Search Results (26)

Accurate volatility forecasting in energy markets is paramount for risk management, derivative pricing, and strategic policy planning. Traditional econometric models like the Heterogeneous Auto-regressive (HAR) model effectively capture the long-memory and multi-component nature of volatility but often fail to account for non-linearities and complex, unseen dependencies. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, excel at capturing these non-linear patterns but can be data-hungry and prone to overfitting, especially in noisy financial datasets. This paper proposes a novel hybrid model, HAR-LSTM-GARCH, which synergistically combines the strengths of the HAR model, an LSTM network, and a GARCH model to forecast the realized volatility of crude oil futures. The HAR component captures the persistent, multi-scale volatility dynamics, the LSTM network learns the non-linear residual patterns, and the GARCH component models the time-varying volatility of the residuals themselves. Using high-frequency data on Brent Crude futures, we compute daily Realized Volatility (RV). Our empirical results demonstrate that the proposed HAR-LSTM-GARCH model significantly outperforms the benchmark HAR, GARCH(1,1), and standalone LSTM models in both statistical accuracy and economic significance, offering a robust framework for volatility forecasting in the complex energy sector. Full article

The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN–Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model’s effectiveness and robustness in the WTI crude oil market via Diebold–Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market. Full article

As a pivotal global commodity, crude oil price volatility directly impacts economic stability and strategic security. Being the most widely traded asset worldwide, it also serves as a key financial barometer and a critical transition fuel in the shift towards renewable energy. Nevertheless, accurate forecasting of crude oil prices remains challenging due to three persistent challenges: (1) the lack of a systematic method to filter out redundant and noisy features for deep learning models; (2) the limited ability of existing models to simultaneously capture both local bidirectional dependencies and global periodic patterns; and (3) the non-adaptive nature of conventional attention mechanisms, which restricts their capacity to dynamically focus on the most informative historical periods. To bridge these gaps, this study introduces a novel forecasting framework with three key contributions. First, we introduce a hierarchical feature selection paradigm based on LightGBM to systematically eliminate data redundancy and noise, thereby constructing an optimal feature subset for subsequent deep modeling. Second, an improved Autoformer encoder, integrated with Bidirectional GRUs, is designed to simultaneously capture local bidirectional dependencies and global periodic patterns, enabling a more comprehensive multi-scale temporal representation. Third, a dynamic fusion mechanism is incorporated to adaptively recalibrate the significance of historical timesteps. This enables the model to focus on periods rich in information, enhancing contextual awareness in predictions. Future research aims to enhance forecasting capabilities by achieving a deeper integration of local and global temporal representations, potentially through exploring advanced gating or sparse attention mechanisms. Full article

This paper investigates the short-term predictability of daily crude oil price movements by employing a multi-method analytical framework that incorporates both econometric and machine learning techniques. Utilizing a dataset of 21 financial and commodity time series spanning ten years of trading days (2015–2024), we explore the dynamics of oil price volatility and its key determinants. In the forecasting phase, we applied seven models. The meta-learner model, which consists of three base learners (Random Forest, gradient boosting, and support vector regression), achieved the highest R² value of 0.532, providing evidence that our complex model structure can successfully outperform existing approaches. This ensemble demonstrated that the most influential predictors of next-day oil prices are VIX, OVX, and MOVE (volatility indices for equities, oil, and bonds, respectively), and lagged oil returns. The results underscore the critical role of volatility spillovers and nonlinear dependencies in forecasting oil returns and suggest future directions for integrating macroeconomic signals and advanced volatility models. Moreover, we show that combining multiple machine learning procedures into a single meta-model yields superior predictive performance. Full article

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions. Full article

To address the challenges in forecasting crude oil and hot-rolled coil futures prices, the aim is to transcend the constraints of conventional approaches. This involves effectively predicting short-term price fluctuations, developing quantitative trading strategies, and modeling time series data. The goal is to enhance prediction accuracy and stability, thereby supporting decision-making and risk management in financial markets. A novel approach, the multi-dimensional fusion feature-enhanced (MDFFE) prediction method has been devised. Additionally, a data augmentation framework leveraging multi-dimensional feature engineering has been established. The technical indicators, volatility indicators, time features, and cross-variety linkage features are integrated to build a prediction system, and the lag feature design is used to prevent data leakage. In addition, a deep fusion model is constructed, which combines the temporal feature extraction ability of the convolution neural network with the nonlinear mapping advantage of an extreme gradient boosting tree. With the help of a three-layer convolution neural network structure and adaptive weight fusion strategy, an end-to-end prediction framework is constructed. Experimental results demonstrate that the MDFFE model excels in various metrics, including mean absolute error, root mean square error, mean absolute percentage error, coefficient of determination, and sum of squared errors. The mean absolute error reaches as low as 0.0068, while the coefficient of determination can be as high as 0.9970. In addition, the significance and stability of the model performance were verified by statistical methods such as a paired t-test and ANOVA analysis of variance. This MDFFE algorithm offers a robust and practical approach for predicting commodity futures prices. It holds significant theoretical and practical value in financial market forecasting, enhancing prediction accuracy and mitigating forecast volatility. Full article

Crude oil is a raw and natural, but nonrenewable, resource. It is one of the world’s most important commodities, and its price can have ripple effects throughout the broader economy. Accurately predicting crude oil prices is vital for investment decisions but it remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results illustrate that the proposed VMD–EMD–Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD–Transformer in forecasting crude oil prices. Details are presented in the empirical study part. Full article

