Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility

Choo, Wei Chong (1998) Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility. Masters thesis, Universiti Putra Malaysia.

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Abstract

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.

Item Type:Thesis (Masters)
Subject:GARCH model - Evaluation
Subject:Stock exchanges - Kuala Lumpur
Chairman Supervisor:Associate Professor Muhammad Idrees Ahmad, PhD
Call Number:FSAS 1998 1
Faculty or Institute:Faculty of Environmental Studies
ID Code:11298
Deposited By: Mohd Nezeri Mohamad
Deposited On:18 Jul 2011 01:29
Last Modified:09 May 2012 01:14

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