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


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



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


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.


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


Adenomon, M. O., Maijamaa, B., & John, D. O. (2022). The effects of COVID-19 outbreak on the Nigerian Stock Exchange performance: Evidence from GARCH models. Journal of Statistical Modeling & Analytics (JOSMA), 4(1), 25–38.

Aliyev, F., Ajayi, R., & Gasim, N. (2020). Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100. The Journal of Economic Asymmetries, 22.

Behera, H., Gunadi, I., & Rath, B. N. (2023). COVID-19 uncertainty, financial markets and monetary policy effects in case of two emerging Asian countries. Economic Analysis and Policy, 78, 173–189.

Braz, M. S., Sáfadi, T., Ferreira, R. A., Morais, M. H. F., Silva, Z., & Da Rocha, C. M. B. M. (2023). Temporal relationship between human and canine visceral leishmaniasis in an urban area in southeastern Brazil: An application of the ARIMAX model. Preventive Veterinary Medicine, 215.

Dinku, T., & Worku, G. (2022). Asymmetric GARCH models on price volatility of agricultural commodities. SN Business & Economics, 2.

Ekinci, A. (2021). Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect. Chaos, Solitons & Fractals, 151.

Fang, J., Gozgor, G., Lau, C. K. M., & Lu, Z. (2020). The impact of Baidu index sentiment on the volatility of China’s stock markets. Finance Research Letters, 32.

Haryanto. (2020). Dampak COVID-19 terhadap pergerakan nilai tukar rupiah dan Indeks Harga Saham Gabungan (IHSG). The Indonesian Journal of Development Planning, 4(2), 151–165.

Hismendi, Masbar, R., Nazamuddin, Majid, M. S. A., & Suriani. (2021). Sectoral stock markets and economic growth nexus: Empirical evidence from Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 11–19.

Hussain, F., Ali, Y., Li, Y., & Haque, M. M. (2023). Real-time crash risk forecasting using artificial-Intelligence based video analytics: A unified framework of generalised extreme value theory and autoregressive integrated moving average model. Analytic Methods in Accident Research, 40, 1–21.

Ichsani, S., Mariana, C., & Andari, D. (2019). Does the Indonesia composite index get affected by the Asia composite index? International Journal of Innovation, Creativity and Change, 6(7), 1–13.

Kasuma, K. A. P., & Nugroho, Y. D. (2020). Tinjauan kasus terkonfirmasi positif COVID-19 terhadap iklim investasi di Indonesia: Peramalan dan korelasi. In Seminar Nasional Official Statistics (pp. 190–195).

Kong, Q., Han, J., Jin, X., Li, C., Wang, T., Bai, Q., & Chen, Y. (2023). Polar motion prediction using the combination of SSA and ARMA. Geodesy and Geodynamics, 14(4), 368–376.

Kyriazis, Ν. A., Daskalou, K., Arampatzis, M., Prassa, P., & Papaioannou, E. (2019). Estimating the volatility of cryptocurrencies during bearish markets by employing GARCH models. Heliyon, 5(8), 1–8.

Lekhal, M., & El Oubani, A. (2020). Does the adaptive market hypothesis explain the evolution of emerging markets efficiency? Evidence from the Moroccan financial market. Heliyon, 6(7), 1–12.

Lyu, Y., Wei, Y., Hu, Y., & Yang, M. (2021). Good volatility, bad volatility and economic uncertainty: Evidence from the crude oil futures market. Energy, 222.

Moffat, I. U., & Akpan, E. A. (2019). White noise analysis: A measure of time series model adequacy. Applied Mathematics,10(11), 989–1003.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.

Noor, N. M., Abdullah, M. M. A. B., Yahaya, A. S., & Ramli, N. A. (2015). Comparison of linear interpolation method and mean method to replace the missing values in environmental data set. Materials Science Forum, 803, 278–281.

Putera, M. L. S. (2020). Non-cash payment transaction projection using ARIMAX : Efect of calendar. Jurnal Matematika, Statistika dan Komputasi, 16(3), 296–310.

Raheem, S. H., Alhusseini, F. H. H., & Alshaybawee, T. (2020). Modelling volatility in financial time series using ARCH models. International Journal of Innovation, Creativity and Change, 12(7), 248–261.

Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using Autoregressive Integrated Moving Average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21, 1–12.

Shahani, R., & Taneja, A. (2022). Dynamics of volatility behaviour and spillover from crude to energy crops: Empirical evidence from India. Energy Nexus, 8, 1–8.

Sheng, C., Zhang, D., Wang, G., & Huang, Y. (2021). Research on risk mechanism of China’s carbon financial market development from the perspective of ecological civilization. Journal of Computational and Applied Mathematics, 381.

Somarajan, S., Shankar, M., Sharma, T., & Jeyanthi, R. (2019). Modelling and analysis of volatility in time series data. In J. Wang, G. Reddy, V. Prasad, & V. Reddy (Eds.), Advances in intelligent systems and computing (pp. 609–618). Springer.

Vukovic, B. D., & Zinurova, R. Y. (2020). Competitive advantages and sustainable development of Russian agrarian sector. In M. Radović-Marković, B. Đukanović, & N. Vuković (Eds.), Economy and ecology: Contemporary trends and contradictions 2020 (pp. 203–207).



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