Abstract
For an Investor, modelling and forecasting the stock prices are very important. Stock price fluctuate as time goes and these changes vary from one point of time to another. These changes can be really dangerous if ignored because the risk of loss it might create. Many models have been created with the purpose of minimizing the risk of loss. In this study, the ARIMA-GARCH model will be used to predict closing price in the stock prices which contain volatility. The reason for using the combination of the two models is due to ARIMA model unable to handle large volatility along with non-linear data. Thus, it is hoped the use of this combined model can solve this problem. The data that is used on this study is the closing price of 2 stocks that is part of the LQ45 index. In this research, the data will be used on the combined model to get the forecast price of the next day. Then, the rest of the forecast price will be found using a process called Walk Forward. After acquiring all the forecasted price, it is found that the combination of ARIMA (1,1,1)-GARCH (1,1) yield the best result in forecasting the stock prices. Then, by using MAE and RMSE to check the error of the results, it can be concluded that the ARIMA-GARCH model is a model that is able to predict stock prices well.
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