markets. This channel is reflected in the direct response of CDS spreads and bond yields to fragmentation shocks, as well as through global risk indicators such as the VIX.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(iii) Expectations Channel\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe expectations channel operates through \u003cb\u003epolicy uncertainty and credibility effects\u003c/b\u003e, influencing investor perceptions of fiscal sustainability and macroeconomic stability. Increased uncertainty regarding trade policies and geopolitical developments can amplify sovereign risk by raising doubts about future economic performance and government solvency.\u003c/p\u003e \u003cp\u003eThese channels are not mutually exclusive and may interact dynamically, reinforcing or offsetting each other depending on country-specific characteristics such as fiscal capacity, trade openness, and financial market development.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4. Transmission Mechanism: Fragmentation \u0026rarr; Trade \u0026rarr; Sovereign Risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 4 illustrates the integrated transmission mechanism linking geoeconomic fragmentation to sovereign risk. A fragmentation shock\u0026mdash;originating from geopolitical tensions, trade restrictions, or policy uncertainty\u0026mdash;first affects the \u003cb\u003etrade structure\u003c/b\u003e by reducing bilateral trade intensity and increasing supply chain reconfiguration. These changes then propagate through the \u003cb\u003ereal economy\u003c/b\u003e, affecting growth and fiscal balances, and through \u003cb\u003efinancial markets\u003c/b\u003e, influencing investor sentiment and risk pricing. The combined effect of these channels leads to an increase in sovereign bond yields and CDS spreads, with feedback loops reinforcing the initial shock.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eModel Specification and Identification Strategy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Specification and Identification Strategy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndogenous\u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExogenous\u003c/p\u003e \u003cp\u003e/ Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIdentification\u003c/p\u003e \u003cp\u003eScheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKey\u003c/p\u003e \u003cp\u003eRestriction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLog-Lik.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline SVAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesky ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRAG, TI, CDS,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIX, GDP, INF, FB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCholesky ordering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFRAG ordered first (exog.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2005Q1\u0026ndash;2025Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1,842.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,722.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,810.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel VAR (FE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRAG, TI, CDS,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry FE, Year FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGMM (Arellano-Bond)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInstrument: lagged FRAG(-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2005Q1\u0026ndash;2025Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1,798.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,639.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,724.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel VAR (LSDV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeast-sq. dummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRAG, TI, CDS, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOLS\u0026thinsp;+\u0026thinsp;bootstrapped SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCountry-specific intercepts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2005Q1\u0026ndash;2025Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1,809.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,658.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,741.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVAR (Sign Restr.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSign restrictions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRAG, TI, CDS, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIX, GDP, INF, FB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSign restriction (Uhlig)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFRAG\u0026uarr;\u0026rarr;TI\u0026darr;, FRAG\u0026uarr;\u0026rarr;CDS\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2005Q1\u0026ndash;2025Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1,851.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,741.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,829.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Projections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJord\u0026agrave; (2005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it},\\:CD{S}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFRAG, controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026ndash;8 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLP-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIV: GPR as instrument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2005Q1\u0026ndash;2025Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVAR (Sub-sample)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesky ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRAG, TI, CDS, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIX, GDP, INF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCholesky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePre-COVID only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2005Q1\u0026ndash;2019Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1,298.