Application of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in Forecasting the Volatility of Optimal Portfolio Stock Returns of the MNC36 Index

Abstract

Investment is a capital investment made by investors through the purchase of several stocks that are usually long-term with the hope that investors will benefit from increased stock prices. The most commonly used risk indicator in investing is volatility. Therefore, it is necessary to carry out modeling that can overcome the effects of heteroscedasticity to predict future volatility. Efforts are made to overcome the effects of heteroscedasticity by applying the Generalized Autoregressive Conditional Heterossexicity (GARCH) Model in Forecasting the Volatility of Optimal Portfolio Stock Returns on the MNC36 Index. This type of research is applied research that begins with reviewing the problem, analyzing relevant theories, and reviewing the problem and its application. Based on the results of data analysis using the residual normality test through the Jarque-Bera test, it was obtained that the GARCH model has a normal residual and is not heteroscedasticity so that it can be used as a forecasting model. BNGA shares obtained the most stable forecast results with almost constant volatility, indicating that this stock has the lowest risk compared to BBCA and BMRI stocks.