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

Authors

  • Yenni Angraini IPB University
  • Adelia Putri Pangestika IPB University
  • I Made Sumertajaya IPB University

DOI:

https://doi.org/10.21512/comtech.v15i1.10610

Keywords:

Generalized Autoregressive Conditional Heteroscedasticity (GARCH), forecasting accuracy, Jakarta Composite Index (JCI)

Abstract

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 Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.

Dimensions

Plum Analytics

Author Biographies

Yenni Angraini, IPB University

Department of Statistics, Faculty of Mathematics and Natural Sciences

Adelia Putri Pangestika, IPB University

Department of Statistics, Faculty of Mathematics and Natural Sciences

I Made Sumertajaya, IPB University

Department of Statistics, Faculty of Mathematics and Natural Sciences

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Published

2024-05-21
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