Crude Oil Price Series Research Articles

Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss

Prediction methods have garnered significant attention in intelligent decision-making. Most existing approaches to predicting crude oil prices prioritize accuracy and stability while providing precise prediction intervals (PIs) that can offer valuable insights. Thus far, we introduce a novel hybrid model to forecast future crude oil prices. Our approach leverages the variational mode decomposition (VMD) to simplify the complexity of the original time series, yielding a set of subseries. These subseries are then modeled using a deep neural network architecture called a gated recurrent unit (GRU). To address the prediction uncertainty, we employed the pinball loss function rather than the mean square error (MSE) to guide the proposed VMD-GRU. This adaptation extends the traditional GRU-based point forecasting to probabilistic forecasting by estimating quantiles. We evaluated our proposed model on a well-established crude oil price series by conducting both single- and multi-step-ahead forecasting analyses. Our findings underscore the efficacy of the combined model, demonstrating its superior predictive performance compared to benchmark models.

A Crude Oil Spot Price Forecasting Method Incorporating Quadratic Decomposition and Residual Forecasting

The world economy is affected by fluctuations in the price of crude oil, making precise and effective forecasting of crude oil prices essential. In this study, we propose a combined forecasting scheme, which combines a quadratic decomposition and optimized support vector regression (SVR). In the decomposition part, the original crude oil price series are first decomposed using empirical modal decomposition (CEEMDAN), and then the residuals of the first decomposition (RES) are decomposed using variational modal decomposition (VMD). Additionally, this work proposes to optimize the support vector regression model (SVR) by the seagull optimization algorithm (SOA). Ultimately, the empirical investigation created the feature-variable system and predicted the filtered features. By computing evaluation indices like MAE, MSE, R2, and MAPE and validating using Brent and WTI crude oil spot, the prediction errors of the CEEMDAN -RES.-VMD -SOA-SVR combination prediction model presented in this paper are assessed and compared with those of the other twelve comparative models. The empirical evidence shows that the combination model being proposed in this paper outperforms the other related comparative models and improves the accuracy of the crude oil price forecasting model.

A novel crude oil price forecasting model using decomposition and deep learning networks

Crude oil prices are susceptible to a variety of elements, including military, political, financial, and diplomatic forces, resulting in price fluctuations that are random, abrupt, and chaotic. Based on the chaotic nature of crude oil price series, this paper proposes a model for predicting crude oil prices that incorporates variational modal decomposition algorithm (VMD), phase space reconstruction technique (PSR), convolutional neural network (CNN), and bidirectional long and short-term memory network (BiLSTM). Specifically, the noises in the original data are removed using VMD. Next, the crude oil price is reconstructed using PSR. Then the reconstructed and denoised phase space is fed into the CNN-BiLSTM model for multi-step predictions. The empirical results show that the proposed model achieves the lowest MAPEs as 0.662%, 2.283%, 3.829%, and 4.44% and the lowest MSEs as 0.335, 3.942, 10.047 and 13.681. The Diebold Mariano (DM) test also demonstrates the proposed model’s superiority in the domain of crude oil price forecasting.

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

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.

Study of the Modal Evolution of the Causal Relationship between Crude Oil, Gold, and Dollar Price Series

The rise and fall of crude oil prices, gold prices, and US dollar exchange rates is of great significance to investors. It is necessary to explore the evolution characteristics of the causal relationship between crude oil, gold, and US dollar, so as to provide reference for investors. We selected the ICE Brent crude oil continuous futures, the ICE WTI crude oil continuous futures, the NYMEX light crude oil continuous futures, the COMEX gold continuous futures, and the US dollar index as the research objects. Based on the theory of pattern causality, the causal relationship between crude oil, gold, and US dollar is divided into positive causal relationship (T), negative causal relationship (F), and disordered causal relationship (D). The causal modal evolution network of crude oil and gold prices against the US dollar index (BWCGG-U) and the causal relationship modal evolution network of the US dollar index on the price of crude oil and gold (U-BWCGG) were constructed. Complex network and sliding window are used to study the evolution characteristics of short-term causality between crude oil and gold and US dollar in 60 days. The results show that (1) the modes of causality between crude oil, gold, and the US dollar index are constantly changing, with a few key modes. The key modes are often transmitted to themselves. (2) The most critical modes of two networks are all FFFF, and the negative causality between gold and the US dollar index is more stable. Investors based on the gold (dollar index) price to judge the dollar index (gold futures) investment program are more reliable. (3) When crude oil and gold show causal modes of FFFF and DDDF, respectively, the emergence and transformation of the modes can be maintained for a period of time to provide a certain guarantee for investors’ investment. However, its internal conversion rate is high, which needs to be judged in combination with the actual situation to improve the forecasting ability.

