Estimating volatility of stock index returns by using symmetric Garch models

This paper utilizes Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to estimate volatility of financial asset returns of three Asian markets namely; Kuala Lumpur Composite Index (KLCI) of Malaysia, Jakarta Stock Exchange Composite Index (JKSE) of Indonesia and Straits Time...

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Main Author:	Islam, Mohd Aminul
Format:	Article
Language:	English
Published:	IDOSI Publications 2013
Subjects:	HG4501 Stocks, investment, speculation
Online Access:	http://irep.iium.edu.my/41039/ http://irep.iium.edu.my/41039/ http://irep.iium.edu.my/41039/ http://irep.iium.edu.my/41039/1/Middle-East_Journal_of_Scientific_Research.pdf

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recordtype	eprints
spelling	iium-410392015-02-05T06:38:12Z http://irep.iium.edu.my/41039/ Estimating volatility of stock index returns by using symmetric Garch models Islam, Mohd Aminul HG4501 Stocks, investment, speculation This paper utilizes Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to estimate volatility of financial asset returns of three Asian markets namely; Kuala Lumpur Composite Index (KLCI) of Malaysia, Jakarta Stock Exchange Composite Index (JKSE) of Indonesia and Straits Times Index (STI) of Singapore. Two symmetric GARCH models with imposing names such as the GARCH (1, 1) and the GARCH-in-Mean or GARCH-M (1, 1) are considered in this study. The study covers the period 02/01/2007 – 31/12/2012 comprising daily observations of 1477 for KLCI, 1461 for JKSE and 1493 for STI excluding the public holidays. We choose to apply GARCH models as they are especially suitable for high frequency financial market data such as stock returns which has a time-varying variance. Unlike the linear structural models, GARCH models are found useful in explaining a number of important features commonly observed in most financial time series such as leptokurtosis, volatility clustering and leverage or asymmetric effects. In this paper, we applied the symmetric GARCH models to examine their capability in explaining the volatility clustering and leptokurtic characteristic of the financial data. In addition, we also empirically tested the positive correlation hypothesis between the expected risk and the expected return usually predicted in financial application. Our results provide strong evidence that daily stock returns can be characterized by these two symmetric GARCH models. From the results of risk-return hypothesis test in GARCH-M model, we found evidence of positive correlation between the risk and return for all markets as expected. However, only for Indonesian market which is found to be more volatile than the other two 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 two markets are positive but statistically insignificant suggesting that increased risk does not necessarily produce higher return. IDOSI Publications 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/41039/1/Middle-East_Journal_of_Scientific_Research.pdf Islam, Mohd Aminul (2013) Estimating volatility of stock index returns by using symmetric Garch models. Middle-East Journal of Scientific Research, 18 (7). pp. 991-999. ISSN 1990-9233 http://www.idosi.org/mejsr/mejsr.htm 10.5829/idosi.mejsr.2013.18.7.11806
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 Estimating volatility of stock index returns by using symmetric Garch models
description	This paper utilizes Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to estimate volatility of financial asset returns of three Asian markets namely; Kuala Lumpur Composite Index (KLCI) of Malaysia, Jakarta Stock Exchange Composite Index (JKSE) of Indonesia and Straits Times Index (STI) of Singapore. Two symmetric GARCH models with imposing names such as the GARCH (1, 1) and the GARCH-in-Mean or GARCH-M (1, 1) are considered in this study. The study covers the period 02/01/2007 – 31/12/2012 comprising daily observations of 1477 for KLCI, 1461 for JKSE and 1493 for STI excluding the public holidays. We choose to apply GARCH models as they are especially suitable for high frequency financial market data such as stock returns which has a time-varying variance. Unlike the linear structural models, GARCH models are found useful in explaining a number of important features commonly observed in most financial time series such as leptokurtosis, volatility clustering and leverage or asymmetric effects. In this paper, we applied the symmetric GARCH models to examine their capability in explaining the volatility clustering and leptokurtic characteristic of the financial data. In addition, we also empirically tested the positive correlation hypothesis between the expected risk and the expected return usually predicted in financial application. Our results provide strong evidence that daily stock returns can be characterized by these two symmetric GARCH models. From the results of risk-return hypothesis test in GARCH-M model, we found evidence of positive correlation between the risk and return for all markets as expected. However, only for Indonesian market which is found to be more volatile than the other two 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 two markets are positive but statistically insignificant suggesting that increased risk does not necessarily produce higher return.
format	Article
author	Islam, Mohd Aminul
author_facet	Islam, Mohd Aminul
author_sort	Islam, Mohd Aminul
title	Estimating volatility of stock index returns by using symmetric Garch models
title_short	Estimating volatility of stock index returns by using symmetric Garch models
title_full	Estimating volatility of stock index returns by using symmetric Garch models
title_fullStr	Estimating volatility of stock index returns by using symmetric Garch models
title_full_unstemmed	Estimating volatility of stock index returns by using symmetric Garch models
title_sort	estimating volatility of stock index returns by using symmetric garch models
publisher	IDOSI Publications
publishDate	2013
url	http://irep.iium.edu.my/41039/ http://irep.iium.edu.my/41039/ http://irep.iium.edu.my/41039/ http://irep.iium.edu.my/41039/1/Middle-East_Journal_of_Scientific_Research.pdf
first_indexed	2023-09-18T20:58:47Z
last_indexed	2023-09-18T20:58:47Z
_version_	1777410483156942848

Estimating volatility of stock index returns by using symmetric Garch models

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