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2,632.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,705.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVAR (Post-GFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesky ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRAG, TI, CDS, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIX, GDP, INF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCholesky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePost-GFC only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2010Q1\u0026ndash;2025Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;1,512.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,062.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,133.5\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the different econometric specifications employed in the analysis and their respective identification strategies. The baseline SVAR model, based on Cholesky decomposition, provides a benchmark for identifying fragmentation shocks, while alternative specifications\u0026mdash;such as sign-restricted SVAR and local projections\u0026mdash;offer robustness to different identifying assumptions. Panel VAR models account for cross-country heterogeneity and address endogeneity through GMM estimation. Sub-sample analyses (pre-COVID and post-GFC) further ensure the stability of results across different economic regimes. Overall, the consistency of findings across models strengthens the credibility of the empirical results and confirms the robustness of the estimated relationship between fragmentation, trade reconfiguration, and sovereign risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Baseline Effects\u003c/h2\u003e \u003cp\u003eThe baseline results provide strong evidence that \u003cb\u003egeoeconomic fragmentation shocks significantly increase sovereign risk\u003c/b\u003e across both the Euro Area and the United States. Following a positive fragmentation shock, both \u003cb\u003esovereign bond yields\u003c/b\u003e and \u003cb\u003eCDS spreads\u003c/b\u003e exhibit a statistically significant and persistent increase, confirming Hypothesis (H1).\u003c/p\u003e \u003cp\u003eThe estimated impulse response functions (IRFs) indicate that the effect materializes rapidly\u0026mdash;within the first quarter\u0026mdash;and peaks between the \u003cb\u003e4th and 8th quarters\u003c/b\u003e, suggesting medium-term persistence. For the Euro Area, the magnitude of the response is particularly pronounced, with CDS spreads increasing more strongly than bond yields, reflecting heightened default risk perceptions. In contrast, U.S. responses are more moderate but remain statistically significant.\u003c/p\u003e \u003cp\u003eThese findings highlight that fragmentation operates as a \u003cb\u003esystemic risk factor\u003c/b\u003e, affecting both real and financial dimensions of sovereign risk. The stronger response in CDS spreads relative to yields suggests that markets primarily interpret fragmentation as an increase in \u003cb\u003ecredit risk\u003c/b\u003e, rather than solely through macroeconomic fundamentals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5. Impulse Response Functions (IRFs): Fragmentation Shock\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 5 shows that a one-standard-deviation shock to the fragmentation index leads to a \u003cb\u003epositive and persistent response\u003c/b\u003e in sovereign yields and CDS spreads. The response is hump-shaped, with peak effects occurring after several quarters, indicating delayed transmission through trade and financial channels. Confidence intervals confirm statistical significance across most horizons, particularly for Euro Area countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Role of Trade Reconfiguration\u003c/h2\u003e \u003cp\u003eTo better understand the mechanisms underlying these effects, the analysis decomposes the impact of fragmentation shocks into \u003cb\u003etrade and non-trade components\u003c/b\u003e. The results demonstrate that \u003cb\u003etrade reconfiguration plays a central role\u003c/b\u003e in amplifying sovereign risk.\u003c/p\u003e \u003cp\u003eSpecifically, declines in \u003cb\u003eEU\u0026ndash;US trade intensity\u003c/b\u003e and increases in \u003cb\u003esupply chain concentration\u003c/b\u003e significantly magnify the impact of fragmentation shocks. Countries experiencing stronger trade diversion or reshoring pressures exhibit larger increases in sovereign spreads, confirming Hypothesis (H2). The reshoring indicator shows a nuanced effect: while it may partially mitigate exposure to external shocks in the long run, it tends to \u003cb\u003eincrease short-term costs and fiscal pressures\u003c/b\u003e, thereby raising sovereign risk in the short to medium term.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 6. IRFs with Trade Channel Decomposition\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 6 illustrates that a substantial portion of the response of sovereign risk is transmitted through the \u003cb\u003etrade channel\u003c/b\u003e, particularly in the Euro Area. When trade variables are held constant, the response of CDS spreads and yields is significantly attenuated, confirming the importance of trade reconfiguration as a key transmission mechanism.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cb\u003eVariance Decomposition (Trade vs Financial Channels)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance Decomposition (Trade vs Financial Channels)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHorizon\u003c/p\u003e \u003cp\u003e(Quarters)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFragm.