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SARIMAX–GARCH Model to Forecast Composite Index with Inflation Rate and Exchange Rate Factors

M. Fariz Fadillah Mardianto, Elly Pusporani, Diana Ulya, I Kadek Pasek Kusuma Adi Putra, Rico Ramadhan

Abstract


Investors should consider the Indonesia Composite Index (ICI) as a key indicator before making investment decisions, as it reflects the performance of industries and the broader economic growth. In Indonesia, the ICI exhibits fluctuating movements, making accurate forecasting essential for understanding the country's economic conditions, which are closely tied to capital flows, growth, and tax revenues. This study aims to forecast the ICI using the SARIMAX-GARCH model, incorporating macroeconomic factors such as the inflation rate and exchange rate. The findings reveal that both variables significantly impact the ICI, with the model achieving a Mean Absolute Percentage Error (MAPE) of 0.952% for training data and 5.233% for test data. The model's performance is supported by an R² value of 0.9782 and a Mean Squared Error (MSE) of 0.0003. This research not only improves the accuracy of ICI forecasts but also supports Indonesia's 8th Sustainable Development Goal (SDG) for decent work and economic growth.

 

Doi: 10.28991/HIJ-2024-05-03-014

Full Text: PDF


Keywords


Indonesia Composite Index; SARIMAX-GARCH; Inflation Rate;, Exchange Rate; Sustainable Development Goals.

References


Nugraha, N. M., Novan, D., & Nugraha, S. (2020). The Influence of Macroeconomic Factors on the Volatility of Composite Price Stock Index: A Study on the Indonesia Stock Exchange. International Journal of Psychosocial Rehabilitation, 24(01), 2507–2513.

Sumaryoto, Nurfarkhana, A., & Anita, T. (2021). The Impact of Money Supply and the Inflation Rate on Indonesia Composite Index: Case Study in Indonesia Stock Exchange 2008-2017. Business and Accounting Research International Journal, 5(2), 196–213.

Republika. (2024). JCI throughout 2022 Strengthens 4.09 Percent. Republika, Głogów, Poland. Available online: https://ekonomi.republika.co.id/berita/rnp7n1383/ihsg-sepanjang-2022-menguat-409-persen (accessed on August 2024).

Kristanto, M. E., & Idris. (2016). Analysis of the Influence of Inflation, Exchange Rates, and Interest Rates on the Joint Movement of JCI Stock Returns and Trading Volume for the January 2006–December 2015 Period. Diponegoro Journal of Management, 3(2337–3806), 1–15.

Pardede, N., Hidayat, R. R., & Sulasmiyati, S. (2016). The Influence of World Crude Oil Prices, Inflation, Central Bank Rate, and Exchange Rate on the Stock Price Index of the Mining Sector in ASEAN (Study in Indonesia, Singapore, and Thailand for the Period of July 2013–December 2015.

Geetha, C., Chong, V., Mohidin, R., & Chandran, V. V. (2011). The Relationship Between Inflation and Stock Market: Evidence from Malaysia, United States and China. International Journal of Economics and Management Sciences, 1(2), 1–16.

Akua Miyanti, G. A. D., & Wiagustini, L. P. (2018). The Effect of Fed Interest Rates, Oil Prices and Inflation on the Composite Stock Price Index (IHSG) on the Indonesia Stock Exchange. E-Journal of Economics and Business, Udayana University, 5, 1261. doi:10.24843/eeb.2018.v07.i05.p02.

Utama, E. Y. (2016). The Effect of SBI Interest Rates, Inflation, and Money Supply on the Composite Stock Price Index (IHSG) on the Indonesia Stock Exchange (BEI). Faculty of Economics, Universitas Negeri Yogyakarta, 4(1), 11–13.

Suriyani, N. K., & Sudiarta, G. (2018). The Effect of Interest Rates, Inflation and Exchange Rates on Stock Returns on the Indonesian Stock Exchange. E-Journal of Management of Udayana University, 7(6), 3172–3200. doi:10.24843/EJMUNUD.2018.v07.i06.p12.

