Search Results (144)

Using 2143 daily observations from January 2018 to March 2026, this study examines risk transmission among European Union Allowance (EUA) prices, green-bond indices, environmental, social, and governance (ESG) equity indices, conventional financial assets, and digital assets. The empirical design combines vector autoregression with an exogenous variable and generalised forecast-error variance decomposition (VAR-X/GFEVD) for the full-sample and rolling analyses with quantile vector autoregression with an exogenous variable and GFEVD (QVAR-X/GFEVD) for the quantile-specific analysis. The preferred eight-asset VAR-X specification produces a full-sample Total Connectedness Index (TCI) of 39.31%, while the corresponding 100-day rolling VAR-X analysis produces a mean TCI of 43.03%. The rolling TCI reaches a maximum of 54.17% on 20 July 2022 and a COVID-19-period maximum of 48.80% on 8 June 2020, indicating that system-wide connectedness increases during periods of market stress. The Cboe Volatility Index (VIX), S&P 500, and ESG equity index are consistently the dominant net transmitters, whereas EUAs, Bitcoin, crude oil, and green bonds are net receivers. The EUA net-receiver position becomes less negative after 2024, but this temporal association should not be interpreted as evidence that strengthened Phase IV provisions caused greater carbon-market stability. The quantile-specific results reveal non-monotonic state dependence, with the TCI ranging from 18.28% at τ = 0.6 to 23.52% at τ = 0.4 rather than increasing uniformly toward the lower tail. Full article

Forecasting USD/CNY exchange rate volatility is of substantial practical significance for the management of cross-border capital flows and the formulation of monetary policies. In recent years, multiple external shocks arising from China–US geopolitical tensions and sharp fluctuations in the international crude oil market have become increasingly intertwined. Traditional forecasting models are often unable to capture such sudden risk signals in a timely manner, while a single model is also insufficiently adaptable to multi-scale volatility structures. To address these challenges, this paper develops a deep-learning-based dynamic forecasting framework that integrates a China–US geopolitical risk feature system (CUGRI) with international oil price shock signals. Specifically, CUGRI is constructed from news texts published by 15 authoritative Chinese and US media outlets, combining a finance-specific language model with a geopolitical-domain sentiment lexicon to build a five-dimensional daily risk quantification feature system. At the modeling level, this paper proposes a GRU-BiLSTM-Transformer dynamic fusion model, which adaptively assigns fusion weights according to the recent forecasting performance of each sub-model. All empirical analyses are implemented under the Python programming environment with the PyTorch deep learning framework. Using nearly ten years of daily data, this paper conducts out-of-sample forecasting tests. The empirical results show that the proposed dynamic fusion model achieves an out-of-sample $R^2$ of 0.342, while reducing RMSE and MAE by 4.01% and 3.72%, respectively, relative to the best-performing baseline model. CUGRI exhibits significant incremental predictive value, with forecasting gains substantially higher during periods of elevated geopolitical risk than under normal conditions. Oil price shocks provide complementary predictive information, and the dynamic weighting mechanism further improves the accuracy of combined forecasts. In practice, the findings are relevant to cross-border firms and financial institutions when tracking changes in geopolitical and energy-market risks and adjusting foreign-exchange hedging strategies. For monetary and regulatory authorities, the model helps identify periods when external shocks may amplify USD/CNY volatility and supports exchange-rate risk surveillance and macro-financial stability analysis. Full article

The Caspian Sea’s unique environment and intense hydrocarbon extraction make it a high-risk, understudied region for oil spill modelling. This review assesses the physical, chemical, and biological processes governing oil spill transport and fate, and evaluates the principal numerical tools available for the Caspian Sea context. The weathering processes are reviewed from foundational formulations to operational implementations. Key research challenges identified include the absence of photo-oxidation from operational models, limited laboratory data for Caspian crude oil types, and simplified biodegradation parameterizations. Hydrodynamic forcing uncertainty, arising from the lack of a dedicated operational ocean model, remains the dominant source of trajectory forecast error. Seven operational oil spill modelling tools and the ROMS hydrodynamic platform are reviewed. Only OSCAR and MIKE 21 have documented applications to the Caspian Sea, representing a significant regional gap. ROMS is identified as the most suitable hydrodynamic platform for future operational forecasting. Finally, the integration of machine learning and deep learning methods, including neural network trajectory prediction and SAR detection, is discussed as a promising frontier for improving forecast accuracy in this data-sparse environment. Full article

