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Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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17 pages, 463 KB

Open AccessEditor’s ChoiceArticle

Heterogeneous Regional Convergence in the European Union: Club Dynamics, Structural Breaks, and Spatial Spillovers

by Greta Mockevičienė and Mindaugas Butkus

Economies 2026, 14(6), 228; https://doi.org/10.3390/economies14060228 - 13 Jun 2026

Viewed by 944

Abstract

This study examines income convergence among EU NUTS-2 regions from 2000 to 2023 using a combination of Phillips-Sul (PS) club convergence methodology,

β

-convergence, and spatial econometric models. The results reveal that regional convergence in Europe is heterogeneous and nonlinear: four stable convergence [...] Read more.

This study examines income convergence among EU NUTS-2 regions from 2000 to 2023 using a combination of Phillips-Sul (PS) club convergence methodology,

β

-convergence, and spatial econometric models. The results reveal that regional convergence in Europe is heterogeneous and nonlinear: four stable convergence clubs emerge, while overall convergence is rejected. Convergence was faster before 2012 and weakened afterward. A single income threshold and two structural breaks (2005 and 2012) mark shifts in growth dynamics. Spatial models reveal that neighboring regions affect each other’s growth, indicating that regional development in Europe depends on both local conditions and interactions across regions. Full article

(This article belongs to the Section Economic Development)

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17 pages, 464 KB

Open AccessEditor’s ChoiceArticle

Geopolitical Shocks and Regime-Dependent Oil Price Volatility: Evidence from Middle East Escalations in 2025–2026

by Katarzyna Czech and Michał Wielechowski

Economies 2026, 14(5), 185; https://doi.org/10.3390/economies14050185 - 16 May 2026

Viewed by 4911

Abstract

Geopolitical tensions remain an important source of uncertainty for global oil markets. This study examines whether recent geopolitical shocks related to escalating tensions in the Middle East in 2025–2026 were associated with changes in oil price volatility regimes. The analysis is based on daily WTI crude oil prices covering the period from 1 January 2024 to 10 April 2026. A two-regime Markov-switching GARCH model is used to identify low- and high-volatility states. The regime classification is further supported by return-variance tests, episode-level descriptive statistics, and a sensitivity analysis of alternative probability thresholds. The results show that the oil market remained in a low-volatility regime for most of the sample, but three distinct high-volatility episodes were identified, i.e., in early April 2025, June 2025, and late February to April 2026. These episodes differed in duration, direction, and intensity. The 2026 episode was the longest and most persistent high-volatility period, with the highest conditional volatility, the highest average probability of the high-volatility regime, and the widest daily price ranges. The sensitivity analysis confirms that the identification of the three main episodes is robust to stricter probability thresholds. The findings suggest that recent geopolitical shocks coincided with distinct volatility regime episodes in the oil prices, with direct military escalation in the Middle East being associated with the strongest and most persistent market turbulence. Full article

(This article belongs to the Section Macroeconomics, Monetary Economics, and Financial Markets)

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18 pages, 2476 KB

Open AccessEditor’s ChoiceArticle

Structural Spillovers Among Bitcoin, Ethereum, Gold, and U.S. Equities: Evidence from the 2024 Spot ETF Institutionalization Regime

by Wisam Bukaita and Xinrui Li

Economies 2026, 14(4), 143; https://doi.org/10.3390/economies14040143 - 19 Apr 2026

Cited by 2 | Viewed by 2423

Abstract

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption. Full article

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73 pages, 2487 KB

Open AccessEditor’s ChoiceArticle

Beyond Shocks: How ESG Fundamentals Shape Geopolitical Risk Across Countries

by Fabio Anobile, Alberto Costantiello, Carlo Drago, Massimo Arnone and Angelo Leogrande

Economies 2026, 14(3), 96; https://doi.org/10.3390/economies14030096 - 17 Mar 2026

