Oil Price Trends Research Articles

A Time Trend and Persistence Analysis of Sunflower Oil and Olive Oil Prices in the Context of the Russia-Ukraine War

The imbalances between sunflower oil production (Ukraine) and olive oil production (Europe) due to the substitution effects caused by the Russian invasion of Ukraine on 24 February 2022 affected the prices. Given that Ukraine is the largest producer of global sunflower oil and Europe is the largest territory that produces olive oil, this scientific article tries to analyze the global prices of both vegetable oils and understand how the war between Russia and Ukraine has affected them. Advanced statistical and econometric methods to carry out this analysis have been used. It is found that the prices of both variables separately have similar behavior and that the shock caused by the war will be transitory, with the original price trend recovering in the long term using fractional integration methods. In a multivariate analysis, using a causality test in the frequency domain we observe that both variables are related to each other, and the effects of war will have an impact in the long term, with olive oil being the cause. A negative relationship between both variables measured with a wavelet analysis is also observed. Furthermore, if this trend continues, the price of olive oil would prevail over the price of sunflower in the war between Russia and Ukraine. Finally, a 12-month prediction is presented using artificial neural networks, where the price of olive oil will be high for at least 11 more months. The price of sunflower oil is predicted to last for only 5 more months.

Seeing is believing: Forecasting crude oil price trend from the perspective of images

AbstractIn this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

The dynamic impact of oil shocks on the Saudi stock market: new evidence through dynamic simulated ARDL approach

PurposeThe paper investigates the dynamic relationship between oil prices, the USA dollar exchange rate and the Saudi stock market index.Design/methodology/approachThe authors perform a novel dynamic simulated the autoregressive distributed lag (ARDL) on weekly data from 2010 to 2021.FindingsThe authors' work reveals three main results: First, a cointegration relationship exists between oil prices and the Saudi stock market index. Second, the Saudi stock market is strongly affected by fluctuations in oil prices in both the short and long run. Third, the exchange rate of the USA dollar has a slight influence on the movements of the Saudi stock market. The simulations show that the Saudi stock market index has a long-run upward trend after an oil price shock, while the dollar index rises moderately after a similar shock. Moreover, the first months of the COVID-19 pandemic coincided with a significant decline in the Saudi stock market index, particularly the substantial drop in oil prices.Practical implicationsThese findings encourage domestic and foreign investors to benefit from an upward trend in oil prices, especially after the opening of the Saudi market to foreign investment. On the other hand, it raises questions about the Saudi economy's dependence on oil as the sole vehicle for output growth. It highlights the urgent need for diversification and productivity growth in the non-oil sector and other renewable natural resources to increase Saudi competitiveness.Originality/valueThe novelty of the research lies in the following. First, the authors apply one of the latest developments in time-series modeling techniques. This dynamic ARDL simulation model provides a worthwhile alternative way to explore dynamic correlations in the short and long run and assess the choc effects. Secondly, the study would enable us to track the impact of the COVID-19 health crisis on the Saudi stock market.

The impact of Russia–Ukraine war on crude oil prices: an EMC framework

As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.

The Effect of the Chinese Industry Sector in Predicting Oil Price: Evidence from Information Geometric Causal Inference and GWO-ELM

The COVID-19 outbreak and the implementation of peak and carbon neutral policies have severely impacted oil price volatility and the industrial sector. Exploring the impact mechanisms between oil prices and industries is particularly important for accurate forecasting of crude oil prices. As one of the world’s largest commodity consumers, China’s crude oil market is more representative and susceptible to external factors than that of developed countries. In this paper, we propose an analytical forecasting framework based on the causal effects between Shanghai crude oil prices and various industries in China to improve the forecasting accuracy of crude oil prices. Information geometric causal inference (IGCI) is applied to detect causal relationships between 31 different industries in China and Shanghai crude oil prices in the three time periods before, during and after COVID-19, and industries with strong causal information effects on crude oil prices in the long run are screened out as additional features. An oil price forecasting model based on Gray Wolf Optimization and Extreme Learning Machine (GWO-ELM) is proposed. Considering the small amount of data for Shanghai crude oil, this paper proposes a cross-learning data approach to solve the problem. Experimental results show that the GWO-ELM model outperforms RF, LSTM, GRU, and migration learning-based Tr-LSTM and Tr-Adaboost models in the task of Shanghai crude oil futures price prediction, and find that industry characteristics with long-term causal effects on oil prices can improve the model prediction accuracy. Our proposed analytical prediction can capture the oil price trend more accurately through the information of the industry and solve the problem of insufficient training data for the model. The application of this framework is expected to provide new methods and ideas for data mining of crude oil and other futures prices.

