Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index

Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressiv...

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Bibliographic Details
Main Authors: Yenni Angraini, Adelia Putri Pangestika, I Made Sumertajaya
Format: Article
Language:English
Published: Bina Nusantara University 2024-05-01
Series:ComTech
Subjects:
generalized autoregressive conditional heteroscedasticity (garch)
forecasting accuracy
jakarta composite index (jci)
Online Access:https://journal.binus.ac.id/index.php/comtech/article/view/10610
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