Effects of Investor Sentiment and Geopolitical Risk on Stock Return Volatility in East African Frontier Markets: Evidence from a GARCH-X Framework
DOI:
https://doi.org/10.37075/FABA.2026.1.07Keywords:
investor sentiment, Geopolitical risk, GARCH, modelling, East African marketsAbstract
Purpose: In an environment of rising global uncertainty and increasing sensitivity to behavioural and geopolitical shocks, this study examines the effects of investor sentiment and geopolitical risk on stock return volatility in East African frontier markets.
Design/Methodology/Approach: Daily stock index returns data for Nairobi, Dar es Salaam, Uganda, and Rwanda from 2014 to 2025 are analysed using ARMA-GARCH and extended ARMA-GARCH-X models. Investor sentiment is constructed using principal component analysis (PCA) of market-based proxies of international indicators, and geopolitical risk is measured using the Caldara-Iacoviello index. Investor sentiment and geopolitical risk are incorporated into both the mean and variance equations within a GARCH-X framework to assess their effects on return dynamics and time-varying volatility.
Findings: Volatility exhibits strong persistence, clustering, and heavy tails across East African frontier markets. Investor sentiment exerts a consistent influence on volatility, while geopolitical risk plays a weaker and more episodic role. Asymmetric volatility responses are evident in most markets, and the limited predictability of returns in the mean equation remains broadly consistent with weak-form efficiency, although the overall results better support an adaptive market interpretation.
Practical implications: The sensitivity of volatility to sentiment and geopolitical risk highlights the importance of incorporating behavioural and external risk indicators into risk management and portfolio allocation decisions in frontier markets.
Originality/Value: This study contributes to the limited literature on East African frontier markets by providing direct evidence on the role of investor sentiment and geopolitical risk in explaining volatility, extending beyond traditional volatility modelling frameworks to offer novel perspectives.
Paper Type: Research Paper
Downloads
References
Adrian, T., N. Boyarchenko, and D. Giannone. 2019. Vulnerable growth. American Economic Review, 109(4): 1263–1289. https://doi.org/10.1257/aer.20161923.
Akaike, H. 1978. A Bayesian analysis of the minimum AIC procedure. Annals of the Institute of Statistical Mathematics, 30(1): 9–14. https://doi.org/10.1007/BF02480194.
Alabi, O. O., K. Ayinde, O. E. Babalola, H. A. Bello, and E. C. Okon. 2020. Effects of multicollinearity on type I errors of heteroskedasticity tests. Open Journal of Statistics, 10(4): 664–677. https://doi.org/10.4236/ojs.2020.104041.
Anderson, H. M., F. Vahid, and K. Nam. 2020. Asymmetric nonlinear smooth transition GARCH models. Journal of Financial Econometrics, 18(1): 120–150. https://doi.org/10.1007/978-1-4615-5129-4_10.
Atenya, M. K. 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.
Baker, M., and J. C. Stein. 2004. Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3): 271–299. https://doi.org/10.1016/j.finmar.2003.11.005.
Baker, M., and J. Wurgler. 2006. Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4): 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x.
Baker, M., and J. Wurgler. 2007. Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2): 129–152. https://doi.org/10.1257/jep.21.2.129.
Balcilar, M., R. Gupta, and C. Pierdzioch. 2016. Uncertainty and gold price: Evidence from a quantile-on-quantile regression. Resources Policy, 49: 74–80. https://doi.org/10.1016/j.resourpol.2016.04.004.
Baur, D. G., and B. M. Lucey. 2010. Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2): 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x.
Biau, C. 2018. Common Capital Market Infrastructure for East Africa: Options for the Way Forward. Milken Institute. https://milkeninstitute.org/sites/default/files/reports-pdf/FINAL-Capital-Market-Infrastructure-in-East-Africa.pdf.
Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3): 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
Bollerslev, T., R. F. Engle, and D. B. Nelson. 1994. ARCH models. In R. F. Engle, and D. L. McFadden, eds. Handbook of Econometrics. Vol. 4. Amsterdam: Elsevier, 2959–3038. https://doi.org/10.1016/S1573-4412(05)80018-2.
Bouri, E., R. Demirer, R. Gupta, and H. Marfatia. 2019. Geopolitical risks and movements in Islamic bond and equity markets: A note. Defence and Peace Economics, 30(3): 367–379. https://doi.org/10.1080/10242694.2018.1424613.
Brogaard, J., and A. Detzel. 2015. The asset-pricing implications of government economic policy uncertainty. Management Science, 61(1): 3–18. https://doi.org/10.1287/mnsc.2014.2044.
Brooks, C. 2019. Introductory Econometrics for Finance. 4th ed. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108524872.
