Search Results (145)

For a long time, the explanation of the various determinants of oil price fluctuations and their impact on economic activity has been based on the supply and demand mechanism. However, with various volatile changes in the international situation in recent years, such as threats to public health and an increase in regional conflicts, special attention has been paid to the geopolitical context as an additional driver of oil price fluctuations. This study examines the relationship between oil price changes and GDP growth and other macroeconomic variables from the perspective of the vulnerability of oil-importing and oil-exporting countries to unexpected oil price shocks, driven by tense geopolitical events, in three European countries (Norway, Germany, and Poland). We apply the Structural Vector Autoregressive (SVAR) model and orthogonalized impulse response functions, based on quarterly data, in regard to two samples: the first spans 1995Q1–2019Q4 (pre-2020 sample), with relatively gradual changes in oil prices, and the second spans 1995Q1–2024Q2 (whole sample), with sudden fluctuations in oil prices due to geopolitical developments. A key finding of this research is that vulnerability to unpredictable oil price shocks related to geopolitical tensions is higher than in regard to expected gradual changes in oil prices, both in oil-importing and oil-exporting countries. Different causality patterns and stronger responses in regard to GDP growth during the period, including in regard to tense geopolitical events in comparison to the pre-2020 sample, lead to the belief that economies are not more resilient to oil price shocks as has been suggested by some studies, which referred to periods that were not driven by geopolitical events. Our research also suggests that countries implementing policies to reduce oil dependency and promote investment in alternative energy sources are better equipped to mitigate the adverse effects of oil price shocks. Full article

Cities serve as the primary arenas for achieving the strategic objectives of “carbon peak and carbon neutrality”. This study employed the LMDI method to systematically analyze the evolution trend of energy-related carbon emissions in Hong Kong and their influencing factors from 1980 to 2023. The main findings are as follows: (1) Hong Kong’s energy consumption structure remains dominated by coal and oil. Influenced by energy prices, significant shifts in this structure occurred across different periods. Imported electricity from mainland China, in particular, has exerted a promoting effect on the optimization of its energy consumption mix. (2) Economic output and population concentration are the primary drivers of increased carbon emissions. However, the contribution of economic growth to carbon emissions has gradually weakened in recent years due to a lack of new growth drivers. (3) Energy consumption intensity, energy consumption structure, and carbon intensity are the primary influencing factors in curbing carbon emissions. Among these, the carbon reduction impact of energy consumption intensity is the most significant. Hong Kong should continue to adopt a robust strategy for controlling total energy consumption to effectively mitigate carbon emissions. Additionally, it should remain vigilant regarding the potential implications of future energy price fluctuations. It is also essential to sustain cross-border energy cooperation, primarily based on electricity imports from the Pearl River Delta, while simultaneously expanding international and domestic supply channels for natural gas. Full article

Understanding the price dynamics of oil futures is crucial for advancing green finance strategies and supporting sustainable energy transitions. This study investigates the macroeconomic and energy market determinants of oil futures prices through Granger causality, cointegration analysis, and the error correction model, using daily data. It focuses on the influence of economic development levels, exchange rate fluctuations, and inter-energy price linkages. The empirical findings indicate that (1) oil futures prices exhibit strong correlations with other energy prices, macroeconomic factors, and exchange rate variables; (2) economic development significantly affects oil futures prices, while exchange rate impacts are statistically insignificant based on the daily data analyzed; (3) there exists a stable long-term equilibrium relationship between oil futures prices and variables representing economic activity, exchange rates, and energy market trends; (4) oil futures prices exhibit significant short-term dynamics while adjusting steadily toward a long-run equilibrium driven by macroeconomic and energy market fundamentals. By enhancing the accuracy of oil futures price forecasting, this study offers practical insights for managing financial risks associated with fossil energy markets and contributes to the formulation of low-carbon investment strategies. The findings provide a valuable reference for integrating energy pricing models into sustainable finance and climate-aligned portfolio decisions. Full article

