Optimal portfolio: &amp;#x201C;Empirical investigation of food industry listed in KLSE&amp;#x201D;

Shahruddin, Sharifah Nazatul Shima Syed M.; Tafri, Fauziah Hanim; Lazam, Norazliani Md

doi:10.1109/icssbe.2012.6396574

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Generalized Autoregressive Conditional Heteroscedasticity (G1

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2018

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“…Hence, GARCH estimation model has been employed by [11] which introduced for estimating time-varying risk and provides effective tools for estimating the volatilities of stock price.…”

Section: Generalized Autoregressive Conditional Heteroscedasticity (Gmentioning

confidence: 99%

Predicting Stock Market Index Using Hybrid Intelligence Model

Johari¹,

Farid²,

Nasrudin³

et al. 2018

IJET

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Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARI-MA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.

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“…Hence, GARCH estimation model has been employed by [11] which introduced for estimating time-varying risk and provides effective tools for estimating the volatilities of stock price.…”

Section: Generalized Autoregressive Conditional Heteroscedasticity (Gmentioning

confidence: 99%