Cited by 2 | Viewed by 2393

Abstract

This paper examines the connection between Environmental, Social, and Governance (ESG) factors and the risk of geopolitics, as defined by the Geopolitical Risk (GPR) index. The concept of geopolitical risk is conventionally defined as the direct result of political incidents, war, and international tensions. The current study argues that the concept should be understood in a more structural and sustainable manner, relating to the underlying forces driving geopolitical risk. The main research question is whether and how the three pillars of ESG factors contribute to explaining and understanding cross-country and over-time variations in geopolitical risk. In an effort to avoid information loss associated with the ESG index’s aggregate nature, the three factors are considered separately and the three pillars are analyzed individually. The empirical context is a balanced cross-country panel dataset including 42 countries over the 2000–2023 time period. Data for the three factors are obtained from the World Bank dataset to standardize and compare data across countries and over time. The GPR index measures the level of geopolitical risk and is defined by Dario Caldara and Matteo Iacoviello. The GPR index captures the level of geopolitical tensions by analyzing media signals. The combination of the three sources enables direct connections and correlations among the three factors and the internationally recognized GPR index. The paper uses an integrated methodological approach that combines results from three distinct methods. The first method uses panel data analysis to estimate average marginal effects while controlling for unobserved heterogeneity. The second method uses clustering to identify structural patterns and divide countries into groups based on their unique characteristics and risk profiles. The third method uses machine learning regressions and nonparametric analysis to capture the complex relationships and interactions in the data. The three-step method is used for each pillar to ensure consistency and comparability. The results suggest that the three factors contribute to the GPR index in a unique manner. The environment and energy structure contribute to the GPR index as a risk multiplier; the social factor relates to exposure to instability; and the governance factor is a central stabilizing factor. The paper makes a unique contribution to the literature by defining the three factors and their relationship to the GPR index in a clear, sustainable manner. Full article

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15 pages, 309 KB

Open AccessEditor’s ChoiceArticle

Geopolitical Shocks and Crude Oil Market Tail Risk: Evidence from the Russia–Ukraine Conflict

by Charalampos Vasilios Basdekis, Apostolos G. Christopoulos, Konstantinos Gkillas and Ludovica Grifa

Economies 2026, 14(3), 92; https://doi.org/10.3390/economies14030092 - 12 Mar 2026

Cited by 1 | Viewed by 5417

Abstract

This study examines the impact of the Russia–Ukraine war on crude oil tail risk using the Conditional Autoregressive Value at Risk (CAViaR) framework. We analyzed 2364 daily observations of West Texas Intermediate (WTI) crude oil futures spanning 1 January 2015 to 11 December 2023, thereby capturing both the pre-war period and the conflict regime. To operationalize the geopolitical shock, we identify four theoretically grounded event dates (21 February, 24 February, 11 May, and 15 June 2022) associated with military escalation and energy-supply disruptions, and incorporate them as exogenous dummy variables. Methodologically, we implement a two-step approach. First, we estimate 1-day Value at Risk (VaR) at the 5% and 1% levels using four alternative CAViaR specifications (Adaptive, Symmetric, Asymmetric, and Indirect GARCH(1,1)) within a rolling-window framework to capture the dynamic evolution of tail risk. Second, we regress the resulting VaR series on geopolitical-event indicators to quantify the marginal effect of war-related developments on downside risk. The empirical results show tail risk increases in oil-market after the most important geopolitical events in all the model specifications across the market characteristics. The Indirect GARCH(1,1) CAViaR model exhibited the highest sensitivity, producing event coefficients of 0.795 (5% VaR) and 0.710 (1% VaR), both significant at the 1% level. Our adaptive specification has magnitudes that are even higher at the extreme tail (2.002 at 1% VaR), further supporting increased vulnerability during periods of escalation in conflict. Evidence from the asymmetric model would also indicate stronger market response to unfavorable news, in line with loss-sensitive investor behavior. In sum, the outcomes indicate that the Russia–Ukraine war considerably elevated the downside risk of crude oil markets and that geopolitical events have economically and statistically significant effects on the tail dynamics. Incorporating event-based geopolitical indicators in the framework of CAViaR, contributes to the literature in energy-market risk modeling and applies practical information to investors, risk managers, and policymakers operating under a dynamic environment characterized by geopolitical uncertainty. Full article

(This article belongs to the Special Issue Exchange Rate Dynamics and Fiscal Policy in an Era of Polycrisis: Inflation, Geopolitical Risks, and Global Uncertainty)

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