Indonesia’s dependence on fossil fuel exports, particularly coal and crude oil, presents a dual challenge: sustaining economic growth while addressing rising CO₂ emissions. Despite significant attention to domestic energy consumption, the environmental implications of export activities remain underexplored. This study examines the dynamic relationship between energy exports, crude oil prices, and CO₂ emissions in Indonesia using a Vector Autoregressive (VAR) model with annual data from 2002 to 2022. The analysis incorporates Impulse Response Functions (IRFs) and Forecast Error Variance Decomposition (FEVD) to trace short- and long-term interactions among variables. Findings reveal that coal exports are strongly persistent and positively linked to past emission levels, while oil exports respond negatively to both coal and emission shocks—suggesting internal trade-offs. CO₂ emissions are primarily self-driven yet increasingly influenced by oil export fluctuations over time. Crude oil prices, in contrast, have limited impact on domestic emissions. This study contributes a novel export-based perspective to Indonesia’s emission profile and demonstrates the value of dynamic modeling in policy analysis. Results underscore the importance of integrated strategies that balance trade objectives with climate commitments, offering evidence-based insights for refining Indonesia’s nationally determined contributions (NDCs) and sustainable energy policies. Full article

Rising population, combined with declining home food production, in Arab nations has resulted in increased food imports that intensifies their dependence on international markets for vital food supplies. These nations face challenges in achieving food security because crude oil price volatility creates difficulties in managing the expenses of imported food products. This research calculates the income and price elasticities of imported food demand to understand consumer behavior changes in response to income and price variations, which helps to explain their impact on regional food security. To our knowledge, this research presents the first analysis of imported food consumption patterns across Arab countries according to their income brackets. This study employs the static Almost Ideal Demand System model to examine food import data spanning from 1961 to 2020. The majority of imported food categories demonstrate inelastic price and income demand, which means that their essential food consumption remains stable despite cost fluctuations. The need for imports makes Arab nations vulnerable to external price changes, which endangers their food security. This research demonstrates why governments must implement policies through subsidies and taxation to reduce price volatility risks while ensuring food stability, which will lead to sustained food security for these nations. Full article

High-frequency fluctuations in the international crude oil market have led to multilevel characteristics in China’s domestic refined oil pricing mechanism. To address the poor fitting performance of single deep learning models on oil price data, which hampers accurate gasoline price prediction, this paper proposes a gasoline price prediction method based on a combined xLSTM–XGBoost model. Using gasoline price data from June 2000 to November 2024 in Sichuan Province as a sample, the data are decomposed via STL decomposition to extract trend, residual, and seasonal components. The xLSTM model is then employed to predict the trend and seasonal components, while XGBoost predicts the residual component. Finally, the predictions from both models are combined to produce the final forecast. The experimental results demonstrate that the proposed xLSTM–XGBoost model reduces the MAE by 14.8% compared to the second-best sLSTM–XGBoost model and by 83% compared to the traditional LSTM model, significantly enhancing prediction accuracy. Full article

This paper revisits the international finance trilemma by analyzing how different monetary policy objectives and exchange rate regimes shape the transmission of global risk shocks. Using a structural vector autoregressive model with exogenous variables (SVARX), we examine the monetary policy responses and exchange rate fluctuations of ASEAN+4 economies—China, Japan, Korea, and Hong Kong—to external shocks including U.S. monetary policy changes, oil price fluctuations, global policy uncertainty, and financial risk during 2010–2022. Economies are grouped according to their trilemma configurations: floating exchange rates with free capital flows, fixed exchange rates, and capital control regimes. Our findings broadly support the trilemma hypothesis: fixed-rate economies align with U.S. interest rate movements, capital control economies retain greater monetary autonomy, and open, floating regimes show partial responsiveness. More importantly, monetary responses vary by global shock type: U.S. monetary policy drives the most synchronized policy reactions, while oil price and uncertainty shocks produce more heterogeneous outcomes. Robustness checks include alternative model specifications, where global shocks are treated as endogenous, and extensions, such as using Japan’s monetary base as a proxy for unconventional monetary policy. These results refine the empirical understanding of the trilemma by showing that its dynamics depend not only on institutional arrangements but also on the nature of global shocks—underscoring the need for more tailored and, where possible, regionally coordinated monetary policy strategies. Full article

