Predicting Stock Price Volatility Using ARCH-GARCH Models
April 28, 2025
Downloads
This paper aims to apply ARCH-GARCH models to evaluate their ability to predict stock returns volatility in financial markets to help better allocate resources, manage risks, and improve investment decisions. This study was conducted on daily data of Apple Inc. stock prices during the period from January 2, 2024, to December 30, 2024, with 251 observations. The results indicate that ARCH-GARCH models provide good daily forecasts of stock price movements and are able to capture the fluctuation series by choosing the appropriate model for the prediction process. The ARCH(1) model was the most suitable for predicting the stock returns series of the study sample.
Sabbar, . T. O. . (2025). Predicting Stock Price Volatility Using ARCH-GARCH Models. International Journal on Economics, Finance and Sustainable Development, 7(4), 198–204. Retrieved from https://journals.researchparks.org/index.php/IJEFSD/article/view/5437
1. MCMillan, D., Speight, A., & Apgwilym, O. (2000). Forecasting UK stock market volatility. Applied Financial Economics, 10(4), 435-448.
2. Vasudevan, R. D., & Vetrivel, S. C. (2016). Forecasting stock market volatility using GARCH models: Evidence from the Indian stock market. Asian Journal of Research in Social Sciences and Humanities, 6(8), 1565-1574.
3. Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of economic perspectives, 15(4), 157-168.
4. Akgiray, V. (1989). Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts. Journal of Business, 62:55-80.
5. Fama, E. (1965). The Behaviour of Stock Market Prices. Journal of Business, 38:34-105.
6. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business, 36: 394-419.
7. Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.
8. Garcia, R. C., Contreras, J., Van Akkeren, M., & Garcia, J. B. C. (2005). A GARCH forecasting model to predict day-ahead electricity prices. IEEE transactions on power systems, 20(2), 867-874.
9. Alam, M. Z., Siddikee, M. N., & Masukujjaman, M. (2013). Forecasting volatility of stock indices with ARCH model. International Journal of Financial Research, 4(2), 126.
10. Arowolo, W. B. (2013). Predicting stock prices returns using GARCH model. The International Journal of Engineering and Science, 2(5), 32-37.
11. Lestari, E. P., Prajanti, S. D. W., Wibawanto, W., & Adzim, F. (2022). ARCH-GARCH analysis: An approach to determine the price volatility of red chili. AGRARIS: Journal of Agribusiness and Rural Development Research, 8(1), 90-105.
12. Dinardi, F. B. (2019). Forecasting the stock market using ARIMA and ARCH/GARCH approaches (Master's thesis, Universidade NOVA de Lisboa (Portugal)).
13. AL-Masoodi, S. O. J.& Pele, D.T (2023). Using ARCH and GARCH mathematical models to predict the general budget deficit for the period from (2008) to (2023). Wasit Journal for Pure sciences, 2(4), 269-277.
14. Huang, X., You, P., Gao, X., & Cheng, D. (2023, July). Check for updates Stock Price Prediction Based on ARIMA-GARCH and LSTM. In Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) (Vol. 14, p. 438). Springer Nature.
15. Yunita, I. (2016, March). Volatility Modeling Using ARCH/GARCH Method: Aplication on Asia Pasific Index. In 3rd International Seminar and Conference on Learning Organization (pp. 182-189). Atlantis Press.
16. Toong, D., Goh, K. W., & Sim, Y. W. (2023). Estimation of stock market index volatility using the GARCH model: Causality between stock indices. Asian Economic and Financial Review, 13(3), 162-179.
2. Vasudevan, R. D., & Vetrivel, S. C. (2016). Forecasting stock market volatility using GARCH models: Evidence from the Indian stock market. Asian Journal of Research in Social Sciences and Humanities, 6(8), 1565-1574.
3. Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of economic perspectives, 15(4), 157-168.
4. Akgiray, V. (1989). Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts. Journal of Business, 62:55-80.
5. Fama, E. (1965). The Behaviour of Stock Market Prices. Journal of Business, 38:34-105.
6. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business, 36: 394-419.
7. Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.
8. Garcia, R. C., Contreras, J., Van Akkeren, M., & Garcia, J. B. C. (2005). A GARCH forecasting model to predict day-ahead electricity prices. IEEE transactions on power systems, 20(2), 867-874.
9. Alam, M. Z., Siddikee, M. N., & Masukujjaman, M. (2013). Forecasting volatility of stock indices with ARCH model. International Journal of Financial Research, 4(2), 126.
10. Arowolo, W. B. (2013). Predicting stock prices returns using GARCH model. The International Journal of Engineering and Science, 2(5), 32-37.
11. Lestari, E. P., Prajanti, S. D. W., Wibawanto, W., & Adzim, F. (2022). ARCH-GARCH analysis: An approach to determine the price volatility of red chili. AGRARIS: Journal of Agribusiness and Rural Development Research, 8(1), 90-105.
12. Dinardi, F. B. (2019). Forecasting the stock market using ARIMA and ARCH/GARCH approaches (Master's thesis, Universidade NOVA de Lisboa (Portugal)).
13. AL-Masoodi, S. O. J.& Pele, D.T (2023). Using ARCH and GARCH mathematical models to predict the general budget deficit for the period from (2008) to (2023). Wasit Journal for Pure sciences, 2(4), 269-277.
14. Huang, X., You, P., Gao, X., & Cheng, D. (2023, July). Check for updates Stock Price Prediction Based on ARIMA-GARCH and LSTM. In Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) (Vol. 14, p. 438). Springer Nature.
15. Yunita, I. (2016, March). Volatility Modeling Using ARCH/GARCH Method: Aplication on Asia Pasific Index. In 3rd International Seminar and Conference on Learning Organization (pp. 182-189). Atlantis Press.
16. Toong, D., Goh, K. W., & Sim, Y. W. (2023). Estimation of stock market index volatility using the GARCH model: Causality between stock indices. Asian Economic and Financial Review, 13(3), 162-179.
2.png)