Crude Oil Price Data Research Articles

Multistep Brent oil price forecasting with a multi-aspect aeta-heuristic optimization and ensemble deep learning model

Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude oil price forecasting, achieving accurate forecasts remains challenging. Deep learning models’ performance is heavily influenced by hyperparameters tuning, and they are expected to perform differently under various circumstances. Furthermore, price volatility is also sensitive to external factors such as world events. To address these limitations, we propose a hybrid approach that integrates metaheuristic optimisation with an ensemble of five widely used neural network architectures for time series forecasting. Unlike existing methods that apply metaheuristics to optimise hyperparameters within the neural network architecture, we exploit the GWO metaheuristic optimiser at four levels: feature selection, data preparation, model training, and forecast blending. The proposed approach has been evaluated for forecasting three-ahead days using real-world Brent crude oil price data, and the obtained results demonstrate that the proposed approach improves the forecasting performance measured using various benchmarks, achieving 0.000127 of MSE.

Statistical inference for innovation distribution in ARMA and multi-step-ahead prediction via empirical process

The kernel distribution estimator (KDE) is proposed based on residuals of the innovation distribution in the autoregressive moving-average (ARMA) time series. The deviation between KDE and the innovation distribution function is shown to converge to the Brownian bridge, leading to the construction of a Kolmogorov–Smirnov smooth simultaneous confidence band for the innovation distribution function. Additionally, an empirical cumulative distribution function (CDF) based on prediction residuals is introduced for the multi-step-ahead prediction error distribution function. This empirical process weakly converges to a Gaussian process with a specific covariance function. Furthermore, a quantile estimator is derived from the empirical CDF of prediction residuals, and multi-step-ahead prediction intervals (PIs) for future observations are established using these estimated quantiles. The PIs achieve the nominal prediction level asymptotically for the finite variance ARMA model. Simulation studies and analysis of crude oil price data confirm the validity of the asymptotic theory.

Forecast performance of noncausal autoregressions and the importance of unit root pretesting

AbstractBased on a large simulation study, this paper investigates which strategy to adopt in order to choose the most accurate forecasting model for mixed causal‐noncausal autoregressions (MAR) data generating processes: always differencing (D), never differencing (L), or unit root pretesting (P). Relying on recent econometric developments regarding forecasting and unit root testing in the MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 10% level is a good compromise. In fact, it never departs too much from the best model in terms of forecast accuracy, unlike the L (respectively, D) strategy when the DGP becomes very persistent (respectively, less persistent). This approach is illustrated using recent monthly Brent crude oil price data.

Selection of Modelling for Forecasting Crude Palm Oil Prices Using Deep Learning (GRU & LSTM)

The unstable crude palm oil (CPO) prices have an impact on assessments of economic growth and environmental sustainability, as well as market strategies, international trade discussions, and consumer pricing expectations for products made from CPO. Therefore, it is crucial to identify the best prediction method to accurately forecast this cost. This research aims to develop an accurate time series data prediction model for crude palm oil prices using GRU and LSTM methods. The study also aims to identify the best-performing model by comparing their performance. This study uses LSTM and GRU methods as well as Bi-LSTM as a comparison, using crude palm oil price data from the Indonesian Ministry of Trade (August 1, 2018–August 31, 2023) from Medan (1064 data points) and Rotterdam SPOT (617 data), Rotterdam Forward 1 Month (1,022 data), and Rotterdam Forward 2 Month (950 data). Each dataset is then split into training and testing data with a 70:30 ratio. The hyperparameters used are (a) learning rate: (0.001); (b) batch size: 100; (c) node: (512); (d) optimizer: Adam; (e) and epoch: 50. The results of the forecast are highly accurate with a MAPE of less than 10%. Overall, both LSTM and GRU techniques demonstrate excellent performance in forecasting crude palm oil prices, but they may need to be modified based on data features. The ARIMA method can also be considered for forecasting. Future studies may consider optimizing parameters and model structures. Doi: 10.28991/ESJ-2024-08-03-05 Full Text: PDF

Fitting Alternative Autoregressive and Moving Average Models to Nigeria Crude Oil Prices

