dc.contributor.author |
Zhang, Yunhan
|
|
dc.contributor.author |
Gabauer, David
|
|
dc.contributor.author |
Gupta, Rangan
|
|
dc.contributor.author |
Ji, Qiang
|
|
dc.date.accessioned |
2024-06-27T04:56:53Z |
|
dc.date.available |
2024-06-27T04:56:53Z |
|
dc.date.issued |
2024-08 |
|
dc.description.abstract |
By introducing a new generalized forecast error variance decomposition (GFEVD) approach that splits the same into its contemporaneous and lagged components, we investigate the risk spillover effects of different order moments, derived from intraday data, for the top 10 banks and top 10 oil and gas companies in the U.S., covering the period from December 29, 2017 to December 30, 2022. The study finds that, first, the dynamic total connectedness of all order moments is heterogeneous over time driven by economic events. Second, except realized volatility spillovers, the vast majority of overall spillovers are attributable to contemporaneous spillovers, while only a tiny fraction is associated with lagged spillovers. Finally, realized skewness (crash risk) and realized kurtosis (extreme events) in banks and oil and gas companies originate mainly from intra-industry rather than inter-industry transmission. |
en_US |
dc.description.department |
Economics |
en_US |
dc.description.librarian |
hj2024 |
en_US |
dc.description.sdg |
SDG-08:Decent work and economic growth |
en_US |
dc.description.sponsorship |
The National Natural Science Foundation of China. |
en_US |
dc.description.uri |
https://www.elsevier.com/locate/eneeco |
en_US |
dc.identifier.citation |
Zhang, Y., Gabauer, D., Gupta, R. & Ji, Q. 2024, 'How connected is the oil-bank network? Firm-level and high-frequency evidence', Energy Economics, vol. 136, art. 107684, pp. 1-16, doi : 10.1016/j.eneco.2024.107684. |
en_US |
dc.identifier.issn |
0140-9883 (print) |
|
dc.identifier.issn |
1873-6181 (online) |
|
dc.identifier.other |
10.1016/j.eneco.2024.107684 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/96683 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.rights |
© 2024 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Energy Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Energy Economics, vol. 136, art. 107684, pp. 1-16, 2024, doi : 10.1016/j.eneco.2024.107684. |
en_US |
dc.subject |
Generalized forecast error variance decomposition (GFEVD) |
en_US |
dc.subject |
GFEVD decomposition |
en_US |
dc.subject |
Dynamic connectedness |
en_US |
dc.subject |
Higher moments |
en_US |
dc.subject |
Banking connectedness |
en_US |
dc.subject |
Time-varying parameter vector autoregressive (TVP-VAR) |
en_US |
dc.subject |
SDG-08: Decent work and economic growth |
en_US |
dc.title |
How connected is the oil-bank network? Firm-level and high-frequency evidence |
en_US |
dc.type |
Preprint Article |
en_US |