This paper investigates the performance of GARCH-MIDAS and AO-GARCH-MIDAS models in predicting stock market volatility based on considering the volatility effects of macroeconomic variables. The traditional GARCH-MIDAS model only considers the level effects of macroeconomic variables, while this paper, by introducing the volatility effects of macroeconomic variables, in particular, combines the realized volatility with macroeconomic variables (e.g., CPI, M2, UD). The results of the MCS test based on MAE and MSE show that the AO-GARCH-MIDAS family of models performs well in out-of-sample forecasting, especially the AO-GARCH-MIDAS-RV+UD model, which is the best performer under both assessment metrics and shows strong forecasting ability. In contrast, traditional GARCH-MIDAS models and combined models based on macroeconomic variables (e.g., GARCH-MIDAS-RV+UD and GARCH-MIDAS-UD) perform weakly in out-of-sample forecasts. Overall, this paper shows that the AO-GARCH-MIDAS model, which takes into account the volatility effects of macroeconomic variables, significantly improves forecasting accuracy when dealing with complex economic environments and abnormal volatility, providing more reliable stock market volatility forecasting results.
Ahmad, N., & Ramzan, M. (2016). Stock market volatility and macroeconomic factor volatility. International Journal of Research in Business Studies and Management, 3(7).
Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. Statistical methodology, 1(1-2), 105-128.
Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH?MIDAS approach. Journal of Forecasting, 32(7), 600-612.
Chang, I., Tiao, G. C., & Chen, C. (1988). Estimation of time series parameters in the presence of outliers. Technometrics, 30(2), 193-204.
Chen, C., & Liu, L.-M. (1993). Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88(421), 284-297.
Degiannakis, S., Livada, A., & Panas, E. (2008). Rolling-sampled parameters of ARCH and Levy-stable models. Applied Economics, 40(23), 3051-3067.
Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.
Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of forecasting, 15(1), 1-9.
Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric reviews, 26(1), 53-90.
Gong, X., Wang, M., & Shao, L. (2022). The impact of macro economy on the oil price volatility from the perspective of mixing frequency. International Journal of Finance & Economics, 27(4), 4487-4514.
Liu, R., Yang, J., & Ruan, C. (2020). The Macroeconomic Influence of China Futures Market: A GARCH-MIDAS Approach. Advances in Harmony Search, Soft Computing and Applications 15,
Liu, T., Choo, W., Tunde, M. B., Wan, C., & Liang, Y. (2024). Enhancing stock volatility prediction with the AO-GARCH-MIDAS model. PloS one, 19(6), e0305420.
Ma, Y. r., Ji, Q., & Pan, J. (2019). Oil financialization and volatility forecast: Evidence from multidimensional predictors. Journal of Forecasting, 38(6), 564-581.
Mo, D., Gupta, R., Li, B., & Singh, T. (2018). The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets. Economic Modelling, 70, 543-560.
Park, B. J. (2002). An outlier robust GARCH model and forecasting volatility of exchange rate returns. Journal of Forecasting, 21(5), 381-393.
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.
Wu, C., Wang, X., Luo, S., Shan, J., & Wang, F. (2020). Influencing factors analysis of crude oil futures price volatility based on mixed-frequency data. Applied Sciences, 10(23), 8393.
Yaya, O. S., Ogbonna, A. E., & Vo, X. V. (2022). Oil shocks and volatility of green investments: GARCH-MIDAS analyses. Resources Policy, 78, 102789.
Yu, X., & Huang, Y. (2021). The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach. Physica A: Statistical Mechanics and its Applications, 570, 125794.
Zhang, M., Zhao, Y., & Nan, J. (2022). Economic policy uncertainty and volatility of treasury futures. Review of Derivatives Research, 25(1), 93-107.
Liu, T., Choo, W., Tunde, M. B., & Xinping, H. (2024). Application of AO-GARCH-MIDAS Model Based on Volatility Effect in Stock Market Volatility Forecasting. International Journal of Academic Research in Business and Social Sciences, 14(12), 3509–3521.
Copyright: © 2024 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
