Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic

Zaheer Anwer, Ashraf Khan, Muhammad Abubakr Naeem, Aviral Kumar Tiwari

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

COVID-19 led restrictions make it imperative to study how pandemic affects the systemic risk profile of global commodities network. Therefore, we investigate the systemic risk profile of global commodities network as represented by energy and nonenergy commodity markets (precious metals, industrial metals, and agriculture) in pre- and post-crisis period. We use neural network quantile regression approach of Keilbar and Wang (Empir Econ 62:1–26, 2021) using daily data for the period 01 January 2018–27 October 2021. The findings suggest that at the onset of COVID-19, the two firm-specific risk measures namely value at risk and conditional value of risk explode pointing to increasing systemic risk in COVID-19 period. The risk spillover network analysis reveals moderate to high lower tail connectedness of commodities within each sector and low tail connectedness of energy commodities with the other sectors for both pre- and post-COVID-19 periods. The Systemic Network Risk Index reveals an abrupt increase in systemic risk at the start of pandemic, followed by gradual stabilization. We rank commodities in terms of systemic fragility index and observe that in post COVID-19 period, gold, silver, copper, and zinc are the most fragile commodities while wheat and sugar are the least fragile commodities. We use Systemic Hazard Index to rank commodities with respect to their risk contribution to global commodities network. During post COVID-19 period, the energy commodities (except natural gas) contribute most to the systemic risk. Our study has important implications for policymakers and the investment industry.

Original languageEnglish
JournalAnnals of Operations Research
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • COVID-19
  • CoVaR
  • Commodities
  • Energy
  • Neural network quantile regression

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research

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