Business, Economics and Management

Abstract: Between 28 February and 31 March 2026, Brent crude went from $72.48 to a peak of \(118.35 per barrel, the Dutch Title Transfer Facility (TTF) gas benchmark doubled, and the Asian Japan Korea Marker (JKM) spot price for liquefied natural gas (LNG) rose by more than 140%. The trigger was the war against Iran and the closure of the Strait of Hormuz, through which about one fifth of the world's oil and LNG used to pass. This article aims to explain a consequence of that shock which the energy literature and the public-debt literature have each studied on their own but almost never together: supply-driven inflation quietly erodes the real value of sovereign liabilities. Three days before the first airstrikes, the Institute of International Finance reported a record \)348 trillion in global debt. We trace the transmission channels of the 2026 shock (LNG supply, shipping and war-risk insurance, fertilizers and food), review the dilemma it created for the European Central Bank (ECB) and the Federal Reserve, and compute a simple liquidation arithmetic: each percentage point of unanticipated inflation transfers roughly $407 billion from bondholders to the United States Treasury alone. The article argues that the observed tolerance of 3-5% inflation in 2026 is best read as the initial phase of a financial-repression regime, and that this liquidation channel is a privilege of reserve-currency issuers, while energy-importing emerging economies suffer the opposite effect.

Abstract: This paper examines the dynamic effects of economic policy uncertaintyon key macroeconomic outcomes in Sweden—industrial production, inflation, and unemployment—over the period 2014M01–2025M04. Using the Swedish News-Based Economic Policy Uncertainty Index and applying the Local Projections framework of Jordà, we trace the impulse responses of each variable to a one-standard-deviation EPU shock across a 12-month horizon. Our findings reveal a distinct temporal transmission pattern: industrial production contracts immediately and significantly, with the largest decline occurring around the second month following the shock. In contrast, inflation responds with a notable delay, exhibiting insignificant short-run effects but turning significantly negative from the eighth month onward, consistent with demand-driven disinflation. Unemployment rises more gradually but persistently, becoming statistically significant from the second month and remaining elevated throughout the horizon. These results underscore the importance of a dynamic, horizon-specific approach to understanding uncertainty shocks in a small open economy. From a policy standpoint, the findings highlight the value of forward-looking communication, automatic stabilizers, and targeted labor-market interventions to mitigate short-run output losses and persistent employment deterioration.

Abstract: This study provides a comparative econometric analysis of the impact of artifi-cial intelligence (AI) and digitalization on economic growth in Saudi Arabia over the period 2000–2024. Due to the absence of direct AI investment data, proxy variables including internet usage, government expenditure, inflation, and patent applications are employed. The empirical framework integrates Ordinary Least Squares (OLS), ARIMAX, and ARDL/ECM models to distinguish between short-run dynamics and long-run relationships. The results indicate that short-run effects are generally weak, with only one variable showing marginal statistical significance and a transitional effect. Comparative analysis reveals that although ARIMAX demonstrates better in-sample fit, it suffers from diagnostic limitations and high forecasting errors. In contrast, the ARDL model emerges as the most appropriate framework for capturing long-run equilibrium relationships. The findings suggest that the economic impact of AI and digitalization is primarily long-term rather than immediate. This study contributes by providing a structured comparison between econo-metric approaches and offers policy-relevant insights aligned with Saudi Vision 2030.

Abstract: Logistics infrastructure plays a strategic role in supply chain integration, regional connectivity, and the territorial organization of freight flows in emerging economies. However, the socioeconomic performance of logistics complexes is not automatic or homogeneous, as it depends on institutional capacity, linkages with local business networks, public investment, logistics services, and the development conditions of each region. This study proposes, applies, and validates a transferable composite index to assess the territorial and socioeconomic performance of nine logistics complexes in Colombia. The methodology compares a baseline prior to the operation or consolidation of each complex with a five-year follow-up observation, integrating economic, business, fiscal, social, labor, and distributive variables. The variables were transformed to a base of 100, weighted using principal component analysis, and assessed through adequacy tests, sensitivity analysis, entropy weighting, Spearman rank correlation, and Winsorization. The results show relative improvements across all cases, although with substantial differences in magnitude and internal composition. The Caribbean Logistics Complex and the Bogotá Free Trade Zone obtain the highest index values, while the remaining complexes show positive but more moderate progress. The main contribution of this study is to provide a decision-support tool for logistics planning, benchmarking, infrastructure prioritization, and territorial performance assessment, particularly in emerging economies where logistics investment decisions require the integration of economic development, supply chain connectivity, public investment, employment, social investment, and inequality-reduction criteria.

