Tail risk connectedness between US industries

Nguyen, Linh H.; Nguyen, Linh X. D.; Tan, Linzhi

Abstract

We use the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression technique to construct and analyse the complete tail risk connectedness network of the whole US industry system. We also investigate the empirical relationship between input–output linkages and the tail risk spillovers among US industries. Our findings identify the tail-risk drivers, tail-risk receivers, and tail-risk distributors among industries and confirm that the actual trade flow between industries is a major driver of their tail risk connectedness.

Citation

Nguyen, L. H., Nguyen, L. X. D., & Tan, L. (2021). Tail risk connectedness between US industries. International Journal of Finance and Economics, 26(3), 3624-3650. https://doi.org/10.1002/ijfe.1979

• Journal Article Type: Article
• Acceptance Date: Jun 18, 2020
• Online Publication Date: Jul 15, 2020
• Publication Date: 2021-07
• Deposit Date: Aug 21, 2023
• Publicly Available Date: Aug 23, 2023
• Journal: International Journal of Finance and Economics
• Print ISSN: 1076-9307
• Electronic ISSN: 1099-1158
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 3
• Pages: 3624-3650
• DOI: https://doi.org/10.1002/ijfe.1979
• Keywords: Tail risk spillovers; Tail risk network; Business linkage; Input-Output; Quantile regression
• Public URL: https://nottingham-repository.worktribe.com/output/24574614
• Publisher URL: https://onlinelibrary.wiley.com/doi/10.1002/ijfe.1979