Modeling and Forecasting Volatility of the Malaysian and the Singaporean stock indices using Asymmetric GARCH models and Non-normal Densities
Ahmed Shamiri () and
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Abu Hassan: University Kebangsaan Malaysia
Econometrics from University Library of Munich, Germany
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.
Keywords: ARCH-Models; Asymmetry; Stock market indices and volatility modeling; SAS/ETS software. (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin, nep-fmk and nep-for
Note: Type of Document - pdf; pages: 25
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0509015
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