Brown, G. W., and M. T. Cliff. 2005. Investor sentiment and asset valuation. Journal of Business, 78(2): 405–440. https://doi.org/10.1086/427633.
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. New York: Springer.
Caldara, D., and M. Iacoviello. 2022. Measuring geopolitical risk. American Economic Review, 112(4): 1194–1225. https://doi.org/10.1257/aer.20191823.
Calvo, G. A., and C. M. Reinhart. 2002. Fear of floating. Quarterly Journal of Economics, 117(2): 379–408. https://doi.org/10.1162/003355302753650274.
Campbell, J. Y., M. Lettau, B. G. Malkiel, and Y. Xu. 2001. Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. Journal of Finance, 56(1): 1–43. https://doi.org/10.1111/0022-1082.00318.
Chowdhury, S. S. H. 2023. Spillover of sentiments between the GCC stock markets. Global Business Review, 24(6): 1434–1453. https://doi.org/10.1177/0972150920935595.
Coulibaly, D., and H. Kempf. 2010. Does inflation targeting decrease exchange rate pass-through in emerging countries? Documents de Travail du Centre d'Economie de la Sorbonne, No. 2010.49. Université Paris 1 Panthéon-Sorbonne. https://doi.org/10.2139/ssrn.1706025.
De Bondt, W. F. M., and R. Thaler. 1985. Does the stock market overreact? Journal of Finance, 40(3): 793–805. https://doi.org/10.1111/j.1540-6261.1985.tb05004.x.
Eissa, M. A., and H. Al Refai. 2024. Context-dependent responses to geopolitical risk in Middle Eastern and African stock markets: An asymmetric volatility spillover study. International Review of Economics and Finance, 94: 103402. https://doi.org/10.1016/j.iref.2024.103402.
Enders, W. 2012. Applied Econometric Time Series. 3rd ed. Hoboken, NJ: Wiley.
Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4): 987–1007. https://doi.org/10.2307/1912773.
Fama, E. F. 1970. Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2): 383–417. https://doi.org/10.2307/2325486.
Ferreira, J. J., S. Gomes, J. M. Lopes, and J. Z. Zhang. 2023. Ticking time bombs: The MENA and SSA regions' geopolitical risks. Resources Policy, 85: 103938. https://doi.org/10.1016/j.resourpol.2023.103938.
Finter, P., A. Niessen-Ruenzi, and S. Ruenzi. 2012. The impact of investor sentiment on the German stock market. Zeitschrift für Betriebswirtschaft, 82: 133–163. https://doi.org/10.1007/s11573-011-0536-x.
Gkillas, K., R. Gupta, and M. E. Wohar. 2018. Volatility jumps: The role of geopolitical risks. Finance Research Letters, 27: 247–258. https://doi.org/10.1016/j.frl.2018.03.014.
Glosten, L. R., R. Jagannathan, and D. E. Runkle. 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5): 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x.
He, Z. 2023. Geopolitical risks and investor sentiment: Mechanistic insights from machine learning. North American Journal of Economics and Finance, 67: 101947. https://doi.org/10.1016/j.najef.2023.101947.
Kahneman, D., and A. Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica, 47(2): 263–291. https://doi.org/10.2307/1914185.
Kamuhanda, R. 2020. Determinants of Capital Market Performance in Uganda: A Case Study of Uganda Securities Exchange. Unpublished Master’s Thesis, Makerere University. https://makir.mak.ac.ug/items/f106e482-07a6-4a40-b09a-14fad7eecf8e.
Kamazima, B. K., and J. K. Omurwa. 2018. The determinants of emerging financial markets development: A case study of the Dar es Salaam Stock Exchange, Tanzania. European Journal of Business and Management, 10(17): 92–108. https://doi.org/10.7176/EJBM/10-17-10.
Kengere, H. A. 2023. Volatility persistence in East African stock markets. European Journal of Economic and Financial Research, 7(1): 223–250. https://doi.org/10.46827/ejefr.v7i1.1454.
Kilian, L., and C. Park. 2009. The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4): 1267–1287. https://doi.org/10.1111/j.1468-2354.2009.00568.x.
Kunjal, D. 2023. Does geopolitical risk matter for ETF flows in emerging markets? Finance, Accounting and Business Analysis, 5(2): 102–112. https://faba.bg/index.php/faba/article/view/169.
Kuttu, S. 2018. Asymmetric mean reversion and volatility in African real exchange rates. Journal of Economics and Finance, 42(3): 575–590. https://doi.org/10.1007/s12197-017-9412-z.
Lo, A. W. 2004. The adaptive markets hypothesis. Journal of Portfolio Management, 30(5): 15–29. https://doi.org/10.3905/jpm.2004.442611.