\u003c/p\u003e \u003cp\u003eShock (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003cp\u003eChannel (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003cp\u003eChannel (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpectations\u003c/p\u003e \u003cp\u003eChannel (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDomestic\u003c/p\u003e \u003cp\u003eFiscal (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGlobal Risk\u003c/p\u003e \u003cp\u003e(VIX) (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDom. shock\u003c/p\u003e \u003cp\u003e95% CI [lo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDom. shock\u003c/p\u003e \u003cp\u003e95% CI [hi]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTrade ch.\u003c/p\u003e \u003cp\u003e95% CI [lo]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuro Area (Agg.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuro Area (Agg.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuro Area (Agg.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e21,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e30,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e24,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e19,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e41,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e22,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e22,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8,5\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the variance decomposition results, quantifying the relative contribution of different channels to sovereign risk dynamics. Several important findings emerge.\u003c/p\u003e \u003cp\u003eFirst, \u003cb\u003efragmentation shocks account for an increasing share of sovereign risk variance over time\u003c/b\u003e, rising from approximately 12\u0026ndash;18% at short horizons to over 20\u0026ndash;30% at longer horizons in peripheral European countries. Second, the \u003cb\u003etrade channel becomes increasingly dominant over time\u003c/b\u003e, particularly in countries such as Italy, Spain, and Greece, where it explains up to \u003cb\u003e24\u0026ndash;28% of the variation\u003c/b\u003e in sovereign spreads at longer horizons.\u003c/p\u003e \u003cp\u003eThird, the \u003cb\u003efinancial channel remains consistently important\u003c/b\u003e, especially in core economies and the United States, where it accounts for a larger share of short-term fluctuations. The \u003cb\u003eexpectations channel\u003c/b\u003e, while smaller in magnitude, contributes non-negligibly by amplifying uncertainty effects.\u003c/p\u003e \u003cp\u003eImportantly, the decomposition reveals significant \u003cb\u003eheterogeneity across countries\u003c/b\u003e. Peripheral economies exhibit stronger sensitivity to trade shocks, reflecting higher external vulnerability and weaker fiscal positions. In contrast, core economies and the United States show relatively higher contributions from financial and global risk factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Europe vs United States Comparison\u003c/h2\u003e \u003cp\u003eA key contribution of this study is the comparative analysis between Europe and the United States. The results reveal \u003cb\u003estructural differences in the transmission of fragmentation shocks\u003c/b\u003e, supporting Hypothesis (H4).\u003c/p\u003e \u003cp\u003eIn the Euro Area, sovereign risk is primarily driven by the \u003cb\u003etrade channel\u003c/b\u003e, reflecting the region\u0026rsquo;s high degree of trade openness and reliance on global value chains. Fragmentation-induced trade disruptions translate directly into lower growth and fiscal stress, particularly in peripheral countries.\u003c/p\u003e \u003cp\u003eIn contrast, the United States exhibits a stronger \u003cb\u003efinancial channel\u003c/b\u003e, with sovereign risk responding more to changes in global risk sentiment and investor behavior than to trade reconfiguration. This reflects the unique role of U.S. financial markets as global safe assets and the relatively lower dependence of the U.S. economy on external trade.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7. Comparative IRFs: EU vs US\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 7 confirms that the response of sovereign risk to fragmentation shocks is \u003cb\u003elarger and more persistent in the Euro Area\u003c/b\u003e compared to the United States. While both regions experience increases in CDS spreads and yields, the magnitude and duration of the response are significantly higher in Europe, particularly in peripheral economies.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cb\u003eElasticities of Sovereign Risk to Fragmentation Shocks\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eElasticities of Sovereign Risk to Fragmentation Shocks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSVAR Elast.\u003c/p\u003e \u003cp\u003e(h\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSVAR Elast.\u003c/p\u003e \u003cp\u003e(h\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSVAR Elast.\u003c/p\u003e \u003cp\u003e(h\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLP Elast.\u003c/p\u003e \u003cp\u003e(h\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLP Elast.\u003c/p\u003e \u003cp\u003e(h\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003cp\u003e[lo, h\u0026thinsp;=\u0026thinsp;4]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003cp\u003e[hi, h\u0026thinsp;=\u0026thinsp;4]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003cp\u003e(h\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade vs\u003c/p\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDirection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuro Area (Agg.