Brent Crude

About: Brent Crude is a research topic. Over the lifetime, 548 publications have been published within this topic receiving 9879 citations.

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Abstract: Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting

Abstract: Crude oil is one of the main energy sources and its prices have gained increasing attention due to its important role in the world economy Accurate prediction of crude oil prices is an important issue not only for ordinary investors, but also for the whole society To achieve the accurate prediction of nonstationary and nonlinear crude oil price time series, an adaptive hybrid ensemble learning paradigm integrating complementary ensemble empirical mode decomposition (CEEMD), autoregressive integrated moving average (ARIMA) and sparse Bayesian learning (SBL), namely CEEMD-ARIMA&SBL-SBL (CEEMD-A&S-SBL), is developed in this study Firstly, the decomposition method CEEMD, which can reduce the end effects and mode mixing, was employed to decompose the original crude oil price time series into intrinsic mode functions (IMFs) and one residue Then, ARIMA and SBL with combined kernels were applied to predict target values for the residue and each single IMF independently Finally, the predicted values of the above two models for each component were adaptively selected based on the training precision, and then aggregated as the final forecasting results using SBL without kernel-tricks Experiments were conducted on the crude oil spot prices of the West Texas Intermediate (WTI) and Brent crude oil to evaluate the performance of the proposed CEEMD-A&S-SBL The experimental results demonstrated that, compared with some state-of-the-art prediction models, CEEMD-A&S-SBL can significantly improve the prediction accuracy of crude oil prices in terms of the root mean squared error (RMSE), the mean absolute percent error (MAPE), and the directional statistic (Dstat)

Information spillover and market connectedness: multi-scale quantile-on-quantile analysis of the crude oil and carbon markets

Abstract: ABSTRACT The asymmetric interdependence of carbon futures and crude oil futures prices in different market conditions remains unsettled in the literature. This study aims to quantify the crude oil price impacts on carbon price across the carbon-oil distribution in Phase III. For this purpose, we employ two novel methods, namely the quantile Granger causality test and the quantile-on-quantile regression methods. To detect the short-, medium-, and long-term impacts of the crude oil price on carbon price, we further decompose the sample series into six components using the wavelet method. Accordingly, we are able to estimate the nexus between carbon futures and crude oil futures prices in different time and frequency domains. Through a series of robustness checks, we find our results stable and robust, indicating that the crude oil impact on carbon price is asymmetric, conditional on the whole carbon and crude oil price distributions. Furthermore, the crude oil impact varies across different time scales, and shows negative signs throughout the whole carbon price distribution in the short term and basically positive signs in the medium and long term. Based on the above findings, we highlight several important policy implications to promote better market regulation and portfolio optimization.