Stock Return Modeli Using the Generalized Autoregressive Conditional Heteroscedasticity (Garch) Method at Bank Rakyat Indonesia - Toyib Abdullah; Sulistijanti, Wellie (Journal of Applied Statistics and Data Mining, 2022)
Stocks are time series data in the financial sector, which usually have a tendency to fluctuate rapidly from time to time so that the variance of the error will always change over time or is not constant, or is often called a case of heteroscedasticity. The time series model to model this condition is the Autoregressive Conditional Heteroscedasticity (ARCH) model. ARCH models require large orders in modeling variations because financial data has a large level of volatility. To overcome orders that are too large in the ARCH model, a generalization of the ARCH model is carried out, this model is known as Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The results of the research show that the general picture of the closing price of BRI shares in the period January 2015 to December 2018 experienced unstable fluctuations and the highest closing price was in 2017. The best model used to print BRI share returns is the ARIMA model (29.0, 1) GARCH (1,1) so that the resulting model is as follows: Z_t=〖-0.084777e〗_(t-26) and σ_t^2=0.000241+〖0.149753α〗_(t-1)^2+ 〖0.334886σ〗_(t-1)^2. From the results of forecasting the return value, there was one period that experienced a loss and four periods that experienced a profit, with a loss in the 11/01/2018 period of 0.000116 and the highest profit in the 10/31/2018 period of 0.000039.