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Tail risk connectedness between US industries

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  • Linh H. Nguyen
  • 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.

Suggested Citation

  • Linh H. Nguyen & Linh X. D. Nguyen & Linzhi Tan, 2021. "Tail risk connectedness between US industries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3624-3650, July.
    • Handle: RePEc:wly:ijfiec:v:26:y:2021:i:3:p:3624-3650
    DOI: 10.1002/ijfe.1979
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    2. Lang, Chunlin & Hu, Yang & Corbet, Shaen & Hou, Yang (Greg), 2024. "Tail risk connectedness in G7 stock markets: Understanding the impact of COVID-19 and related variants," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    3. Abdullah, Mohammad & Sarker, Provash Kumer & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Rehman, Mohd Ziaur, 2024. "Tail risk intersection between tech-tokens and tech-stocks," Global Finance Journal, Elsevier, vol. 61(C).

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