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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Main Author:	Islam, Mohd Aminul
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	HG4501 Stocks, investment, speculation
Online Access:	http://irep.iium.edu.my/33420/ http://irep.iium.edu.my/33420/1/IRIE_2013.pdf

id	iium-33420
recordtype	eprints
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 Conference or Workshop Item 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.
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	HG4501 Stocks, investment, speculation
spellingShingle	HG4501 Stocks, investment, speculation Islam, Mohd Aminul Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI)
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.
format	Conference or Workshop Item
author	Islam, Mohd Aminul
author_facet	Islam, Mohd Aminul
author_sort	Islam, Mohd Aminul
title	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_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_sort	modeling volatility using garch (1, 1) model: the case of kuala lumpur composite index (klci)
publishDate	2013
url	http://irep.iium.edu.my/33420/ http://irep.iium.edu.my/33420/1/IRIE_2013.pdf
first_indexed	2023-09-18T20:48:18Z
last_indexed	2023-09-18T20:48:18Z
_version_	1777409823451643904

Modeling volatility using GARCH (1, 1) Model: The case of Kuala Lumpur Composite Index (KLCI)

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