I Kadek Bellyoni Dwijaya, Abdul Azis. R, Sri Dewi Fitrianingsih, Nunung Apriani. "The Era of High Interest Rates: Sectoral Stock Price Index Volatility in Indonesia" International Research Journal of Economics and Management Studies, Vol. 3, No. 9, pp. 176-185, 2024.
This study aims to determine the impact of interest rate hikes on sectoral index stock prices on the Indonesia Stock Exchange; in addition, we predict the volatility of sectoral indices using monthly data for the period 2021-2024. The research method uses simple correlation to determine the impact of interest rates on sectoral index stock prices and volatility methods using ARIMA, ARCH, and GARCH to determine the best model from our analysis. Our findings show that an increase in interest rates has a significant positive impact on the finance banking sector, energy mineral sector, and utility sector. It has a negative impact on the industry sector and no significant impact on the distribution sector. The volatility modeling of the finance banking sector is ARIMA (1,1,2), the distribution sector is ARIMA (0,1,5), the industry sector is ARIMA (2,0,1), the energy mineral sector is ARCH (0,1,3). The utility sector is GARCH (1,1,0). The positive effect of interest rates shows that some sectoral indices can serve as an indication of economic strength that tends to improve; investors' response to monetary policy is not bad news, so they are more confident in investing in certain sectors.
[1] M. Bobasu, A., Quaglietti, L., & Ricci, “Tracking global economic uncertainty: implications for the euro area,” IMF Econ. Rev., pp. 1–38, 2023.
[2] E. Caggiano, G., & Castelnuovo, “Global financial uncertainty,” J. Appl. Econom., vol. 38, no. 3, pp. 432–449, 2023.
[3] L. Zhang and C. Bai, J., Zhang, Y., & Cui, “Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators,” Res. Int. Bus. Financ., vol. 65, p. 101949, 2023.
[4] Y. Abadi, J., Brunnermeier, M., & Koby, “The reversal interest rate,” Am. Econ. Rev., vol. 113, no. 8, pp. 2084–2120, 2023.
[5] Y. M. Kasim and I. K. B. Muslimin, & Dwijaya, “Market reaction to the Covid-19 pandemic: Events study at stocks listed on LQ45 index,” Cogent Bus.
Manag., vol. 9, no. 1, p. 2024979, 2022.
[6] G. M. Box, G. E. P., & Jenkins, Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1970.
[7] C. K. Ariyo, A. A., Adewumi, A. O., & Ayo, “Stock price prediction using the ARIMA model,” in In 2014 UKSim-AMSS 16th international conference
on computer modelling and simulation, IEEE, 2014, pp. 106–112.
[8] R. A. Brockwell, P. J., & Davis, Introduction to Time Series and Forecasting (2nd ed.). New York: Springer, 2002.
[9] J. D. Hamilton, Time series analysis. Princeton University Press, 2020.
[10] R. F. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, vol. 50, no. 4,
pp. 987–1007, 1982.
[11] T. Bollerslev, “Generalized Autoregressive Conditional Heteroskedasticity,” J. Econom., vol. 31, no. 3, pp. 307–327, 1986.
[12] H. Alzyadat, J. A., Abuhommous, A. A. A., & Alqaralleh, “Testing the conditional volatility of Saudi Arabia stock market: Symmetric and asymmetric
autoregressive conditional heteroskedasticity (garch) approach,” Acad. Account. Financ. Stud. J., vol. 25, no. 2, pp. 1–9, 2021.
[13] R. Elhini, M., & Hammam, “The impact of COVID-19 on the standard & poor 500 index sectors: A multivariate generalized autoregressive conditional
heteroscedasticity model,” J. Chinese Econ. Foreign Trade Stud., vol. 14, no. 1, pp. 18–43, 2021.
[14] J. B. Taylor, “Discretion versus policy rules in practice,” Carnegie-Rochester Conf. Ser. public policy, vol. 39, pp. 195–214, 1993.
