Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
The performance of generalised autoregressive conditional heteroscedasticity (GARCH) model and its modifications in forecasting stock market volatility are evaluated using the rate of returns from the daily stock market indices of Kuala Lumpur Stock Exchange (KLSE). These indices include Composi...
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| 主要作者: | |
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| 格式: | Thesis |
| 语言: | English English |
| 出版: |
1998
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| 在线阅读: | http://psasir.upm.edu.my/id/eprint/11298/1/FSAS_1998_1_A.pdf http://psasir.upm.edu.my/id/eprint/11298/ |
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| 总结: | The performance of generalised autoregressive conditional
heteroscedasticity (GARCH) model and its modifications in forecasting stock
market volatility are evaluated using the rate of returns from the daily stock market
indices of Kuala Lumpur Stock Exchange (KLSE). These indices include
Composite Index, Tins Index, Plantations Index, Properties Index and Finance
Index. The models are stationary GARCH, unconstrained GARCH, non-negative
GARCH, GARCH in mean (GARCH-M), exponential GARCH (EGARCH) and
integrated GARCH.
The parameters of these models and variance processes are estimated jointly
using maximum likelihood method. The performance of the within-sample
estimation is assessed using several goodness-of-fit statistics and the accuracy of
the out-of-sample forecasts is judged using mean squared error. |
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