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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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Autor principal: Choo, Wei Chong
Formato: Thesis
Idioma:English
English
Publicado em: 1998
Assuntos:
GARCH model - Evaluation
Stock exchanges - Kuala Lumpur
Acesso em linha:http://psasir.upm.edu.my/id/eprint/11298/1/FSAS_1998_1_A.pdf
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id oai:psasir.upm.edu.my:11298
record_format eprints
spelling oai:psasir.upm.edu.my:11298 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. GARCH model - Evaluation Stock exchanges - Kuala Lumpur English
institution UPM IR
collection UPM IR
language English
English
topic GARCH model - Evaluation
Stock exchanges - Kuala Lumpur
spellingShingle GARCH model - Evaluation
Stock exchanges - Kuala Lumpur
Choo, Wei Chong
Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility
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
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
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score 13.4562235

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