Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic

Weng, Futian; Zhang, Hongwei; Yang, Cai

doi:10.1016/j.resourpol.2021.102148

Cited by 52 publications

(14 citation statements)

References 53 publications

(55 reference statements)

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“…It is also unclear how oil prices will behave as the pandemic gradually peters out. An attempt to incorporate genetic mutations of the virus to forecast crude oil futures has been made by (Weng et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Pandemics and Oil Shocks

Mohammed

Barrales‐Ruiz

2022

Preprint

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This paper investigates the role of the COVID-19 pandemic in oil markets, focusing on the great oil price crash in April 2020. Using a 5-variable structural vector autoregression (SVAR) model, the study identifies an oil price shock arising from the pandemic together with supply, demand, and financial market shocks to global oil markets. The results show that a pandemic shock causes a delayed decrease in oil prices. Moreover, financial market conditions that affect financial investment decisions play a significant role in oil price movements. The study also computes the forecast error variance decomposition and finds that the impact of a pandemic shock, financial speculation shock, and aggregate demand shock are crucial in the short run. The findings offer new opportunities for applications in energy research.

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Section: Discussionmentioning

confidence: 99%

Pandemics and Oil Shocks

Mohammed

Barrales‐Ruiz

2022

Preprint

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“…Wu et al [36] forecast crude oil prices by using convolutional neural network (CNN) and variational mode decomposition (VMD) to extract and process text features in online news. Weng et al [37] proposed a modeling framework, the genetic algorithm regularization online extreme learning machine with forgetting factor (GA-RFOS-ELM), to estimate the effects of news during COVID-19 on the volatility of crude oil futures. Stifanic et al [38] integrated the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks to predict commodity and stock price movement during COVID-19.…”

Section: Forecasting Considering Covid-19mentioning

confidence: 99%

A New Container Throughput Forecasting Paradigm under COVID-19

et al. 2022

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COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition–ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA.

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“…Crude oil price variations have a substantial influence on the worldwide economy. 20 Weng, Zhang 68 proved that COVID-19 pandemic news has more predictive information, which is essential for temporary volatility prediction of crude oil futures. According to Niu, Liu, 69 outbreak reports acquired through panic index and country sentiment index could be used to estimate China's petroleum products fluctuations.…”

Section: Overview Of the Literaturementioning

confidence: 99%

Evidence from the nonlinear autoregressive distributed lag model on the asymmetric influence of the first wave of the COVID-19 pandemic on energy markets

Armeanu

Gherghina

Vasile

et al. 2022

Energy & Environment

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The COVID-19 pandemic remained a global risk factor and integrated into various means in the functioning of companies, economies and financial markets. Therefore, this paper investigates how COVID-19 influences the energy market in the main financial markets (China, France, Germany, Italy, Spain and the United States), using time series from February 28, 2020, to November 3, 2020. The goal of this research is to investigate the asymmetric impact of COVID-19 from leading financial markets on energy commodities. In this regard, the non-linear auto-regressive distributed lag (NARDL) framework is employed to capture the long-run asymmetric reactions. The econometric design allows to explore the long-term asymmetric reactions of dependent variables through positive and negative partial sum decompositions of changes in the explanatory variables. The quantitative results show a significant long-run asymmetric interdependence between the number of new SARS-CoV-2 incidence and mortality and the daily percent change in close price of future contracts pertaining to Brent oil, crude oil WTI, carbon emissions, gasoline RBOB, heating oil, Chukyo kerosene, and natural gas. Furthermore, no asymmetry is found in the case of ethanol and fuel oil futures. The novelty of this article is the study of the impact of COVID-19 on the energy sector during the first two waves of COVID-19 by applying the NARDL model that allows to capture long-term asymmetric reactions. Certainly, further research on this topic is necessary due to the permanent shifts in the pandemic, as well as the availability of longer data periods on COVID-19.

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Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic

Cited by 52 publications

References 53 publications

Pandemics and Oil Shocks

Pandemics and Oil Shocks

A New Container Throughput Forecasting Paradigm under COVID-19

Evidence from the nonlinear autoregressive distributed lag model on the asymmetric influence of the first wave of the COVID-19 pandemic on energy markets

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