Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index
DOI:
https://doi.org/10.21512/comtech.v15i1.10610Keywords:
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.
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References
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. https://doi.org/10.22452/josma.vol4no1.3
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. https://doi.org/10.1016/j.jeca.2020.e00167
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. https://doi.org/10.1016/j.eap.2023.03.001
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. https://doi.org/10.1016/j.prevetmed.2023.105921
Dinku, T., & Worku, G. (2022). Asymmetric GARCH models on price volatility of agricultural commodities. SN Business & Economics, 2. https://doi.org/10.1007/s43546-022-00355-7
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. https://doi.org/10.1016/j.chaos.2021.111227
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. https://doi.org/10.1016/j.frl.2019.01.011
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. https://doi.org/10.13106/jafeb.2021.vol8.no4.0011
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. https://doi.org/10.1016/j.amar.2023.100302
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). https://doi.org/10.34123/semnasoffstat.v2020i1.720
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. https://doi.org/10.1016/j.geog.2022.12.004
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. https://doi.org/10.1016/j.heliyon.2019.e02239
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. https://doi.org/10.1016/j.heliyon.2020.e04429
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. https://doi.org/10.1016/j.energy.2021.119924
Moffat, I. U., & Akpan, E. A. (2019). White noise analysis: A measure of time series model adequacy. Applied Mathematics,10(11), 989–1003. https://doi.org/10.4236/am.2019.1011069
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. https://doi.org/10.4028/www.scientific.net/MSF.803.278
Putera, M. L. S. (2020). Non-cash payment transaction projection using ARIMAX : Efect of calendar. Jurnal Matematika, Statistika dan Komputasi, 16(3), 296–310. https://doi.org/10.20956/jmsk.v16i3.8546
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. https://doi.org/10.1186/s12874-021-01235-8
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. https://doi.org/10.1016/j.nexus.2022.100152
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. https://doi.org/10.1016/j.cam.2020.112990
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. https://doi.org/10.1007/978-981-13-3393-4_62
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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