{"title":"The volatility of the Dow Jones Pharmaceuticals and Biotechnology Index in the context of the Coronavirus crisis","authors":"F. Darie, Illena Tache","doi":"10.51410/JCGIRM.7.2.3","DOIUrl":null,"url":null,"abstract":"This paper’s analysis was triggered by the outbreak of the new virus\nCOVID-19. In December 2019, the Chinese officials alerted the World\nHealth Organization (WHO) of the existence of an unknown deadly virus.\nCoronavirus has rapidly spread across the world - to Europe, Middle East\nand the USA, forcing the World Health Organization to declare COVID-19\na global pandemic. Its spread has generated major concerns for the health\nand economic sectors. Meanwhile, all countries hope for the development\nof a vaccine. Using as a research method the EGARCH model, this paper\ninvestigates if it can be applied to model the trend of volatility of the\npharmaceuticals and biotechnology markets, especially during the health\ncrisis. More specifically, this paper tries to identify whether different\nspecifications of univariate GARCH models can usefully anticipate\nvolatility in the stock indices market. The study uses estimates from both a\nsymmetric and an asymmetric GARCH models, namely GARCH (1, 1) and\nEGARCH models, for the Dow Jones Pharmaceuticals and Biotechnology\nindex (DJUSPN). The dataset is extracted from “Investing.com” and covers\nthe period September 2019 - August 2020, resulting in a total of\napproximately 252 daily closing prices. The data focuses on the response of\nthe highest capitalized pharmaceutical and biotechnology companies from\nthe US to combat the outbreak of the coronavirus. This study concludes that\nthe EGARCH model is better than the unconditional volatility and the\nconditional GARCH (1, 1) volatility and it is best suited for modelling and\nforecasting the fluctuations of the stock indexes.","PeriodicalId":147045,"journal":{"name":"Journal of corporate governance, insurance and risk management","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of corporate governance, insurance and risk management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51410/JCGIRM.7.2.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper’s analysis was triggered by the outbreak of the new virus
COVID-19. In December 2019, the Chinese officials alerted the World
Health Organization (WHO) of the existence of an unknown deadly virus.
Coronavirus has rapidly spread across the world - to Europe, Middle East
and the USA, forcing the World Health Organization to declare COVID-19
a global pandemic. Its spread has generated major concerns for the health
and economic sectors. Meanwhile, all countries hope for the development
of a vaccine. Using as a research method the EGARCH model, this paper
investigates if it can be applied to model the trend of volatility of the
pharmaceuticals and biotechnology markets, especially during the health
crisis. More specifically, this paper tries to identify whether different
specifications of univariate GARCH models can usefully anticipate
volatility in the stock indices market. The study uses estimates from both a
symmetric and an asymmetric GARCH models, namely GARCH (1, 1) and
EGARCH models, for the Dow Jones Pharmaceuticals and Biotechnology
index (DJUSPN). The dataset is extracted from “Investing.com” and covers
the period September 2019 - August 2020, resulting in a total of
approximately 252 daily closing prices. The data focuses on the response of
the highest capitalized pharmaceutical and biotechnology companies from
the US to combat the outbreak of the coronavirus. This study concludes that
the EGARCH model is better than the unconditional volatility and the
conditional GARCH (1, 1) volatility and it is best suited for modelling and
forecasting the fluctuations of the stock indexes.