Oil inventories are essential in alleviating realized and anticipated supply shocks and represent a key market indicator. This study examines the responses of global and country oil inventories to supply shocks under tight and loose market conditions. We utilize an expanded version of the GVAR model, adding the OECD oil inventories variable, incorporating major oil-producing countries: Iran, Russia, and Venezuela, and extending the coverage period. Our simulations indicate that a negative global supply shock significantly affects oil inventories under “tight” market conditions. The model correctly predicts the trajectory of changes to oil inventories in South Korea following a supply shock to Russian production in tight markets and Iranian output in loose markets. This case also shows that commercial players, using their inventories as a buffer, can negate government attempts to maintain constant levels of reserves. Overall, the response to the oil inventory tends to vary across producing and importing countries and market conditions. Such dynamics highlight potential problems with specific policies, such as using inventories as a buffer to alleviate price fluctuations or disrupting the oil production of individual countries through sanctions, as these measures oftentimes result in unintended consequences due to complex interconnections of the global oil market. Full article

In this study, Gaussian process (GP) regression is used to normalize observed commodity data and produce predictions at densely interpolated time intervals. The methodology is applied to an empirical oil price dataset. A Gaussian kernel with data-dependent initialization is used to calculate prediction means and confidence intervals. This approach generates synthetic data points from the denoised dataset to improve prediction accuracy. From this augmented larger dataset, a procedure is developed for estimating an upcoming crash-like behavior of the commodity price. Finally, multiple data-science-driven algorithms are used to demonstrate how data densification using GP regression improves the detection of forthcoming large fluctuations in a particular commodity dataset. Full article

To address the challenges in forecasting crude oil and hot-rolled coil futures prices, the aim is to transcend the constraints of conventional approaches. This involves effectively predicting short-term price fluctuations, developing quantitative trading strategies, and modeling time series data. The goal is to enhance prediction accuracy and stability, thereby supporting decision-making and risk management in financial markets. A novel approach, the multi-dimensional fusion feature-enhanced (MDFFE) prediction method has been devised. Additionally, a data augmentation framework leveraging multi-dimensional feature engineering has been established. The technical indicators, volatility indicators, time features, and cross-variety linkage features are integrated to build a prediction system, and the lag feature design is used to prevent data leakage. In addition, a deep fusion model is constructed, which combines the temporal feature extraction ability of the convolution neural network with the nonlinear mapping advantage of an extreme gradient boosting tree. With the help of a three-layer convolution neural network structure and adaptive weight fusion strategy, an end-to-end prediction framework is constructed. Experimental results demonstrate that the MDFFE model excels in various metrics, including mean absolute error, root mean square error, mean absolute percentage error, coefficient of determination, and sum of squared errors. The mean absolute error reaches as low as 0.0068, while the coefficient of determination can be as high as 0.9970. In addition, the significance and stability of the model performance were verified by statistical methods such as a paired t-test and ANOVA analysis of variance. This MDFFE algorithm offers a robust and practical approach for predicting commodity futures prices. It holds significant theoretical and practical value in financial market forecasting, enhancing prediction accuracy and mitigating forecast volatility. Full article

The crude oil market is one of the most significant sectors in the global economy. Fluctuations in oil prices impact the financial performance of national economies. Crude oil prices are also the basis of many popular financial derivatives on the financial market. Binary-temporal representation state models enable the precise modelling and development of financially efficient decision-support systems in the crude oil market. Existing models are primarily based on the main technical analysis methods: trend and moving average analysis. In this paper, with the aim of enhancing forecasting efficiency, we introduce the concept of determining the widely used RSI indicator for binary-temporal representation and propose a new state model based on its readings. We also present empirical research on the proposed model applied to the oil market, using historical data from the past six years. The results confirm the validity of the approach adopted. Full article

This study examines the interplay between oil price volatility, sustainability-driven initiatives, and the MSCI UAE Index, highlighting the challenges that oil-dependent economies face in balancing financial stability with sustainability transitions. Using a dataset of 2707 daily observations from 2014 to 2024, we applied linear regression, ARCH, GARCH, and TARCH models to analyze volatility dynamics across two key periods: the 2014–2016 oil price collapse and the 2019–2023 phase marked by the COVID-19 pandemic and increasing sustainability efforts. Our findings indicate that oil price fluctuations significantly impact the MSCI UAE Index, with GARCH models confirming persistent volatility and TARCH models revealing asymmetrical effects, where negative shocks intensify market fluctuations. While the initial sustainability policy announcements contributed to short-term volatility and investor uncertainty, they ultimately fostered market confidence and long-term stabilization. Unlike previous studies focusing solely on oil price volatility in emerging markets, this research integrates sustainability policy announcements into financial modeling, providing novel empirical insights into their impact on financial stability in oil-exporting economies. The findings suggest that stabilization funds, dynamic portfolio strategies, and transparent regulatory policies can mitigate oil price volatility risks and enhance market resilience during sustainability transitions, offering valuable insights for investors, policymakers, and financial institutions navigating the UAE’s evolving economic landscape. Full article

