Oil Price Forecasting Research Articles

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

The oil industries are an important part of a country’s economy. The crude oil’s price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries (OPEC) oil price forecasting. Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods. More precisely, the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA (1,1,1), ARIMA (0,1,1), autoregressive moving average (ARMA) (0,1), vector autoregression (VAR), random walk (RW), support vector machine (SVM), and random forests (RF) models by 99.94%, 99.62%, 99.87%, 99.65%, 7.55%, 98.38%, and 99.35%, respectively, according to mean square error metric.

Crude oil price prediction using temporal fusion transformer model

In this paper, we applied the temporal fusion transformer model to the crude oil price movement modeling and forecasting. The temporal fusion transformer model has been adopted in the crude oil price forecasting model using the attention mechanism, to capture the different level of autocorrelations among observations in the crude oil prices. Empirical evaluation of the transformer based multi-horizon ahead crude oil price forecasting model has been conducted. Experiment results show that the introduction of transformer model in the forecasting process has improved the forecasting accuracy significantly at longer time horizon.

Keyword Research-based stock market oil price forecast validity test with neural network

Stock market and global economic processes are often based on speculation, and in certain oligopoly markets, decisions are preceded by the prediction of the expected decisions of other players, i.e. we can also speak of speculative operation. In today's fast-paced world, the online press has a great influence on opinion formation through expert articles and analyses. In this article, we examine the extent to which Wall Street Journal articles influence or can influence investor inclination. In other words, the future price change can be predicted from the analysis of the articles, or more precisely, how effectively it can be predicted.

Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

In this study, the crude oil spot price is forecast using Bayesian symbolic regression (BSR). In particular, the initial parameters specification of BSR is analysed. Contrary to the conventional approach to symbolic regression, which is based on genetic programming methods, BSR applies Bayesian algorithms to evolve the set of expressions (functions). This econometric method is able to deal with variable uncertainty (feature selection) issues in oil price forecasting. Secondly, this research seems to be the first application of BSR to oil price forecasting. Monthly data between January 1986 and April 2021 are analysed. As well as BSR, several other methods (also able to deal with variable uncertainty) are used as benchmark models, such as LASSO and ridge regressions, dynamic model averaging, and Bayesian model averaging. The more common ARIMA and naïve methods are also used, together with several time-varying parameter regressions. As a result, this research not only presents a novel and original application of the BSR method but also provides a concise and uniform comparison of the application of several popular forecasting methods for the crude oil spot price. Robustness checks are also performed to strengthen the obtained conclusions. It is found that the suitable selection of functions and operators for BSR initialization is an important, but not trivial, task. Unfortunately, BSR does not result in forecasts that are statistically significantly more accurate than the benchmark models. However, BSR is computationally faster than the genetic programming-based symbolic regression.

Modelling the impact of disease outbreaks on the international crude oil supply chain using Random Forest regression

PurposeThe crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression.Design/methodology/approachThis study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression.FindingsThe findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further.Originality/valueThis is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.

An integrated model for crude oil forecasting: Causality assessment and technical efficiency

In light of the central role of crude oil in the economy and the complex mechanisms involved in forecasting crude oil prices, this study proposes a two-stage model that optimally selects driving predictors for crude oil price forecasting by integrating Granger causality test (GCT) and stochastic frontier analysis (SFA). In the first stage, GCT is used to perform causality assessments for 92 predictors across eight categories of factors (demand, supply, inventory, financial market, macroeconomy, economic policy uncertainty, geopolitical risk, and technical indicator). In the second stage, SFA is employed to assess the forecasting power of the preliminarily selected predictors in terms of technical efficiency by using multiple evaluation measures. By collecting a data sample which spans a 21-year period from January 1, 2000 to December 31, 2020, we conduct a comprehensive empirical study by employing rolling time window technique. The empirical results demonstrate that the two-stage model significantly outperforms eight competing models in terms of four forecasting techniques (linear regression, artificial neural network, support vector regression, and random forest). The proposed model's outperformance is robust to different time windows, different forecast horizons, alternative proxies of crude oil prices, and different business conditions. We also explore the time-varying characteristics of predictors for crude oil price forecasting and confirm that financial factors remain vital determinants affecting oil prices.

