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All Journal ESTIMASI: Journal of Statistics and Its Application
Zulfanita Dien R
a:1:{s:5:"en_US";s:28:"UIN Raden Mas Said Surakarta";}
Mathematics

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Aplikasi Model Autoregressive Conditional Heteroscedastic-Generalized Auto Autoregressive Conditional Heteroscedastic pada Data Return Saham Bank Syariah Indonesia Zulfanita Dien R; Siswanto
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24799

Abstract

The increase of the financial sector, financial information is used in the economy to model and predict the movement of capital market stocks, so investors can easily understand investment risks. Financial sector data is in the form of time series data. Financial data  is found that does not fit the assumption of heteroscedasticity, so a model is needed that can maintain heteroscedasticity. Model Autoregressive Conditional Heteroscedasticity-Generalized Autoregressive Conditional Heteroscedastic is one of the econometric models used to model heteroscedasticity data in time series. The data in this study is BSI's daily closing price data taken from 4 January 2021 to 31 August 2022 with 406 data. Based on the selection of a time series model on Bank Syariah Indonesia (BSI), the best models are ARMA (11.0) and ARCH models (1). So that the ARMA (11.0)-ARCH (1) model can be the best model for modeling and predicting BSI stock return prices.
Co-Authors Siswanto Siswanto