The need to compare the efficiency between the Autoregressive Integrated moving average (ARIMA (p,d,q)) models when modelling Crude oil price is the motivation behind this research. The research focuses on different orders of Autoregressive Integrated moving average models. The trend Analysis of the original series were plotted and was observed that crude oil prices were not stationary. The data were transformed by taking a natural log and the series becomes stationary after first differenced. The ACF and PACF of the stationary time series were also plotted which were the basis for the suggested ARIMA models. Error variances for the suggested ARIMA (p,d,q) models were derived and estimated as the basis for model performance comparison. Empirically, Crude Oil Price data spanning from January 2006 to July 2023 were used for the analysis. Findings from the study has revealed that, ARIMA (2,1,1) with the least error variance outperformed the other suggested models. The study further stated the estimated models for forecast of the future value of the crude oil price. The study recommends the use of error variance as a criterion for best model suggestion and ARIMA (2,1,1) was selected as the best model for modelling Nigeria Crude oil price.

Crude Oil Price Bubble Identification and Risk Assessment From Different Spot Markets: Establishing a Sustainable Energy Policy

Crude oil, as a strategic energy resource for social development, has witnessed substantial price fluctuations in recent years, impacting the sustainability of economy, ecological environment, and energy security. This paper utilized weekly crude oil price data from January 2003 to March 2023 to examine price bubbles in different markets using the SADF and GSADF methods, identified the duration of price bubbles by comparing the BSADF series with critical value series, assessed the risk of price bubbles in different markets using the established comprehensive indicators, and explored sustainable energy policies from the perspectives of both crude oil importers and exporters. The findings of this study indicate that bubbles exist in the world’s major crude oil markets, with linkages and differences in their origin and termination across different markets. The risk of price bubbles varies widely across different crude oil markets and has significant geographical characteristics, with a higher risk in the markets such as OPEC, Oman, and Dubai, and a lower risk in the markets such as WTI, Brent, and Daqing. This paper proposes sustainable energy policies from the perspective of energy importers and exporters. This is of great importance to enhance the ability of different countries to cope with the risk of crude oil price bubbles and to ensure the sustainability of economic development, ecological environment, and energy security.

Power of Unit Root Tests Against Nonlinear and Noncausal Alternatives with an Application to the Brent Crude Oil Price

Abstract The increasing sophistication of economic and financial time series modelling creates a need for a test of the time dependence structure of the series which does not require a proper specification of the alternative. Indeed, the latter is unknown beforehand. Yet, the stationarity has to be established before proceeding to the estimation and testing of causal/noncausal or linear/nonlinear models as their econometric theory has been developed under the maintained assumption of stationarity. In this paper, we propose a new unit root test statistics which is both asymptotically consistent against all stationary alternatives and still keeps good power properties in finite sample. A large simulation study is performed to assess the power of our test compared to existing unit root tests built specifically for various kinds of stationary alternatives, when the true DGP is either causal or noncausal, linear or nonlinear stationary. Based on various sample sizes and degrees of persistence, it turns out that our new test performs very well in terms of power in finite sample, no matter the alternative under consideration. The proposed approach is illustrated using recent Brent crude oil price data.

Early warning of critical transitions in crude oil price

Critical transitions in the crude oil price are important in market management. Previous methods have focused on generic early warning signals in complex systems if a critical transition is approaching and ignored the specific feature of the oil price. This paper proposes a heteroscedastic network model in which early warnings of critical transitions are identified based on the community structure of the network representing the dynamic process of a time series. Using WTI crude oil price data, we detect early warning signals of critical transitions. Our findings indicate that major switches exist between early warnings and critical transitions in different periods, and the corresponding features can be analyzed based on the fundamentals and expectations of traders. Importantly, based on the network indicators associated with early warnings, the fundamental features may be similar during certain periods, and the changes in fundamentals and expectations before and after critical transitions are not random. A new complex system perspective is used to explore early warnings for critical transitions, and useful implications for energy-related market investors and policy-makers are provided.

Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.

The Impact of Changes in Crude Oil price on the Yield and Volatility of Manufacturing: Evidence from the US and China

This study is based on the data of WTI international futures crude oil price, China manufacturing Index and the United States Dow Jones Manufacturing Index from November 23, 2021 to June 16, 2022. This paper uses VAR and ARMA-GARCH models to analyze the changes in yield and volatility of manufacturing in the US and China. The results show that the change of crude oil price under the situation of Russia-Ukraine conflict has a negative effect on the return rate of manufacturing industry in China and the United States, but the impact on the return rate of manufacturing industry in China is greater than that of Dow Jones, and the impact on the return rate of manufacturing industry in the United States will gradually disappear as time goes by. In order to reduce the adverse impact of oil price fluctuations on manufacturing industry, this study puts forward some suggestions, such as adopting diversified ways of oil import and export trade and actively developing and utilizing other energy sources.

