Volatility linked 'ESG-metric' for sustainability risk in corporate world


As environmental, social, and governance (ESG) factors continue to gain prominence in the corporate world where risk keeps on evolving, their role as a critical measure of sustainability risk is increasingly recognized.Data analytics and artificial intelligence have transformed how financial institutions approach ESG risk monitoring and reporting. By appreciating the power of these technologies, institutions can be able to dig deeper into their data, make more informed decisions, and improve the transparency and accuracy of their ESG reports. Data analytics and artificial intelligence enable financial institutions to process massive amounts of data from a variety of sources, such as internal systems, public databases, and social media platforms. This extensive data analysis enables a thorough assessment of ESG risks, resulting to valuable insights that would otherwise go unnoticed. Machine learning algorithms are critical in ESG risk monitoring created using softwares e.g R. These algorithms can be trained to detect patterns in the data.This article tries to give a directive in quantifying the risk brought about by ESG factors in conjunction with other risk metrics. The study details the theoretical understanding of ESG i.e. literature review and the quantification of ESG-metric to enhance measuring of sustainability risk. My research illustrates evidence to show how ESG factors serve as vital indicators of sustainability risk. I will try to assess the relationship between volatility, VaR among other covariates and ESG factors, and try to come up with an R-model with a quantified metric called ESG-metric derived from ESG scores which will enable quantification of sustainability risk in the corporate world which will be a step of miles towards risk mitigation. 

 

  • INTRODUCTION


Sustainability risk is an unforeseen situation that impacts a company financially and reputation when it fails to effectively address ESG factors. These factors incorporate a wide range of issues, e.g. environmental ones include climate change leading to climate-related risks, waste management and resource usage, social issues include the relationship of institutions to employees, customers, community, or other institutions while governance issues are related to institutions policies, government policies, and business/corporate ethics. Lately, the world is concentrating on climate risk mitigation; on September 2023 Kenya hosted Climate Summit 2023 without forgetting the COP(s); climate-related summits, according to International Meteorological Organization (IMO) we are experiencing a global warming of 1.5°C which has affected the agricultural sector directly hence impacting the corporate world in one way or the other. In Kenya, these impacts were realized in the savings and credit sector, e.g. according to the annual SACCO(s) supervision report 2022 released by SASRA (SACCO regulatory authority) has revealed that 49 agriculture-based SACCOs have topped the charts in loan defaults by the ratio of 18.42 percent, more than double the industry average among other examples due to climate-related crises. Social and governance also have greatly affected the sector e.g. increased withdrawals due to economic hardships as a result of policies laid down among other evidence from Daily Nation newspaper and other relevant data sources. As much as some factors are beyond the company's control some of them can be controlled hence I thought of this model.

Related sources:

  • https://www2.deloitte.com/ie/en/pages/audit/articles/esg_risks_the_reporting_challenge.html
  • https://www.ilo.org/wcmsp5/groups/public/---ed_dialogue/---act_emp/documents/publication/wcms_848404.pdf


  • Model review:

                                      

            This part illustratively shows the relationship between volatility and ESG factors.


Volatility is the degree of variation in the price, value, or returns of a financial instrument or asset over a specific period of time from its mean. Higher volatility indicates instability of prices or value of an asset, while lower volatility shows stable and predictable movements. Away from that volatility can mainly result from changes in supply and demand and other factors e.g. ESG factors hence the relationship between volatility and ESG-metric. Since R-models quantify volatility, it is possible to develop a model that quantifies sustainability risk by coming up with ESG scores are an ESG metric to measure sustainability risk. This shows a direct relationship between volatility and sustainability risk.



By Mutharimi Phineas

(CEO, The Kenyactuary)

 

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