This paper investigated the use of linear models to forecast crude oil futures prices (WTI) on a monthly basis, emphasizing their importance for financial markets and the global economy. The main objective was to develop predictive models using time series analysis techniques, such as autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), as well as ARMA variants adjusted by genetic algorithms (ARMA-GA) and particle swarm optimization (ARMA-PSO). Exponential smoothing techniques, including SES, Holt, and Holt-Winters, in additive and multiplicative forms, were also covered. The models were integrated using ensemble techniques, by the mean, median, Moore-Penrose pseudo-inverse, and weighted averages with GA and PSO. The methodology adopted included pre-processing that applied techniques to ensure the stationarity of the data, which is essential for reliable modeling. The results indicated that for one-step-ahead forecasts, the weighted average ensemble with PSO outperformed traditional models in terms of error metrics. For multi-step forecasts (3, 6, 9 and 12), the ensemble with the Moore-Penrose pseudo-inverse showed better results. This study has shown the effectiveness of combining predictive models to forecast future values in WTI oil prices, offering a useful tool for analysis and applications. However, it is possible to expand the idea of applying linear models to non-linear models. Full article

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. Full article

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes. Full article

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions. Full article

The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition–reconstruction–ensemble for crude oil price forecasting is proposed. Based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, in this paper we construct a recursive CEEMDAN decomposition–reconstruction–ensemble model considering the complexity traits of crude oil data. In this model, the steps of mode reconstruction, component prediction, and ensemble prediction are driven by complexity traits. For illustration and verification purposes, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Thus, the proposed recursive CEEMDAN decomposition–reconstruction–ensemble model can be an effective tool to forecast oil price in the future. Full article

The whole world has entered the era of the Vuca. Some traditional methods of problem analysis begin to fail. Complexity science is needed to study and solve problems from the perspective of complex systems. As a complex system full of volatility and uncertainty, price fluctuations have attracted wide attention from researchers. Therefore, through a literature review, this paper analyzes the research on complex theories on price prediction. The following conclusions are drawn: (1) The price forecast receives widespread attention year by year, and the number of published articles also shows a rapid rising trend. (2) The hybrid model can achieve higher prediction accuracy than the single model. (3) The complexity of models is increasing. In the future, the more complex methods will be applied to price forecast, including AI technologies such as LLM. (4) Crude-oil prices and stock prices will continue to be the focus of research, with carbon prices, gold prices, Bitcoin, and others becoming new research hotspots. The innovation of this research mainly includes the following three aspects: (1) The whole analysis of all the articles on price prediction using mathematical models in the past 10 years rather than the analysis of a single field such as oil price or stock price. (2) Classify the research methods of price forecasting in different fields, and found the common problems of price forecasting in different fields (including data processing methods and model selection, etc.), which provide references for different researchers to select price forecasting models. (3) Use VOSviewer to analyze the hot words appearing in recent years according to the timeline, find the research trend, and provide references for researchers to choose the future research direction. Full article

Crude oil has dual attributes of finance and energy. Its price fluctuation significantly impacts global economic development and financial market stability. Therefore, it is necessary to predict crude oil futures prices. In this paper, a hybrid forecast model of EEMD-CNN-ILSTM for crude oil futures price is proposed, which is based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Long Short-Term Memory (ILSTM). ILSTM improves the output gate of Long Short-Term Memory (LSTM) and adds important hidden state information based on the original output. In addition, ILSTM adds the learning of cell state at the previous time in the forget gate and input gate, which makes the model learn more fully from historical data. EEMD decomposes time series data into a residual sequence and multiple Intrinsic Mode Functions (IMF). Then, the IMF components are reconstructed into three sub-sequences of high-frequency, middle-frequency, and low-frequency, which are convenient for CNN to extract the input data’s features effectively. The forecast accuracy of ILSTM is improved efficiently by learning historical data. This paper uses the daily crude oil futures data of the Shanghai Energy Exchange in China as the experimental data set. The EEMD-CNN-ILSTM is compared with seven prediction models: Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), LSTM, ILSTM, CNN-LSTM, CNN-ILSTM, and EEMD-CNN-LSTM. The results of the experiment show the model is more effective and accurate. Full article

The global financial markets are greatly affected by crude oil price movements, indicating the necessity of forecasting their fluctuation and volatility. Crude oil prices, however, are a complex and fundamental macroeconomic variable to estimate due to their nonlinearity, nonstationary, and volatility. The state-of-the-art research in this field demonstrates that conventional methods are incapable of addressing the nonlinear trend of price changes. Additionally, many parameters are involved in this problem, which adds to the complexity of such a prediction. To overcome these obstacles, a Mutual Information-Based Network Autoregressive (MINAR) model is developed to forecast the West Texas Intermediate (WTI) close crude oil price. To this end, open, high, low, and close (OHLC) prices of crude oil are collected from 1 January 2020 to 20 July 2022. Afterwards, the Mutual Information-based distance is utilized to establish the network of OHLC prices. The MINAR model provides a basis to consider the joint effects of the OHLC network interactions, the autoregressive impact, and the independent noise and establishes an intelligent tool to estimate the future fluctuations in a complex, multivariate, and noisy environment. To measure the accuracy and performance of the model, three validation measures, namely, RMSE, MAPE, and UMBRAE, are applied. The results demonstrate that the proposed MINAR model outperforms the benchmark ARIMA model. Full article