Crude oil price analysis and forecasting: A perspective of “new triangle”

Lu, Quanying; Li, Yuze; Chai, Jian; Wang, Shouyang

doi:10.1016/j.eneco.2020.104721

Cited by 54 publications

(13 citation statements)

References 43 publications

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“…In recent years, several studies have been conducted on the relationship between oil prices and shale oil production. Most of these studies examined the effects of shale oil production on oil prices (M anescu and Nuño, 2015;Su et al, 2017;Bataa and Park, 2017;Kang et al, 2017;Salisu and Adediran, 2018;Lu et al, 2020;Monge et al, 2017;Kim, 2018); However, only some of these studies have investigated the effect of unconventional oil production on oil prices (Umekwe and Baek, 2017;Hosseini and Shakouri, 2016). Bataa and Park (2017) explored the effect of the shale oil revolution on crude oil prices through a SVAR model.…”

Section: Oil Price and Shale Oil Productionmentioning

confidence: 99%

“…Mănescu and Nuño (2015) using an advanced general equilibrium model stated that the decline in oil prices in the second half of 2014 was mainly due to the positive oil supply shock. Lu et al (2020) analyzed and forecasted crude oil prices. They indicated that the effect of US shale oil production on oil prices is gradually increasing but relatively low.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Numerous studies have examined the relationship between oil prices and shale oil production (M anescu and Nuño, 2015;Su et al, 2017;Bataa and Park, 2017;Kang et al, 2017;Monge et al, 2017;Kim, 2018;Salisu and Adediran, 2018;Lu et al, 2020), while most have investigated the effects of shale oil supply on oil prices. Only a handful of studies The ratio of total primary energy imports to total primary energy consumption in the USA IJDI 21,2 have looked at the effect of oil prices on unconventional oil production.…”

Section: Introductionmentioning

confidence: 99%

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Oil price shocks on shale oil supply and energy security: a case study of the United States

Kazemzadeh

Shadmehri

Salari

et al. 2022

IJDI

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Purpose The purpose of this study is to examine oil price shocks on US shale oil supply and energy security during the period 2000q1–2020q4. Design/methodology/approach In this study, the Shannon–Wiener index was used to calculate energy security, and then a structural vector autoregression (VAR) was applied to measure the effect of oil price shocks. Findings The results of the variance decomposition indicate that oil prices account for about 20% of changes in US shale oil production, while it explains only about 3% of changes in energy security. Finally, historical decomposition confirms the results of impulse response functions. Originality/value The novelty of this study is that so far, no study has examined the effect of oil price shock on shale oil production and energy security in the USA using the structural VAR model. This study also used the latest Shannon–Wiener index as a measure of energy security in the USA. The reason for selecting this index is that, in addition to considering the share of the total consumption of each primary energy, the share of energy imports from each country as well as the political risk of energy exporting countries to the USA are also included.

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Section: Oil Price and Shale Oil Productionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Oil price shocks on shale oil supply and energy security: a case study of the United States

Kazemzadeh

Shadmehri

Salari

et al. 2022

IJDI

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“…Plenty of studies have found strong evidence of dependence between stock markets and crude oil markets. Standard & Poor's 500 (S&P500) index has been widely employed in crude oil forecasting models (Lu et al, 2020). In addition, a persistent lead-lag relationship between S&P500 and WTI futures has been found (Kyrtsou et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Understanding the determinants of the crude oil future market is of great interest. Previous research mainly examines the contributions of supply and demand fundamentals (Pan et al, 2017;Meng and Liu, 2019), macroeconomic factors (Aloui et al, 2016;Meng and Liu, 2019), financial factors (Kyrtsou et al, 2016;Lu et al, 2020) and political factors (Chen et al, 2016;…”

Section: Introductionmentioning

confidence: 99%

New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach

Shao¹,

Shao²,

Yang³

2021

Preprint

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This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newlylaunched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.

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Forecasting interval‐valued returns of crude oil: A novel kernel‐based approach

Yang,

Xu,

Wei

et al. 2024

Journal of Forecasting

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This paper proposes a novel kernel‐based generalized random interval multilayer perceptron (KG‐iMLP) method for predicting high‐volatility interval‐valued returns of crude oil. The KG‐iMLP model is constructed by utilizing the distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval‐valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG‐iMLP for crude oil price forecasting.

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Crude oil price analysis and forecasting: A perspective of “new triangle”

Cited by 54 publications

References 43 publications

Oil price shocks on shale oil supply and energy security: a case study of the United States

Oil price shocks on shale oil supply and energy security: a case study of the United States

New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach

Forecasting interval‐valued returns of crude oil: A novel kernel‐based approach

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