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Tail risk connectedness between US industries

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

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Authors

LINH NGUYEN LINH.NGUYEN2@NOTTINGHAM.AC.UK
Assistant Professor

Linh X. D. Nguyen

Linzhi Tan



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.

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

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