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

By Ishaan Dawar, Anupam Dutta, Elie Bouri, Tareq Saeed. Renewable Energy, 2021

238 citations recorded by the source corpus.

Abstract Unlike previous studies examining the association between crude oil and renewable energy stock prices under average conditions, we employ a quantile-based regression approach offering a more comprehensive dependence structure under diverse market conditions. Using weekly data covering crude oil prices (WTI market) and three clean energy stock indices (the Wilderhill Energy Index, MAC Global Solar Energy Index, and S&P Global Clean Energy Index), quantile regression analyses provide solid evidence of the decreasing dependence of clean energy stock returns on crude oil returns. The lagged effect of WTI oil returns on clean energy stock returns is generally significant, which indicates that clean energy stock returns react differently to new information on oil returns under different market conditions. We further check for asymmetrical effects of oil returns on clean energy stock returns in various market conditions and find a strong effect of negative oil returns during bearish periods and an insignificant effect during bullish episodes.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/3ed89c6a8d7befd26449afdf9ddafffbea79589a

Citations in the corpus

• A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, unknown): “…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).”