Pratikno, D. (2009). The Influence of Rupiah Exchange Rate, Inflation, SBI, and Dow Jones Index on the Movement of the Composite Stock Price Index (IHSG) in the Indonesia Stock Exchange. Journal of Economics, Universitas Sumatra Utara Medan, 16–99.

Tampubolon, K. M., H. M. Roy Sembel, & Sidharta, J. (2021). The Effect of Inflation, Interest Rate, Exchange Rate and Gross Domestic Product Growth on Composite Stock Price Index (Study at Indonesia Stock Exchange Period of 2010-2019). Fundamental Management Journal, 6(1), 66–90. doi:10.33541/fjm.v6i1.2826.

Hasanudin. (2021). The Effect of Inflation, Exchange Rate, BI Rate and Dow Jones Index to Indonesia Composite Index in Indonesia Stock Exchange on the Year 2013-2018. International Journal of Science and Society, 3(3), 50–60. doi:10.54783/ijsoc.v3i3.353.

Amin, M. Z. (2012). The Effect of Inflation Rate, SBI Rate, Dollar Value (USD/IDR), and Dow Jones Index (DJIA) to Movement of Composite Stock Price Index In Indonesia Stock Exchange (IDX)(Periode 2008–2011). Thesis Journal FEB UB, 1–17.

Christopher, A., Sediono, E. A., & Suliyanto, Dan M. F. (2021). Application of the ARIMAX-GARCH Model in Modeling and Forecasting the Volume of Electronic Money Transactions in Indonesia. Journal of Mathematics Education, Science and Technology, 2(6), 241–256.

Hamjah, M. A. (2014). Forecasting Major Fruit Crops Productions in Bangladesh using Box-Jenkins ARIMA Model. Journal of Economics and Sustainable Development, 5(7), 96–107.

Chyon, F. A., Suman, M. N. H., Fahim, M. R. I., & Ahmmed, M. S. (2022). Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning. Journal of Virological Methods, 301, 114433. doi:10.1016/j.jviromet.2021.114433.

Abonazel, M. R., & Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP using ARIMA models. Reports on Economics and Finance, 5(1), 35–47. doi:10.12988/ref.2019.81023.

Wei, W. (2006). The Oxford Handbook of Quantitative Methods in Psychology Vol2: Statistical Analysis. Oxford University Press, Oxford, United Kingdom.

Kawakita, S., & Takahashi, H. (2022). Time-series analysis of population dynamics of the common cutworm, Spodoptera litura (Lepidoptera: Noctuidae), using an ARIMAX model. Pest Management Science, 78(6), 2423–2433. doi:10.1002/ps.6873.

Paul, R. K. (2015). ARIMAX-GARCH-WAVELET model for forecasting volatile data. Model Assisted Statistics and Applications, 10(3), 243–252. doi:10.3233/MAS-150328.

Simms, L. E., Engebretson, M. J., & Reeves, G. D. (2022). Removing Diurnal Signals and Longer-Term Trends from Electron Flux and ULF Correlations: A Comparison of Spectral Subtraction, Simple Differencing, and ARIMAX Models. Journal of Geophysical Research: Space Physics, 127(2), 2021 030021. doi:10.1029/2021JA030021.

Alharbi, F. R., & Csala, D. (2022). A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach. Inventions, 7(4), 94. doi:10.3390/inventions7040094.

Bari, S. H., Rahman, M. T., Hussain, M. M., & Ray, S. (2015). Forecasting Monthly Precipitation in Sylhet City Using ARIMA Model. Civil and Environmental Research, 7(1), 69–77.

Adesi, G. B., Engle, R. F., & Mancini, L. (2014). A GARCH option pricing model with filtered historical simulation. Simulating Security Returns: A Filtered Historical Simulation Approach, 21(3), 66–108. doi:10.1057/9781137465559.0009.

Endri, E., Aipama, W., Razak, A., Sari, L., & Septiano, R. (2021). Stock price volatility during the COVID-19 pandemic: The GARCH model. Investment Management and Financial Innovations, 18(4), 12–20. doi:10.21511/imfi.18(4).2021.02.

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. doi:10.1016/j.ijforecast.2015.12.003.

Yao, K. C., Hsueh, H. W., Huang, M. H., & Wu, T. C. (2022). The Role of GARCH Effect on the Prediction of Air Pollution. Sustainability (Switzerland), 14(8), 4459. doi:10.3390/su14084459.

