Usman, Mustofa and Barusman, M Yusuf S and Virginia, Erica (2018) Application of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model for Forecasting. Journal of Engineering and Applied Sciences, 13 (10). pp. 3418-3422. ISSN 1818-7803 (Online) 1816-949X

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Abstract

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 five week period is very sound and all the forecast values are within 95% confidence interval. Keywords: Volatility, heteroscedasticity, ARIMA, GARCH model

Item Type: Article
Subjects: Q Science > QA Mathematics
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Magister Ilmu Matematika
Depositing User: MUSTOFA US
Date Deposited: 14 Nov 2018 02:47
Last Modified: 14 Nov 2018 02:47
URI: http://repository.lppm.unila.ac.id/id/eprint/9835

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