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Modelling time-varying volatility using GARCH models: evidence from the Indian stock market
Background: In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts. An empirical study of the returns of the NSE indices revealed a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758444/ https://www.ncbi.nlm.nih.gov/pubmed/36567684 http://dx.doi.org/10.12688/f1000research.124998.2 |
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author | Ali, Farman Suri, Pradeep Kaur, Tarunpreet Bisht, Deepa |
author_facet | Ali, Farman Suri, Pradeep Kaur, Tarunpreet Bisht, Deepa |
author_sort | Ali, Farman |
collection | PubMed |
description | Background: In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts. An empirical study of the returns of the NSE indices revealed an autoregressive conditional heteroskedastic trend in the Indian stock market. Methods: Using GARCH 1, 1 (generalized autoregressive conditional heteroskedasticity) and FIGARCH (fractionally integrated GARCH), we examine investor behaviour and the persistence of long-term volatility. Results: The empirical findings of the estimated models suggest that shocks persist for a long time in NSE returns. Furthermore, bad news has a greater impact on stock volatility than good news. The return on assets is stable but highly volatile, even though the Indian economy has experienced the global crash to some extent. Conclusions: Models of volatility derived from the GARCH equation provide accurate forecasts and are useful for portfolio allocation, performance measurement, and option valuation. |
format | Online Article Text |
id | pubmed-9758444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-97584442022-12-23 Modelling time-varying volatility using GARCH models: evidence from the Indian stock market Ali, Farman Suri, Pradeep Kaur, Tarunpreet Bisht, Deepa F1000Res Research Article Background: In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts. An empirical study of the returns of the NSE indices revealed an autoregressive conditional heteroskedastic trend in the Indian stock market. Methods: Using GARCH 1, 1 (generalized autoregressive conditional heteroskedasticity) and FIGARCH (fractionally integrated GARCH), we examine investor behaviour and the persistence of long-term volatility. Results: The empirical findings of the estimated models suggest that shocks persist for a long time in NSE returns. Furthermore, bad news has a greater impact on stock volatility than good news. The return on assets is stable but highly volatile, even though the Indian economy has experienced the global crash to some extent. Conclusions: Models of volatility derived from the GARCH equation provide accurate forecasts and are useful for portfolio allocation, performance measurement, and option valuation. F1000 Research Limited 2022-12-08 /pmc/articles/PMC9758444/ /pubmed/36567684 http://dx.doi.org/10.12688/f1000research.124998.2 Text en Copyright: © 2022 Ali F et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ali, Farman Suri, Pradeep Kaur, Tarunpreet Bisht, Deepa Modelling time-varying volatility using GARCH models: evidence from the Indian stock market |
title | Modelling time-varying volatility using GARCH models: evidence from the Indian stock market |
title_full | Modelling time-varying volatility using GARCH models: evidence from the Indian stock market |
title_fullStr | Modelling time-varying volatility using GARCH models: evidence from the Indian stock market |
title_full_unstemmed | Modelling time-varying volatility using GARCH models: evidence from the Indian stock market |
title_short | Modelling time-varying volatility using GARCH models: evidence from the Indian stock market |
title_sort | modelling time-varying volatility using garch models: evidence from the indian stock market |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758444/ https://www.ncbi.nlm.nih.gov/pubmed/36567684 http://dx.doi.org/10.12688/f1000research.124998.2 |
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