This study develops a rigorous, leakage-free forecasting framework for monthly Robusta coffee prices using historical observations from January 1975 to December 2025. A comprehensive set of explanatory variables is constructed from lagged coffee prices, moving averages, logarithmic returns, rolling volatility, and exogenous variables such as the Oceanic Niño Index (ONI), the U.S. Dollar Index, and Brent crude oil prices. To ensure methodological fairness, all predictors are generated exclusively from information available at the forecast origin, and all competing models are evaluated under a unified expanding-window walk-forward validation framework. Seven forecasting models are compared: Naïve, Exponential Smoothing (ETS), ARIMA, ARIMAX, Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Forecasting performance is evaluated using R², RMSE, MAE, and MAPE, while Taylor diagrams and the Diebold–Mariano test are employed to assess model agreement and differences in predictive accuracy. The results show that XGBoost achieves the highest forecasting accuracy (R² = 0.956, RMSE = 0.264), followed closely by the Naïve (R² = 0.954, RMSE = 0.271) and ARIMA (R² = 0.954, RMSE = 0.270) benchmarks, whereas ARIMAX and ETS provide comparable performance and the deep learning models (LSTM and GRU) produce substantially larger prediction errors. Feature importance analysis further indicates that the first lag of coffee price is the dominant predictor, accounting for approximately 94% of the predictive gain in XGBoost. Overall, the findings demonstrate that rigorous leakage-free validation is essential for reliable forecasting research and that, for monthly Robusta coffee prices, increased model complexity does not necessarily yield superior predictive performance. Full article

This paper develops a hybrid fractional-order framework for medium-horizon forecasting of wheat, corn, and soybean prices, combining a Caputo fractional differential equation with exogenous macro-climatic drivers (weather, crude oil, exchange rate, inflation) and a machine learning residual-correction layer. Existence, uniqueness, and Ulam–Hyers stability are established for both Caputo and Atangana–Baleanu formulations via Banach fixed-point theory, with numerical illustration through a fractional Adams–Bashforth–Moulton predictor–corrector scheme. Formal unit-root tests (ADF, KPSS, Phillips–Perron) confirm $I\left(1\right)$ behaviour in log-price levels and stationarity in first differences. Residuals are corrected using XGBoost and two feedforward neural networks (MLP-A, MLP-B), producing a family of hybrid forecasting models. Using 25 years of monthly FRED data (January 2000–December 2024), multi-method long-memory diagnostics (Hurst exponent, Lo’s modified $R/S$ test, DFA, local Whittle estimation) confirm near unit-root fractional integration in log-price levels ($H\in [0.985,1.044]$), with estimated fractional orders $\widehat{\alpha }\in \{0.737,0.884,0.451\}$ for wheat, corn, and soybean. Under a strict rolling-origin protocol (17 origins, horizons $h\in \{1,5,6,12\}$ months), conventional benchmarks remain competitive at $h=1$, but their MAPE degrades to 16.6–31.9% at $h=12$, while Frac+XGBoost error stays flat at 3.9–14.5%. This horizon-robust advantage holds across three market regimes (COVID-19 pandemic shock, 2021–2022 super-cycle, 2023–2024 normalisation) and is confirmed by Holm–Bonferroni-corrected Diebold–Mariano tests ($p<0.001$ at $h=12$). The model’s structural advantage emerges at $h\ge 5$ months, supporting procurement planning, food-security buffer stocks, and import budgeting. Full article

