The Role of Macroeconomic Variables in Forecasting Equity Market Volatility in the East Aftican Community

Authors

  • Lin Wen Sheng SILC Business School, Shanghai University, China
  • Mutebi Jade SILC Business School, Shanghai University, China

Keywords:

GARCH, MIDAS, stock market, forecast volatility, East African Community

Abstract

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH MIDAS model improved its forecasting ability, however, for Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCHMIDAS model.

References

Andreou, E., Ghysels, E., & Kourtellos, A. (2010). Regression models with mixed sampling frequencies. Journal of Econometrics, 158(2), 246–261. https://doi.org/10.1016/j.jeconom.2010.01.004

Chen, X., & Ghysels, E. (2011). News - Good or bad - and its impact on volatility predictions over multiple horizons. Review of Financial Studies, 24(1), 46–81. https://doi.org/10.1093/rfs/hhq071

Chen, Z., Ye, Y., & Li, X. (2022). Forecasting China’s crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic. Resources Policy, 75. https://doi.org/10.1016/j.resourpol.2021.102453

Engle, R. F., Ghysels, E., & Sohn, B. (2013). STOCK MARKET VOLATILITY AND MACROECONOMIC FUNDAMENTALS. In Source: The Review of Economics and Statistics (Vol. 95, Issue 3). https://about.jstor.org/terms

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. https://doi.org/10.1016/j.jempfin.2020.05.007

Fang, Y., Fan, Y., Haroon, M., & Dilanchiev, A. (2023). Exploring the relationship between global economic policy and volatility of crude futures: A two-factor GARCH-MIDAS analysis. Resources Policy, 85. https://doi.org/10.1016/j.resourpol.2023.103766

Ghysels, E., Santa-Clara, P., & Valkanov, R. (n.d.). The MIDAS Touch: Mixed Data Sampling Regression Models *.

Ghysels, E., Sinko, A., & Valkanov, R. (n.d.). MIDAS Regressions: Further Results and New Directions *.

Ghysels, E., & Valkanov, R. (n.d.). Granger Causality Tests with Mixed Data Frequencies.

Kotze, G. L. 2007. Forecasting inflation with high frequency asset price data. Working Paper. University of Stellenbosch.

Liang, C., Xia, Z., Lai, X., & Wang, L. (2022). Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model. Energy Economics, 116. https://doi.org/10.1016/j.eneco.2022.106437

Maqsood, A., Safdar, S., Shafi, R., & Lelit, N. J. (2017). Modeling Stock Market Volatility Using GARCH Models: A Case Study of Nairobi Securities Exchange (NSE). Open Journal of Statistics, 07(02), 369–381. https://doi.org/10.4236/ojs.2017.72026

Marselline, K. A. (2019). The status quo of East African stock markets: Integration and volatility. African Journal of Business Management, 13(5), 176–187. https://doi.org/10.5897/ajbm2019.8742

Namugaya, J., Waititu, A. G., & Ka Diongue, A. (2019). Forecasting stock returns volatility on Uganda securities exchange using TSK fuzzy-GARCH and GARCH models. Reports on Economics and Finance, 5(1), 1–14. https://doi.org/10.12988/ref.2019.81022

Namugaya, J., Weke, P. G. O., & Charles, W. M. (2014). Modelling stock returns volatility on Uganda securities exchange. Applied Mathematical Sciences, 8(101–104), 5173–5184. https://doi.org/10.12988/ams.2014.46394

Raza, S. A., Masood, A., Benkraiem, R., & Urom, C. (2023). Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach. Energy Economics, 120. https://doi.org/10.1016/j.eneco.2023.106591

Salisu, A. A., Gupta, R., & Demirer, R. (2022). Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model. Energy Economics, 108. https://doi.org/10.1016/j.eneco.2022.105934

Salisu, A. A., Ogbonna, A. E., Lasisi, L., & Olaniran, A. (2022). Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach. North American Journal of Economics and Finance, 62. https://doi.org/10.1016/j.najef.2022.101755

Segnon, M., Gupta, R., & Wilfling, B. (2023). Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2022.11.007

Tumala, M. M., Salisu, A. A., & Gambo, A. I. (2023). Disentangled oil shocks and stock market volatility in Nigeria and South Africa: A GARCH-MIDAS approach. Economic Analysis and Policy, 78, 707– 717. https://doi.org/10.1016/j.eap.2023.04.009

Wang, L., Wu, J., Cao, Y., & Hong, Y. (2022). Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both? Energy Economics, 111. https://doi.org/10.1016/j.eneco.2022.106056

Wang, L., Zhao, C., Liang, C., & Jiu, S. (2022). Predicting the volatility of China’s new energy stock market: Deep insight from the realized EGARCH-MIDAS model. Finance Research Letters, 48. https://doi.org/10.1016/j.frl.2022.102981

Zhao, J. (2022). Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach. Resources Policy, 79. https://doi.org/10.1016/j.resourpol.2022.103031

Zhou, Z., Fu, Z., Jiang, Y., Zeng, X., & Lin, L. (2020). Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model. Finance Research Letters, 34. https://doi.org/10.1016/j.frl.2019.08.006

Downloads

Published

2024-01-13

How to Cite

Sheng, L. W., & Jade, M. (2024). The Role of Macroeconomic Variables in Forecasting Equity Market Volatility in the East Aftican Community. ESI Preprints, 25, 222. Retrieved from https://esipreprints.org/index.php/esipreprints/article/view/753

Issue

Vol. 25 (2024): ESI Preprints

Section

Preprints

License

Copyright (c) 2024 ESI Preprints

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.