Uncertainty and crude oil market volatility: new evidence

Liang, Chao; Wei, Yu; Li, Xiafei; Zhang, Xuhui; Zhang, Yifeng

doi:10.1080/00036846.2019.1696943

Cited by 119 publications

(38 citation statements)

References 47 publications

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“…This finding is timely in the midst of COVID-19. This finding is consistent with the literature demonstrating that market volatility is due to economic policy and financial market uncertainty (see, Aloui et al, 2016;Baker et al, 2016;Hailemariam et al, 2019;Liang et al, 2019;Mei et al, 2019;Wei et al, 2017;Wen et al, 2019).…”

Section: Results and Discussion Results And Discussionsupporting

confidence: 93%

“…Neither the study of energy market volatility nor its linkage with uncertainty are new. To-date, the literature has been dominated by debate on what constitutes a better predictor of energy market volatility in a horserace between the economic policy uncertainty (EPU) and the equity market uncertainty (EMU); see Wei et al (2017), Hailemariam et al (2019), Liang et al (2019), and Mei et al (2019) for studies on EPU and Aloui et al (2016), Baker et al (2016) and Wen et al (2019) for studies on EMU. The COVID-19 pandemic has introduced a different dimension to the functioning of energy markets (see Devpura & Narayan, 2020;Huang & Zheng, 2020;Narayan, 2020).…”

Section: Introduction Introductionmentioning

confidence: 99%

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Uncertainty Due to Infectious Diseases and Energy Market Volatility

Salisu

Adediran

2020

Energy RESEARCH LETTERS

129

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Motivated by the COVID-19 pandemic, we examine the role of uncertainty due to infectious diseases in predicting energy market volatility using the new dataset on Equity Market Volatility-Infectious Diseases (EMV-ID). We find that the new measure of market uncertainty is a good predictor of energy market volatility in both in-sample and out-of-sample tests. These results have implications for portfolio diversification strategies, which we set aside for future research.

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Section: Results and Discussion Results And Discussionsupporting

confidence: 93%

Section: Introduction Introductionmentioning

confidence: 99%

Uncertainty Due to Infectious Diseases and Energy Market Volatility

Salisu

Adediran

2020

Energy RESEARCH LETTERS

129

View full text Add to dashboard Cite

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“…[ 56 ] and Liang et al. [ 33 ]; we also apply this statistic, which is defined as:

where

is the actual realized range-based volatility,

is the estimation from model

, and

is the volatility forecasting from the benchmark model.…”

Section: Empirical Designmentioning

confidence: 99%

Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

Wang

et al. 2020

Energy

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This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China’s oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-term volatilities with regard to Chinese oil futures market. Moreover, our proposed model, the Markov-regime MIDAS with including the OVX (MS-MIDAS-RV-OVX), significantly outperforms the MIDAS and other competing models. Unsurprising results further confirm that OVX indeed contain predictive information for oil realized volatility (especially significant and robust in middle-term and long-term horizons) and regime switching is useful to deal with the structural break within the energy market. We carry out economic value analysis and discuss OVX’s asymmetric effects concerning different trading hours and good (bad) OVX, and find OVX performs better in day-time trading hours and the good OVX is more predictive for the oil futures RV than the bad OVX. The further discussion also confirms our previous conclusions are robust during the highly volatile period of the COVID-19 pandemic.

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“…In our study, we employ the superior volatility measures, realized volatility (RV), which has been addressed in the existing literature (e.g., Ji & Zhang, 2019;Liang, Wei, Li, Zhang, & Zhang, 2019;Liang, Wei, & Zhang, 2020;Luo, Ji, Klein, Todorova, & Zhang, 2020;Ma, Liao, Zhang, & Cao, 2019;Qiu, Zhang, Xie, & Zhao, 2019;Tang, Xiao, Wahab, & Ma, 2020;Wang, Pan, & Wu, 2017;Wen, Gong, & Cai, 2016;Wen, Zhao, Zhang, & Hu, 2019;Zhang, Ma, Wang, & Liu, 2019). Subsequently, based on the RV, Corsi (2009) proposes the Heterogeneous Autoregressive (HAR) model.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, based on the RV, Corsi (2009) proposes the Heterogeneous Autoregressive (HAR) model. It has become the workhorse of the forecasting model for crude oil futures volatility, as it applies simple linear regression techniques and its consistent superior performance (Degiannakis & Filis, 2017;Liang, Wei, Li, Zhang, & Zhang, 2019;Liang, Wei, & Zhang, 2020;Luo, Ji, Klein, Todorova, & Zhang, 2020;Ma, Wei, Liu, & Huang, 2018;Sévi, 2014;Wang, Pan, & Wu, 2017;Wen, Gong, & Cai, 2016;Wen, Zhao, Zhang, & Hu, 2019). Furthermore, existing studies employ different jump test (daily and intraday) to extract the jump component.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect

Wang

Wahab

et al. 2021

Journal of Forecasting

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This study explores the forecasting ability of jump, jump intensity, and leverage effect for an emerging futures market, China's crude oil futures market, using different kinds of HAR-type models. From an in-sample perspective, we find that the HAR components, monthly leverage effect, jump size, and jump intensity have positive effects on future oil volatility. Moreover, out-of-sample results show that a forecasting model with jump and jump intensity cannot only achieve a superior forecasting performance under low volatility level but also increase the economic value. Subsequently, we examine the effect of decompositions of jump information, the results show signed jump components can improve the accuracy. Finally, we extend our empirical analysis considering different forecast horizons, COVID-19 pandemic, and different trading hours. Our empirical results are robust and consistent.

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Uncertainty and crude oil market volatility: new evidence

Cited by 119 publications

References 47 publications

Uncertainty Due to Infectious Diseases and Energy Market Volatility

Uncertainty Due to Infectious Diseases and Energy Market Volatility

Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect

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