Oil price assumptions for macroeconomic policy

We evaluate the economic usefulness of oil price forecasts by means of conditional forecasting of five US macroeconomic indicators. First, we forecast oil prices using a mixed sampling frequency framework, where oil prices are driven by information available at high-frequency; and subsequently we proceed with our macroeconomic conditional forecasts. Overall, there is diminishing importance of oil price forecasts for inflation projections, whereas the reverse holds true for inflation expectations, industrial production and producers price index. An array of arguments is presented as to why this might be the case. Our findings remain robust to alternative forecasting frameworks and model specifications.

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.

A novel hybrid method for oil price forecasting with ensemble thought

As a globally hard currency, crude oil has influenced all aspects of the world, especially because volatility in its price seriously affect economies, politics and wars. The prediction of oil prices is challenging due to remarkable price volatility, uncertainty, elusiveness, and complexity. There have been many papers adopting traditional machine learning (ML), economic approaches or combinations of the two; however, these papers have rarely focused on the performance of individual models and ignored less popular but complex models. Considering the above difficulties and situations, in this paper, we design an advanced approach for valuable and robust forecasting of crude oil prices to investigate the differences among the three other popular methods. For this purpose, the Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM) are employed in the proposed hybrid method. The merits of this hybridization lie in the fact that the ensemble model is capable of handling volatile features such as nonlinearity, noncyclicity, and market interrelationships existing in oil price time series; incorporating the strengths of the three single models; not easily overfitting; and finally achieving a better performance. The results reveal that the hybrid method achieves the highest Directional Accuracy (DA) and the lowest errors (0.9612, 13.7417, and 0.0368 of DA, MAE, and MAPE, respectively) compared with the results of three other methods-the RF (0.9569, 14.2699, and 0.0385, respectively), XGB (0.9526, 14.5111, and 0.0387, respectively), and the LGBM (0.9526, 14.4022, and 0.0384, respectively)-in experiments on real datasets.

Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling.

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.

Canola and soybean oil price forecasts via neural networks

Forecasts of commodity prices are vital issues to market participants and policy-makers. Those of cooking section oil are of no exception, considering its importance as one of main food resources. In the present study, we assess the forecast problem using weekly wholesale price indices of canola and soybean oil in China during January 1, 2010–January 3, 2020, by employing the non-linear auto-regressive neural network as the forecast tool. We evaluate forecast performance of different model settings over algorithms, delays, hidden neurons, and data splitting ratios in arriving at the final models for the two commodities, which are relatively simple and lead to accurate and stable results. Particularly, the model for the price index of canola oil generates relative root mean square errors of 2.66, 1.46, and 2.17% for training, validation, and testing, respectively, and the model for the price index of soybean oil generates relative root mean square errors of 2.33, 1.96, and 1.98% for training, validation, and testing, respectively. Through the analysis, we show usefulness of the neural network technique for commodity price forecasts. Our results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula

This paper introduces methodologies in forecasting oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. We also apply Bayesian variable selection and nonlinear principal component analysis (NLPCA) for data dimension reduction. With a reduced number of important covariates, we also forecast oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. To apply real data to the proposed methods, we select monthly log returns of 2 oil prices and 74 large-cap, major S&P 500 stock prices across the period of February 2001–October 2019. We conclude that vine copula regression with NLPCA is superior overall to other proposed methods in terms of the measures of prediction errors.

A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events.

This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00500-022-07276-5.

Spillover of international crude oil prices on China's refined oil wholesale prices and price forecasting: Daily-frequency data of private enterprises and local refineries

Compared with retail prices of state-owned companies used in almost all existing studies, China's refined oil wholesale prices of private enterprises and local refineries are more affected by the market and better reflect the real supply-demand situation. For the first time, this paper applies own-monitored daily-frequency wholesale prices of China's private enterprises and local refineries during 2013–2020 to derive spillover effects of international crude oil prices on China's refined oil prices through the VAR-BEKK-GARCH (vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroscedasticity) model, and then tries to forecast wholesale prices through the PCA-BP (principal component analysis-back propagation) neural network model. Results show that international crude oil prices have significant mean spillover and volatility spillover effects on China's refined oil wholesale prices. Changes in crude oil prices are the Granger cause of changes in refined oil wholesale prices. With the improvement of China's oil-pricing mechanism in 2016, the volatility spillover from the international crude oil market to China's refined oil market gradually increases, and the BRENT price variation has an increasing impact on the refined oil wholesale price variation. The PCA-BP model could serve as a candidate tool for forecasting China's refined oil wholesale prices.

Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm

Crude oil price forecasting provides reference for stabilizing the energy economic, significantly improving the investment and operation decision-making. However, owing to the complexity and significant fluctuations of crude oil market, it is difficult to achieve accurate and reliable crude oil price forecasting. In this study, we develop a novel hybrid crude oil price forecasting model. Firstly, the Hampel identifier is employed to remove outliers in crude oil price series. Subsequently, complete ensemble empirical mode decomposition is used to reduce noise. Then a novel modified multi-objective water cycle algorithm is proposed to optimize the parameters of echo state network. Finally, deterministic and uncertainty prediction is conducted to verify the model performance. The results reveal that the proposed hybrid model outperforms various contract models in deterministic and interval predictions, as well as in daily and weekly forecasting. Therefore, the proposed hybrid model is a reliable tool for crude oil price forecasting and serve as a reference for decision making in energy economic market.

Geopolitical risk and oil price volatility: Evidence from Markov-switching model

This study explores the predictability of geopolitical risk (GPR) on oil market volatility using autoregressive Markov-regime switching model, and obtains several remarkable findings. First, in-sample results show that high GPR can lead to high fluctuations in oil market. Considering different market states, GPR has different effects. Second, out-of-sample results indicate that GPR has useful information to forecast oil market volatility. Compared to expansions, GPR has a more powerful ability for forecasting oil price volatility during recessions, which are robust to different robustness tests. Third, GPR is effective in long-term forecast horizons. Moreover, geopolitical risk threats and acts are helpful in forecasting oil price volatility, especially geopolitical risk threats.

Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework

Crude oil is an important global commodity, and its price fluctuation affects the political and economic security of a country. Therefore, it is necessary to conduct crude oil price forecasting. Based on the forecasting research of multi-source information and decomposition-ensemble, we combine the two into a model and propose a multi-perspective crude oil price forecasting model under a new decomposition-ensemble framework. Specifically, the crude oil price series is decomposed and reconstructed into several modes through variational mode decomposition (VMD) and fuzzy entropy (FE). Further, we screen the effective predictors from structured and unstructured multi-source data using the Granger causality test, and select the optimal input features through random forest - recursive feature elimination (RF-RFE). Finally, each reconstruction mode is individually forecasted on the basis of the selected different input features and the forecasting values obtained are combined and integrated; the final result is obtained from the integrating prediction results through the error evaluation criterion. The West Texas Intermediate (WTI) daily spot price is adopted to validate the performance of our proposed model. The empirical results show that compared with the benchmark models, the proposed model can significantly improve forecasting accuracy.

Modelling of Volatility in Nigeria Crude Oil Price Using symmetric and asymmetric GARCH models

Accurate modelling of volatility is important in finance, particularly as it relates to the modelling and forecasting of crude oil prices. This paper examines which of the model can best handle the price volatility of the Nigeria Crude Oil market. The models consider in this work are the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and Power Autoregressive Conditional Heteroskedasticity (PARCH). The analysis displays the evaluation of the enactment of Nigerian crude oil price for GARCH (1, 1), EGARCH (1, 1) and PARCH (1, 1) models. The data used was collected from the Central Bank of Nigeria website for twelve (12) years (2010 – 2021) which make up of 2422 observations. Time Series was carried out using Eviews 9 and the result shows that the GARCH model outperforms EGARCH and PARCH models based on the value of Akaike Information Criteria.

Global economic conditions index and oil price predictability

The numerous academics rely on exogenous drivers to improve the accuracy of oil price forecasting. This study mainly explores whether a new indicator of global economic conditions proposed by Baumeister et al. (2020) can successfully predict the oil price. Our empirical results reveal that the global economic conditions index can extremely improve the accuracy in forecasting oil price in terms of univariate and bivariate analysis. In addition, compared with 14 traditional macroeconomic variables, global economic conditions index exhibits incremental predictive content in forecasting oil price. The longer forecasting horizons analysis confirms the superior forecasting performance of the global economic condition index for short-term (h = 2 and h = 3). Our findings can provide important implications to market participants in crude oil market.

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.