Modelling of crude oil price data using hidden Markov model

PurposeOne of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit extreme fluctuations during periods of different times of market uncertainty, it became hard to the governments to predict accurately the prices of crude oil in order to build their financial budgets. Therefore, this study aims to analyse and model crude oil price using the hidden Markov process (HMM).Design/methodology/approachTraditional mathematical approaches of time series may be not give accurate results to measure and analyse the crude oil price, since the latter has an unstable and fluctuating nature, hence, its prediction forms a challenge task. A novel methodology that is so-called the HMM is proposed that takes into account the heterogeneity in prices as well as their hidden state-based behaviour.FindingsUsing the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 into December 2021. The model selection criteria and measures of the prediction performance of each model are applied to choose the best model. Movements of crude oil prices exhibit extreme fluctuations during periods of different times of market uncertainty. The processes of model estimation and the model selection were conducted in Python V.3.10, and it is available from the first author on request.Originality/valueUsing the Bayesian approach, several estimated models with different ranks are fitted to a non-homogeneous data of Iraqi crude oil prices from January 2010 to December 2021.

Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia

Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) model. After modeling, it is proven that the world crude oil price data for the period January 2002 to June 2022 has a heteroscedasticity effect that cannot be overcome if only using the ARIMA model. The results of data processing show that the ARIMA (0,1,2) followed by the ARCH (2) is the best model with a MAPE value of 5,32%. The accuracy values obtained are classifield as very good for forecasting world crude oil prices.

Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm

Crude oil prices play a significant role in the global economy, therefore accurate prediction of oil prices is very important. Therefore, a forecasting model is needed to predict Crude Oil Prices. The purpose of this study is to forecast the price of crude oil from Indonesia (ICP). The data source is from a website published by the Ministry of Energy and Mineral Resources (ESDM), namely monthly crude oil price data specifically for six main types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data used is data for a period of 5 years (2018 – 2022). The data available is in the form of time series data. Dated Brent combined with the Alpha factor for each month and year is a reference in determining the ICP price. Forecasting Indonesian crude oil prices in the future is based on the historical oil price of the previous period. The Data Mining algorithm used for forecasting is Multiple Linear Regression. The dataset processed using training data is 80%, and testing data is 20%. The model produced, on average, has a good level of accuracy in calculating MAPE where for SLC = 9%, Attaka = 45%, Duri = 126%, Belida = 33%, Banyu = 150% and SC = 50%. Based on the MAPE calculation value, the Linear Regression Equation to predict Indonesian Crude Oil Prices (ICP) shows that the model produced by SLC crude oil is very good. Attaka, Belida and SC crude oil yielded fair yields and Duri and Banyu crude oil yielded poor yields.

Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator

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.

An extreme bias-penalized forecast combination approach to commodity price forecasting

Forecast combination, a well-established technique for improving forecasting accuracy, investigates the integration of competing forecasts to produce a composite superior to individual forecasts. In this study, we propose a novel forecast combination method that would reduce overfitting risk and improve forecast’s generalization ability. To capture the extreme bias of a forecast in combination process, we define a measurement PaR for forecast combination. A novel PaR-based loss function with an elastic net is proposed that can effectively trade off the sparsity of weights to mitigate the risk of underfitting or overfitting. An improved artificial bee colony algorithm-based optimization method is introduced to achieve the optimal weights. The experimental results on gold, silver and crude oil price data demonstrate that the proposed forecast combination approach can outperform not only individual models but also combination approaches like simple averaging and other competitive benchmarks. The MAPE achieved by the presented method could decrease by 10.98%, 5.03% and 10.28% in gold, silver and crude oil price forecasting respectively, compared to the best individual model.