[15] M. Clarida, R., Gali, J., & Gertler, “Monetary policy rules and macroeconomic stability: evidence and some theory,” Q. J. Econ., vol. 115, no. 1, pp.
147–180, 2000.
[16] F. Tian, M., Li, W., & Wen, “The dynamic impact of oil price shocks on the stock market and the USD/RMB exchange rate: Evidence from implied
volatility indices,” North Am. J. Econ. Financ., vol. 55, p. 101310, 2021.
[17] F. Ha, J., Kose, M. A., & Ohnsorge, “From low to high inflation: Implications for emerging market and developing economies,” Available SSRN, p.
4074459, 2022.
[18] D. Díaz, F., Henríquez, P. A., & Winkelried, “Stock market volatility and the COVID-19 reproductive number,” Res. Int. Bus. Financ., vol. 59, p.
101517, 2022.
[19] S. Bakry, W., Kavalmthara, P. J., Saverimuttu, V., Liu, Y., & Cyril, “Response of stock market volatility to COVID-19 announcements and stringency
measures: A comparison of developed and emerging markets,” Financ. Res. Lett., vol. 46, p. 102350, 2022.
[20] F. Li, W., Chien, F., Waqas Kamran, H., Aldeehani, T. M., Sadiq, M., Nguyen, V. C., & Taghizadeh-Hesary, “The nexus between COVID-19 fear and
stock market volatility,” Econ. Res. istraživanja, vol. 35, no. 1, pp. 1765–1785, 2022.
[21] D. Xu, “Canadian stock market volatility under COVID-19,” Int. Rev. Econ. Financ., vol. 77, pp. 159–169, 2022.
[22] M. H. Wu, F. L., Zhan, X. D., Zhou, J. Q., & Wang, “Stock market volatility and Russia–Ukraine conflict,” Financ. Res. Lett., vol. 55, p. 103919, 2023.
[23] X. Zhou, H., & Lu, “Investor attention on the Russia-Ukraine conflict and stock market volatility: Evidence from China,” Financ. Res. Lett., vol. 52, p.
103526, 2023.
[24] T. Umar, Z., Polat, O., Choi, S. Y., & Teplova, “The impact of the Russia-Ukraine conflict on the connectedness of financial markets,” Financ. Res.
Lett., vol. 48, p. 102976, 2022.
[25] C. Ospina, R., Gondim, J. A., Leiva, V., & Castro, “An overview of forecast analysis with ARIMA models during the COVID-19 pandemic:
Methodology and case study in Brazil,” Mathematics, vol. 11, no. 14, p. 3069, 2023.
[26] F. Wang, X., Kang, Y., Hyndman, R. J., & Li, “Distributed ARIMA models for ultra-long time series,” Int. J. Forecast., vol. 39, no. 3, pp. 1163–1184,
2023.
[27] W. Fan, “Prediction of monetary fund based on ARIMA model,” Procedia Comput. Sci., vol. 208, pp. 277–285, 2022.
[28] R. Escobar-Anel, M., Gollart, M., & Zagst, “Closed-form portfolio optimization under GARCH models,” Oper. Res. Perspect., vol. 9, p. 100216, 2022.
[29] R. A. Naik, N., Mohan, B. R., & Jha, “GARCH model identification for stock crises events,” Procedia Comput. Sci., vol. 171, pp. 1742–1749, 2020.
[30] D. Marobhe, M., & Pastory, “Modeling stock market volatility using GARCH models case study of Dar es Salaam stock exchange (DSE),” Rev. Integr.
Bus. Econ. Res., vol. 9, no. 2, pp. 138–150, 2020.
[31] Y. Xiang, “Using ARIMA‐GARCH Model to Analyze Fluctuation Law of International Oil Price,” Math. Probl. Eng., vol. 2022, no. 1, p. 3936414,
2022.
ARCH, ARIMA, GARCH, Interest Rate, Stock Index, Volatility.