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Aplikasi Model Autoregressive Conditional Heteroscedastic-Generalized Auto Autoregressive Conditional Heteroscedastic pada Data Return Saham Bank Syariah Indonesia

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 1 (2023) · pp. 44–52

DOI
10.20956/ejsa.vi.24799
ISSN
2721-379X, 2721-3803
Published
2023-02-14
Publisher
Hasanuddin University
Language
en

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.

Keywords

Collaboration network of the authors

12 authors · 40 co-authorship links

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  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Irdam RianiUniversitas Halu Oleo1
La Onu La OlaAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Ayu Pratika PutriAffiliation unknown1
Annurial Fitrayah TaufiqAffiliation unknown1
Ahmad Maulana SyarifAffiliation unknown1
Nurmimie KharinaAffiliation unknown1
Samsir Aditya AniaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Aplikasi Model Autoregressive Conditional Heteroscedastic-Generalized Auto Autoregressive Conditional Heteroscedastic pada Data Return Saham Bank Syariah Indonesia</dc:title>
	<dc:creator xml:lang="en">Dien R, Zulfanita</dc:creator>
	<dc:creator xml:lang="en">Siswanto</dc:creator>
	<dc:subject xml:lang="en">ARCH-GARCH</dc:subject>
	<dc:subject xml:lang="en">Bank Syariah Indonesia</dc:subject>
	<dc:subject xml:lang="en">Heteroscedasticity</dc:subject>
	<dc:subject xml:lang="en">Stock Return</dc:subject>
	<dc:subject xml:lang="en">Time Series</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-02-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/24799</dc:identifier>
	<dc:identifier>10.20956/ejsa.vi.24799</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 1, Januari, 2023 : Estimasi; 44-52</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/24799/9332</dc:relation>
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