Estimating volatility of stock index returns by using symmetric Garch models

• 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/41039/1/Middle-East_Journal_of_Scientific_Research.pdf; http://irep.iium.edu.my/41039/; http://www.idosi.org/mejsr/mejsr.htm
• Institution: Universiti Islam Antarabangsa Malaysia

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.