Quantifying systemic risk in US industries using neural network quantile regression

Muhammad Abubakr Naeem, Sitara Karim, Aviral Kumar Tiwari

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

The study quantified the systemic risk spillovers between top 10 US industries using conditional value-at-risk in a network context by calibrating the marginal effects of a quantile regression process and coving period from January 3, 2007-May 28, 2021 and found significant variations in the risk measured using a nonlinear process. Neural networks identified the manufacturing industry as the center of risk spillovers with the disconnected telecommunication industry in a system-wide neural network portraying its diversification potential. The systematic fragility index, which ranks industries with a high exposure to tail risk in a system, revealed the utilities industry as being the most vulnerable to economically fragile periods. By contrast, the systematic hazard index, which measures the risk contribution of an industry, showed the manufacturing industry as the principal risk contributor. With this tail risk assessment, particularly during distress periods, we stipulate several implications for policymakers, regulators, investors, and financial market participants.

Original languageEnglish
Article number101648
JournalResearch in International Business and Finance
Volume61
DOIs
Publication statusPublished - Oct 2022

Keywords

  • CoVaR
  • Neural networks
  • Quantile regression
  • Systemic risk

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Finance

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