In the context of heightened oil price volatility, mastering technical trading strategies is essential for informed investment and sound decision making. This study explores the effectiveness of contrarian technical trading strategies in the Brent crude oil market, aiming to enhance returns in the face of persistent market fluctuations. Utilizing historical price data, this research formulates trading rules based on overbought and oversold signals derived from the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI). It assesses their performance through a range of Average Holding Period Return (AHPR) metrics, emphasizing the 250-day AHPR as a proxy for one-year returns. The findings show that RSI-based strategies, especially those using a threshold of 25, are most effective in oversold conditions, achieving peak profitability of over 40% in Quarter 2. The conclusions highlight the importance of parameter flexibility, strategic timing, and responsiveness to market dynamics in optimizing the contrarian strategy performance. The implications suggest investors and managers can refine strategies by accounting for behavioral biases, market timing, and flexible parameters, while enhancing big data analytics in technical trading. Full article

This study employs asymmetric quantile regression to investigate the asymmetric impact of WTI crude oil prices and economic policy uncertainty (EPU) on stock market returns from May 2014 to December 2024 in oil-importing (China, India, Germany, Italy, Japan, USA, and South Korea) and oil-exporting (Saudi Arabia, Russia, Iraq, Canada, and the United Arab Emirates) countries. The findings reveal that an increase in oil prices significantly impacts the returns of all countries. For oil-importing countries, an increase in oil prices consistently exhibits a positive impact, with insignificant effects in lower and medium quantiles and significant effects in higher quantiles. Conversely, a decrease in oil prices generally decreases stock market returns across all quantiles. This study offers valuable insights for investors to manage risks and improve the predictability of oil price fluctuations. It also provides strategies and policy implications for capitalists and decision-makers. By addressing contemporary issues and using up-to-date data, the study supports financial institutions and portfolio managers in formulating effective strategies. Full article

This study investigates the multifractal behavior of four leading semiconductor stocks—Intel (INTC), Advanced Micro Devices (AMD), Nvidia (NVDA), and Broadcom (AVGO)—in relation to key financial assets, including the Dow Jones Industrial Average (DJI), the Euro–U.S. Dollar exchange rate (EUR), gold (XAU), crude oil (WTI), and Bitcoin (BTC), using Multifractal Asymmetric Detrended Cross-Correlation Analysis (MF-ADCCA). The analysis is based on daily price return time series from January 2015 to January 2025. Results reveal consistent evidence of multifractality across all asset pairs, with the generalized Hurst exponent exhibiting significant variability, indicative of complex and nonlinear stock price dynamics. Among the semiconductor stocks, NVDA and AVGO exhibit the highest levels of multifractal cross-correlation, particularly with DJI, WTI, and BTC, while AMD consistently shows the lowest, suggesting comparatively more stable behavior. Notably, cross-correlation Hurst exponents with BTC are the highest, reaching approximately 0.54 for NVDA and AMD. Conversely, pairs with EUR display long-term negative correlations, with exponents around 0.46 across all semiconductor stocks. Multifractal spectrum analysis highlights that NVDA and AVGO exhibit broader and more pronounced multifractal characteristics, largely driven by higher fluctuation intensities. Asymmetric cross-correlation analysis reveals that stocks paired with DJI show greater persistence during market downturns, whereas those paired with XAU demonstrate stronger persistence during upward trends. Analysis of multifractality sources using surrogate time series confirms the influence of fat-tailed distributions and temporal linear correlations in most asset pairs, with the exception of WTI, which shows less complex behavior. Overall, the findings underscore the utility of multifractal asymmetric cross-correlation analysis in capturing the intricate dynamics of semiconductor stocks. This approach provides valuable insights for investors and portfolio managers by accounting for the multifaceted and asset-dependent nature of stock behavior under varying market conditions. Full article