期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2019
卷号：10
期号：1
页码：294-301
DOI：10.32479/ijeep.8715
出版社：EconJournals
摘要：Share price as one kind of financial data is the time series data that indicates the level of fluctuations and heterogeneous variances called heteroscedasticity. The method that can be used to overcome the effect of autoregressive conditional heteroscedasticity effect is the generalised form of ARCH (GARCH) model. This study aims to design the best model that can estimate the parameters, predict share price based on the best model and show its volatility. In addition, this paper discusses the prediction-based investment decision model. The findings indicate that the best model corresponding to the data is AR(4)-GARCH(1,1). The model is implemented to forecast the stock prices of Indika Energy Tbk, Indonesia, for 40 days and significantly presented good findings with an error percentage below the mean absolute..
其他摘要：Share price as one kind of financial data is the time series data that indicates the level of fluctuations and heterogeneous variances called heteroscedasticity. The method that can be used to overcome the effect of autoregressive conditional heteroscedasticity (ARCH effect) is the GARCH model. This study aims to design the best model that can estimate the parameters, predict share price based on the best model and show its volatility. In addition, this paper discusses the prediction-based investment decision model. The findings indicate that the best model corresponding to the data is AR(4)-GARCH(1,1). The model is implemented to forecast the stock prices of Indika Energy Tbk, Indonesia, for 40 days and significantly presented good findings with an error percentage below the mean absolute.
关键词：Autoregressive Conditional Heteroscedasticity Effect; Generalised Form of Autoregressive Conditional Heteroscedasticity Model;