Research on crude oil price forecasting based on computational intelligence

<abstract><p>The crude oil market, as a complex evolutionary nonlinear driving system, is by nature a highly noisy, nonlinear and deterministic chaotic series of price series. In this paper, a computational intelligence-based portfolio model is constructed to forecast crude oil prices using weekly price data of West Texas intermediate crude oil (WTI) crude oil futures from 2011 to 2021. First, the WTI crude oil price series are decomposed using the ensemble empirical modal decomposition method (EEMD) and the set of component series is reconstructed using the cluster analysis method. Second, the reconstructed series are modeled and predicted using neural network models such as time-delay neural network (TDNN), extreme learning machine (ELM), multilayer perceptron (MLP) and the GM (1, 1) gray prediction algorithm and the output of the model with the best prediction effect for each component is integrated. Finally, the EGARCH model is used to further optimize the predictive power of the combined model and output the final predicted values. The results show that the combined model based on computational intelligence has higher forecasting accuracy than single models such as GM (1, 1), ARIMA, MLP and the combined EEMD-ELM model for forecasting crude oil futures prices.</p></abstract>

How does economic policy uncertainty respond to the global oil price fluctuations? Evidence from BRICS countries

In this paper, we explore the effect of global crude oil price on the economic policy uncertainty (EPU) in BRICS countries. For this purpose, we have adopted the hybrid wavelet artificial neural network model (wavelet-ANN) and the threshold vector auto-regression model (TVAR) to decompose the global crude oil price series into different market participants' horizons and to examine its comprehensive and detailed impact on different underlying oil price change regimes. The empirical results are as follows: Firstly, we find that oil price has heterogeneous effects on EPU in different BRICS countries. In contrast, the EPU of oil importers among BRICS countries (China and India) significantly negatively affect global oil price in the long run. Secondly, in each country, the influence of oil price on EPU shows a nonlinear and asymmetric feature. For China, a significant effect exists in the high-fluctuation regime in the short run. Moreover, the significant negative response of Brazil's EPU to oil prices also occurred in the high-fluctuation regime. Regarding Russia, the significant negative effect occurs in the low-fluctuation regime. Similar results can also be found in India. In the long run, only in India, the fluctuation of global crude oil price has a significant positive impact on India's EPU.

ANALYSIS OF INTERNATIONAL OIL PRODUCTION COMPETITON USING GAME THEORY

Uluslararası ham petrol piyasasında oyuncuların OPEC (Petrol İhraç Eden Ülkeler Örgütü) üye ülkeleri ile OPEC’e üye olmayan petrol üreten ülkeler (non-OPEC) oldukları varsayımı yaygındır. Söz konusu oyuncuların kâr maksimizasyonu ve/veya petrol piyasalarını kontrol edebilme hedefiyle üretim seviyesi ve fiyat gibi çeşitli strateji kümeleri kullanarak rekabet eğilimine girmeleri söz konusu piyasanın oyun modelleri ile analiz edilebilirliğini ortaya koymaktadır. Bu çalışmanın amacı, OPEC ve non-OPEC ülkelerinin rekabete dayalı petrol üretim miktarlarını oyun teorisi temelinde analiz etmektir. Bu amaç doğrultusunda, oyunculara ait 1972-2019 dönemini kapsayan üretim miktarları ve ham petrol fiyat serileri kullanılmıştır. Petrol piyasalarındaki rekabetin varlığına işaret eden üretim fonksiyonlarına ait katsayılar Tam Bilgi En Küçük Kareler (FMOLS- Fully Modified Ordinary Least Square) yöntemi ile tahmin edilmiştir. Elde edilen modellerden Cournot-Nash ve Stackelberg denge çözümleri hesaplanmıştır. Cournot-Nash denge düzeyine göre Stackelberg denge düzeylerinde lider oyuncunun üretiminin artığı, takipçi oyuncunun üretim düzeyinin ise azaldığı görülmüştür. Bulgular Cournot-Nash denge çıktılarının Stackelberg denge çıktılarından daha yüksek olduğu çalışmalar ile paralellik göstermiştir. Oyuncuların üretim miktarı ve fiyat stratejileri vasıtasıyla optimum üretim düzeyine ulaşmaları için Cournot-Nash dengesinde kalmaları önerilmektedir.