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuro Area (Agg.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10Y Yield (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10Y Yield (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10Y Yield (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10Y Yield (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDS Spread (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10Y Yield (bps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the estimated elasticities of sovereign risk measures with respect to fragmentation shocks. The results provide strong quantitative support for the main hypotheses.\u003c/p\u003e \u003cp\u003eFor the Euro Area, fragmentation shocks increase CDS spreads by approximately \u003cb\u003e8.5 basis points at a 4-quarter horizon\u003c/b\u003e, with even larger effects in peripheral countries such as Italy (18.5 bps) and Greece (32.5 bps). Sovereign bond yields exhibit similar patterns, though with slightly lower magnitudes.\u003c/p\u003e \u003cp\u003eIn contrast, the United States shows more moderate responses, with CDS spreads increasing by approximately \u003cb\u003e4.2 basis points\u003c/b\u003e and yields by \u003cb\u003e2.5 basis points\u003c/b\u003e at the same horizon. The statistical significance of these estimates, confirmed by narrow confidence intervals, underscores the robustness of the results.\u003c/p\u003e \u003cp\u003eThe comparison between SVAR and local projection estimates reveals \u003cb\u003econsistent findings across methodologies\u003c/b\u003e, reinforcing the credibility of the empirical strategy. Moreover, the classification of channels indicates that \u003cb\u003etrade effects dominate in Europe\u003c/b\u003e, while \u003cb\u003efinancial effects dominate in the United States\u003c/b\u003e, consistent with the variance decomposition results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Heterogeneity within Europe\u003c/h2\u003e \u003cp\u003eThe analysis further explores \u003cb\u003eheterogeneity within the Euro Area\u003c/b\u003e, distinguishing between core and peripheral economies. The results show that \u003cb\u003eperipheral countries are significantly more sensitive\u003c/b\u003e to fragmentation shocks, both in terms of magnitude and persistence.\u003c/p\u003e \u003cp\u003eThis heterogeneity can be explained by differences in \u003cb\u003efiscal capacity, debt levels, and trade exposure\u003c/b\u003e. Countries with higher public debt and weaker fiscal positions\u0026mdash;such as Italy, Greece, and Portugal\u0026mdash;experience larger increases in sovereign spreads following fragmentation shocks. Similarly, economies with greater dependence on external trade are more exposed to trade channel transmission.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 8. IRFs: Euro Area Core vs Periphery\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 8 illustrates that the response of sovereign risk is \u003cb\u003esubstantially stronger in peripheral countries\u003c/b\u003e, with larger and more persistent increases in CDS spreads and bond yields. Core countries, while still affected, exhibit more muted responses, reflecting stronger fiscal fundamentals and greater resilience to external shocks.\u003c/p\u003e \u003cp\u003eOverall, the empirical results provide robust evidence that \u003cb\u003egeoeconomic fragmentation significantly increases sovereign risk\u003c/b\u003e, with effects transmitted through both trade and financial channels. The findings highlight the importance of trade structure and fiscal resilience in shaping country-specific responses, offering important insights for policymakers navigating an increasingly fragmented global economy.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Robustness Checks","content":"\u003cp\u003eTo ensure the validity and reliability of the baseline findings, a comprehensive set of robustness checks is conducted along three main dimensions: (i) alternative measures of geoeconomic fragmentation, (ii) subperiod analyses capturing major global shocks, and (iii) alternative econometric specifications. These exercises aim to verify that the estimated relationship between fragmentation and sovereign risk is not driven by specific measurement choices, sample periods, or modeling assumptions.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Alternative Fragmentation Measures\u003c/h2\u003e \u003cp\u003eA first set of robustness tests evaluates the sensitivity of the results to different proxies for geoeconomic fragmentation. While the baseline specification relies on a composite index constructed using principal component analysis, alternative measures are considered to isolate specific dimensions of fragmentation.\u003c/p\u003e \u003cp\u003eUsing an alternative specification based solely on the \u003cb\u003egeopolitical risk (GPR) index\u003c/b\u003e, the estimated elasticity of CDS spreads remains close to the baseline (8.5 basis points at a 4-quarter horizon), confirming the robustness of the results. Similarly, a \u003cb\u003eWTO-based fragmentation proxy\u003c/b\u003e, capturing trade policy interventions and restrictions, yields slightly lower but statistically consistent estimates (7.8 bps), suggesting that the findings are not sensitive to the choice of fragmentation metric.\u003c/p\u003e \u003cp\u003eA more restrictive measure based on a \u003cb\u003esanctions-only index\u003c/b\u003e produces somewhat higher elasticities (9.2 bps), particularly for peripheral economies, indicating that sanctions may represent a more acute form of fragmentation with stronger financial implications. Overall, these results confirm that the positive relationship between fragmentation and sovereign risk is robust across different conceptualizations of fragmentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Subperiod Analysis\u003c/h2\u003e \u003cp\u003eTo assess the stability of the results over time, the sample is divided into several subperiods corresponding to major economic and geopolitical events.