TY  - JOUR
T1  - Dynamic Model of Forecasting Stock Prices
AU - Satria Dwi Kesumah, Fajrin AU - Hendrawaty, Ernie AU - Usman, Mustofa AU - Russel, Edwin AU - Azhar, Rialdi AU - , Widiarti AU - Ananta, Prayudha 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 6
SP  - 1330
EP  - 1336
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.1330.1336
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.1330.1336
KW  - Volatility forecasting
KW  -GARCH
KW  -ARCH effect
KW  -stock price forecasting
KW  -parameters
KW  -investment
AB  - Sharia based investments currently become more popular in Indonesia as an alternative for those who
have a long-term horizon and are seeking an Islamic way in investing their money. However, such long-term
investment allows the existence of heteroscedasticity or heterogeneous variances in the time series data. To
come up with this issue, one way to model the Autoregressive Conditional Heteroscedasticity (ARCH) effect
is GARCH Model. The objective of this study is to obtain the best model estimating the parameters, to forecast
the stock prices and to present its predicted volatility. The results show that the best model as fitted data is
AR (1)-GARCH (1,1). The implication of this model is to predict the share price of Indofood CBP Sukses
Makmur Tbk, Indonesia, for the next 2 months (60 days) and it shows a very reasonable result as the percentage
of error is less than the mean.
ER  -