Lo, A. W., and R. Zhang. 2024. The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics. Oxford: Oxford University Press. https://doi.org/10.1093/oso/9780199681143.001.0001.
Marobhe, M., and D. Pastory. 2020. Modeling stock market volatility using GARCH models: Evidence from the Dar es Salaam Stock Exchange. Review of Integrative Business and Economics Research, 9(2): 138–150.
Mbanga, C., A. F. Darrat, and J. C. Park. 2019. Investor sentiment and aggregate stock returns: The role of investor attention. Review of Quantitative Finance and Accounting, 53(2): 397–428. https://doi.org/10.1007/s11156-018-0753-2.
Mensi, W., F. Z. Boubaker, K. H. Al-Yahyaee, and S. H. Kang. 2018. Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25: 230–238. https://doi.org/10.1016/j.frl.2017.10.032.
Muguto, L. 2022. Analysis of Stock Return Volatility and Its Response to Investor Sentiment: An Examination of Emerging and Developed Markets. Unpublished Doctoral Dissertation, University of KwaZulu-Natal. https://researchspace.ukzn.ac.za/.
Muguto, H. T., L. Rupande, and P.-F. Muzindutsi. 2019. Investor sentiment and foreign financial flows: Evidence from South Africa. Zbornik Radova Ekonomski Fakultet u Rijeka, 37(2): 473–498. https://doi.org/10.18045/zbefri.2019.2.473.
Muguto, L., and P.-F. Muzindutsi. 2022. A comparative analysis of the nature of stock return volatility in BRICS and G7 markets. Journal of Risk and Financial Management, 15(2): 85. https://doi.org/10.3390/jrfm15020085.
Musembi, M. M., R. Mugo, and D. Ochieng. 2020. Symmetric and asymmetric effects of investor sentiment on equity market performance in Kenya. International Journal of Management and Commerce Innovations, 8(1): 438–447. https://www.researchpublish.com/upload/book/paperpdf-1598703691.pdf.
Nelson, D. B. 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2): 347–370. https://doi.org/10.2307/2938260.
Obalade, A. A., and P.-F. Muzindutsi. 2019. The adaptive market hypothesis and the day-of-the-week effect in African stock markets: The Markov switching model. Comparative Economic Research: Central and Eastern Europe, 22(3): 145–162. https://doi.org/10.2478/cer-2019-0028.
Rahman, M. L., and A. Shamsuddin. 2019. Investor sentiment and the price-earnings ratio in the G7 stock markets. Pacific-Basin Finance Journal, 55: 46–62. https://doi.org/10.1016/j.pacfin.2019.03.003.
Rohilla, A., A. K. Singh, N. Tripathi, and V. Bhandari. 2023. Does investor sentiment affect volatility in the Indian stock market: An ARDL approach. International Journal of Banking Risk and Insurance, 11(1): 12. https://doi.org/10.48001/jbmis.1101004.
Rosengren, E. S. 2019. Perspectives on the U.S. economic outlook. Speech, Federal Reserve Bank of Boston, 17 December. https://www.bostonfed.org/home/news-and-events/speeches/2019/perspectives-on-the-us-economic-outlook.aspx.
Salisu, A. A., A. E. Ogbonna, L. Lasisi, and A. Olaniran. 2022. Geopolitical risk and stock market volatility in emerging markets: A GARCH-MIDAS approach. North American Journal of Economics and Finance, 62: 101755. https://doi.org/10.1016/j.najef.2022.101755.
Smales, L. A. 2017. The importance of fear: Investor sentiment and stock market returns. Applied Economics, 49(34): 3395–3421. https://doi.org/10.1080/00036846.2016.1259752.
Smales, L. A. 2021. Geopolitical risk and volatility spillovers in oil and stock markets. Quarterly Review of Economics and Finance, 80: 358–366. https://doi.org/10.1016/j.qref.2021.03.008.
Statman, M. 2024. Harry Markowitz's two intellectual children: Mean-variance and behavioral portfolio theories. Journal of Portfolio Management, 50(8). https://doi.org/10.3905/jpm.2024.50.8.024.
Tsay, R. S. 2005. Analysis of Financial Time Series. 2nd ed. Hoboken, NJ: Wiley. https://doi.org/10.1002/0471746193.
Yunvirusaba, N., J. Aduda, and A. Kube. 2019. Volatility spillovers between East African stock markets. International Journal of Economics and Finance, 11(10): 32–41. https://doi.org/10.5539/ijef.v11n10p32.
Zhang, M. Z. 2021. Stock Returns and Inflation Redux: An Explanation from Monetary Policy in Advanced and Emerging Markets. International Monetary Fund. https://doi.org/10.5089/9781513586755.001