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003epre-Global Financial Crisis (2005\u0026ndash;2008)\u003c/b\u003e period exhibits significantly lower elasticities (4.2 bps), reflecting a relatively stable and integrated global economic environment. In contrast, during the \u003cb\u003eGlobal Financial Crisis (2008\u0026ndash;2012)\u003c/b\u003e, the estimated effects increase substantially (15.8 bps), highlighting the role of crises as amplifiers of fragmentation shocks.\u003c/p\u003e \u003cp\u003eIn the \u003cb\u003epost-Eurozone crisis period (2015\u0026ndash;2019)\u003c/b\u003e, the results stabilize at intermediate levels (6.5 bps), suggesting partial normalization of financial conditions. Excluding the \u003cb\u003eCOVID-19 shock (2020Q1\u0026ndash;Q2)\u003c/b\u003e or the \u003cb\u003eUkraine conflict period (2022Q1\u0026ndash;Q3)\u003c/b\u003e does not materially alter the results, with elasticities remaining close to baseline estimates. However, when these crisis periods are included, the magnitude of the effects increases (up to 11.2 bps), indicating that \u003cb\u003eextreme events amplify the transmission of fragmentation shocks\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThese findings confirm that while the strength of the relationship varies across periods, the overall positive effect of fragmentation on sovereign risk remains stable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Alternative Econometric Specifications\u003c/h2\u003e \u003cp\u003eA third set of robustness checks examines the sensitivity of the results to different econometric approaches and identification strategies.\u003c/p\u003e \u003cp\u003eFirst, a \u003cb\u003esign-restricted SVAR model\u003c/b\u003e is estimated, imposing theoretically consistent restrictions (e.g., fragmentation shocks reduce trade intensity and increase CDS spreads). The resulting elasticities (9.8 bps) are close to the baseline, albeit with wider confidence intervals, reflecting the less restrictive identification scheme.\u003c/p\u003e \u003cp\u003eSecond, \u003cb\u003elocal projection methods with instrumental variables (LP-IV)\u003c/b\u003e are used to provide a non-parametric alternative to VAR models. The estimates (9.1 bps) closely match the baseline results, confirming the robustness of the findings to different dynamic specifications.\u003c/p\u003e \u003cp\u003eThird, \u003cb\u003ebootstrapped standard errors (1,000 replications)\u003c/b\u003e are employed to account for potential small-sample biases. The results remain statistically significant, with only a slight widening of confidence intervals.\u003c/p\u003e \u003cp\u003eFinally, \u003cb\u003epanel VAR models estimated using GMM techniques\u003c/b\u003e produce consistent results (7.9 bps), confirming that the findings are robust to the inclusion of country fixed effects and the treatment of endogeneity.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. \u003cb\u003eRobustness Results Summary\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Results Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobustness Check\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFragm. Elast.\u003c/p\u003e \u003cp\u003e(CDS, h\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003cp\u003e[lo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003cp\u003e[hi]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSign.\u003c/p\u003e \u003cp\u003e(p-val)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStability vs\u003c/p\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCountries\u003c/p\u003e \u003cp\u003ePassing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKey\u003c/p\u003e \u003cp\u003eDeviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePass?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlt. GPR measure (Baseline)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR-Cholesky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWTO-based fragm. proxy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR-Cholesky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMinor γ diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanctions-only index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR-Cholesky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlightly higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePeriphery larger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-GFC (2005\u0026ndash;2008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR sub-sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower (expected)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSmaller magnitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFC period (2008\u0026ndash;2012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR sub-sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMuch higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCrisis amplifier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Euro crisis (2015\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR sub-sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSlightly lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 excl. (drop 2020Q1\u0026ndash;Q2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR excl.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVery stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNegligible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUkraine shock excl. (drop 2022Q1\u0026ndash;Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR excl.