Forecasting oil futures price volatility with economic policy uncertainty: a CARR-MIDAS model

Wu, Xinyu; Cui, Hao; Wang, Lu

doi:10.1080/13504851.2021.1977232

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Worryingly, the spike in fossil energy and global commodity prices triggered by the Russo-Ukrainian war could further hamper the global economic recovery after the COVID-19 pandemic. In turn, fluctuation of the GEPU index may backfire on global energy markets, creating a vicious cycle (Ma et al 2019, Wu et al 2021. The outbreak of the Russo-Ukrainian war is expected to skew our estimates.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Post-COVID-19 economic and environmental policy and recovery based on recurrent neural network and long short-term memory network

Xiong²,

Chen³

et al. 2022

Environ. Res. Commun.

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COVID-19 has brought significant impacts on the global economy and environment. The Global Economic-and-environmental Policy Uncertainty (GEPU) index is a critical indicator to measure the uncertainty of global economic policies. Its prediction provides evidence for the good prospect of global economic and environmental policy and recovery. This is the first study using the monthly data of GEPU from January 1997 to January 2022 to predict the GEPU index after the COVID-19 pandemic. Both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models have been adopted to predict the GEPU. In general, the RNN outperforms the LSTM networks, and most results suggest that the GEPU index will remain stable or decline in the coming year. A few results point to the possibility of a short-term increase in GEPU, but still far from its two peaks during the first year of the COVID-19 pandemic. This forecast confirms that the impact of the epidemic on global economic and environmental policy will continue to wane. Lower economic and environmental policy uncertainty facilitates global economic and environmental recovery. Economic recovery brings more opportunities and a stable macroeconomic environment, which is a positive sign for both investors and businesses. Meanwhile, for the ecological environment, the declining GEPU index marks a gradual reduction in the direct impact of policy uncertainty on sustainable development, but the indirect environmental impact of uncertainty may remain in the long run. Our prediction also provides a reference for subsequent policy formulation and related research.

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Section: Discussionmentioning

confidence: 99%

Prediction of Post-COVID-19 economic and environmental policy and recovery based on recurrent neural network and long short-term memory network

Xiong²,

Chen³

et al. 2022

Environ. Res. Commun.

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Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

Xie

Zhang

2022

The North American Journal of Economics and Finance

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Closer is more important: The impact of Chinese and global macro-level determinants on Shanghai crude oil futures volatility

Yu,

Xiao,

Cifuentes-Faura

2024

QFE

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<p>Using the GARCH-MIDAS model, we investigated the impact of Chinese and global macro-level determinants on the return volatility of Shanghai crude oil futures, covering Chinese and global economic policy uncertainty, Chinese and global crude oil demand as well as production, Chinese crude oil import, and global crude oil speculation. The in-sample empirical results showed that Chinese crude oil demand, Chinese crude oil production, Chinese economic policy uncertainty, and global crude oil speculation have significant impact on the long-term volatility component of Shanghai crude oil futures. The out-of-sample prediction results show that Chinese current crude oil production and previous crude oil import have the relatively best predictive power for the return volatility of Shanghai crude oil futures. As a whole, Chinese domestic macro-factors have a stronger impact and higher predictive power on the return volatility of Shanghai crude oil futures compared with corresponding global macro-factors. Besides, the global crude oil speculation is the global macro-level determinant, which deserves most attention.</p>

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Forecasting oil futures price volatility with economic policy uncertainty: a CARR-MIDAS model

Cited by 3 publications

References 15 publications

Prediction of Post-COVID-19 economic and environmental policy and recovery based on recurrent neural network and long short-term memory network

Prediction of Post-COVID-19 economic and environmental policy and recovery based on recurrent neural network and long short-term memory network

Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

Closer is more important: The impact of Chinese and global macro-level determinants on Shanghai crude oil futures volatility

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