Abstract
A novel network with Wavelet denoising-GARCHSK and Mixed CoVaR method is proposed to construct full-sample and dynamic networks for investigating the risk spillover effects across international crude oil and Chinese stock sectors before and after the COVID-19 outbreak. The empirical results denote that the total bidirectional oil-sector risk spillover effects increase rapidly after the COVID-19 outbreak. Interestingly, sectors shift from net risk receivers to net risk contributors in the oil-sector risk transfer effects during the pandemic period. Second, unlike the pre-COVID-19 period, Shanghai crude (SC) replaces Brent as the largest oil risk transmitter to stocks during the COVID-19 period. Third, there are notable sectoral features in the oil-sector risk spillovers, which differ across different periods. After the burst, Energy has an incredibly weak connection with crude oil, while the sectors, which oil products are input for, become close with crude oil. Far more surprising is that the petroleum-independent sectors have increasing closer risk transfer effects with crude, even becoming the largest risk contributors to oil, after that. Finally, the oil-sector relationships during the same period are time-varying but stable. This paper provides policymakers and investors with new method and insight into the oil-sector relationships.
Author supplied keywords
Cite
CITATION STYLE
Zhu, P., Tang, Y., & Lu, T. (2023). How Connected is Crude Oil to Stock Sectors Before and After the COVID-19 Outbreak? Evidence from a Novel Network Method. Fluctuation and Noise Letters, 22(3). https://doi.org/10.1142/S0219477523500244
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.