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModest reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull sample (COVID+Ukraine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher (expected)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCrisis episodes add\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSign-restriction SVAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSign restr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSimilar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWider CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Projections (LP-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLP-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsistent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGP robust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBootstrapped SE (1000 rep)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVAR+bootstrap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWider CI only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel VAR (GMM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanel GMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsistent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePanel controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e✓\u003c/b\u003e\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e synthesizes the results of all robustness checks, demonstrating a high degree of consistency across specifications. The estimated elasticity of CDS spreads with respect to fragmentation shocks remains within a relatively narrow range (approximately \u003cb\u003e7.5 to 11.2 basis points\u003c/b\u003e), and all estimates are statistically significant at conventional levels.\u003c/p\u003e \u003cp\u003eImportantly, the majority of countries (9\u0026ndash;10 out of 10) pass each robustness test, indicating that the results are not driven by specific country characteristics. Minor deviations are observed in certain cases\u0026mdash;such as slightly larger effects in peripheral economies under the sanctions-based index\u0026mdash;but these do not alter the overall conclusions.\u003c/p\u003e \u003cp\u003eThe stability of the results across alternative measures, subsamples, and econometric techniques provides strong support for the validity of the baseline findings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 9. Stability of Results Across Subsamples\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure 9 visually confirms the robustness of the results by illustrating the evolution of estimated elasticities across different subsamples. While some variation is observed\u0026mdash;particularly during crisis periods\u0026mdash;the overall pattern remains consistent, with fragmentation shocks exerting a positive and statistically significant effect on sovereign risk in all cases. The figure highlights the resilience of the main findings and underscores the importance of fragmentation as a persistent driver of sovereign risk dynamics.\u003c/p\u003e \u003cp\u003eOverall, the robustness analysis demonstrates that the empirical results are \u003cb\u003ehighly stable and not sensitive to alternative specifications\u003c/b\u003e, reinforcing the conclusion that geoeconomic fragmentation is a key determinant of sovereign risk in advanced economies.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eThe empirical findings of this study provide compelling evidence that \u003cb\u003egeoeconomic fragmentation constitutes a systemic source of sovereign risk\u003c/b\u003e, with significant implications for both macroeconomic stability and financial market dynamics. The results confirm that fragmentation shocks\u0026mdash;arising from trade tensions, geopolitical conflicts, and policy uncertainty\u0026mdash;translate into higher sovereign bond yields and CDS spreads through multiple, interrelated transmission channels.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Interpretation of Fragmentation Effects\u003c/h2\u003e \u003cp\u003eThe positive and persistent impact of fragmentation on sovereign risk can be interpreted as the outcome of \u003cb\u003esimultaneous real and financial disruptions\u003c/b\u003e. On the real side, fragmentation weakens economic performance by reducing trade efficiency, disrupting global value chains, and lowering productivity. These effects translate into \u003cb\u003ereduced fiscal revenues and deteriorating debt dynamics\u003c/b\u003e, thereby increasing perceived default risk.\u003c/p\u003e \u003cp\u003eOn the financial side, fragmentation amplifies \u003cb\u003euncertainty and risk aversion\u003c/b\u003e, leading to higher risk premia and tighter financial conditions. Investors respond to geopolitical instability by reallocating portfolios, often away from more vulnerable economies, which results in increased borrowing costs. The stronger response observed in CDS spreads relative to bond yields suggests that markets interpret fragmentation primarily as a \u003cb\u003ecredit risk shock\u003c/b\u003e, rather than a purely cyclical or monetary phenomenon.\u003c/p\u003e \u003cp\u003eImportantly, the persistence of the effects over medium-term horizons indicates that fragmentation is not a transitory disturbance but rather a \u003cb\u003estructural shift in the global economic environment\u003c/b\u003e, with lasting implications for sovereign risk pricing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Trade vs Financial Dominance in Transmission\u003c/h2\u003e \u003cp\u003eA key insight of the analysis is the differentiated role of \u003cb\u003etrade and financial channels\u003c/b\u003e in transmitting fragmentation shocks across regions.