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.

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.

Oil Prices, Emission Permits Trade of Carbon, and the Dependence Between Their Quantiles

The relations between carbon and oil market is concerned by many scholars but little research has focused on the dependence between their quantiles. We use Quantile on Quantile Regression method to study the impact of WTI crude oil price and Daqing crude oil price on carbon price and use wavelet analysis to clean and decompose the time series. Results show that the impact of crude oil on carbon is heterogeneous. Research based on the original sequence shows that crude oil price has a positive impact on carbon price at all quantile levels. Research based on decomposition sequence shows that the positive impact of crude oil on carbon begins to weaken, the zero effect begins to increase, and the negative impact also begins to appear. However, the negative impact on carbon price becomes stronger with the stability of the time series data obtained from the decomposition of crude oil price series gradually improving, while the positive impact gradually weakens.

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.

Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning

Accurately forecasting crude oil prices has drawn much attention from researchers, investors, producers, and consumers. However, the complexity of crude oil prices makes it a very challenging task. To this end, this paper presents a novel scheme by integrating variational mode decomposition (VMD) and random sparse Bayesian learning (RSBL, SBL-based prediction with random lags and random samples), namely VMD-RSBL, for the forecasting task. The proposed VMD-RSBL contains three stages. First, crude oil price series is decomposed into a couple of components by VMD. The decomposed components exhibit simpler characteristics than the raw prices and hence are easy to forecast. Second, RSBL is employed to predict each component individually. Specifically, for each component, the proposed scheme builds a group of predictors with SBL on different subsets of samples (random samples) and random lags, and then the average of all the predictors is taken as the forecasting result of the individual component. At last, the forecasting results of all the components are added as the final forecasting prices. We perform extensive experiments, and the results demonstrate that the proposed VMD-RSBL significantly outperforms many state-of-the-art schemes in terms of several evaluation indicators.

Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach

AbstractThe Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is the primary objective of this study. The other goal of this paper is to expand on the researchers’ previous work by examining long memory and volatilities simultaneously, by using the ARFIMA-sGARCH hybrid model and comparing it against the ARFIMA-fGARCH hybrid model. Consequently, the hybrid models were configured with the monthly Brent crude oil price series for the period from January 1979 to July 2019. These datasets were considered as the global economy is currently facing significant challenges resulting from noticeable volatilities, especially in terms of the Brent crude prices, due to the outbreak of COVID-19. To achieve these goals, an R/S analysis was performed and the aggregated variance and the Higuchi methods were applied to test for the presence of long memory in the dataset. Furthermore, four breaks have been detected: in 1986, 1999, 2005, and 2013 using the Bayes information criterion. In the further section of the paper, the Hurst Exponent and Geweke-Porter-Hudak (GPH) methods were used to estimate the values of fractional differences. Thus, some ARFIMA models were identified using AIC (Akaike Information Criterion), BIC (Schwartz Bayesian Information Criterion), AICc (corrected AIC), and the RMSE (Root Mean Squared Error). In result, the following conclusions were reached: the ARFIMA(2,0.3589648,2)-sGARCH(1,1) model and the ARFIMA(2,0.3589648,2)-fGARCH(1,1) model under normal distribution proved to be the best models, demonstrating the smallest values for these criteria. The calculations conducted herein show that the two models are of the same accuracy level in terms of the RMSE value, which equals 0.08808882, and it is this result that distinguishes our study. In conclusion, these models can be used to predict oil prices more accurately than others.

