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The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

The economic model derived from the supply and demand of crude oil prices is a significant component that measures economic development and sustainability. Therefore, it is essential to mitigate crude oil price volatility risks by establishing models that will effectively predict prices. A promising approach is the application of long short-term memory artificial neural networks for time-series forecasting. However, their ability to tackle complex time series is limited. Therefore, a decomposition-forecasting approach is taken. Furthermore, machine learning model accuracy is highly dependent on hyper-parameter settings. Therefore, in this paper, a modified version of the salp swarm algorithm is tasked with determining satisfying parameters of the long short-term memory model to improve the performance and accuracy of the prediction algorithm. The proposed approach is validated on real-world West Texas Intermediate (WTI) crude oil price data throughout two types of experiments, one with the original time series and one with the decomposed series after applying variation mode decomposition. In both cases, models were adjusted to conduct one, three, and five-steps ahead predictions. According to the findings of comparative analysis with contemporary metaheuristics, it was concluded that the proposed hybrid approach is promising for crude oil price forecasting, outscoring all competitors.

This paper makes a novel attempt to model the nonlinear association between renewable energy consumption and crude oil prices concerning four net oil-importing South Asian economies: Bangladesh, India, Pakistan and Sri Lanka. Using annual data from 1990 to 2018, the long-run elasticity estimates confirm the nonlinear nexus and suggest that although rising crude oil prices do not facilitate renewable energy consumption initially, upon reaching a threshold level of crude oil price, further hikes in the oil prices are likely to elevate the renewable energy consumption figures. The estimated real oil price threshold, in this regard, is predicted to be around 135 US dollars per barrel, which is way above the prevailing oil price level. Identical nonlinearity is also confirmed in the context of the oil prices and renewable energy share in total final energy consumption volumes. Moreover, the nexus between renewable electricity share in aggregate electricity outputs and crude oil prices is also seen to exhibit nonlinearity. However, rising crude oil prices were not found to enhance the renewable electricity shares. Besides, the causality results implicated that movements in crude oil prices influenced the renewable energy transition process across the concerned South Asian economies. Thus, these results, in a nutshell, impose critically important policy implications for attainment of energy security and environmental sustainability in South Asia, particularly via curbing the traditional imported crude oil-dependencies of these nations.

There has always been a complex relationship between uncertainty and crude oil prices. Three types of uncertainty, i.e., economic policy uncertainty, geopolitical risk uncertainty, and climate policy uncertainty (EPU, GPR, and CPU for short), have exacerbated abnormal fluctuations in the energy market, making crude oil prices volatile more and more frequently, especially from the perspective of the financial attribute of crude oil. Based on the time-series data related to uncertainties and crude oil prices from December 2001 to March 2021, this paper uses the quantile-on-quantile regression (QQR) method to explore the overall impact of various uncertainties on crude oil prices. Moreover, this paper adopts the QQR method based on the wavelet transform to investigate the heterogeneous effects of various uncertainties on crude oil prices at different time scales. The following conclusions are obtained. First, there are significant differences in the overall impact of the three types of uncertainties on crude oil prices, and this heterogeneity is reflected in quantiles of the peak impact intensity, the impact direction, and the fluctuation change. Second, the impact intensities of the three types of uncertainties on crude oil prices are significantly different at different time scales. This is mainly reflected in the different periods of significant impact of the three uncertainties on crude oil prices. Third, the impact directions and fluctuations of the three types of uncertainties on crude oil prices are heterogeneous at different time scales.

The uncertainty in the evolution of crude oil price fluctuation has a significant impact on economic stability. Based on the decomposition of crude oil price fluctuation by the state-space model, this paper studies the fluctuation trend of crude oil prices and its causes. The nonlinearity autoregressive distribute lag approach (NARDL) model is used to capture the influence mechanism characteristics of crude oil prices at different positions and different fluctuation trends. An event study model with dummy variables is constructed to compare the effects of different types of events on crude oil price fluctuations. The empirical results indicate that the fluctuation of crude oil prices tends to strengthen on the whole, and there is a remarkable correlation between this trend and the influencing mechanism of crude oil price, namely, the fluctuation source structure. The influence mechanism of crude oil price fluctuation is asymmetric when the crude oil price is at different positions and under different trends. There is a strong correlation between event shocks and event types in the evolution of crude oil price fluctuation.

