Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets
Abstract In recent two decades, modelling and forecasting stock market volatility have been very important research topic in the Chinese financial researchers’ community. A number of literatures have been discussed the characteristic of volatility in emerging markets. China’s stock market is perta...
| Main Author: | |
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2019
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| Online Access: | https://eprints.nottingham.ac.uk/57363/ |
| _version_ | 1848799471690842112 |
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| author | Zhang, Jiahao |
| author_facet | Zhang, Jiahao |
| author_sort | Zhang, Jiahao |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Abstract
In recent two decades, modelling and forecasting stock market volatility have been very important research topic in the Chinese financial researchers’ community. A number of literatures have been discussed the characteristic of volatility in emerging markets. China’s stock market is pertained to emerging market. Meanwhile, it has been quite volatile since the stock market was established. In attempt to contribute to literature, this paper will apply three different types of GARCH model into Shanghai Stock Exchange Composite index to conduct empirical analysis. Also, the characteristic of the Shanghai Stock Exchange Composite index from the perspective of econometric will evaluated in this dissertation.
The finding shows that the SSE return series distribution appears leptokurtosis with significant ARCH effects and GARCH effects. Meanwhile, the return series exhibits the significant feature of clustering and time-varying. Furthermore, by applying the symmetric model (GARCH (1,1)) and asymmetric model (EGARCH (1,1) and TGARCH (1,1)), the result show that during the ten years’ time period, the GARCH (1,1) model actually outperforms than the other two models. However, in the sample of year 2015, it can be conducted that the TGARCH (1,1) model is superior to GARCH (1,1) and EGARCH (1,1) model. In addition, the recommendation indicated that the Chinese government should strengthen the stock market regulations and reduce market intervention. |
| first_indexed | 2025-11-14T20:36:12Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-57363 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:36:12Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-573632022-11-30T10:33:25Z https://eprints.nottingham.ac.uk/57363/ Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets Zhang, Jiahao Abstract In recent two decades, modelling and forecasting stock market volatility have been very important research topic in the Chinese financial researchers’ community. A number of literatures have been discussed the characteristic of volatility in emerging markets. China’s stock market is pertained to emerging market. Meanwhile, it has been quite volatile since the stock market was established. In attempt to contribute to literature, this paper will apply three different types of GARCH model into Shanghai Stock Exchange Composite index to conduct empirical analysis. Also, the characteristic of the Shanghai Stock Exchange Composite index from the perspective of econometric will evaluated in this dissertation. The finding shows that the SSE return series distribution appears leptokurtosis with significant ARCH effects and GARCH effects. Meanwhile, the return series exhibits the significant feature of clustering and time-varying. Furthermore, by applying the symmetric model (GARCH (1,1)) and asymmetric model (EGARCH (1,1) and TGARCH (1,1)), the result show that during the ten years’ time period, the GARCH (1,1) model actually outperforms than the other two models. However, in the sample of year 2015, it can be conducted that the TGARCH (1,1) model is superior to GARCH (1,1) and EGARCH (1,1) model. In addition, the recommendation indicated that the Chinese government should strengthen the stock market regulations and reduce market intervention. 2019-08-28 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/57363/1/final%20dissertation%200828.pdf Zhang, Jiahao (2019) Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets. [Dissertation (University of Nottingham only)] |
| spellingShingle | Zhang, Jiahao Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets |
| title | Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets |
| title_full | Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets |
| title_fullStr | Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets |
| title_full_unstemmed | Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets |
| title_short | Modelling and Forecasting Volatility by GARCH models: The Empirical Evidence of China’s Stock Markets |
| title_sort | modelling and forecasting volatility by garch models: the empirical evidence of china’s stock markets |
| url | https://eprints.nottingham.ac.uk/57363/ |