Crude Oil Price Data Research Articles

Measuring the impact of COVID-19 surveillance variables over the international oil market

Coronavirus (COVID-19) has turned to be an alarm for the whole world both in terms of health and economics. It is striking the global economy and increasing the unpredictability of the financial market in several ways. Significantly, the pandemic spread stimulated the social distancing which led to the lockdown of the countries’ businesses, financial markets, and daily life events. International oil markets have accommodated the crude oil prices during the early COVID-19 period. However, after the first 50 days, Saudi Arabia has surged the market with oil, which caused a certain decrease in crude oil prices, internationally. Saudi Arabia is one of the biggest oil reserves in the world. International trade is based on oil reservoirs which in turn, have been significantly dislodged by the pandemic. Therefore, it is crucial to study the impact of COVID-19 on the international oil market. The purpose of this study is to investigate the short-term and long-term impact of COVID-19 on the international oil market. The daily crude oil price data is used to analyze the impact of daily price fluctuation over COVID-19 surveillance variables. The correlation between surveillance variables and international crude oil prices is calculated and analyzed. Consequently, the project will help in stabilizing the expected world economic crises and particularly will provide the implications for the policymakers in the oil market.

Comparative Performance of ARIMA and GARCH Model in Forecasting Crude Oil Price Data

This study compares the performance of Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity models in forecasting Crude Oil Price data as obtained from (CBN 2019) Statistical Bulletin. The forecasting of Crude Oil Price, plays an important role in decision making for the Nigeria government and all other sectors of her economy. Crude Oil Prices are volatile time series data, as they have huge price swings in a shortage or an oversupply period. In this study, we use two time series models which are Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heterocedasticity (GARCH) models in modelling and forecasting Crude Oil Prices. The statistical analysis was performed by the use of time plot to display the trend of the data, Autocorrelation Function (ACF), Partial Autocorrelation Functions (PACF), Dickey-Fuller test for stationarity, forecasting was done based on the best fit models for both ARIMA and GARCH models. Our result shows that ARIMA (3, 1, 2) is the best ARIMA model to forecast monthly Crude Oil Price and we also found GARCH (1, 1) model is the best GARCH model and using a specified set of parameters, GARCH (1, 1) model is the best fit for our concerned data set.

Robust information share measures with an application on the international crude oil markets

AbstractThis paper proposes a robust estimation approach of the popular information share using a robust vector error correction model. Via simulation studies, we show that the proposed measures lead to more accurate estimates than the existing measures in the presence of outliers. The proposed measures are then investigated using high‐frequency crude oil prices data of the West Texas Intermediate and Brent over November 2019–October 2020. The results suggest that the abnormally large price movement in April 2020 may cause biased estimates of the ordinary measures, whereas the robust measures produce rather consistent results.

Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil

Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. From the analysis and comparison of the prediction results, we find that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models. In addition, the LLE-RNN/LSTM models can most successfully capture the nonlinear characteristics of crude oil prices. When the moving window size is twenty, that is, when crude oil price data are lagging by almost a month, each model can minimize its error, and the LLE-RNN /LSTM models have the best robustness.

Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results

This study contributes to the growing literature that focuses on predicting crude oil spot price returns out-of-sample by conditioning on the news-based uncertainty measures pioneered by Baker et al. (2016). With the aim of providing new empirical results useful for future research, we apply a comprehensive Bayesian model averaging (BMA) framework that incorporates the following aspects: (i): Parameter instability, (ii): Model uncertainty, and (iii): Besides the conditional mean process, it allows predictors of the candidate models in the model set to impact the variable being predicted through the conditional volatility process or both processes. Applied to monthly news-based uncertainty and crude oil price data from 1985m1 through 2020m12, we observe that accounting for model uncertainty and allowing predictors to impact crude oil price returns exclusively through the conditional volatility process lead to the most consistent pattern of point (density) prediction accuracy gains relative to the benchmark. In contrast, the approach predominately relied on in the current literature, namely, allowing predictors to impact returns only through the conditional mean process does not lead to the same degree of point (density) prediction accuracy gains. Likewise, any further prediction accuracy gains from (i) are at best modest once (ii) and (iii) are accounted for. The largest relative gain occurs when predicting the left tail of the conditional return distribution one-month ahead. The statistical evidence of predictability also translates to economic gains.

