Oil Prices and Stock Prices of Clean Energy: New Evidence from Chinese Subsectoral Data

Commodity Prices and the Stock Market in Thailand

“…Some literatures suggested that oil price risk impacts stock price returns in both developed and emerging markets including Chang et al (2010), Hamma et al (2014), Caporale et al (2015), Gupta (2016), Tian (2016), Ulussever (2017) while others suggested that the results were mixed or there was no significant influence of oil price risk on stock markets. (Alom, 2015;Bastianin et al, 2016;Degiannakis et al, 2018;Yıldırım et al, 2018;Alio et al, 2019;Lv et al, 2019;Singhal et al, 2019) Some evidence regarding the latter case is presented in the following paragraph. Bastianin et al (2016) investigated the impact of oil price shocks on the stock market volatility in the G7 countries and found that the stock market volatility did not respond to oil supply shocks, however, demand shocks had significant impact on the stock market volatility.…”

“…Singhal et al (2019) have studied the association between gold prices, oil prices, and equity market in Mexico and found that although both gold prices and energy prices have significant impact on equity markets, gold prices have a more positive effect on equities than oil. Moreover, in the Chinese markets, Lv et al (2019) employed GARCH-M model to This Journal is licensed under a Creative Commons Attribution 4.0 International License discuss the influence of oil price and stock price in sub-industries such as wind and solar energy. In addition, the spillover effects between oil risk and stock volatility in these sub-industries are also explored.…”

Tail risk connectedness in clean energy and oil financial market

“…More recently, Lv et al. ( 2021 ), by using a K-GARCH-M model, studied the spillover effect between oil price and Chinese clean energy subsectors (such as hydropower, solar energy, nuclear power, wind energy, new energy and new-energy vehicles). Their analysis was conducted at sectoral index levels.…”

“…In our research, using the the Tail-Event driven NETwork (TENET, Härdle et al, 2016 ) risk model, we are able to offer fresh information on the degree of interconnection (spillover effect) between markets, providing a detailed picture of the relationship. Although many works have analysed this relationship using several different methodologies such as the VAR and VECM models (Henriques & Sadorsky, 2008 ; Kumar et al, 2012 ; Managi & Okimoto, 2013 ; Bondia et al, 2016 ; Chai et al, 2022 ); wavelets (Reboredo et al, 2017 ); copulas (Reboredo & Ugolini, 2018 ); GARCH and variants (Sadorsky, 2012a ; Lv et al, 2021 , 2012 , 2014 ) and framework and variants (Ahmad, 2017 ; Ferrer et al, 2018 ; Pham, 2019 ; Nasreen et al, 2020 ; Foglia & Angelini, 2020 ), none of these methods have been able to capture extreme spillover effects from a network perspective at the firm level. Our paper is close to a recent work of Saeed et al.…”

“…Finally, we are able to investigate the contribution of each green and brown firm by considering their tail interconnects with other energy companies (i.e., we consider all the systems) in contrast to Lv et al. ( 2021 ), who, by BEKK-GARCH-M could analyse only the bidirectional relationship.…”