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Tail risk connectedness between US industries
Author(s) -
Nguyen Linh H.,
Nguyen Linh X. D.,
Tan Linzhi
Publication year - 2021
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.1979
Subject(s) - social connectedness , tail risk , econometrics , lasso (programming language) , quantile regression , economics , construct (python library) , quantile , regression , computer science , mathematics , statistics , world wide web , psychotherapist , programming language , psychology
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.