Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression

Dawar, Ishaan; Dutta, Anupam; Bouri, Elie; Saeed, Tareq

doi:10.1016/j.renene.2020.08.162

Cited by 213 publications

(63 citation statements)

References 36 publications

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“…In general, the risk shock has the greatest impact on the new energy markets followed by the crude oil demand shock, and the crude oil supply shock has a smaller impact. These conclusions are consistent with the studies of Reboredo and Ugolini (2018) and Dawar et al (2021) finding that crude oil prices played an important role in dynamic changes of new energy stocks. Figure 1 shows the connectedness for the crude oil price shocks and the new energy markets.…”

Section: Empirical Results and Analysissupporting

confidence: 92%

“…Corbet et al (2020) showed that the decline of crude oil prices during the COVID-19 period had a positive spillover effect on the new energy market. Dawar et al (2021) used the quantile regression method to analyze the dependency link between WTI crude oil prices and new energy stocks; they showed that the affection of the lag return of crude oil prices to new energy stocks was significant and also found that the affection of crude oil returns on new energy stocks was asymmetric.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Effect of Structural Oil Price Shocks on New Energy Stock Markets

Zhou

Geng

2021

Front. Environ. Sci.

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This paper decomposes daily crude oil shocks into demand shock, supply shock and risk shock. Then, it employs Diebold and Yilmaz connectedness index approach to explore the differences for the time-varying effect of different types of structural shocks on new energy stock markets in China, Europe and the United States during the period 10 June 2009–30 October 2018. The new findings show that: 1) There are time-varying features of structural shocks to all new energy markets. 2) The crude oil demand shock and risk shock have a large explanatory ability on the returns of all new energy stock markets, while the crude oil supply shock has a small impact. 3) The influences of crude oil demand shocks on the market returns of new energy in China, Europe and the United States are 1.31%, 8.64%, and 4.47%, respectively; however, the affection of crude oil risk shocks to the market returns of new energy in the same markets are 3.17%, 7.91%, and 21.51%, respectively. 4) The crude oil demand shock and supply shock have little impact on any new energy market volatilities, but the effects of crude oil risk shocks to China and the United States’ new energy market volatilities are 2.44% and 3.14%, respectively.

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Section: Empirical Results and Analysissupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Dynamic Effect of Structural Oil Price Shocks on New Energy Stock Markets

Zhou

Geng

2021

Front. Environ. Sci.

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“…There is literature on clean energy equity dynamics that largely focuses on the impact of oil prices on clean energy stock returns (Bondia et al 2016;Dutta 2017;Dutta et al 2018;Elie et al 2019;Gupta 2017;Henriques and Sadorsky 2008;Kumar et al 2012;Maghyereh et al 2019;Managi and Okimoto 2013;Reboredo 2015;Reboredo et al 2017b;Reboredo and Ugolini 2018;Uddin et al 2019;Wen et al 2014). Popular modelling approaches include multifactor models (Henriques and Sadorsky 2008;Gupta 2017;Reboredo et al 2017b;Bohl et al 2013;Sadorsky 2012), vector autoregressions (Kumar et al 2012;Dutta et al 2018), or other approaches like wavelets (Maghyereh et al 2019;Reboredo et al 2017a), copulas (Reboredo 2015), and quantiles (Uddin et al 2019;Dawar et al 2021). While this research is important in establishing that oil prices have a significant impact on clean energy stock prices the focus has not been on forecasting clean energy stock prices.…”

Section: Discussionmentioning

confidence: 99%

A Random Forests Approach to Predicting Clean Energy Stock Prices

Sadorsky

2021

JRFM

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Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.

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“…Several studies find a statistically significant impact of energy commodity prices on green equity returns, for example, Kumar et al (2012); Managi and Okimoto (2013); Nasreen et al (2020). However, other studies find evidence for the decoupling of clean energy and fossil fuel markets, for example, Dawar et al (2020); Kyritsis and Serletis (2019); Ferrer et al (2018). The lack of consensus in the literature could be due to the use of a global green equity index, which masks the heterogeneous behavior among sub-sectors of the green equity market.…”

Section: Discussionmentioning

confidence: 99%

“…Uddin et al (2019) study the cross-quantile dependence between clean energy stock and oil price, aggregate stock price, exchange rate, and gold price. Dawar et al (2020) employ quantile regressions and find decreasing dependence between clean energy stock returns and crude oil returns. Yahya et al (2020) study the cross-quantile dependence between clean energy stock and non-ferrous metal prices and find a time-varying and asymmetric dependence between these variables.…”

Section: Related Literaturementioning

confidence: 99%

How Integrated are Regional Green Equity Markets? Evidence from a Cross-Quantilogram Approach

Pham

2021

JRFM

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Rising concerns over climate change have increased investors’ and policymakers’ interests in environmentally friendly investments, which have led to the rapid expansion of the green equity market recently. Previous studies have focused on analyzing the green equity market at the aggregate level, thereby overlooking the heterogeneity across green equity sub-sectors. This paper contributes to the literature by investigating how interdependence between green equity markets and other financial assets varies across regions, market conditions, and investment horizons. To this end, the paper employs the recently developed cross-quantilogram framework, which measures the cross-quantile dependence across time series without any moment condition requirement. The results show that within the green equity market, movements in the U.S. market can predict movements in the Asian and European markets during all market conditions. In contrast, the Asian and European green equity markets only predict movements in the U.S. market during bearish periods. The paper also finds that regional green equity markets respond differently to movements in other financial assets, such as energy commodity and general stock returns. In addition, the interdependence among regional green equity and other assets varies across market conditions and investment horizons. These results have important implications for environmentally friendly investors and policymakers.

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Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression

Cited by 213 publications

References 36 publications

Dynamic Effect of Structural Oil Price Shocks on New Energy Stock Markets

Dynamic Effect of Structural Oil Price Shocks on New Energy Stock Markets

A Random Forests Approach to Predicting Clean Energy Stock Prices

How Integrated are Regional Green Equity Markets? Evidence from a Cross-Quantilogram Approach

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