Crude oil price forecasting is an important research area in the international bulk commodity market. However, as risk factors diversify, price movements exhibit more complex nonlinear behavior. Hence, this study provides a comprehensive analysis of forecasting Brent crude oil prices by comparing various hybrid combinations of linear and nonlinear time series models. To this end, first, the logarithmic transformation is used to stabilize the variance of the crude oil prices time series; second, the original time series of log crude oil prices is decomposed into two new subseries, such as a long-run trend series and a stochastic series, using the Hodrick–Prescott filter; and third, two linear and two nonlinear time series models are considered to forecast the decomposed subseries. Finally, the forecast results for each subseries are combined to obtain the final day-ahead forecast result. The proposed modeling framework is applied to daily Brent spot prices from 1 January 2013 to 27 December 2022. Six different accuracy metrics, pictorial analysis, and a statistical test are performed to verify the proposed methodology’s performance. The experimental results (accuracy measures, pictorial analysis, and statistical test) show the efficiency and accuracy of the proposed hybrid forecasting methodology. Additionally, our forecasting results are comparatively better than the benchmark models. Finally, we believe that the proposed forecasting method can be used for other complex financial time data to obtain highly efficient and accurate forecasts.

This study investigates the long- and short-run effects of crude oil price (COP) and economic policy uncertainty (EPU) on China’s green bond index (GBI) using the quantile autoregressive distributed lag model. The empirical results show that COP and EPU produce a significant positive and negative influence on GBI in the long-run across most quantiles, respectively, but their short-run counterparts are opposite direction and only significant in higher quantiles. Thus, major contributions are made accordingly and shown in the following aspects. The findings emphasise the importance of understanding how COP and EPU affect China’s green bond market for the first time. In addition, both the long- and short-run effects are captured, but long-run shocks primarily drive the green bond market. Finally, time- and quantile-varying analyses are adopted to explain the nexus between COP and EPU to GBI, which considers not only different states of the bond market but also events that occur in different time periods. Some detailed policies, such as a unified and effective green bond market, an early warning mechanism of oil price fluctuation, and prudent economic policy adjustments, are beneficial for stabilising the green finance market. First published online 19 December 2022

Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

Crude oil plays an important role in the global economy, as it contributes one-third of the energy consumption worldwide. However, despite its importance in policymaking and economic development, forecasting its price is still challenging due to its complexity and irregular price trends. Although a significant amount of research has been conducted to improve forecasting using external factors as well as machine-learning and deep-learning models, only a few studies have used hybrid models to improve prediction accuracy. In this study, we propose a novel hybrid model that captures the finer details and interconnections between multivariate factors to improve the accuracy of petroleum oil price prediction. Our proposed hybrid model integrates a convolutional neural network and a recurrent neural network with skip connections and is trained using petroleum oil prices and external data directly accessible from the official website of South Korea’s national oil corporation and the official Yahoo Finance site. We compare the performance of our univariate and multivariate models in terms of the Pearson correlation, mean absolute error, mean squared error, root mean squared error, and R squared (R2) evaluation metrics. Our proposed models exhibited significantly better performance than the existing models based on long short-term memory and gated recurrent units, showing correlations of 0.985 and 0.988, respectively, for 10-day price predictions and obtaining better results for longer prediction periods when compared with other deep-learning models. We validated that our proposed model with skip connections outperforms the benchmark models and showed that the convolutional neural network using gated recurrent units with skip connections is superior to the compared models. The findings suggest that, to some extent, relying on a single source of data is ineffective in predicting long-term changes in oil prices, and thus, to develop a better prediction model based on time-series based data, it is necessary to take a multivariate approach and develop an efficient computational model with skip connections.

This study examines the forecasting power of the gas price and uncertainty indices for crude oil prices. The complex characteristics of crude oil price such as a non-linear structure, time-varying, and non-stationarity motivate us to use a newly proposed approach of machine learning tools called XGBoost Modelling. This intelligent tool is applied against the SVM and ARIMAX (p,d,q) models to assess the complex relationships between crude oil prices and their forecasters. Empirical evidence shows that machine learning models, such as the SVM and XGBoost models, dominate traditional models, such as ARIMAX, to provide accurate forecasts of crude oil prices. Performance assessment reveals that the XGBoost model displays superior prediction capacity over the SVM model in terms of accuracy and convergence. The superior performance of XGBoost is due to its lower complexity and costs, high accuracy, and rapid processing times. The feature importance analysis conducted by the Shapley additive explanation method (SHAP) highlights that the different uncertainty indexes and the gas price display a significant ability to forecast future WTI crude prices. Additionally, the SHAP values suggest that the oil implied volatility captures valuable forecasting information of gas prices and other uncertainty indices that affect the WTI crude oil price.