@article{MAKHILLJEAS2018131016206,
    title = {Application of Generalized Autoregressive Conditional
Heteroscedasticity (GARCH) Model for Forecasting},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {10},
    pages = {3418-3422},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.3418.3422},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.3418.3422},
    author = {M. Yusuf S.,Mustofa,Riyama and},
    keywords = {Volatility,heteroscedasticity,ARIMA,GARCH Model,forecasting,Comfeed Indonesia},
    abstract = {Financial data sometimes have not only high volatility but also heterogeneous variances. The Box
Jenkins method cannot be used to overcome a model which has an effect of heteroscedasticity. One of the
models can be used to overcome the effect of heteroscedasticity is GARCH Model. The aims of this study are
to find the best model, to estimate the parameters of the best model and to predict the share price data of JAPFA
Comfeed Indonesia over the period of June 2015 to October 2016. The best model which fits to the data is
ARIMA (0, 1, 2) and GARCH (1, 1). The application of the two models for forecasting the share price data of
JAPFA Comfeed Indonesia for the next 5 weeks period is very sound and all the forecast values are within 95%
confidence interval.}
    }