Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

Chen, Zhonglu; Ye, Yong; Li, Xiafei

doi:10.1016/j.resourpol.2021.102453

Cited by 19 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Input parameters of the hybrid MLR-FFANN model 22)- (24). The MAE, MAPE, and RMSE evaluation criteria results from the training and testing data of different FFANN structures for all working cases of the AGDE algorithm are given in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, it has been reported that the leverage effect is the strongest predictor of the COVID-19 pandemic based on the samples collected during the pandemic. 24 In another study, Wu et al analyzed the US oil markets based on social media information using convolutional NN (CNN) during the COVID-19 process. The results obtained from the experimental studies showed that although social media information contributes to the estimation of oil price, production and consumption, the oil inventory does not affect the estimation accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…It was determined that the MIDAS‐RV‐CJ model was more successful in the short term, while the MIDAS‐RV‐L model gave better results in long‐term predictions. Furthermore, it has been reported that the leverage effect is the strongest predictor of the COVID‐19 pandemic based on the samples collected during the pandemic 24 . In another study, Wu et al analyzed the US oil markets based on social media information using convolutional NN (CNN) during the COVID‐19 process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

View full text Add to dashboard Cite

The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression-feedforward artificial neural network (MLR-FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID-19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR-FFANN approach was to simultaneously optimize the β polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR-FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID-19 precautions.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In recent studies, Chen, Z. et al [31] used the MIDAS modeling framework, which is a common mixed data sampling approach, to investigate how leverage and jumps affect the ability to forecast the realized volatility (RV) of China's crude oil futures. This study's findings can be utilized to better predict the volatility of China's crude oil futures, reduce investment risks, and provide higher profits.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models

Iftikhar,

Zafar,

Turpo-Chaparro

et al. 2023

Mathematics

View full text Add to dashboard Cite

Crude oil price forecasting is an important research area in the international bulk commodity market. However, as risk factors diversify, price movements exhibit more complex nonlinear behavior. Hence, this study provides a comprehensive analysis of forecasting Brent crude oil prices by comparing various hybrid combinations of linear and nonlinear time series models. To this end, first, the logarithmic transformation is used to stabilize the variance of the crude oil prices time series; second, the original time series of log crude oil prices is decomposed into two new subseries, such as a long-run trend series and a stochastic series, using the Hodrick–Prescott filter; and third, two linear and two nonlinear time series models are considered to forecast the decomposed subseries. Finally, the forecast results for each subseries are combined to obtain the final day-ahead forecast result. The proposed modeling framework is applied to daily Brent spot prices from 1 January 2013 to 27 December 2022. Six different accuracy metrics, pictorial analysis, and a statistical test are performed to verify the proposed methodology’s performance. The experimental results (accuracy measures, pictorial analysis, and statistical test) show the efficiency and accuracy of the proposed hybrid forecasting methodology. Additionally, our forecasting results are comparatively better than the benchmark models. Finally, we believe that the proposed forecasting method can be used for other complex financial time data to obtain highly efficient and accurate forecasts.

show abstract

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

Qiao,

Pan,

Liang

et al. 2024

Journal of Forecasting

View full text Add to dashboard Cite

This paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting.

show abstract

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

Cited by 19 publications

References 45 publications

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

Contact Info

Product

Resources

About