Comparing the performances of symmetric and asymmetric generalized autoregressive conditionally heteroscedasticity models based on long-memory models under different distributions

This research compares the symmetric and asymmetric effects of generalized autoregressive conditional heteroscedasticity (GARCH)-type models to investigate the volatility of the autoregressive fractionally integrated moving average (ARFIMA) model using the monthly Brent crude oil price series for the period of January 1979–July 2019. The best model of volatility is determined by comparing 13 hybrid models of GARCH (sGARCH, fGARCH, EGARCH, TGARCH, IGARCH, AVGARCH, NGARCH, NAGARCH, APARCH, apARCH, GJRGARCH, gjrGARCH, and csGARCH) in terms of symmetric and asymmetric effects at the level of (1,1). R/S analysis is used to achieve this target. The aggregated variance method, the Higuchi method, and the structural break test are performed to determine the presence of long memory in the dataset. Furthermore, the Hurst exponent method and the Geweke and Porter–Hudak method are used to estimate the fractional difference values. The ARFIMA(2,0.3589648,2)–IGARCH(1,1) model under normal distribution is selected as the best model based on the Akaike information criterion, Schwartz Bayesian information criterion, and by the smallest value for root-mean-squared error, in which this model can be used to predict more accurately than other models.

Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns

The aim of this paper is to investigate the decoupling and recoupling of WTI and Brent prices also with respect to the debate on the regionalisation-globalisation of the two oil markets.To this purpose, we employ the Dynamic Time Warping (DTW) algorithm to identify decoupling events between the two crude oil price series. DTW has been employed for classification and clustering aims in many fields, but in this paper we make a slightly different application of DTW with respect to those provided by the literature, demonstrating how DTW can be employed also to investigate the decoupling between the two oil benchmarks. Our analysis reveals that the two oil benchmarks decouple and recouple according to WTI local market conditions. Therefore, we found evidence that WTI-Brent market is not fully integrated at all times. We also propose two DTW-based indexes: Relative-Alignment Index (RAI) and Warping Index. The first confirms that the greatest decoupling between WTI and Brent occurs because of WTI local market conditions and is useful in highlighting the main decoupling between our crude oil series over time, while the second provides information on the time window of crude oil price decoupling. Lastly, we provide some policy implications based on our results. • We investigate the decoupling-recoupling of WTI/Brent prices (regionalisation-globalisation of both oil markets). • We employ the Dynamic Time Warping algorithm to identify decoupling events between the two crude oil price series. • The two oil benchmarks decouple and recouple according to WTI local market conditions. • We propose two DTW-based indexes: Relative-Alignment Index (RAI) and Warping Index. • RAI confirms the role of WTI local market conditions. WI gives information on the time window of crude oil price decoupling.

Crude oil price prediction based on LSTM network and GM (1,1) model

PurposeAccording to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.Design/methodology/approachFirst, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.FindingsThe model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.Originality/valueThis paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

Oil Price Forecasting Using a Time-Varying Approach

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

Forecasting Volatility of Crude oil Prices using Box-Jenkins's Autoregressive Moving Average: Evidence from Indian Chemical Industry

The current paper deals with to forecast volatility in crude oil prices in Indian economy. In the current study volatility is measured through change in monthly crude oil prices per barrel. The monthly data of crude oil price have taken from January 1995 to May, 2017. The different unit root tests are applied to test check change in crude oil price series is stationary or non stationary. Box-Jenkins's Autoregressive Moving Average of Box-Jenkins methodology has been used for developing a forecasting model. Minimum Akaike Information Criteria (AIC) has been opted to arrive at fit good ARMA model. According to this criteria (4, 3)(0,0) was observed as one of the best model to predict the volatility in future crude oil prices. Forecasted volatility in prices may be utilized for calculating future spot price and hedging future risk. Moreover, forecasted prices volatility of crude oil will also beneficial to oil companies, policy makers for formulating different economic policies and taking some crucial economic decision.