Şentaş, A., Tashiev, İ., Küçükayvaz, F., Kul, S., Eken, S., Sayar, A., & Becerikli, Y. (2020). Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification. Evolutionary Intelligence, 13, 83-91. doi:10.1007/s12065-018-0167-z.

Ghozali, I. (2016). Multivariate Analysis Application Using IBM SPSS 20. Semarang: Badan Penerbit Universitas Diponegoro, Indonesia.

Bagwell, C. B., Hill, B. L., Herbert, D. J., Bray, C. M., & Hunsberger, B. C. (2016). Sometimes simpler is better: VLog, a general but easy-to-implement log-like transform for cytometry. Cytometry Part A, 89(12), 1097–1105. doi:10.1002/cyto.a.23017.

Xue, D., & Hua, Z. (2016). ARIMA Based Time Series Forecasting Model. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 9(2), 93–98. doi:10.2174/2352096509999160819164242.

Garima Jain, E., & Mallick, B. (2017). A Study of Time Series Models ARIMA and ETS. In International Journal of Modern Education and Computer Science, 9(4), 57-63. doi:10.5815/ijmecs.2017.04.07.

Abunofal, M., Poshiya, N., Qussous, R., & Weidlich, A. (2021). Comparative Analysis of Electricity Market Prices Based on Different Forecasting Methods. 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, 1–6. doi:10.1109/PowerTech46648.2021.9495034.

Shadkam, A. (2020). Using SARIMAX to forecast electricity demand and consumption in university buildings. Doctoral dissertation, University of British Columbia, British Columbia, Canada.

Krueger, D., Mitman, K., & Perri, F. (2016). Macroeconomics and Household Heterogeneity. Handbook of Macroeconomics, Elsevier, Volume 2, 843–921. doi:10.1016/bs.hesmac.2016.04.003.

Moffat, I. U., Akpan, E. A., & Abasiekwere, U. A. (2017). A time series evaluation of the asymmetric nature of heteroscedasticity: an EGARCH approach. International Journal of Statistics and Applied Mathematics, 2(6), 111–117.

Aue, A., Horváth, L., & Pellatt, D. F. (2017). Functional Generalized Autoregressive Conditional Heteroskedasticity. Journal of Time Series Analysis, 38(1), 3–21. doi:10.1111/jtsa.12192.

Dritsaki, C. (2017). An Empirical Evaluation in GARCH Volatility Modeling: Evidence from the Stockholm Stock Exchange. Journal of Mathematical Finance, 07(02), 366–390. doi:10.4236/jmf.2017.72020.

Rosyadi, M. I., Widowati, A., & Jamil, P. C. (2020). Analysis of Correlation Between Indonesian Composite Index and Economic Growth in Indonesia. European Journal of Business and Management, 12(8), 42–46. doi:10.7176/ejbm/12-8-07.

Lim, K., & McNelis, T. (2013). The Impact of Economic Structure on Stock Market Performance: A Comparative Study of Indonesia and Malaysia. Pacific-Basin Finance Journal, 24(2), 256–271.

Sutrisno, B. (2024). Macroeconomic Variables and Stock Market Volatility: An Empirical Analysis of Indonesia. Asian Economic and Financial Review, 14(1), 24-38.

Devianto, D., Permathasari, P., Yollanda, M., & Wirahadi Ahmad, A. (2020). The Model of Artificial Neural Network and Nonparametric MARS Regression for Indonesian Composite Index. IOP Conference Series: Materials Science and Engineering, 846(1), 12007. doi:10.1088/1757-899X/846/1/012007.

Wijaya, S., & Handoko, F. (2023). Forecasting the Composite Index with SARIMAX-GARCH Models in the Context of the COVID-19 Pandemic. Indonesian Journal of Economics and Business, 32(4), 89–105.

Pratama, R., & Susilo, E. (2024). Examining the Relationship between Inflation, Exchange Rates, and Stock Market Indices: Evidence from Indonesia. Global Journal of Economics, 19(1), 75–90.


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DOI: 10.28991/HIJ-2024-05-03-014

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