Abstract: The relationship between public education spending and economic growth remains contested in developing economies, where quality, not just quantity, of investment may matter most. This study examines whether public education expenditure has driven economic growth in Egypt over 1980-2023, addressing the under-studied role of edu-cation quality and structural breaks linked to major reforms. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach with Zivot-Andrews structural break and CUSUM/CUSUMSQ stability tests, the study models short- and long-run effects of education expenditure on GDP growth, controlling for capital formation, labor par-ticipation, trade openness, and foreign investment, and incorporating quality proxies such as student-teacher ratios and completion rates. The bound F-statistic (6.23) con-firm long-run cointegration, and the error-correction coefficient (-0.835, p< 0.01) indi-cates rapid adjustment to equilibrium. Physical capital is the strongest driver of long-run growth, while education expenditure shows a weakly negative long-run as-sociation (significant at 10%), suggesting allocative inefficiency rather than absent re-turns; quality proxies show significant adjustment effects. Stability tests confirm the long-run relationship is structurally invariant once short-run dynamics are accounted for. Improving the efficiency and quality of education spending, rather than its volume, thus appears essential for Egypt to advance Vision 2030, SDG 4, and SDG 8.

Abstract: Electricity-generation mixes can converge in a final-year snapshot while following different transformation pathways, which matters for identifying energy-security exposures. This article analyzes EU-27 electricity-generation mixes for 2000–2024 (27 countries, 25 years, eight generation components) as compositional time trajectories. Shares were regularized by simple multiplicative replacement, transformed with the isometric log-ratio transformation, compared using multivariate dynamic time warping with a Sakoe–Chiba constraint, and clustered with partitioning around medoids (PAM; k-medoids). The selected four-cluster solution is represented by Spain, Bulgaria, Malta, and Latvia and describes broad renewable diversification, solid-fossil-fuel legacy transformation, high-concentration island transition, and renewable-dominant restructuring. The raw-share baseline and the compositional trajectory solution agree weakly (Adjusted Rand Index = 0.1891), while endpoint-only clustering for 2024 also diverges from full-trajectory clustering (Adjusted Rand Index = 0.1950; 10 countries change assignment after label alignment). Across 40 robustness specifications, 14 countries are core cases and 13 are border cases. This boundary sensitivity is a substantive diagnostic: stable interpretation of border countries requires information beyond generation shares. Final mix similarity is therefore not a substitute for pathway similarity, and structural energy-security exposure should be interpreted through diversification, concentration, and the sequence of fuel substitution.

Abstract: Italy's special legal status for "innovative" small and medium-sized enterprises (SMEs) grants fiscal, financial and administrative benefits intended to strengthen competitiveness, yet whether the status marks a distinctive profile of realised firm performance remains empirically underexplored. Using ten years of balance-sheet data assembled within the LUCE (LUtech Campus Ecosystem) research project on 4,043 firms (2,873 innovative and 1,170 ordinary), we compare the two populations across six performance dimensions—performance persistence, revenue growth, labour productivity, operating profitability, earnings volatility and financial stability. Because the populations differ systematically in size, sector and location, we use propensity-score matching (1,031 balanced pairs) and interpret the resulting differential as a conditional innovative-status premium rather than as a causal effect. Innovative SMEs display a large and robust revenue-growth premium—a median growth rate roughly three-and-a-half times that of matched ordinary peers (+17.3 percentage points per year; rank-biserial 0.53)—coexisting with a fragility penalty of higher earnings volatility and lower financial stability; operating profitability is higher but does not survive our robustness battery, and labour productivity is marginally lower. A within-firm event study around the registration date shows that the growth advantage largely predates registration, indicating that the status certifies and renders visible already-dynamic firms rather than causally upgrading them. The premium is strongly and significantly heterogeneous across space—broadest in the South, where local institutions are weakest—consistent with an institutional-substitution boundary condition that a formal region-by-status interaction confirms. The results are robust to nine alternative estimators, multiple-testing correction and hidden-bias diagnostics. We read the innovative-firm register as an informative screening and monitoring device rather than as a policy whose causal returns we measure.