\u003c/p\u003e \u003cp\u003eIn the Euro Area, the results highlight the \u003cb\u003edominance of the trade channel\u003c/b\u003e, reflecting the region\u0026rsquo;s deep integration into global trade networks and its reliance on external demand. Fragmentation-induced trade disruptions directly affect economic growth and fiscal balances, particularly in peripheral economies with higher external exposure and limited fiscal space. This explains the stronger and more persistent responses observed in countries such as Italy, Spain, and Greece.\u003c/p\u003e \u003cp\u003eIn contrast, the United States exhibits a stronger \u003cb\u003efinancial channel\u003c/b\u003e, where sovereign risk is more sensitive to changes in global risk sentiment and investor behavior. This reflects the central role of U.S. financial markets in the global system and the status of U.S. Treasury securities as safe assets. As a result, fragmentation shocks are transmitted primarily through \u003cb\u003erisk premia and capital market dynamics\u003c/b\u003e, rather than through trade flows.\u003c/p\u003e \u003cp\u003eThese findings underscore that the \u003cb\u003etransmission of fragmentation is inherently asymmetric\u003c/b\u003e, depending on structural characteristics such as trade openness, financial market depth, and institutional credibility. The coexistence of trade-driven and finance-driven mechanisms suggests that fragmentation operates as a \u003cb\u003emulti-dimensional shock\u003c/b\u003e, requiring integrated analytical and policy approaches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Policy Implications\u003c/h2\u003e \u003cp\u003e \u003cb\u003eStrategic Autonomy vs Economic Costs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOne of the central policy debates highlighted by this study concerns the trade-off between \u003cb\u003estrategic autonomy and economic efficiency\u003c/b\u003e. While policies such as reshoring, diversification of supply chains, and trade decoupling may enhance resilience to geopolitical risks, the results indicate that they also entail \u003cb\u003esignificant economic and financial costs\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn particular, reduced trade integration and increased production costs can weaken growth prospects and fiscal capacity, thereby increasing sovereign risk. Policymakers therefore face a delicate balance: pursuing strategic autonomy without undermining the benefits of international economic integration. The findings suggest that \u003cb\u003eselective and targeted approaches\u003c/b\u003e, rather than broad-based decoupling, may help mitigate these trade-offs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDebt Sustainability and Risk Pricing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results also have important implications for \u003cb\u003edebt sustainability and sovereign risk pricing\u003c/b\u003e. As fragmentation increases borrowing costs, especially for fiscally vulnerable countries, it may exacerbate existing debt dynamics and raise concerns about long-term solvency. This is particularly relevant for Euro Area peripheral economies, where high debt levels and limited fiscal space amplify the impact of external shocks.\u003c/p\u003e \u003cp\u003eFrom a financial market perspective, the findings indicate that investors increasingly incorporate \u003cb\u003egeoeconomic risks into sovereign risk assessments\u003c/b\u003e, suggesting a shift in the determinants of risk premia. Traditional macroeconomic indicators\u0026mdash;such as growth and inflation\u0026mdash;are now complemented by measures of geopolitical and trade-related uncertainty.\u003c/p\u003e \u003cp\u003eThese dynamics call for enhanced \u003cb\u003epolicy coordination and risk management frameworks\u003c/b\u003e, including:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStrengthening fiscal buffers to absorb external shocks,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEnhancing transparency and credibility in economic policymaking,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromoting diversified and resilient trade structures,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeepening financial integration to reduce fragmentation-induced volatility.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn sum, the discussion highlights that geoeconomic fragmentation is not merely a trade phenomenon but a \u003cb\u003esystem-wide transformation with profound macro-financial consequences\u003c/b\u003e. Its impact on sovereign risk depends critically on the interplay between trade structures, financial markets, and policy responses, underscoring the need for coordinated strategies to navigate an increasingly fragmented global economy.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThis paper examines the macro-financial consequences of \u003cb\u003egeoeconomic fragmentation\u003c/b\u003e by developing an integrated empirical framework linking fragmentation dynamics to \u003cb\u003etrade reconfiguration\u003c/b\u003e and \u003cb\u003esovereign risk\u003c/b\u003e. Using a combination of SVAR, panel VAR, and local projection methods over the period 2005\u0026ndash;2025, the analysis provides robust evidence that fragmentation shocks significantly increase sovereign bond yields and CDS spreads across advanced economies.\u003c/p\u003e \u003cp\u003eThe findings highlight three key results. First, geoeconomic fragmentation acts as a \u003cb\u003esystemic risk factor\u003c/b\u003e, with persistent effects on sovereign risk that extend beyond short-term market fluctuations. Second, \u003cb\u003etrade reconfiguration mechanisms\u003c/b\u003e\u0026mdash;including declining trade intensity, supply chain concentration, and reshoring\u0026mdash;play a central role in transmitting fragmentation shocks to sovereign risk, particularly in highly open economies. Third, the transmission of these effects is \u003cb\u003eheterogeneous across regions\u003c/b\u003e, with the trade channel dominating in the Euro Area and the financial channel playing a more prominent role in the United States.