ANALISIS VOLATILITAS SAHAM PERUSAHAAN MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Abstract

The rate of return is one of the important factors for investors in making decisions when they want to invest. The amount of return obtained can determine the level of price fluctuations over a certain period or can be called volatility. Stock volatility needs to be reviewed to measure how big the level of fluctuation in the price of a stock is, so in this study discussed the analysis of the volatility of PT. AMAG uses the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The results showed that the stock return log data of PT. AMAG has volatility as evidenced by the presence of elements of heteroscedasticity in the Autoregressive Integrated Moving Average (ARIMA) model, so it is necessary to continue with the GARCH model testing to overcome the problem of heteroscedasticity or the residual variance is not constant. The best models obtained are ARIMA (0,0,1) and GARCH (1,1), using these models the results of forecasting the stock price index of PT. AMAG for 30 periods did not differ much from the actual data with a MAPE value of 1.44%.