Short-term Petroleum Price Prediction Based on LSTM and LightGBM

With the recent severe fluctuations in the international petroleum market, accurately predicting the direction of oil prices has enormous importance for multiple industries. This study employ the single-unit LSTM model and the multi-unit LSTM model based on word frequency to predict short-term petroleum prices. Results show that the multi-unit LSTM model provides better prediction performance. This study also utilized the LightGBM prediction model taking seven distinct variables into considerate. The result indicates that the model proposed is effective in forecasting crude oil price trends. In conclusion, LSTM model has better interpretability in the time dimension, while the LightGBM model has higher overall prediction accuracy than LSTM. And this study provides ideas for the construction of other oil price forecasting models.

What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting

Crude oil price series are nonlinear and highly volatile, making it difficult to obtain satisfactory performance for traditional statistical-based forecasting methods. To improve forecasting accuracy, this study proposes a novel learning paradigm by integrating the trajectory similarity method with machine learning models based on the decomposition-ensemble framework. In the proposed learning paradigm, raw data of international crude oil prices are first decomposed using variational mode decomposition (VMD), after which, using sample entropy (SE), the resulting essential modal functions are divided into high and low frequencies. The process aims to reorganize the data by using the forecasting properties of different models. Finally, to obtain the final forecasting results, two models, i.e., the trajectory similarity method (TS) and long short term memory neural network (LSTM), are applied to predict and sum up the low and high-frequency subseries, respectively. As sample data for validation, this study selected the international crude oil price series of West Texas Intermediate (WTI) and Brent. Experimental results showed that the proposed VMD-SE-TS/LSTM learning paradigm significantly outperforms all other benchmark models, including the single models without decomposition and the hybrid models with decomposition. The proposed approach performs best in different evaluation metrics and statistical tests under different horizons, indicating that the proposed VMD-SE-TS/LSTM learning paradigm is effective and robust in crude oil price forecasting.

Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends

Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends

Impact of Paris Agreement on financing strategy: Evidence from global FPSO industry

This study explores the impact of the Paris Agreement on the determinants of firm-level capital structure decisions of listed contractor-owned Floating Production Storage and Offloading (FPSO) companies in the oil and gas (O&G) industry from 2000 to 2019. The study identified various financing structures between contractor-owned FPSO companies due to their individual and institutional characteristics. Tangibility, profitability, market-to-book (growth), size and effective tax rates are critical determinants of capital structure. Overall, the results support applying the pecking-order theory (PoT) from a firm-level and macro-economic context. The 2015 Paris Agreement ratification significantly impacted the capital structure determinants; the dynamic association has changed in the post-Paris period. Besides, the impact of the global financial crisis on leverage ratios was potentially mitigated by the upward trend in Brent crude oil price between 2007 and 2013.

Energy Subsidy and Price Dynamics In Indonesia

The downward trend of the world oil prices in recent years has put profound consideration among countries to reform the energy subsidy policies. Examination of the inflationary impact of the reform has become essential due to possible welfare consequences. This study aims to reveal the impact of energy subsidy on the prices in Indonesia by utilizing the autoregressive distributed lag (ARDL) method to capture short-run dynamics and long-run cointegration between dependent and independent variables in the period 1980-2017. The finding of this research indicated that the impact of the energy subsidy is significant for the short-run and the long-run periods. Regarding the result, the government of Indonesia is advised to reform carefully by applying energy subsidy policy gradually and protecting vulnerable poor households.

Defending global oil price security: Based on the perspective of uncertainty risk

This study uses the time-varying parameter/stochastic volatility vector autoregression (TVP-SV-VAR) model to explore the impact of uncertainty risk on oil prices. Economic policy uncertainty and geopolitical risk were used as proxy variables for economic and political uncertainty risk. The study results indicate that oil price is driven jointly by two uncertain risk factors, where the impact of economic policy uncertainty is significantly greater than that of geopolitical risks. However, with the use of oil futures hedging tools and the improvement of the oil market mechanism, the relationship between oil supply and demand is relatively stable, and their impact on oil prices is gradually weakening. We also investigated the influence of severe political and economic uncertainty event shocks on oil price. The differences in oil prices due to economic or political events are mainly reflected through oil demand channels. The global financial crisis and 9/11 terrorist attacks had higher negative impacts on oil demand and prices than other events. This study discusses the impact of uncertain risk on oil prices and can aid market participants and government decision-makers in accurately predicting the trend of oil price and improving risk response abilities to cope with oil emergencies.

