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All Journal Journal of Mathematics Technology and Education
Hutasoit, Anggiarmi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Parameter Estimation of GARCH Model with Bootstrap Approximation: Penaksiran Paramater Model GARCH Menggunakan Pendekatan Bootstrap Hutasoit, Anggiarmi; Sutarman
Journal of Mathematics Technology and Education Vol. 1 No. 3 (2022): Journal of Mathematics Technology and Education
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jomte.v1i3.9261

Abstract

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.
Co-Authors Sutarman