Abstract: Accurate measurement and forecasting of inflation expectations are essential for sustaining financial stability and supporting effective economic decision-making. Conventional forecasting approaches rely predominantly on survey-based expectations and low-frequency macroeconomic indicators, which are subject to publication delays and have limited responsiveness to rapidly shifting economic conditions. To address this challenge, a novel inflation nowcasting framework is proposed that integrates expectations from a survey of market participants conducted by the Central Bank of the Republic of Türkiye with high-frequency search query data from Google Trends. By capturing real-time shifts in consumer attention and behavior, the proposed framework reduces the information gap that occurs prior to the publication of official statistics. The forecasting model is developed using AutoTS, an automated machine learning (AutoML) framework that remains relatively unexplored in the inflation forecasting literature. Empirical evaluations demonstrate that the presented methodology reduces forecast errors by 32.99% relative to survey-based expectations, achieving an out-of-sample symmetric mean absolute percentage error of 18.18%. A main advantage of the model is its adaptability to structural breaks and regime shifts in inflation patterns. For example, at a monthly inflation rate of 4.84% in January 2026, the absolute forecast error is limited to 0.22 percentage points, compared to 1.08 percentage points for expert forecasts. Similarly, for a disinflationary episode in November 2025, with monthly inflation declining to 0.87%, an estimate of 0.95% is generated, thereby mitigating the adjustment lag commonly associated with conventional expert-based expectations. These results indicate that search query data possess meaningful incremental predictive value that is not completely captured by traditional surveys. Consequently, integrating high-frequency behavioral signals with expert expectations through an AutoML framework improves the timeliness and accuracy of inflation nowcasts, particularly during periods of heightened volatility and rapid regime shifts.

Abstract: Return on Equity (ROE) is one of the most closely monitored financial ratios by shareholders and potential investors. A negative ROE can send an unfavourable message to investors. This study identifies the financial ratios that influence ROE and determines the best machine learning technique for predicting it. The imbalanced dataset, sourced from the Integrated Real-time Equity System (IRESS), consisted of all companies listed on the Johannesburg Stock Exchange in 2019. The dataset was balanced using original observations from previous years (dataset 2) and the SMOTE and ROSE oversampling methods. Additionally, we assessed the performance of three shrinkage methods— Lasso, Elastic Net, and Ridge Regression—specifically for feature selection. The model evaluation metrics used are sensitivity, specificity, precision, F1 score, and accuracy. The best predictors of ROE identified by the classifiers were net profit margin, interest cover, earnings per share, earnings yield, and price-to-earnings ratio. The Random Forest method emerged as the most effective feature selection technique. With the SMOTE and ROSE datasets, performance decreased after feature selection. After oversampling, it was not easy to choose between the SMOTE and ROSE datasets because RF performed very well on both, with key metrics above 99%. The study encourages investors to prioritize companies with low price-to-earnings ratios, high net profit margins, high interest cover, high earnings per share, and high earnings yield when making investment decisions, using the Random Forest method.

Abstract: This paper examines the determinants of industrial added value in ten major oil-importing economies over the period 1993–2024, with particular attention to the role of oil prices. Using a cross-sectionally augmented autoregressive distributed lag (CS-ARDL) mean group estimator, the study accounts for cross-sectional dependence, heterogeneous dynamics, non-stationarity, and long-run cointegration across countries. The empirical model includes gross fixed capital formation, foreign direct investment, labor force participation, trade openness, consumer price inflation, and the OPEC oil basket price as explanatory variables. The results show that domestic structural factors are the main drivers of industrial performance. In the short run, investment, labor force participation, and trade openness increase industrial value added, while inflation reduces it. In the long run, capital deepening, labor market participation and trade integration remain positively associated with industrial value added, whereas inflation has a negative effect. By contrast, oil prices do not exert a statistically significant direct effect in either the short or long run. These findings suggest that large oil-importing economies have partly adapted to oil-price risk through structural adjustment, greater openness, and macroeconomic stabilization. The evidence implies that industrial policy in such economies should prioritize investment, labor market participation, trade integration, and price stability, while treating oil-price volatility as an external risk rather than a dominant structural constraint.

