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.

Determination of Accuracy at Backpropagation Method in Prediction Crude Oil Prices

We analyzed the performance of the artificial neural network with the backpropagation method in predicting crude oil prices in this paper, including the case of crude oil price predictions. The training results obtained that the MSE value was 0.00099762 with 135 Epoch, in the network testing the MSE value was 0.093336. Meanwhile, the predicted value is determined by the target value with a contribution of 99% with a significant effect. Thus the accuracy level is determined by the target value and the predicted value. The accuracy of the system is obtained for 83,6%.

A study of univariate forecasting methods for crude oil price

PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

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.

Study on the correlation between Crude oil price and new energy stock price in China and America

In today's new world situation, the consumption structure of energy is constantly changing. All countries attach importance to the use of new energy, vigorously promoting the development of new energy-related industries.Traditional energy and new energy are interchangeable, so there is a complex relationship between crude oil futures market and new energy stock market. China, as an economy with strong energy demand and high dependence on oil, will be affected by changes in oil futures prices. America's new energy policy has two striking sides. On the one hand, due to the lack of consensus, the US has so far failed to come up with new energy development plans and targets at the national level. On the other hand, a series of supportive policies launched by the federal and local governments have enabled the U.S. wind and solar industries to maintain a high growth rate in recent years. In view of this, the research takes WTI crude oil price, Zhongzheng New energy Index and China crude oil price as the research object, analyzes the interaction among them by using VAR model and GARCH model, and predicts the volatility of crude oil price and new energy stock price.