This study examines the evolution of dependence structures and tail risk transmission among technology equities, Bitcoin, Gold, and Crude Oil during 1 January 2016–1 January 2026. The analysis focuses on NVIDIA (NVDA), AMD, Tesla (TSLA), Bitcoin (BTC), Gold and Oil, covering major disruptions including the COVID-19 pandemic and the Russia–Ukraine conflict. An integrated methodological framework combines DCC-GARCH modeling, R-vine copulas, tail dependence analysis, complexity measures and machine learning-based forecasting techniques. The findings reveal volatility persistence and time-varying correlations, especially between technology equities and BTC during crisis periods. Regime analysis reveals that dependence structures are not stable in time. Lower-tail dependence intensifies during periods of market stress, indicating increased downside risk transmission. Gold remains weakly connected to the other assets, while Bitcoin has the strongest exposure to extreme downside co-movements. Complexity analysis based on the Scale-Dependent Lyapunov Exponent (SDLE) indicates heterogeneous dynamics across scales, characterized by local divergence and stabilization at broader scales. Forecast results based on Random Forest and XGBoost models provide limited predictive gains over benchmark specifications, suggesting that dependence and tail risk modeling offer better insight than short-horizon return predictions. These results are important for monitoring tail risk transmission for financial stability policies. Full article

Understanding complex systems requires analytical tools capable of covering nonlinear dynamics, structural complexity, and informational uncertainty simultaneously. In this context, chaos theory, fractal analysis, and entropy measures provide complementary perspectives for examining any irregular behavior in natural and socio-economic systems. This paper examined the relation between the Geopolitical Risk Index and the World Uncertainty Index to the volatility of West Texas Intermediate crude oil, gold, and Bitcoin over the period October 2010–February 2026. The analysis was motivated by the recent intensification of geopolitical tensions, particularly conflicts involving Iran, the United States, and Israel, which have significantly heightened uncertainty in global energy and financial markets. The empirical analysis first investigated the underlying complexity of the variables using entropy, chaos, and fractionality measures. Results from the Shannon, R-T entropy, Kolmogorov–Sinai complexity, Hurst, H-M and Lo’s R/S statistics, Phillips, and GPH fractionality tests consistently indicate entropy, fractal persistence, and long-range dependence across the series. In addition, the largest Lyapunov exponents and Hurst coefficients confirmed the presence of chaotic dynamics. The results reveal strong regime heterogeneity with geopolitical shocks exerting significantly stronger effects during high-uncertainty periods. Forecast comparisons show that regime-switching models outperform linear specifications, highlighting the importance of fractal and nonlinear dynamics in understanding financial market responses to geopolitical risk. Full article

Price volatility in fresh food commodities can weaken supply-chain coordination, disturb market expectations, and increase short-term risks to food availability and affordability. This issue is more pronounced for specialty crops with seasonal production, concentrated supply, limited storability, and high sensitivity to climate, trade, energy, and online-attention shocks. This study develops a trajectory-regularized physics-informed multi-source forecasting framework for daily wholesale prices of garlic, scallion, and ginger in China from 2014 to 2024. The framework, denoted as STL–ETO–EMA–PILSTM, integrates Seasonal-Trend decomposition using LOESS (STL), Efficient Multi-scale Attention (EMA), Long Short-Term Memory (LSTM), an economically motivated physics-informed trajectory residual constraint, and Exponential-Trigonometric Optimization (ETO), using production, climate, macroeconomic, trade, crude-oil, and online-attention indicators. In this framework, the physics-informed component is implemented as a trajectory residual constraint inspired by price-adjustment inertia and local continuity, rather than as a conventional PINN based on strict governing physical equations. In one-step-ahead forecasting, the model outperformed conventional machine learning baselines and additional time-series baselines, including naive persistence, Transformer Encoder, and PatchTST, with MAE values of 0.0853, 0.0581, and 0.1409 for garlic, scallion, and ginger, respectively, and R² values above 0.996. Leakage-prevention procedures, walk-forward validation, multi-horizon forecasting, and Diebold–Mariano tests were used to strengthen result credibility. Multi-step forecasting showed clear performance degradation as the horizon increased, supporting the positioning of the framework as a short-term market-monitoring tool rather than a long-horizon structural projection model. Permutation-based feature-importance and interaction analyses revealed crop-specific price drivers. The framework provides an interpretable tool for fresh food price forecasting, market stability monitoring, and short-term operational risk monitoring in fresh food supply chains. Full article

