Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility
This paper aims to model volatility of daily index returns for four Asian markets namely; Kuala Lumpur Composite Index of Malaysia, Jakarta Stock Exchange Composite Index of Indonesia, Straits Times Index of Singapore and the Stock Index of Korea over the period 03/01/2007 – 31/07/2013 excluding the...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Asian Network for Scientific Information
2014
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/36034/ http://irep.iium.edu.my/36034/1/59767-59767_JAS.pdf http://irep.iium.edu.my/36034/4/JAS_Published_Article.pdf |
| _version_ | 1848781151547686912 |
|---|---|
| author | Islam, Mohd Aminul |
| author_facet | Islam, Mohd Aminul |
| author_sort | Islam, Mohd Aminul |
| building | IIUM Repository |
| collection | Online Access |
| description | This paper aims to model volatility of daily index returns for four Asian markets namely; Kuala Lumpur Composite Index of Malaysia, Jakarta Stock Exchange Composite Index of Indonesia, Straits Times Index of Singapore and the Stock Index of Korea over the period 03/01/2007 – 31/07/2013 excluding the public holidays. We utilized generalized autoregressive conditional heteroskedasticity models as these models are particularly suitable for high frequency financial time series such as the daily stock returns which has a time-varying variance. Unlike the linear structural models, these models are found very useful in explaining the most stylized facts about index returns such as leptokurtosis, volatility clustering and asymmetric or leverage effects. The key results of this study are as follows. Firstly, the conditional variance process is highly persistent in all markets. Secondly, the relationship between the expected risk and the expected return is positive as expected but the relationship is not statistically significant for all markets except Indonesia. For Indonesian market which is found to be more volatile than the other three markets, the estimated coefficient of risk premium appeared to be statistically significant indicating that increased risk leads to a rise in the returns. The risk-premium coefficients for other three markets are although positive but not statistically significant suggesting that increased risk does not necessarily produce higher return for those three markets. Finally, the asymmetric effects is exist in all cases signifying that negative shock produces higher impact on future volatility than the positive shock of the same magnitude. |
| first_indexed | 2025-11-14T15:45:00Z |
| format | Article |
| id | iium-36034 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T15:45:00Z |
| publishDate | 2014 |
| publisher | Asian Network for Scientific Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-360342015-04-29T05:21:09Z http://irep.iium.edu.my/36034/ Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility Islam, Mohd Aminul HG4501 Stocks, investment, speculation This paper aims to model volatility of daily index returns for four Asian markets namely; Kuala Lumpur Composite Index of Malaysia, Jakarta Stock Exchange Composite Index of Indonesia, Straits Times Index of Singapore and the Stock Index of Korea over the period 03/01/2007 – 31/07/2013 excluding the public holidays. We utilized generalized autoregressive conditional heteroskedasticity models as these models are particularly suitable for high frequency financial time series such as the daily stock returns which has a time-varying variance. Unlike the linear structural models, these models are found very useful in explaining the most stylized facts about index returns such as leptokurtosis, volatility clustering and asymmetric or leverage effects. The key results of this study are as follows. Firstly, the conditional variance process is highly persistent in all markets. Secondly, the relationship between the expected risk and the expected return is positive as expected but the relationship is not statistically significant for all markets except Indonesia. For Indonesian market which is found to be more volatile than the other three markets, the estimated coefficient of risk premium appeared to be statistically significant indicating that increased risk leads to a rise in the returns. The risk-premium coefficients for other three markets are although positive but not statistically significant suggesting that increased risk does not necessarily produce higher return for those three markets. Finally, the asymmetric effects is exist in all cases signifying that negative shock produces higher impact on future volatility than the positive shock of the same magnitude. Asian Network for Scientific Information 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/36034/1/59767-59767_JAS.pdf application/pdf en http://irep.iium.edu.my/36034/4/JAS_Published_Article.pdf Islam, Mohd Aminul (2014) Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility. Journal of Applied Sciences, 14 (7). pp. 641-650. ISSN 1812-5662 (O), 1812-5654 (P) http://scialert.net/abstract/?doi=jas.0000.59767.59767 10.3923/jas.2014.641.650 |
| spellingShingle | HG4501 Stocks, investment, speculation Islam, Mohd Aminul Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| title | Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| title_full | Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| title_fullStr | Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| title_full_unstemmed | Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| title_short | Applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| title_sort | applying generalized autoregressive conditional heteroscedasticity models to model univariate volatility |
| topic | HG4501 Stocks, investment, speculation |
| url | http://irep.iium.edu.my/36034/ http://irep.iium.edu.my/36034/ http://irep.iium.edu.my/36034/ http://irep.iium.edu.my/36034/1/59767-59767_JAS.pdf http://irep.iium.edu.my/36034/4/JAS_Published_Article.pdf |