MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL
Aggregate risk is an aggregation of single risks that are both independent and interdependent. In this study, aggregate risk is constructed from two interdependent random risk variables. The dependence between two random variables can be determined through the size of dependence and joint distributi...
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Universitas Pattimura
2025-07-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17176 |
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| author | Asysta Amalia Pasaribu Anang Kurnia |
| author_facet | Asysta Amalia Pasaribu Anang Kurnia |
| author_sort | Asysta Amalia Pasaribu |
| collection | DOAJ |
| description | Aggregate risk is an aggregation of single risks that are both independent and interdependent. In this study, aggregate risk is constructed from two interdependent random risk variables. The dependence between two random variables can be determined through the size of dependence and joint distribution properties. However, not all distributions have joint distribution properties; the joint distributions may be unknown, so motivating the use of the Copulas in this study is needed. Sometimes, the Copula model is introduced to construct joint distribution properties. The Copula model in this research is used in financial policies such as investment. In the investment sector, the aggregate risk comes from the sum of the single risks and returns. The model used in aggregate return is the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. The data used in this study is the closing price data for Apple and Microsoft stocks from January 01, 2010, to January 01, 2024. The best model selection is the model with the GARCH-Bivariate Normal approach with the smallest MSE value. Model GARCH(1,1)-Bivariate Normal is the best model for the volatility model of aggregate return. |
| format | Article |
| id | doaj-art-1b4c6a194d074443bb8a511b1fed6494 |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-1b4c6a194d074443bb8a511b1fed64942025-08-20T03:02:54ZengUniversitas PattimuraBarekeng1978-72272615-30172025-07-011932069208210.30598/barekengvol19iss3pp2069-208217176MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMALAsysta Amalia Pasaribu0Anang Kurnia1Statistics and Data Science Department, School of Data Science, Mathematics and Informatics, IPB University, IndonesiaStatistics and Data Science Department, School of Data Science, Mathematics and Informatics, IPB University, IndonesiaAggregate risk is an aggregation of single risks that are both independent and interdependent. In this study, aggregate risk is constructed from two interdependent random risk variables. The dependence between two random variables can be determined through the size of dependence and joint distribution properties. However, not all distributions have joint distribution properties; the joint distributions may be unknown, so motivating the use of the Copulas in this study is needed. Sometimes, the Copula model is introduced to construct joint distribution properties. The Copula model in this research is used in financial policies such as investment. In the investment sector, the aggregate risk comes from the sum of the single risks and returns. The model used in aggregate return is the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. The data used in this study is the closing price data for Apple and Microsoft stocks from January 01, 2010, to January 01, 2024. The best model selection is the model with the GARCH-Bivariate Normal approach with the smallest MSE value. Model GARCH(1,1)-Bivariate Normal is the best model for the volatility model of aggregate return.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17176dependencesgarch-copulagarch-bivariate normalrisk aggregatevolatility |
| spellingShingle | Asysta Amalia Pasaribu Anang Kurnia MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL Barekeng dependences garch-copula garch-bivariate normal risk aggregate volatility |
| title | MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL |
| title_full | MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL |
| title_fullStr | MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL |
| title_full_unstemmed | MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL |
| title_short | MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL |
| title_sort | model approach of aggregate return volatility garch 1 1 copula vs garch 1 1 bivariate normal |
| topic | dependences garch-copula garch-bivariate normal risk aggregate volatility |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17176 |
| work_keys_str_mv | AT asystaamaliapasaribu modelapproachofaggregatereturnvolatilitygarch11copulavsgarch11bivariatenormal AT anangkurnia modelapproachofaggregatereturnvolatilitygarch11copulavsgarch11bivariatenormal |