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

The accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. Several classical econometrics and computational approaches show promising results for the ordinary time series forecasting tasks, but they are not satisfactory in crude oil price forecasting. Ensemble empirical mode decomposition (EEMD) not only resolves the problem of nonlinearity and nonstationarity of time series prediction but also creates some problems (i.e., mood mixing and splitting). In this study, we proposed a new hybrid method that combines the median ensemble empirical mode decomposition and group method of data handling (MEEMD-GMDH) to reduce mood splitting problems and forecast crude oil price. MEEMD is achieved by replacing the mean operator with the median operator during the EEMD process. For testing and validation purposes of the different models, the two-seat stamp benchmarked crude oil price data are used (i.e., Brent and West Texas Intermediate (WTI)). To check the proposed model performance, different evaluation measures are used including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Diebold-Mariano (DM) test. All the forecasting accuracy measures confirmed that our proposed model performs well in crude oil prices forecasting as compared to other hybrid models.

Linear and Non-Linear Modelling of Nigerian Crude Oil Prices

To model Nigeria crude oil prices, this analysis compared univariate linear models to univariate nonlinear models. The data for this analysis was gathered from the Central Bank of Nigeria (CBN) Monthly Statistical Bulletin. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the cycle of expansion and contraction. At lag one, the Augmented Dickey-Fuller test was used to test for stationarity. For univariate linear ARIMA (p, d, q)) and univariate non-linear MS-AR, seven models were estimated for the linear model and two for the non-linear model. The best model was chosen based on the criterion of least information criterion, AIC (2.006612), SC (2.156581), and the maximum log-likelihood of (-150.5480) for the crude oil prices were used to pick MS-AR (1) for the series. In analysing crude oil prices data, the MS-AR model proposed by Hamilton outperforms the linear autoregressive models proposed by Box- Jenkins. The model was used to predict the series' values over a one-year cycle (12 months).

Multivariate generalized autoregressive score model (case study: vegetable oils and crude oil price data)

A multivariate econometric model can be used to forecast volatilities and dynamic correlations between various assets. Volatilities and dynamic correlations forecasting are important applied in risk management, hedging, asset allocation, and asset pricing options. This study uses a multivariate Generalized Autoregressive Score (GAS) model to analyze and forecast volatilities and dynamic correlations of the weekly prices of Crude Palm Oil, coconut oil, soybean oil, and crude oil. The GAS model is a new framework based on a score-driven time series model for updating time-varying parameters. Another multivariate econometric model is the Dynamic Conditional Correlation (DCC) GARCH model as a comparison. The empirical experiments regarding the distribution of return data show that the data can be approximated by the multivariate t-student distribution. The likelihood ratio test in the multivariate GAS model shows that the time-varying parameters in the GAS model are volatility, correlation, and location parameters. The evaluation performance model based on RMSE and MAE show that the multivariate GAS model has better performance in estimating and forecasting volatilities than the DCC GARCH model. Multivariate GAS models have better performance in estimating and forecasting the dynamic correlation on returns of CPO and soybean oil.

Forecasting Crude Oil Price Using Event Extraction

Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.

Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series

Several authors have used different classical statistical models to fit the Nigerian Bonny Light crude oil price but the application of machine learning models and Fuzzy Time Series model on the crude oil price has been grossly understudied. Therefore, in this study, a classical statistical model—Autoregressive Integrated Moving Average (ARIMA), two machine learning models—Artificial Neural Network (ANN) and Random Forest (RF) and Fuzzy Time Series (FTS) Model were compared in modeling the Nigerian Bonny Light crude oil price data for the periods from January, 2006 to December, 2020. The monthly secondary data were collected from the Nigerian National Petroleum Corporation (NNPC) and Reuters website and divided into train (70%) and test (30%) sets. The train set was used in building the models and the models were validated using the test set. The performance measures used for the comparison include: The modified Diebold-Mariano test, the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe Efficiency (NSE) values. Based on the performance measures, ANN (4, 1, 1) and RF performed better than ARIMA (1, 1, 0) model but FTS model using Chen’s algorithm outperformed every other model. The results recommend the use of FTS model for forecasting future values of the Nigerian Bonny Light Crude oil. However, a hybrid model of ARIMA-ANN or ARIMA-RF should be built and compared with Chen’s algorithm FTS model for the same data set to further verify the power of FTS model using Chen’s algorithm.

An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm

Abstract The identification of the temporal scales related to market activities is crucial for understanding the dynamics of international crude oil prices. Standard analysis techniques fail in producing consistently good results due to the non-linear behaviour of the oil market. In this paper we propose an innovative approach based on the concurring application of a new non-linear data analysis method, Fast Iterative Filtering (FIF), and a multi-scale statistical analysis (Standardized Mean Test). This approach proves to be able to separate automatically crude oil price data into three components: a long term trend, an intermediate or middle period behaviour, and a transitory or short-run behaviour. The economic meaning of each component is clearly identified as: high frequency variations, caused by normal supply-demand disequilibrium; medium term fluctuations, driven by geopolitical, financial, and technological shocks; a low frequency trend reflecting the global business cycle. All these results make the proposed approach a more performing tool for analysing oil price data structure and dynamics. Such a method, if coupled with different prediction techniques (e.g., ARIMA, ARCH, etc., or ANN, SVM, etc.), can potentially show higher performance than existing hybrid models in forecasting crude oil prices.

Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection

ABSTRACT In this paper, we present a sequential hypothesis test for the detection of the distribution of jump size in Lévy processes. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analysed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the data set, and the deterministic component is implemented to improve the Barndorff-Nielsen & Shephard model, a commonly used stochastic model for derivative and commodity market analysis.

ACCURATE ESTIMATED MODEL OF VOLATILITY CRUDE OIL PRICE

Crude oil price (COP) data are time-series data that are assessed as having both volatility and heteroscedasticity variance. One of the best models that can be applied to address the heteroscedasticity problem is GARCH (generalized autoregressive conditional heteroscedasticity) model. The purpose of this study is to construct the best-fitted model to forecast daily COP as well as to discuss the prepared recommendation for reducing the impact of daily COP movement. Daily COP data are observed for the last decade, i.e., from 2009 to 2018. The finding with the error of less than 0.0001 is AR (1) – GARCH (1,1). The implementation of the model is applicable for both predicting the next 90 days for the COP and its anticipated impact in the future. Because of the increasing prediction, it is recommended that policymakers convert energy use to renewable energy to reduce the cost of oil use.Keywords: Crude Oil Price, Heteroscedasticity, Subsidy, GARCH ModelJEL Classifications: C5, C53, O4, O42, Q4, Q42DOI: https://doi.org/10.32479/ijeep.9513

Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

The Effect of Crude Oil Price Shocks on Indonesia Stock Market Performance

The main purpose of this study is to observe the effects of the crude oil price shocks on Indonesia stock market performance to complete the literature on Indonesia stock market behavior. We examined the effects of the crude oil price shocks on Indonesia stock market performance through the cointegration relationship mechanism between IHSG and the crude oil price and between IHSG and the global stock market indices. The Brent crude oil price data taken from FRED economic data, the Federal Reserve Bank of St.Lois, and the stock market indices data taken from yahoo.finance.com. By using a vector error correction model (VECM) approach, we found that IHSG has significant long-run relationships with the crude oil price and the global stock market indices. This finding indicates that the effects of the crude oil price shocks on IHSG transmitted directly through the cointegration mechanism between IHSG and the crude oil price, and indirectly through the cointegration mechanism between IHSG and the global stock market indices.

Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM

Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices.

Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks

Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate prediction and eventually lead to poor accuracy or the wrong result. Empirical mode decomposition (EMD) and ensemble EMD (EEMD) solve the problems of non-stationary time series forecasting, but they also generate new problems of mode mixing and reconstruction errors. We propose a hybrid method that is combination of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer gated recurrent unit (ML-GRU) neural network to solve the abovementioned issues. This not only deals with the issue of mode mixing effectively, but also makes the reconstruction error of data close to zero. Multi-layer GRU has an excellent ability of nonlinear data-fitting. The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models.

Risk spillover effects from global crude oil market to China’s commodity sectors

We analyzed the impact of upside and downside global crude oil price fluctuation on China’s commodity sectors. To do this, we utilized a series of copula functions, both static and dynamic, to assess the average spillovers across marginal and joint extreme movements in the upward/downward directions between crude oil and China’s commodity sectors. The time-varying Copula-conditional value-at-risk (CoVaR) method can well analyze the spillover effects of upside and downside global crude oil price fluctuation on China’s commodity sectors. The weekly data of crude oil price and China’s commodity sectors are applied and the empirical results indicate that there are upside and downside risk spillover effects from global crude oil to China’s commodity sectors. The degree of the downside spillover effect from crude oil price is larger than that of the upside spillover effect, indicating asymmetric spillover effects. Due to the greater impact of downside risk spillover effects on China’s commodity sectors, investors should more carefully consider the downside risk from global crude oil when assessing portfolio risk.

SYMMETRIC AND ASYMMETRIC EFFECT OF CRUDE OIL PRICES AND EXCHANGE RATE ON BOND YIELDS IN INDONESIA

This study aims to examine the symmetric and asymmetric effects of crude oil prices and exchange rate on bond yields in Indonesia. Dubai crude oil prices are used as a proxy for crude oil price data and the IDR/USD exchange rate is used as a proxy for exchange rate. Meanwhile, the 10-year Indonesian bond yields are used as a proxy for bond yields. Data on Dubai crude oil prices, the IDR/USD exchange rate, and the 10-year Indonesian government bond yields are time-series data from January 2007 to April 2019. The results of the test using the ARDL and NARDL models show that (1) in the long-run, neither the crude oil prices nor the exchange rate has symmetric and asymmetric effects on the bond yields, and (2) in the short-run, both of them have symmetric and asymmetric effects on the bond yields.

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.