Abstract: This study investigates the dynamic transmission of geopolitical risk across the Brent crude oil market, gold market, U.S. Dollar Index (DXY), and the Thai stock market using a Bayesian Time-Varying Coefficient Vector Autoregressive (Bayesian TVC-VAR) model. Monthly data covering the period from January 1990 to December 2025 are employed to capture the evolving effects of major geopolitical events, including the Gulf War, the Asian Financial Crisis, the September 11 terrorist attacks, the Global Financial Crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict. The analysis integrates Time-Varying Impulse Response Functions (TVIRFs), Generalized Forecast Error Variance Decomposition (GFEVD), the Total Connectedness Index (TCI), directional connectedness measures (TO, FROM, NET, and NPDC), and network analysis to examine both the magnitude and direction of shock transmission.The empirical findings indicate that geopolitical risk generates substantial time-varying spillover effects across commodity, foreign exchange, and equity markets. The intensity and direction of connectedness vary considerably across different geopolitical regimes, with oil and the U.S. dollar emerging as dominant transmitters of shocks during periods of heightened uncertainty, whereas gold primarily serves as a safe-haven asset that absorbs market disturbances. The Thai stock market exhibits greater vulnerability to external shocks during global crises, reflecting its high degree of integration with international financial markets. The network analysis further reveals that the topology of financial connectedness changes significantly during major geopolitical events, highlighting shifts in dominant transmission channels over time. This study contributes to the literature by providing a comprehensive Bayesian time-varying connectedness framework that simultaneously evaluates geopolitical risk, commodity markets, foreign exchange, and an emerging stock market. The findings offer valuable implications for investors, portfolio managers, central banks, and policymakers seeking to improve portfolio diversification, risk management, and financial stability under geopolitical uncertainty.

Abstract: This article examines how administrative business records can strengthen place-based support for micro, small and medium-sized enterprises (MSMEs) in peripheral entrepreneurial ecosystems. It develops a structured evidence map and an exploratory business-demography analysis of Chamber of Commerce records from Valledupar, Colombia. The evidence map organises MSME constraints into formalisation, finance, digitalisation, managerial capabilities, innovation and markets, sustainability and governance. The registry analysis describes active business structure, sectoral and territorial concentration, cancellations, early survival and exploratory cancellation hazards. The active classified base contained 25,444 units; 97.7% were microenterprises. Commerce and repair accounted for 44.8%, and the core municipality of Valledupar concentrated 70.9%. Cancellation records were dominated by administrative clean-up (67.1%), requiring careful separation of registry updating from economic closure. Nevertheless, cancelled firms showed early vulnerability: the median duration was 5.03 years and 48.9% cancelled before five years. The article contributes to small business research by linking evidence mapping, business demography and territorial policy sequencing. It argues that MSME support should move beyond registration counts towards early-warning systems, capability-building routes and differentiated sectoral interventions.

Abstract: Commercial format diversity serves as a key indicator reflecting the health and resilience of the commercial structure in rail transit station areas. This study focuses on the rail transit station areas in Shanghai, integrates multi-source geospatial data with Partial Least Squares Structural Equation Modeling (PLS-SEM), and systematically examines the differential influence paths of six categories of factors—urban form, location, facility configuration, passenger flow, land rent, and commercial spatial form—on commercial format diversity between urban core and suburban areas. Multi-group analysis (MGA) is further used to test whether path coefficients differ across concentric zones around stations. The findings are as follows: (1)Passenger flow characteristics are the primary determinant of commercial format diversity and serve as a key mediator through which most other factors (except location) exert their effects. Location characteristics, particularly the distance to the rail transit station, primarily play a moderating role. Urban form characteristics function as the “spatial framework” providing a fundamental regulatory influence, with significant interaction effects with station distance. (2)In urban core areas, the temporal rhythm of passenger flow dominates: weekday passenger flow positively influences commercial evenness, while weekend passenger flow exacerbates uneven distribution, forming a self-balancing regulatory network. In suburban areas, an initial increase followed by a decrease is observed, with an “optimal synergistic zone” existing approximately 600–1000 m from stations where rail transit and other public service facilities jointly promote commercial format diversity. (3)Land rent mainly affects the number of format categories rather than their evenness or dominance. These findings reveal systematic differences in the influence paths of commercial format diversity between urban core and suburban rail transit station areas, as well as the distance-dependent zonal variation patterns of these effects, providing empirical evidence for differentiated spatial design in the context of station-city integration.

