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Modeling and Forecasting Volatility of the Malaysian and the Singaporean stock indices using Asymmetric GARCH models and Non-normal Densities

  • Ahmed Shamiri

    (University Kebangsaan Malaysia)

  • Abu Hassan

    (University Kebangsaan Malaysia)

This 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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File URL: http://econwpa.repec.org/eps/em/papers/0509/0509015.pdf
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Paper provided by EconWPA in its series Econometrics with number 0509015.

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Length: 25 pages
Date of creation: 08 Sep 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0509015
Note: Type of Document - pdf; pages: 25
Contact details of provider: Web page: http://econwpa.repec.org

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