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This paper proposes a novel kernel‐based generalized random interval multilayer perceptron (KG‐iMLP) method for predicting high‐volatility interval‐valued returns of crude oil. The KG‐iMLP model is constructed by utilizing the DK distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval‐valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward DK distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG‐iMLP for crude oil price forecasting. [ABSTRACT FROM AUTHOR]

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Practitioners and policy makers rely on accurate crude oil forecasting to avoid price risks and grasp investment opportunities, but the core of existing predictive models for such prices is based on point-valued inputs and outputs, which may suffer from informational loss of volatility. This paper addresses this issue by proposing a modified threshold autoregressive interval-valued models with interval-valued factors (MTARIX), as extended by Sun et al. [Threshold autoregressive models for interval-valued time series. J. Econom., 2018, 206, 414–446], to analyze and forecast interval-valued crude oil prices. In contrast to point-valued data methods, MTARIX models simultaneously capture nonlinear features in price trend and volatility, and this informational gain can produce more accurate forecasts. Several interval-valued factors and point-valued threshold variables are analyzed, including supply and demand, speculation, stock market, monetary market, technical factor, and search query data. Empirical results suggest that MTARIX models with appropriate threshold variables outperform other competing forecast models (ACIX, CR-SETARX, ARX, and VARX). The findings indicate that oil price range information is more valuable than oil price level information in forecasting crude oil prices. [ABSTRACT FROM AUTHOR]

Crude oil prices are of vital importance for market participants and governments to make energy policies and decisions. In this paper, we apply a newly proposed autoregressive conditional interval (ACI) model to forecast crude oil prices. Compared with the existing point-based forecasting models, the interval-based ACI model can capture the dynamics of oil prices in both level and range of variation in a unified framework. Rich information contained in interval-valued observations can be simultaneously utilized, thus enhancing parameter estimation efficiency and model forecasting accuracy. In forecasting the monthly West Texas Intermediate (WTI) crude oil prices, we document that the ACI models outperform the popular point-based time series models. In particular, ACI models deliver better forecasts than univariate ARMA models and the vector error correction model (VECM). The gain of ACI models is found in out-of-sample monthly price interval forecasts as well as forecasts for point-valued highs, lows, and ranges. Compared with GARCH and conditional autoregressive range (CARR) models, ACI models are also superior in volatility (conditional variance) forecasts of oil prices. A trading strategy that makes use of the monthly high and low forecasts is further developed. This trading strategy generally yields more profitable trading returns under the ACI models than the point-based VECM. [ABSTRACT FROM AUTHOR]

This paper introduces the Poisson distribution, power-law distribution, and logarithmic-normal distribution as the prior distributions to construct Bayes statistical probability inference model for the simulation of the monthly crude oil price change point trends. Based on basic statistical cognition and product partition model (PPM), the historical change points are defined, identified, and analyzed. The PPM-KM integration model is established by combining PPM model and K-means method to measure, cluster, and identify the posterior probability of change points. The appearance probabilities of change points under different scenarios are calculated and compared for single recursive probabilistic predictions. The results showed there were 37 significant change points during 1986–2015. In different time points, unbalance of market supply-demand structure, sudden geopolitical event, the US dollar index, and global economic development situation have become the main reasons for oil price catastrophes. The monthly crude oil price change point complied with the power-law distribution hypothesis. It provides a new analytical perspective and is beneficial to governments, enterprises and investors to understand the market trends, avoid investment risks and make the right investment decisions. [ABSTRACT FROM AUTHOR]

This paper proposes two types of dummy variables for an interval regression model to assess the impact of economic shocks/crises on an interval time series (ITS), e.g. daily intervals of energy prices. We present different economic interpretations of the two types of dummy variables for an interval regression model. Particularly, we discuss how they measure the direction and magnitudes of the change of an ITS caused by an economic crisis, and develop the corresponding hypothesis tests. A main advantage of the proposed ITS modelling approach over traditional point-based methods is that it can assess the change in both the trend and volatility of an asset price process simultaneously. This is due to the informational gain of an ITS sample over a point-valued sample, e.g. closing prices, since an interval observation contains both the trend and variation information of a price process in a given period. Using the proposed interval framework, we focus on the impact of the subprime mortgage crisis in the commodity market as a case study based on the ITS of monthly crude oil future price data. Empirical results suggest a strong evidence that the subprime crisis has lowered the level/trend and increased the volatility of crude oil prices. We also show that the trend of crude oil future prices moves towards an equilibrium state driven by the variation of the price process in last period, and the speculation index, as a proxy of crude oil market liquidity, is significant in explaining the dynamics of crude oil prices. Both findings provide quantitative evidence for theoretical results in the previous literature. [ABSTRACT FROM AUTHOR]

To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the final prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity. [ABSTRACT FROM AUTHOR]