Abstract: The abrupt removal of Nigeria’s long-standing fuel subsidy regime in May 2023 precipitated one of the most significant supply-side price shocks in the country’s post-independence economic history, triggering a cascade of inflationary pressures that challenged conventional monetary policy frameworks and exposed structural vulnerabilities in the economy’s price-setting architecture. This paper investigates the long-memory properties and volatility persistence of Nigerian inflation dynamics in the pre- and post-subsidy liberalization periods using Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) models. Employing monthly consumer price index data from January 2010 to December 2024, the study first estimates the fractional differencing parameter d using both the Geweke–Porter-Hudak (GPH) semiparametric estimator and the exact local Whittle approach, before fitting fully parametric ARFIMA(p,d,q) and ARFIMA-FIGARCH(p,d,q)-(P,δ,Q) specifications to characterize simultaneously the long-range dependence in the conditional mean and in the conditional variance of inflation. The empirical results reveal statistically significant long-memory in both the level and volatility of Nigerian inflation, with the fractional integration parameter increasing markedly from d≈0.61 in the pre-reform period to d≈0.87 in the post-reform period, approaching but not quite reaching the unit root boundary. The FIGARCH estimates confirm that volatility shocks to the inflation process are also highly persistent, with the fractional volatility integration parameter δ rising from approximately 0.44 to 0.69 following the subsidy removal. These findings imply that the energy price shock embedded in the subsidy liberalization has fundamentally altered the stochastic regime of Nigerian inflation, generating near-permanent inflationary inertia that standard short-memory models would dramatically underestimate. The paper discusses implications for monetary policy transmission, inflation targeting feasibility, and the design of compensatory fiscal mechanisms in the post-reform period.

Abstract: The growing misalignment between workforce competences and the skill requirements of digitally evolving occupations is a critical barrier to SME competitiveness in the Industry 4.0 and 5.0 transitions. This study develops and demonstrates, through a working prototype, an ESCO-aligned analytical framework for skill-gap detection and adaptive reskilling, produced within the LUCE (LUtech Campus Ecosystem) project. Its contribution is theoretical, methodological, and technological rather than purely applicative. Drawing on human capital theory, the knowledge-based view of the firm, and skill-biased technical change, it conceptualizes reskilling as the relaxation of a firm-level human-capital–technology complementarity constraint, using ESCO to render this constraint observable and commensurable across firms. Methodologically, it defines the skill gap as a standardized, ontology-grounded measure; technologically, it integrates functions usually kept separate — performance analytics, skill assessment, and learning provision — into a single pipeline, instantiated as a proof-of-concept Intelligent Learning Management System. Workforce competences are extracted from anonymized employee CVs via a deterministic, rule-based Natural Language Processing procedure, mapped to ESCO preferred labels, alternative labels, and concept URIs, and linked to occupational requirements through occupation–skill relations. A Skill Gap Indicator, defined as the complement of evidenced ESCO competence coverage, yields a mean of 0.956, interpreted as a conservative upper-bound estimate rather than a literal measure of absent competences. The prototype then translates detected gaps into targeted training recommendations. As a research prototype, the system is validated along its technical and analytical dimensions rather than for training effectiveness; the relationship between gap closure and firm performance is advanced as a proposition for future longitudinal evaluation.

Abstract: This study analyzes long memory, multifractality and forecasting performance in coffee, Brent oil and gold futures, three international commodity markets of strategic relevance for Colombia. Daily futures prices from 2016 to 2025 were examined using logarithmic returns, descriptive statistics, rolling volatility, Hurst R/S, detrended fluctuation analysis (DFA), multifractal detrended fluctuation analysis (MF-DFA), out-of-sample forecasting models, conditional volatility models and Monte Carlo simulation. The results show heterogeneous long-memory evidence across commodities and market regimes. R/S estimates suggested persistence in the three markets, while DFA moderated this conclusion. MF-DFA confirmed multifractal behavior in all series, with gold and Brent showing wider heterogeneity than coffee. Forecasting results showed that simple models, particularly random walk and drift specifications, were difficult to outperform in one-step-ahead predictions. Monte Carlo simulations generated probabilistic price scenarios for 30-, 60- and 90-trading-day horizons, and an ex-post validation with 2026 observed prices showed that six of nine realized prices fell within the simulated P5-P95 intervals. Overall, the findings suggest that commodity futures relevant to Colombia exhibit differentiated forms of temporal complexity, risk and scenario uncertainty that cannot be fully captured by linear models or average volatility measures.