Forecasting crude oil prices volatility by reconstructing EEMD components using ARIMA and FFNN models

The energy sector which includes gas and oil is concerned to explore and develop refined oil and it’s a multitrillion business. As crude oil is a very important source of energy, and it has a very valuable impact on a country’s economic growth, national security, and social stability. Therefore, accurately predicting the crude oil price volatility is a very important topic of research and still, it is a challenge for researchers to accurately forecast crude oil prices. Therefore, this study is conducted to address the said problem significantly. This research presents a novel hybrid method for reconstructing EEMD IMFs that involves two steps. Visual analysis of Average Mutual Information (AMI) graphs were used to rebuild IMFs. EEMD IMFs were split into two components called stochastic and deterministic. In the proposed method, reconstruction of IMFs of EEMD was done at two stages to see if the stochastic components have more variation. Later, ARIMA and FFNN models were used to test the suggested method’s performance. For this purpose, Brent crude oil prices data was used, and the hybrid model EEMD-S2D1D2-ARIMA/FFNN outperformed the other existing hybrid model with minimum MAE = 0.2323, RMSE = 0.3058 and MAPE = 0.5273. A simulation study was also conducted to check the robustness of the proposed method for N = 50, 500, 1,000, 2000, 5,000, and 7,500. The simulation results also confirm that the unpredictability present in the reconstructed IMFs of the hybrid models EEMD-ARIMA/FFNN and EEMD-SD-ARIMA/FFNN has been reduced by the proposed hybrid models.

Spillover effect of crude oil futures market: An empirical research from emerging market

The world crude oil market is an important place for investors to engage in international financial investment activities, which profoundly impacts the economic activities of all countries. This paper aims to provide empirical support for the decision-making behavior of cross-border enterprises, international investors, and government regulatory authorities. This paper constructs a risk spillover effect measurement model based on the GARCH-CoVaR model. Firstly, the GARCH model fits the logarithmic rate of return of RMB and crude oil price data. Secondly, Copula is used to estimate its joint distribution. Finally, the risk spillover effect is calculated by the Covar model. We selected the emerging market (Shanghai crude oil futures market) as the research object. The research found that the Shanghai crude oil futures market has a significant inverse volatility spillover to the onshore exchange rate market and a two-way positive volatility spillover to the offshore exchange rate market; Then, we conducted a robustness analysis and found that there is a significant two-way risk spillover among offshore exchange rate market and international crude oil market, and there is a downward risk spillover among international crude oil market and onshore exchange rate market.

Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets

Crude oil price predictability has continually been considered as a fundamental argument of finance literature, given its critical propositions for risk management, investment decisions, and commercial and financial policymaking. This work presents an innovative learning framework for efficient predictive modeling of daily and weekly crude oil price (COP) information, which aims to enable sustainable management in oil markets. Firstly, an optimized version of variation mode decomposition (OVMD) is proposed to adaptively decompose the original COP time series into multiple modes based on a set of optimized parameters calculated with a Tree-structured Parzen Estimator (TPE) algorithm. Secondly, an AdaBoost algorithm is redesigned using random forest (RF) to model the future price information in the modes with the high frequency. Thirdly, a new deep network is presented to develop automatically learn spatial–temporal representations from decomposed COP data, where a novel Conv-former module is designed to efficiently extract local as well as global spatial representations without incurring extra computational costs. Followingly, Multiple Long short-term Memory (LSTM) networks are stacked to learn temporal representations from input modes. To further empower the representation power of our framework, a new bidirectional learning module is presented to stack the LSTM layer to learn from COP data in the forward and backward directions. To validate the efficiency of the proposed framework, this work performs experimental simulations and analyses based on a case study from Brent crude oil prices at both daily and weekly scales. The experimental findings show up the competent predictive modeling capabilities of the proposed framework over the cutting-edge methods rendering it as a promising solution to enable sustainable management in crude oil markets. The proposed framework can be generalized to different predictive modeling tasks and hence qualified to be used as a valuable tool for oil portfolio creation, property pricing, and risk management in Crude Oil Markets.

Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy.

The impact of oil price change and its volatility on major global stock markets

This paper investigates the impact of oil price change, and its volatility on the stock market returns of the US, Japan, and China.Moreover it highlights the association between the returns of each market with respect to the returns of the other markets. More specifically, the study uses daily data of three indices to represent the markets which are Standard & Poor 500, Nikkei 225, and Shanghai Composite (SSE), beside daily data of crude oil price. The GARCH model employed to measure the volatility. The findings indicate that, the S&P500 returns are influenced significantly by oil price change, the returns of both N225 and SSE, as well as its own past volatility. Regarding Nikkei 225 returns it influenced by the returns of the S&P500 and SSE, and by its own previous day volatility. In contrast, Shanghai Composite returns are not influenced neither by oil price change, nor by the returns of the two other indices, rather it’s only affected by its own previous volatility, and S&P500 and Nikkei 255 returns are influencing each other. The impact of oil price volatility on the three stock indices returns is insignificant during the sample period.