Sudhi Sharma, Vaibhav Aggarwal, Reepu and Gitanjali Kaur Mehta
This study aims to investigate into the dynamic connection between ESG scores and the volatility term structure for Indian companies listed BSE. The study divides the BSE-100…
Abstract
Purpose
This study aims to investigate into the dynamic connection between ESG scores and the volatility term structure for Indian companies listed BSE. The study divides the BSE-100 listed companies into two panels based on their median ESG scores in 2022, creating high and low ESG scoring groups to capture volatility structure.
Design/methodology/approach
The study employs time-varying symmetric and asymmetric GARCH models and followed by continuous Wavelet to capture volatility structure and explore comparative resilience behavior.
Findings
The study found similar volatility patterns regardless of ESG scores, nudging doubt on the direct impact of ESG on volatility. Additionally, both high- and low-ESG-scored companies displayed high vulnerabilities during the pandemic, raising questions about the effectiveness of ESG frameworks in capturing risks. Finally, by examining the resilience behavior of ESG-scored companies during the pandemic, our study contributes to the evolving understanding of the intersection between ESG performance and crisis response.
Practical implications
The study carries vital implications for investors and policymakers. It highlights the urgent need to strengthen the ESG framework and scores to shield investors from short- and long-term volatilities and economic vulnerabilities.
Originality/value
To the best of the authors’ knowledge, this is the first study investigating the Indian market by examining the volatility structure and resilience behavior of high- and low-ESG-scored companies during the pandemic.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0113
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Ram B. Ramachandran and Chabi Gupta
Purpose: There has been a growing debate on whether generative AI can serve as the modern-day equivalent of white-collar knowledge workers. In a recent post, technology magnate…
Abstract
Purpose: There has been a growing debate on whether generative AI can serve as the modern-day equivalent of white-collar knowledge workers. In a recent post, technology magnate Bill Gates boldly proclaimed that ChatGPT would soon become the quintessential white-collar worker of tomorrow (Dean, 2023). This is indeed an exciting prospect, as generative AI advances at breakneck speed.
Need for the Study: This research delves upon the implications of such advancements for industries reliant on skilled employees. It raises questions about how these individuals will adjust their skillset going forward, given the proliferation of generative AI solutions poised to disrupt traditional roles previously occupied by humans.
Methodology: The study uses an exploratory framework to understand AI’s implications on job roles, productivity, and skill requirements. It introduces generative AI and its relevance, focusing on how it could transform white-collar jobs.
Findings: One thing seems clear: its impact on future employment opportunities. However, this technology still has limitations, potentially leading to unintended consequences. While capable of performing certain functions precisely and accurately, it cannot fully replace the reasoning abilities or cognitive flexibility innate in human workers tasked with knowledge-based work.
Practical Implications: The potential implications for workforce development, policy-making, and future research in AI and labor economics are highlighted. This will also help gain insights into the integration process, benefits, challenges, and the changing nature of white-collar work due to generative AI.