Parameter Estimation of GARCH Model with Bootstrap Approximation: Penaksiran Paramater Model GARCH Menggunakan Pendekatan Bootstrap - Sutarman; Hutasoit, Anggiarmi (Talenta Publisher, 2022)
This study aims to estimate the parameters of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model using a bootstrap approach. In the heteroscedasticity data model, it is determined how much the residual value of the sample used is. The bootstrap approach is a non-parametric and resampling technique used to estimate the parameter. From the sample data implemented, the residual estimation using the Maximum Likelihood Estimation method is - 0.065851304. Furthermore, the residual estimation value using the bootstrap approach is -1.769129241. Thus, the use of the bootstrap approach in the GARCH model results in a smaller residual value than MLE.