\u003c/p\u003e \u003cp\u003eThe main contribution of this study lies in establishing a \u003cb\u003eunified analytical framework linking fragmentation \u0026rarr; trade structure \u0026rarr; sovereign risk\u003c/b\u003e, thereby bridging the gap between the trade and sovereign debt literatures. By incorporating both real and financial transmission channels, the paper provides a more comprehensive understanding of how geopolitical and economic disruptions propagate through the global system.\u003c/p\u003e \u003cp\u003eFrom a broader perspective, the results carry important implications for \u003cb\u003eglobal economic integration\u003c/b\u003e. The shift toward fragmentation and strategic autonomy may enhance resilience to geopolitical shocks, but it also entails significant costs in terms of reduced efficiency, lower growth, and higher borrowing costs. These findings suggest that the ongoing reconfiguration of global trade networks could have lasting consequences for fiscal sustainability and financial stability, particularly in economies with high external exposure and limited policy space.\u003c/p\u003e \u003cp\u003eSeveral avenues for future research emerge from this analysis. First, further work could explore the \u003cb\u003elong-term growth implications\u003c/b\u003e of fragmentation and their interaction with sovereign debt dynamics. Second, extending the analysis to \u003cb\u003eemerging and developing economies\u003c/b\u003e would provide valuable insights into the global distributional effects of fragmentation. Third, incorporating \u003cb\u003efirm-level and sectoral data\u003c/b\u003e could help uncover microeconomic mechanisms underlying trade reconfiguration. Finally, future research could investigate the role of \u003cb\u003epolicy coordination and international institutions\u003c/b\u003e in mitigating the adverse effects of fragmentation and preserving the benefits of global integration.\u003c/p\u003e \u003cp\u003eOverall, this study underscores that geoeconomic fragmentation is reshaping the foundations of the global economy, with profound implications for trade, finance, and sovereign risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A.Z. conceived the research idea, developed the theoretical framework, and designed the empirical methodology. S.A.Z. collected and processed the data, conducted the econometric analysis, and interpreted the results. S.A.Z. wrote the main manuscript text and prepared all figures and tables. S.A.Z. reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author acknowledges the use of ChatGPT (OpenAI) for language editing and proofreading assistance. All scientific content, analysis, and interpretations are solely the responsibility of the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Autor D, Dorn D, Hanson G, Price B (2020) Import competition and the great US employment sag. J Labor Econ. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/705716\u003c/span\u003e\u003cspan address=\"10.1086/705716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiyar S, Ilyina A (2023) \u0026amp; others \u003cem\u003eGeoeconomic Fragmentation and the Future of Multilateralism\u003c/em\u003e. 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Using a comprehensive dataset covering the United States and Euro Area countries over the period 2005\u0026ndash;2025, the analysis employs Structural Vector Autoregression (SVAR), panel VAR, and local projection methods to identify the causal impact of fragmentation shocks on sovereign bond yields and credit default swap (CDS) spreads. The results provide robust evidence that fragmentation significantly increases sovereign risk, with effects that are both statistically significant and persistent over medium-term horizons.\u003c/p\u003e \u003cp\u003eThe findings highlight the central role of \u003cb\u003etrade reconfiguration mechanisms\u003c/b\u003e, including declining bilateral trade intensity, supply chain concentration, and reshoring, in transmitting fragmentation shocks to financial markets. Variance decomposition and elasticity estimates reveal that the \u003cb\u003etrade channel dominates in the Euro Area\u003c/b\u003e, particularly in peripheral economies, while the \u003cb\u003efinancial channel is more pronounced in the United States\u003c/b\u003e, reflecting differences in economic structure and global financial integration.\u003c/p\u003e \u003cp\u003eBy establishing a unified framework linking \u003cb\u003efragmentation \u0026rarr; trade structure \u0026rarr; sovereign risk\u003c/b\u003e, this paper contributes to the literature on international trade, financial economics, and sovereign debt. The results carry important policy implications, suggesting that increasing fragmentation may raise borrowing costs and weaken fiscal sustainability, thereby posing challenges for global economic stability.\u003c/p\u003e","manuscriptTitle":"Geoeconomic Fragmentation, Trade Reconfiguration, and Sovereign Risk: Evidence from Europe–U.S. Economic Relations (2005–2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 14:57:09","doi":"10.21203/rs.3.rs-9393052/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":"b5e17e5c-af3f-49c8-aacd-3c0d1e01b3be","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-04-30T07:05:35+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T07:10:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 14:57:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9393052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9393052","identity":"rs-9393052","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}