Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

Foglia, Matteo; Angelini, Eliana

doi:10.3390/su12239863

Cited by 45 publications

(28 citation statements)

References 47 publications

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“…Therefore, we do not have to make any assumptions about the distribution. Moreover, it allows us to compute a flexible tail impulse response (Patnaik et al 2013), an impulse response function computed in the tails, avoiding the assumption of linearity and mean-to-mean dependence as in VAR models (Ferrer et al 2018;Foglia and Angelini 2020;Henriques and Sadorsky 2008;Kumar et al 2012) . Second, it is a robust approach against serial correlation in the series, which is very relevant for analyzing stock returns.…”

Section: Methodology and Datamentioning

confidence: 99%

“…The study of oil price dynamics and its impact on various markets has been the focus of attention in the literature. In fact, as highlighted by numerous papers (Ahmad 2017;Bondia et al 2016;Ferrer et al 2018;Foglia and Angelini 2020;Henriques and Sadorsky 2008;Kumar et al 2012;Managi and Okimoto 2013;Nasreen et al 2020;Pham 2019;Reboredo 2015;Reboredo andUgolini 2016 2018;Sadorsky 2012;Uddin et al 2019), the dynamics of oil prices impact the performance of renewable companies. This dependence makes it complicated to replace exhaustible energy resources with sustainable energy resources, i.e., substitution effect (Bondia et al 2016;Ferrer et al 2018;Monge and Gil-Alana 2020;Reboredo 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we want to test if there is the asymmetry between the response of clean energy to good days versus bad ones. In recent years, many studies have examined the interaction between the clean (renewable) energy market and the oil market through linear autoregressive models, such as Vector Autoregressive (VAR) (Ahmad 2017;Bondia et al 2016;Ferrer et al 2018;Foglia and Angelini 2020;Henriques and Sadorsky 2008;Kumar et al 2012;Managi and Okimoto 2013). For example, Kumar et al (2012) study the relationship between clean-energy stock price, the stock price of high technology firms, oil price and carbon price.…”

Section: Introductionmentioning

confidence: 99%

“…The equity market performance of renewable energy companies is likely to be more related to factors such as technology innovation, capital spending, legislation or geographic availability than to oil price. More recently, Foglia and Angelini (2020) investigate the volatility connectedness between oil price and clean-energy firms, including the COVID-19 outbreak. Their results show the strong effect of the pandemic on these financial markets with an increase for the volatility spillovers, also supported by the effects of financial contagion.…”

Section: Introductionmentioning

confidence: 99%

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Tail Risk and Extreme Events: Connections between Oil and Clean Energy

Febo

Foglia

Angelini

2021

Risks

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Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an asymmetry effect between the response to good versus bad days. The results show how the two markets influence each other more negatively, i.e., extreme negative events significantly impact the other market. Furthermore, we document how the impact of the shock transmitted by oil prices to clean-energy stocks is less than the amount of shock transmitted oppositely. These findings have important implications for investor and renewable energy policies.

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Section: Methodology and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tail Risk and Extreme Events: Connections between Oil and Clean Energy

Febo

Foglia

Angelini

2021

Risks

Self Cite

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“…Foglia and Angelini [39] study the volatility linkages between oil price and clean energy sector firms (wind, solar, and technology) over the period 2011-2020 with the COVID-19 outbreak. The results indicated a significant change in both static and dynamic volatility linkages around the COVID-19 outbreak.…”

Section: Literature Reviewmentioning

confidence: 99%

The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

2021

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The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

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Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

Wang,

Yang,

Zhou

et al. 2023

Energy Science & Engineering

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As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

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Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

Cited by 45 publications

References 47 publications

Tail Risk and Extreme Events: Connections between Oil and Clean Energy

Tail Risk and Extreme Events: Connections between Oil and Clean Energy

The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

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