A Review of Game Theory and Multi-Criteria Decision-Making Methods with 10 Application to the Oil Production and Price

The oil production and price issues have been discovered a long time ago, and always be a continuous problem to the globe especially during the current global threats of the coronavirus pandemic. This paper provides a literature review that involves game theory and multi-criteria decision-making (MCDM) methods with its applications to oil production and price problems. This paper identifies and analyses the use of the game theory and MCDM methods on oil production and price to compare the situation studied, to determine the model that has been used, the trend of past literature and also the details of the basic elements for the game theory framework. Therefore, the oil production and price problem using the game theory and MCDM methods are reviewed and numerous applications studied from the past works of literature are highlighted. The trend of oil production and price which used the game theory and MCDM methods based on the year 2001 till 2021 is still lacking sources from the Web of Science and Scopus databases. The main contribution of the recent study is the employment of the game theory and MCDM methods to the oil production and price problem.
 Keywords: Game theory; MCDM; oil production; oil price

The Bearing on Indian Frugality and Predicting of Average Crude Oil Rates since 1991 – A Study

In the past few years, the crude oil industry has seen so much volatility in terms of crude oil prices in the international oil market. In this research paper, we explore many aspects of the overall crude oil industry and its impact on the Indian economy, it is crucial to have an idea about price movements in order to ascertain demand and supply of crude oil. In this research paper researchers focus on the Box- Jenkins approach along with normal regression model required used for forecasting crude oil prices and its significant impact on the Indian economy (GDP) since LPG reforms post 1991 through timeseries data available for the specific time period from 1991 to 2018. ARIMA model based on the Box-Jenkins approach is further evidence to check the feasibility of time series data and on the basis of that forecasting of the future trends of crude oil prices are shown. The historical crude oil prices predict future trends of crude oil prices and results on the basis of the proposed model are found to be very conclusive and genuine. Before applying the ARIMA model normality of data is assured followed by unit-roots are checked to make residuals stationary at a desired level of significance in order to have significant forecasting through the ARIMA model used in this research.

An oil imports dependence forecasting system based on fuzzy time series and multi-objective optimization algorithm: Case for China

Oil production and consumption is of great importance for the sustainable development and management of energy and environment. The forecasting of oil imports dependence caused by the gap between production and consumption is particularly crucial in the strategic deployment of oil development. However, researches on oil import dependence forecasting are often limited by the small size of data samples and assumptions, and the previous single-objective optimization algorithms only focus on the improvement of forecasting accuracy but ignore the stability. Therefore, in order to overcome the shortcomings of researches, in this paper, a hybrid forecasting system for oil imports dependence forecasting based on fuzzy time series and multi-objective optimization algorithm is proposed considering the accuracy and stability simultaneously to achieve the balance and optimality and bridge the limitations of small sample forecasting. The proposed forecasting system is compared with other small sample forecasting models and the fuzzy times series model with traditional interval partition methods. The results show that the proposed system is superior to the traditional methods in all indicators for oil import dependence forecasting. Otherwise, the out of sample forecasting results provided in our research indicate that the oil import dependence will maintain an upward trend, but the rate of increase will slow down. The research results can not only provide the basis for the planning and control of oil import and safety, but also benefit the perceptions of oil price trend in the world energy market and the adjustment of energy market structure.

Using ARIMA-GARCH Model to Analyze Fluctuation Law of International Oil Price

It is meaningful and of certain theoretical value for the development of economy through analyzing fluctuation rules of international oil prices and forecasting the future trend of international oil prices. By composing the autoregressive integrated moving average (ARIMA) model and the combination model of autoregressive integrated moving average model-generalized autoregressive conditional heteroskedasticity (ARIMA-GARCH) for analyzing and forecasting international oil prices, study shows that the combination model of ARIMA (1,1,0)-GARCH (1,1) is more suitable for short-term forecasting of international oil prices with higher accuracy that the MAPE of forecasting has reduced from 1.549% to 0.045% and the RMSE of forecasting has reduced from 1.032 to 0.071.