Abstract: During financial crises, markets do not only fall or become more volatile. They may also become more dynamically coupled, with the behaviour of one market becoming more recoverable from another. This study applies Convergent Cross Mapping, a state-space reconstruction method, to examine whether crisis periods strengthen nonlinear coupling between major US and European equity indices. Daily log returns for the Dow Jones Industrial Average, S&P 500, FTSE 100 and DAX are analysed across pre-crisis, crisis and post-crisis windows for the COVID-19 market shock and the Global Financial Crisis. Pairwise bidirectional Convergent Cross Mapping is used to estimate cross-map skill, convergence and directional asymmetry, with a focused lagged analysis of key trans-atlantic pairs during COVID-19. Cross-map skill is interpreted as the strength of the recoverable dynamical footprint between markets. The results show that nonlinear coupling increases during crisis phases. During COVID-19, mean late-library cross-map skill rises from the pre-crisis to the crisis period, and all tested directional relationships satisfy the convergence criterion. The Global Financial Crisis also shows increased cri-sis-period coupling, with stronger persistence into the post-crisis phase. Lagged COVID-19 results suggest a short contemporaneous to three-trading-day coupling hori-zon. The findings position Convergent Cross Mapping as a complementary mathematical modelling framework for identifying recoverable dynamical information between markets during financial stress.

Abstract: Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing energy system resilience, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. An optimized REC configuration is developed to maximize shared photovoltaic generation and minimize external grid dependence. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations and demonstrate that RECs exhibit significantly lower price dependence than standalone consumers. To complement these findings, machine learning techniques and SHAP (SHapley Additive exPlanations) analysis are employed to capture non-linear dynamics and quantify the relative contribution of electricity prices to value formation. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the revised regulatory framework introduces a more stable and counter-cyclical compensation mechanism, strengthening the protective role of RECs. Overall, the study provides robust empirical evidence that renewable energy communities function not only as instruments for renewable deployment but also as effective mechanisms for economic stabilization under volatile market conditions.

Abstract: Expected Shortfall (ES) is a tail functional whose estimation precision is governed by the effective tail sample size nα rather than by the nominal calibration size n. The resulting (nα)−1/2 information limit is well established, yet no practical framework exists for deciding whether two ES forecasts can be meaningfully distinguished over a finite calibration window. This paper converts the asymptotic rate into four operational diagnostics: a plug-in precision benchmark, a sample-size rule, a precision-fragile pairwise comparison screen, and a VaR-first diagnostic linking excess ES dispersion to first-stage quantile miscalibration. An empirical application to global financial assets and heterogeneous forecasters under standard regulatory tail parameters shows that roughly one in five pairwise ES comparisons is precision-fragile, with excess dispersion concentrated in cells with poor VaR calibration. The results suggest that ES forecast rankings at typical tail levels can be constrained by effective tail information rather than by model sophistication.

Abstract: This study investigates the dynamics of white maize price transmission across six regional markets in Mozambique (Manica, Gorongosa, Mutarara, Montepuez, Ribáuè, and Lichinga) and examines their relationship with global energy prices (oil and gas) between 2003 and 2020. The analysis applies wavelet coherence techniques, the Augmented Dickey–Fuller (ADF) test, Johansen cointegration, Vector Error Correction Models (VECM), and Granger causality tests to evaluate both short- and long-run market integration. The results indicate that all series are integrated of order one (I(1)) and that significant long-run cointegration relationships exist among the variables, with three cointegration vectors identified according to the FPE and AIC criteria. Wavelet coherence analysis reveals strong long-run synchronization (1.2–8 years) among regional white maize markets, while short-run coherence (0.1–1.2 years) remains weak, suggesting limited short-term integration. The findings further show that global energy prices do not exert a persistent structural influence on maize price dynamics in Mozambique. The VECM results identify Gorongosa as the main transmission hub of short-run price shocks, exerting positive effects on the other regional markets, whereas oil prices negatively affect Gorongosa and Ribáuè. Granger causality analysis confirms that Gorongosa, Mutarara, and Ribáuè act as the primary price transmitters within the system, while oil prices are mainly driven by exogenous factors. Overall, the findings demonstrate that Mozambican white maize markets are more strongly integrated in the long run than in the short run, highlighting the strategic role of Gorongosa in regional price transmission and suggesting that energy shocks have limited long-term effects on the domestic maize market system.