Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market
Volatility of financial market is a worth considering aspect of option pricing, formulating investment portfolio management and market supervision strategies. The outbreak of the COVID-19 in 2020 has affected financial markets in entire world, and people are mainly concerned about the volatility of...
| Main Author: | |
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2020
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| Online Access: | https://eprints.nottingham.ac.uk/62749/ |
| _version_ | 1848799974217744384 |
|---|---|
| author | WANG, Meng |
| author_facet | WANG, Meng |
| author_sort | WANG, Meng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Volatility of financial market is a worth considering aspect of option pricing, formulating investment portfolio management and market supervision strategies. The outbreak of the COVID-19 in 2020 has affected financial markets in entire world, and people are mainly concerned about the volatility of the stock market.
This study uses the ARMA-GARCH model (Autoregressive conditional heteroskedasticity model) to study the changes in the volatility of the Chinese stock market in the context of the new coronavirus. Using the Shanghai and Shenzhen 300 Index (CSI300), two periods were selected. The first phase is from the beginning of 2016 to the end of 2019, excluding the duration of the 2020 COVID-19 event; the second phase is from January 2016 to May 15, 2020. As expected in the financial time series, in both periods, CSI300 showed some stylized features, such as asymmetric, clustering effect, leptokurtosis, and leverage effect. The study also found that due to the impact of the new coronavirus on the economy, market volatility and leverage have increased significantly, but the persistence has slightly decreased.
The model that best describes the time series data from 2016 to 2019 is ARMA(3,3)-GARCH(1,2), however the model that best describes the time series from 2016 to 2020 is ARMA(3, 3)-EGARCH(1, 2). And ARMA(3,3)-TGARCH(1,2) has the strongest predictive ability, but the comparison of epidemic events affects the predictive ability of the model itself.
Keywords: volatility, leverage effect, COVID-19, ARMA-GARCH model, financial market |
| first_indexed | 2025-11-14T20:44:11Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-62749 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:44:11Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-627492023-04-14T14:29:14Z https://eprints.nottingham.ac.uk/62749/ Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market WANG, Meng Volatility of financial market is a worth considering aspect of option pricing, formulating investment portfolio management and market supervision strategies. The outbreak of the COVID-19 in 2020 has affected financial markets in entire world, and people are mainly concerned about the volatility of the stock market. This study uses the ARMA-GARCH model (Autoregressive conditional heteroskedasticity model) to study the changes in the volatility of the Chinese stock market in the context of the new coronavirus. Using the Shanghai and Shenzhen 300 Index (CSI300), two periods were selected. The first phase is from the beginning of 2016 to the end of 2019, excluding the duration of the 2020 COVID-19 event; the second phase is from January 2016 to May 15, 2020. As expected in the financial time series, in both periods, CSI300 showed some stylized features, such as asymmetric, clustering effect, leptokurtosis, and leverage effect. The study also found that due to the impact of the new coronavirus on the economy, market volatility and leverage have increased significantly, but the persistence has slightly decreased. The model that best describes the time series data from 2016 to 2019 is ARMA(3,3)-GARCH(1,2), however the model that best describes the time series from 2016 to 2020 is ARMA(3, 3)-EGARCH(1, 2). And ARMA(3,3)-TGARCH(1,2) has the strongest predictive ability, but the comparison of epidemic events affects the predictive ability of the model itself. Keywords: volatility, leverage effect, COVID-19, ARMA-GARCH model, financial market 2020-12-01 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/62749/1/20148487%20BUSI4020%20Modelling%20and%20Forcasting%20Volatility%20by%20ARMA-GARCH%20Models%20and%20the%20COVID-19-A%20Study%20of%20the%20Chinese%20Stock%20Market.pdf WANG, Meng (2020) Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market. [Dissertation (University of Nottingham only)] |
| spellingShingle | WANG, Meng Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market |
| title | Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market |
| title_full | Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market |
| title_fullStr | Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market |
| title_full_unstemmed | Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market |
| title_short | Modelling and Forcasting Volatility by ARMA-GARCH Models and the COVID-19-A Study of the Chinese Stock Market |
| title_sort | modelling and forcasting volatility by arma-garch models and the covid-19-a study of the chinese stock market |
| url | https://eprints.nottingham.ac.uk/62749/ |