Nigerian economic policy response to COVID-19: An evaluation of policy actors' views.

COVID‐19 has caused economic hardship globally. Several economies are making efforts to cushion these negative effects. Following the lockdown and downward trend of global oil prices, the Nigerian economy has been hard hit. This paper sheds light on policy actors' views vis‐à‐vis what should be the economic policy response of the Nigerian government to cushion the effects of COVID‐19 and ensure quick economic recovery. A multistage random sampling method was used to select and interview 635 policy actors drawn from academia/research institutes, civil society organizations, development partners, international NGOs, national NGOs, the private sector, and the public sector. The study adopted a participatory development approach. Descriptive and deductive analysis methods were used. The results support direct cash transfers to households, and small and medium scale enterprises (SMEs) as the policy option that would have huge impact in mitigating the economic effects of COVID‐19 in Nigeria. The second most recommended policy option is the eradication of corruption among government officials. The paper recommends that pragmatic actions towards eradicating corrupt practices among government officials should be an integral part of any economic recovery policy in Nigeria and other emerging economies.

Decomposing and reconstructing dynamic risks in the crude oil market based on the VMD and Lempel–Ziv algorithms

<abstract> <p>Crude oil markets have become increasingly uncertain. To study them, we first employ the decomposition-ensemble framework based on the variational mode decomposition (VMD) and Lempel–Ziv algorithms to assess the crude oil dual attributes. Three steps are involved: 1) conditional autoregressive value at risk measures the crude oil risk; 2) they are decomposed by the VMD algorithm into submodes; 3) the Lempel–Ziv algorithm is applied to analyze the crude oil risk for each, thereby identifying the oil commodity or oil financial risks. The results of the empirical analysis reveal significantly different amplitudes for the high- and low-frequency crude oil risk. By summarizing the crude oil risk components, we also conclude that the mean value for the oil commodity risk is 0.04, while that for the oil financial risk is 0. What is more, the oil commodity risk is highly related to downward trends in oil prices, while the oil financial risk exerts the same clustering effect as oil returns.</p> </abstract>

Stock return predictability in China: Power of oil price trend

We study the predictability of stock risk premia in the Chinese stock market using oil factors. In contrast to existing findings that oil price changes render little forecasting power for stock returns, we propose an oil price trend factor which strongly predicts excess stock returns in-sample and out-of-sample at both market- and industry-level. Notably, the predictive power of the oil trend factor is unspanned by the existing predictors and macroeconomic risks. We find that the trend factor negatively predicts equity risk premia which is due to the fact that our trend factor represents oil demand-driven force in determining asset return premium.

A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features

Reliable forecasting of crude oil price has received a prodigious attention by both investment companies and governments. Motivated by this issue, this paper seeks to propose a new hybrid forecasting model for crude oil price trend prediction. For this purpose, the crude oil price series is initially decomposed by variational mode decomposition algorithm, and the multi-modal data features are extracted based on the decomposed modes. The volatility of crude oil prices is simultaneously converted into trend symbols through symbolic time series analysis. Machine learning multi-classifier are then trained with multi-modal data features and historical volatility as input and trend symbols as output. The well-trained models are used to predict the trend symbols of West Texas Intermediate crude oil future price. Empirical results demonstrate that the proposed hybrid forecasting model outperforms its counterparts. Among the classifiers used, the hybrid prediction model using support vector machine classifier exhibits superior predictive ability. The accuracy of the proposed model for predicting high volatility of crude oil prices is evidenced to be better than that of low volatility.

The Tax Cuts and Jobs Act: Searching for Supply-Side Effects

The Tax Cuts and Jobs Act of 2017 (TCJA) instituted the most substantial changes in taxation in decades and was designed to boost the economy via supply-side incentives. This paper reviews these changes and examines the impacts on economic aggregates through 2019. The act clearly reduced revenue. The effect on gross domestic product is difficult to tease out of the data. Investment growth rose after the TCJA was enacted, but it was driven by trends in aggregate demand, oil prices, and intellectual capital that were unrelated to the TCJA’s supply-side incentives. Growth in business formation, employment, and median wages slowed after the TCJA was enacted. International profit shifting fell only slightly, and the boost in repatriated profits primarily led to increased share repurchases rather than new investment.