GARCH Model For Forecasting Stock Return Volatility In The Infrastructure , Utilities And Transportation Sectors

  • Faizul Mubarok Universitas Islam Negeri Syarif Hidayatullah Jakarta
  • Eni Sutrieni Universitas Islam Negeri Syarif Hidayatullah Jakarta
Abstract views: 437 , PDF downloads: 887
Keywords: Volatility, Stock Returns, GARCH, Forecast, Infrastructure


The stock market is continuously changing with uncertainties that can create risks. Prompt information dissemination and rapid capital flow will cause stock price fluctuations, causing volatility in stock prices. This research examines the behavior of volatility patterns in the infrastructure, utility, and transportation sectors using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This study uses monthly data from January 2014 to December 2019. The results show that the volatility of all stocks in the study is influenced by the previous month's error and volatility return. Investors and securities analysis can use these results in making decisions to invest in the infrastructure, utilities, and transportation sectors.


Download data is not yet available.


Alexandrou, G., Koulakiotis, A., & Dasilas, A. (2011). GARCH Modelling of Banking Integration in The Eurozone. Research in International Business and Finance, 25(1), 1–10.

Aliyev, F., Ajayi, R., & Gasim, N. (2020). Modelling Asymmetric Market Volatility With Univariate GARCH Models: Evidence from Nasdaq-100. Journal of Economic Asymmetries, 22, 1–10.

Amini, S., Buchner, A., Cai, C. X., & Mohamed, A. (2020). Why Do Firms Manage Their Stock Price Levels? Journal of International Financial Markets, Institutions and Money, 67, 101220.

Birău, R., Trivedi, J., & Antonescu, M. (2015). Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model. Procedia Economics and Finance, 32(15), 520–525.

Chuang, W., Liu, H. H., & Susmel, R. (2012). The Bivariate GARCH Approach to Investigating the Relation between Stock Returns, Trading Volume, and Return Volatility. Global Finance Journal, 23(1), 1–15.

Domínguez, L. R., & Gámez, L. C. N. (2014). Corporate Reporting on Risks: Evidence from Spanish Companies. Revista de Contabilidad, 17(2), 116–129.

Edmans, A., Jayaraman, S., & Schneemeier, J. (2017). The Source of Information in Prices and Investment-Price Sensitivity. Journal of Financial Economics, 126(1), 74–96.

Fang, T., Lee, T. H., & Su, Z. (2020). Predicting the Long-term Stock Market Volatility: A GARCH-MIDAS Model with Variable Selection. Journal of Empirical Finance, 58, 36–49.

Goryakin, Y., Lobstein, T., James, W. P. T., & Suhrcke, M. (2015). The Impact of Economic, Political and Social Globalization on Overweight and Obesity in the 56 Low and Middle Income Countries. Social Science and Medicine, 133, 67–76.

Herwartz, H. (2017). Stock Return Prediction Under GARCH — An Empirical Assessment. International Journal of Forecasting, 33(3), 569–580.

Ismail, M. T., Audu, B., & Tumala, M. M. (2016). Volatility Forecasting with the Wavelet Transformation Algorithm GARCH Model : Evidence from African Stock Markets. The Journal of Finance and Data Science, 2(2), 125–135.

Lawal, A. I., Somoye, R. O., Babajide, A. A., & Nwanji, T. I. (2018). The Effect of Fiscal and Monetary Policies Interaction on Stock Market Performance: Evidence from Nigeria. Future Business Journal, 4(1), 16–33.

Lin, Z. (2018). Modelling and Forecasting the Stock Market Volatility of SSE Composite Index using GARCH Models. Future Generation Computer Systems, 79(3), 960–972.

Mohamed Dahir, A., Mahat, F., Ab Razak, N. H., & Bany-Ariffin, A. N. (2018). Revisiting the Dynamic Relationship Between Exchange Rates and Stock Prices in BRICS Countries: A Wavelet Analysis. Borsa Istanbul Review, 18(2), 101–113.

Naik, N., Mohan, B., & Jha, R. A. (2020). GARCH-Model Identification based on Performance of Information Criteria. Procedia Computer Science, 171, 1935–1942.

Ningsih, S. R., Sumarjaya, I. W., & Sari, K. (2019). Peramalan Volatilitas Saham Menggunakan Model Exponential Garch Dan Threshold Garch. E-Jurnal Matematika, 8(4), 309.

Prasad, M., Bakry, W., & Varua, M. E. (2020). Examination of Information Release on Return Volatility : A Market and Sectoral Analysis. Heliyon, 6(5), 1–14.

Raneo, A. P., & Muthia, F. (2019). Penerapan Model GARCH Dalam Peramalan Volatilitas di Bursa Efek Indonesia. Jurnal Manajemen Dan Bisnis Sriwijaya, 16(3), 194–202.

Sari, L. K., Achsani, N. A., & Sartono, B. (2017). Pemodelan Volatilitas Return Saham: Studi Kasus Pasar Saham Asia. Jurnal Ekonomi Dan Pembangunan Indonesia, 18(1), 35–52.

Sarwar, S., Tiwari, A. K., & Tingqiu, C. (2020). Analyzing Volatility Spillovers between Oil Market and Asian Stock Markets. Resources Policy, 66, 1–12.

Thampanya, N., Wu, J., Nasir, M. A., & Liu, J. (2020). Fundamental and Behavioural Determinants of Stock Return Volatility in ASEAN-5 Countries. Journal of International Financial Markets, Institutions and Money, 65, 1–26.

Vipul, P. S. (2016). Forecasting Stock Market Volatility Using Realized GARCH Model: International evidence. Quarterly Review of Economics and Finance, 59, 222–230.

Wang, L., Ma, F., Liu, J., & Yang, L. (2020). Forecasting Stock Price Volatility: New Evidence from the GARCH-MIDAS Model. International Journal of Forecasting, 36(2), 684–694.

PlumX Metrics

How to Cite
Mubarok, F., & Sutrieni, E. (2021). GARCH Model For Forecasting Stock Return Volatility In The Infrastructure , Utilities And Transportation Sectors. Eksis: Jurnal Riset Ekonomi Dan Bisnis, 15(2), 87-100.
Section Editor