Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI)
In a dynamic environment, economies go through business cycle which may be considered to be a consequence of the stochastic nature of the financial markets. Past few years, there has been observed a considerable uncertainty in the financial markets in both developed and emerging nations worldwide...
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/33420/ http://irep.iium.edu.my/33420/1/IRIE_2013.pdf |
| _version_ | 1848780744570175488 |
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| author | Islam, Mohd Aminul |
| author_facet | Islam, Mohd Aminul |
| author_sort | Islam, Mohd Aminul |
| building | IIUM Repository |
| collection | Online Access |
| description | In a dynamic environment, economies go through business cycle which may
be considered to be a consequence of the stochastic nature of the financial
markets. Past few years, there has been observed a considerable uncertainty in
the financial markets in both developed and emerging nations worldwide.
Most of the investors as well as the financial analysts are concerned about the
volatility of the asset prices and its resulting effects of uncertainty of the
returns on their investment assets. The primary causes of such asset price
fluctuation are the variability in speculative market prices, unexpected
events, and the instability of business performance (Floros, 2008). The
stochastic nature of the financial market requires quantitative models to
explain and analyze the behavior of stock market returns and hence capable of
dealing with such uncertainty in price movements. In recent, there has been
some remarkable progress in developing sophisticated models to explain and
capture various properties of market variable volatilities and hence to manage
risks associated with them. Some of the models that deal with estimating
volatilities are: Autoregressive Conditional Heteroscedasticity (ARCH) first
developed by Engle (1982), Generalized ARCH or GARCH which was an
extended version of ARCH proposed by Bollerslev (1986) and
Nelson(1991), EGARCH, TGARCH, AGARCH, CGARCH and PGARCH.
These are the further extensions of ARCH model. For our case, we applied
GARCH (1, 1), the most common and popular tool of the GARCH models. |
| first_indexed | 2025-11-14T15:38:32Z |
| format | Proceeding Paper |
| id | iium-33420 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:38:32Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-334202013-12-20T07:05:40Z http://irep.iium.edu.my/33420/ Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) Islam, Mohd Aminul HG4501 Stocks, investment, speculation In a dynamic environment, economies go through business cycle which may be considered to be a consequence of the stochastic nature of the financial markets. Past few years, there has been observed a considerable uncertainty in the financial markets in both developed and emerging nations worldwide. Most of the investors as well as the financial analysts are concerned about the volatility of the asset prices and its resulting effects of uncertainty of the returns on their investment assets. The primary causes of such asset price fluctuation are the variability in speculative market prices, unexpected events, and the instability of business performance (Floros, 2008). The stochastic nature of the financial market requires quantitative models to explain and analyze the behavior of stock market returns and hence capable of dealing with such uncertainty in price movements. In recent, there has been some remarkable progress in developing sophisticated models to explain and capture various properties of market variable volatilities and hence to manage risks associated with them. Some of the models that deal with estimating volatilities are: Autoregressive Conditional Heteroscedasticity (ARCH) first developed by Engle (1982), Generalized ARCH or GARCH which was an extended version of ARCH proposed by Bollerslev (1986) and Nelson(1991), EGARCH, TGARCH, AGARCH, CGARCH and PGARCH. These are the further extensions of ARCH model. For our case, we applied GARCH (1, 1), the most common and popular tool of the GARCH models. 2013 Proceeding Paper NonPeerReviewed application/pdf en http://irep.iium.edu.my/33420/1/IRIE_2013.pdf Islam, Mohd Aminul (2013) Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI). In: IIUM Research, Invention and Innovation Exhibition 2013, 19 - 20 February 2013, Cultural Activity Centre (CAC) and KAED Gallery, IIUM. |
| spellingShingle | HG4501 Stocks, investment, speculation Islam, Mohd Aminul Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) |
| title | Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) |
| title_full | Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) |
| title_fullStr | Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) |
| title_full_unstemmed | Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) |
| title_short | Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI) |
| title_sort | modeling volatility using garch (1, 1) model: the case of kuala lumpur composite index (klci) |
| topic | HG4501 Stocks, investment, speculation |
| url | http://irep.iium.edu.my/33420/ http://irep.iium.edu.my/33420/1/IRIE_2013.pdf |