Grasping the future fluctuation characteristics and trend of oil prices form the basis for a deep understanding of the system mechanisms and development trends of related research fields. However, due to the complex features of the oil price, accurate prediction is very difficult to get. In order to improve the accuracy of international crude oil price predictions, a novel hybrid prediction model is proposed, that is improved on existing decomposition ensemble learning techniques by developing the Dynamic Time Warping Fuzzy Clustering method (FCM-DTW) as a new reconstruction rule. The hybrid model consists of four main steps. First, the West Texas Intermediate (WTI) crude oil spot price is decomposed into a series of relatively stable, different frequency eigenmode components (IMFs) using the adaptive noise complete integration empirical mode decomposition algorithm (CEEMDAN). FCM-DTW is then employed to reconstitute the IMFs into three sub-sequences. Subsequently, an Autoregressive Integrated Moving Average (ARIMA) model is selected according to the data characteristics of the reconstructed sequence and applied to predict the reconstructed components. Finally, a simple additive method is used to integrate the predicted results of each reconstructed component to generate the crude oil price prediction value. The results show that the prediction accuracy of the proposed hybrid model, based on dynamic time warping fuzzy clustering algorithm, is significantly better than the benchmarks considered in this paper. • A decomposition–integration hybrid model of price prediction is proposed. • The model is created using four steps: decomposition, reconstruction, prediction and ensemble. • The Dynamic Time Warping Fuzzy Clustering method (FCM-DTW) is developed to the hybrid model. • The empirical results verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In the big data era, search engine data (SED) have presented new opportunities for improving crude oil price prediction; however, the existing research were confined to single-language (mostly English) search keywords in SED collection. To address such a language bias and grasp worldwide investor attention, this study proposes a novel multilingual SED-driven forecasting methodology from a global perspective. The proposed methodology includes three main steps: (1) multilingual index construction, based on multilingual SED; (2) relationship investigation, between the multilingual index and crude oil price; and (3) oil price prediction, with the multilingual index as an informative predictor. With WTI spot price as studying samples, the empirical results indicate that SED have a powerful predictive power for crude oil price; nevertheless, multilingual SED statistically demonstrate better performance than single-language SED, in terms of enhancing prediction accuracy and model robustness. • A multilingual search engine data (SED)-driven methodology is proposed. • Beyond English SED, multilingual ones are considered to construct an index. • The relationship between oil price and multilingual index is investigated. • The informative multilingual index is applied to oil price forecasting. • Empirical results verify the powerful predictive power of the multilingual SED. [ABSTRACT FROM AUTHOR]

Crude oil price forecasting has garnered considerable attention due to its pivotal role in both market dynamics and economic stability. In this study, we present an attention-based principal component analysis (attention-PCA) methodology designed to improve the performance of oil price forecasting models. The attention-PCA approach enables greater focus on predictor variables with superior forecasting capabilities. Furthermore, we develop a diversity enhancement mechanism for forecast combination by incorporating multiple attention mechanisms, varying numbers of principal components, and a range of forecasting models. The empirical results demonstrates that attention-PCA-based individual forecasting models significantly outperform benchmark models, reducing the Mean Absolute Percentage Error (MAPE) by up to 43.2%. The proposed forecast combination strategy yields the most accurate and diverse forecasts among those evaluated, with the MAPE of the optimal combination model standing at 4.40%. • A semi-heterogeneous forecast combination approach to crude oil price is proposed. • Attention-PCA assigns more attention to factors with superior forecast capacity. • A diversity enhancement mechanism for forecast combination is presented. • The proposed forecast combination method yields both accurate and diverse forecasts. [ABSTRACT FROM AUTHOR]

Crude oil plays an important role in economic development and political stability, and many scholars have been committed to forecasting its price. However, its influencing factors are complex and diverse, and previous studies have rarely focused on the second multivariate decomposition. Therefore, this study introduces financial market factors and crude oil news as forecasters, and proposes a novel hybrid model with two-layer multivariate decomposition. To verify the performance of the proposed model, an empirical study is performed on weekly West Texas Intermediate (WTI) oil spot price. The results suggest that the second multivariate decomposition for the high-frequency subcomponent can significantly improve the forecasting accuracy, and the forecasting performance of the proposed model outperforms all the benchmark models. • A novel model with two-layer multivariate decomposition and news text is proposed. • Crude oil news variables are constructed based on keywords. • A new means to determine lag order is constructed with statistics and chaos theory. [ABSTRACT FROM AUTHOR]

General framework of the proposed multi-scale methodology with multi-factor SED. • A multi-scale method with search engine data is built to forecast oil price. • 3 steps are taken: multi-factor data process, multi-scale analysis and prediction. • Search engine data for diverse oil-related factors is used as powerful predictors. • Multi-scale relationship between oil price and search engine data is investigated. • Empirical study verifies its superiority over both popular and similar benchmarks. With the boom in big data, a promising idea for using search engine data has emerged and improved international oil price prediction, a hot topic in the fields of energy system modelling and analysis. Since different search engine data drive the oil price in different ways at different timescales, a multi-scale forecasting methodology is proposed that carefully explores the multi-scale relationship between the oil price and multi-factor search engine data. In the proposed methodology, three major steps are involved: (1) a multi-factor data process, to collect informative search engine data, reduce dimensionality, and test the predictive power via statistical analyses; (2) multi-scale analysis, to extract matched common modes at similar timescales from the oil price and multi-factor search engine data via multivariate empirical mode decomposition; (3) oil price prediction, including individual prediction at each timescale and ensemble prediction across timescales via a typical forecasting technique. With the Brent oil price as a sample, the empirical results show that the novel methodology significantly outperforms its original form (without multi-factor search engine data and multi-scale analysis), semi-improved versions (with either multi-factor search engine data or multi-scale analysis), and similar counterparts (with other multi-scale analysis), in both the level and directional predictions. [ABSTRACT FROM AUTHOR]