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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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Main Author: Choo, Wei Chong
Format: Thesis
Language:English
English
Published: 1998
Online Access: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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spelling my.upm.eprints.112982012-05-09T01:14:39Z http://psasir.upm.edu.my/id/eprint/11298/ Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility Choo, Wei Chong 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. 1998-04 Thesis NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/11298/1/FSAS_1998_1_A.pdf Choo, Wei Chong (1998) Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility. Masters thesis, Universiti Putra Malaysia. English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description 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.
format Thesis
author Choo, Wei Chong
spellingShingle Choo, Wei Chong
Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
author_facet Choo, Wei Chong
author_sort Choo, Wei Chong
title Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
title_short Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
title_full Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
title_fullStr Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
title_full_unstemmed Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
title_sort generalised autoregressive conditional heteroscedasticity (garch) models for stock market volatility
publishDate 1998
url http://psasir.upm.edu.my/id/eprint/11298/1/FSAS_1998_1_A.pdf
http://psasir.upm.edu.my/id/eprint/11298/
_version_ 1643824797853941760
score 13.211869

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