Modeling and Forecasting Volatility of the Malaysian and the Singaporean stock indices using Asymmetric GARCH models and Non-normal Densities
AbstractThis paper examines and estimate the three GARCH(1,1) models (GARCH, EGARCH and GJR-GARCH) using the daily price data. Two Asian stock indices KLCI and STI are studied using daily data over a 14-years period. The competing Models include GARCH, EGARCH and GJR-GARCH used with three different distributions, Gaussian normal, Student-t, Generalized Error Distribution. The estimation results show that the forecasting performance of asymmetric GARCH Models (GJR-GARCH and EGARCH), especially when fat-tailed asymmetric densities are taken into account in the conditional volatility, is better than symmetric GARCH. Moreover, its found that the AR(1)-GJR model provide the best out-of- sample forecast for the Malaysian stock market, while AR(1)-EGARCH provide a better estimation for the Singaporean stock market.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0509015.
Length: 25 pages
Date of creation: 08 Sep 2005
Date of revision:
Note: Type of Document - pdf; pages: 25
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ARCH-Models; Asymmetry; Stock market indices and volatility modeling; SAS/ETS software.;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-09-17 (All new papers)
- NEP-ECM-2005-09-17 (Econometrics)
- NEP-ETS-2005-09-17 (Econometric Time Series)
- NEP-FIN-2005-09-17 (Finance)
- NEP-FMK-2005-09-17 (Financial Markets)
- NEP-FOR-2005-09-17 (Forecasting)
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