Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors.

Sajid M Chaudhry, Xihui Haviour Chen, Rizwan Ahmed, Muhammad Ali Nasir
Author Information
  1. Sajid M Chaudhry: Economics, Finance & Entrepreneurship Department, Aston Business School, Aston University, Birmingham, UK.
  2. Xihui Haviour Chen: Edinburgh Business School, The Centre for Social and Economic Data Analytics (CSEDA), Heriot-Watt University, Edinburgh, UK.
  3. Rizwan Ahmed: Kent Business School, University of Kent, Canterbury, UK.
  4. Muhammad Ali Nasir: Department, of Economics, University of Leeds, Leeds, UK. ORCID

Abstract

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges.

Keywords

References

  1. Risk Anal. 2021 Mar;41(3):544-557 [PMID: 31379003]
  2. Risk Anal. 2022 Jan;42(1):1-4 [PMID: 35152452]
  3. Risk Anal. 2022 Jan;42(1):206-220 [PMID: 33580512]
  4. Risk Anal. 2023 May 21;: [PMID: 37211620]
  5. Eur J Health Econ. 2018 Nov;19(8):1087-1110 [PMID: 29445942]
  6. Risk Anal. 2023 Oct;43(10):1946-1961 [PMID: 36617495]
  7. Risk Anal. 2025 Mar;45(3):477-495 [PMID: 37480163]
  8. J Clean Prod. 2021 Jan 25;281:125175 [PMID: 33223625]
  9. Risk Anal. 2013 Nov;33(11):1942-51 [PMID: 23614689]
  10. Proc Natl Acad Sci U S A. 2018 Aug 14;115(33):8252-8259 [PMID: 30082409]
  11. Risk Anal. 2023 May;43(5):1011-1031 [PMID: 35752460]
  12. Health Serv Res. 1978 Spring;13(1):16-27 [PMID: 632101]
  13. Risk Anal. 2015 Feb;35(2):193-210 [PMID: 25156415]
  14. Prog Disaster Sci. 2020 Apr;6:100080 [PMID: 34171009]
  15. Risk Anal. 2022 Sep;42(9):1902-1920 [PMID: 33331037]
  16. Front Public Health. 2020 Mar 31;8:80 [PMID: 32296671]

MeSH Term

Humans
Climate Change
Risk Assessment
Models, Theoretical
China
Health Care Sector

Word Cloud

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