{"title":"Demystifying the Impact of COVID-19 on Structural Volatility of Indian Sectoral Market: A Quantile Regression Analysis","authors":"M. Mishra, N. Seth, L. Panda","doi":"10.1177/09722629231159521","DOIUrl":null,"url":null,"abstract":"The present study explores the impact of COVID-19 on the volatility structure of the sectoral market in India. ARMA(p,q)- GJR-GARCH(1, 1)-std model is used to determine the daily conditional volatility for 13 selected sectors over the period starting from January 2020 to December 2021. The quantile regression model is employed to examine the changes in the structure of volatility in each sector over the pandemic duration. The results of the study show that the volatility of Metal, Oil–Gas and PSU are more sensitive to market volatility, whereas the volume of new COVID-19 cases exceeds the threshold limit, and no extreme spillover is observed from the market volatility. In addition to this, Bankex, Metal, Oil–Gas, Private Banks and Power sector volatility are more responsive to news sentiments during the period of increase in new COVID-19 cases. Furthermore, the results also reveal that news sentiments help to control the significant fluctuation in the sectoral market.","PeriodicalId":44860,"journal":{"name":"Vision-The Journal of Business Perspective","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vision-The Journal of Business Perspective","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09722629231159521","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 1
Abstract
The present study explores the impact of COVID-19 on the volatility structure of the sectoral market in India. ARMA(p,q)- GJR-GARCH(1, 1)-std model is used to determine the daily conditional volatility for 13 selected sectors over the period starting from January 2020 to December 2021. The quantile regression model is employed to examine the changes in the structure of volatility in each sector over the pandemic duration. The results of the study show that the volatility of Metal, Oil–Gas and PSU are more sensitive to market volatility, whereas the volume of new COVID-19 cases exceeds the threshold limit, and no extreme spillover is observed from the market volatility. In addition to this, Bankex, Metal, Oil–Gas, Private Banks and Power sector volatility are more responsive to news sentiments during the period of increase in new COVID-19 cases. Furthermore, the results also reveal that news sentiments help to control the significant fluctuation in the sectoral market.
期刊介绍：
Vision-The Journal of Business Perspective is a quarterly peer-reviewed journal of the Management Development Institute, Gurgaon, India published by SAGE Publications. This journal contains papers in all functional areas of management, including economic and business environment. The journal is premised on creating influence on the academic as well as corporate thinkers. Vision-The Journal of Business Perspective is published in March, June, September and December every year. Its targeted readers are researchers, academics involved in research, and corporates with excellent professional backgrounds from India and other parts of the globe. Its contents have been often used as supportive course materials by the academics and corporate professionals. The journal has been providing opportunity for discussion and exchange of ideas across the widest spectrum of scholarly opinions to promote theoretical, empirical and comparative research on problems confronting the business world. Most of the contributors to this journal range from the outstanding and the well published to the upcoming young academics and corporate functionaries. The journal publishes theoretical as well as applied research works.
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