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

• 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/33420/1/IRIE_2013.pdf; http://irep.iium.edu.my/33420/
• Institution: Universiti Islam Antarabangsa Malaysia

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.