Cross-market risk transmission in U.S. energy markets has become increasingly complex as fossil fuel prices, electricity markets, and clean energy financial exposure respond differently to stress episodes. Identifying whether dynamic spillover information contains forward-looking diagnostic value is therefore important for energy market risk monitoring. This study examines a daily six-market U.S. energy return panel covering WTI crude oil, Henry Hub natural gas, Brent crude oil, RBOB gasoline, PJM West electricity, and CELS clean-energy equity exposure from 2016 to 2025. We first estimate time-varying total, directional, and net connectedness using a TVP-VAR-DY framework and then transform the resulting connectedness measures into spillover-based features for supervised high-DSV20-state classification. The results show that energy-market connectedness is clearly time-varying, with crude oil benchmarks occupying central positions and market-level net spillover roles changing across market conditions. Under the retained label-80 Random Forest specification, connectedness-based features provide moderate diagnostic value for identifying future high-DSV20 states. Net WTI, Net Henry Hub, and Net CELS are the most informative spillover-role variables. Additional validation checks indicate that the evidence is best interpreted as support for diagnostic risk monitoring rather than as a high-accuracy forecasting system. The findings highlight the usefulness of dynamic connectedness measures as transparent inputs for energy-market risk assessment. Full article

Against the backdrop of the “dual carbon” goals, carbon emission trading prices serve as a core signal of market operational efficiency. Accurately predicting carbon prices facilitates scientific decision-making, and model optimization is key to improving prediction accuracy. This study takes five major carbon trading pilots in China—Shenzhen, Guangdong, Hubei, Beijing, and Shanghai—as the research objects. An indicator system is constructed from four dimensions: macroeconomy, energy prices, climate and environment, and international markets. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is employed to identify the key influencing factors of carbon prices across different markets. Among them, “WTI crude oil price” and “EUA futures closing price” are consistently significant factors common to all five pilots. On this basis, four models—Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Transformer—are constructed for multi-method prediction comparison. The results show that ARIMAX and GRU achieve the best prediction performance among the four models. To further enhance prediction accuracy, hybrid optimization models are respectively developed: Support Vector Regression (SVR) is used to optimize the nonlinear residuals of ARIMAX (SVR-ARIMAX), and Genetic Algorithm (GA) is used to optimize the key hyperparameters of GRU (GA-GRU). The hybrid models significantly reduce prediction errors in most markets. Specifically, SVR-ARIMAX shows particularly notable improvements in Beijing and Hubei, while GA-GRU outperforms standard GRU in Guangdong, Shenzhen, Shanghai, and Hubei. Based on the optimized models, 12-month-ahead forecasts indicate that the Shenzhen market exhibits high volatility and greatest uncertainty; Guangdong remains relatively stable; Hubei, Beijing, and Shanghai are characterized by narrow-range fluctuations. The findings provide empirical support for corporate emission reduction decision-making, carbon market risk management, and price mechanism improvement. Full article

This study examines crude oil return dynamics in the context of the global energy transition, where decarbonization policies, technological innovation, and shifting energy demand increasingly influence market behavior. We propose a heavy-tailed distributional LSTM framework to jointly model the conditional mean, volatility, and tail risk of West Texas Intermediate (WTI) returns, incorporating key transition-related drivers: carbon allowance returns (ETS), artificial intelligence (AI) activity, electric vehicle (EV) market returns (SPKS), and geopolitical risk (GPR). Granger causality results show that ETS significantly predicts mean returns, reflecting the growing impact of climate policy signals, while AI and EV markets primarily affect volatility, indicating transmission through uncertainty channels. The model adopts a Student-t specification to capture heavy-tailed behavior and extreme price movements. Out-of-sample results reveal limited mean predictability but improved forecasting of return magnitude and tail risk. These findings highlight that, under energy transition dynamics, oil market predictability is increasingly concentrated in the risk dimension rather than in average returns. Full article

