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: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Online Access: | http://irep.iium.edu.my/33420/ http://irep.iium.edu.my/33420/1/IRIE_2013.pdf |
| Summary: | 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. |
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