Brent crude oil prices are strategically important due to their sensitivity to geopolitical developments, financial market stress, and global monetary conditions. This study examines whether strategic risk indicators improve the forecasting performance of Brent crude oil returns within an integrated econometric and machine learning framework. Monthly data from January 2001 to December 2025 are employed, using the Global Geopolitical Risk Index (GPR), the CBOE Volatility Index (VIX), and the U.S. 10-year Treasury yield (DGS10) as key explanatory variables. Methodologically, the analysis first estimates benchmark econometric models, including ARIMAX (AutoRegressive Integrated Moving Average with Explanatory Variable) and ARIMAX-gjrGARCH (Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedasticity, and then implements machine learning models, namely XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), and Random Forest, to capture potential nonlinear relationships. Using sMAPE (Symmetric Mean Absolute Percentage Error), forecast performance is assessed over multiple forecast horizons under a rolling-origin framework. Across several forecasting horizons and train-test split configurations, the empirical results consistently show that machine learning techniques, especially LightGBM, offer superior out-of-sample forecasting accuracy. These findings suggest that the dynamics of Brent crude oil returns are influenced by complex and nonlinear relationships between macro-financial conditions, financial uncertainty, and geopolitical risk. The study concludes that flexible data-driven forecasting frameworks offer stronger predictive performance than benchmark econometric models under strategic risk conditions and provide useful implications for energy market risk management and policy decision-making. Full article

Oil price volatility forecasting remains a central challenge in financial risk management and macroeconomic policy, particularly when market uncertainty stems from expert judgment, geopolitical assessments, or imprecisely quantified fundamentals rather than statistical frequencies. We propose a bivariate uncertain vector autoregressive (UVAR) model to jointly forecast crude oil realized volatility (RV) and the Overall Equity Market Volatility (EMV) tracker within the framework of uncertainty theory, using 204 monthly observations from January 2008 to December 2024. Three cross-validation schemes consistently identify UVAR(1) as optimal, and least-squares estimation reveals an asymmetric bidirectional relationship between the two variables. Residual analysis and uncertain hypothesis testing confirm the adequacy of the fitted model at both $\alpha =0.05$ and $\alpha =0.10$, the conventional significance levels reported in the empirical literature. Relative to a univariate UAR benchmark, UVAR(1) yields lower residual variance and, on average, narrower 95% confidence intervals for both variables and remedies the hypothesis-test failure of UAR(1) for realized volatility; while its fixed-origin ATE is marginally higher on the EMV tracker, this is more than offset by substantial gains on realized volatility, the primary economic variable of interest. Against a probabilistic VAR(1) benchmark, UVAR(1) attains marginally lower out-of-sample sum of squared mean errors while uniquely supporting principled uncertain-statistical inference under non-frequentist data-generating mechanisms. These results provide principled inputs for value-at-risk assessment and portfolio hedging in oil-dependent economies. Full article

Amid the ongoing transformation of global climate governance, climate policy uncertainty has emerged as an increasingly important factor influencing both energy and agricultural commodity markets, with direct implications for energy and food security. Using monthly data from 2008 to 2025, this study applies the TVP-VAR-DY and TVP-VAR-BK frameworks, together with complex network analysis, to investigate spillover dynamics among climate policy uncertainty, energy, and agricultural markets from both time-varying and frequency-based perspectives. The results show that spillover effects evolve substantially over time and become more pronounced during periods of major external shocks, particularly under the influence of short-run factors. Notably, the transmission effect of climate policy uncertainty is stronger for bioenergy-related agricultural commodities, especially soybeans and corn. While the agricultural market exhibits strong internal connectedness, cross-market risk transmission is heterogeneous across commodities, with corn remaining a relatively stable net transmitter of risk. By contrast, crude oil generally acts as a net receiver, whereas climate policy uncertainty behaves as a net receiver in the short run but gradually shifts into a net transmitter over the medium and long term, suggesting a lagged transmission pattern. Robustness checks based on alternative lag lengths, forecast horizons, and CPU proxies confirm that the main connectedness structure is stable and not driven by specific parameter choices. These findings provide useful evidence for policymakers seeking to improve the stability and transparency of climate policy and mitigate cross-market risk, while also offering practical guidance for investors in portfolio allocation and hedging against policy-induced volatility. Full article

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots. Full article