Economic Policy Uncertainty and Stock Market Co-Movements in BRIC Countries: Evidence from Wavelet Coherence and Rolling Bootstrap Granger Causality

Authors

  • Houssem Ben-Ammar University of Sousse, Faculty of Economic Sciences and Management, Tunisia. , Research Laboratory for Economy, Management and Quantitative Finance (LaREMFiQ), IHEC – University of Sousse, Tunisia Corresponding Author
  • Riadh El Abed University of Tunis El Manar, Faculty of Economic Sciences and Management, Tunisia. , Laboratoire d'Economie du développement durable, des ressources naturelles et d'agriculture (LEDDRNA), FSEGT, Tunisia Author

DOI:

https://doi.org/10.47654/v30y2026i1p103-135

Keywords:

EPU, Stock Returns, Wavelet coherence, Bootstrap rolling window

Abstract

Purpose: The relationship between economic policy uncertainty (EPU) and stock returns in the BRIC countries (Brazil, Russia, India, and China) is examined by analyzing both static and dynamic interactions across different time horizons, with particular attention to major global crises.

Design/methodology/approach: Monthly data from 2004 to 2022 are used, and wavelet coherence analysis is applied together with bootstrap rolling-window and full-sample Granger causality tests to assess the dynamic and causal links between EPU and stock returns.

Findings: The results show unidirectional causality from EPU to stock returns in Brazil, Russia, and India. In these countries, higher policy uncertainty reduces stock returns, while no significant causal relationship is found for China. Wavelet coherence results reveal strong short-term co-movements during crisis periods, medium-term synchronization in India and Russia, and persistent long-term correlations in China. The findings highlight the time-varying nature of the EPU–return relationship and its sensitivity to global shocks and institutional conditions.

Originality/value: By integrating wavelet coherence with bootstrap rolling-window Granger causality, the study provides a multi-scale and dynamic framework for analyzing the EPU–stock return nexus in BRIC economies, offering useful insights for portfolio management, risk assessment, and decision-making in the field of Decision Sciences.

Practical/Social implications: The results suggest that investors adopt horizon-sensitive investment strategies, while policymakers improve policy transparency and communication to limit market volatility. Opportunities for future sectoral and cross-market research are also highlighted.

References

Akerlof, G. A. (1978). The market for “lemons”: Quality uncertainty and the market mechanism. In Uncertainty in economics (pp. 235-251). Academic Press. https://doi.org/10.1016/B978-0-12-214850-7.50022-X

Andrews, D. W. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica: Journal of the Econometric Society, 821–856. https://doi.org/10.2307/2951764

Andrews, D. W., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, 1383–1414. https://doi.org/10.2307/2951753

Antonakakis, N., Chatziantoniou, I., & Filis, G. (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters, 120(1), 87–92. https://doi.org/10.1016/j.econlet.2013.04.004

Antonakakis, N., & Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41, 303–319. https://doi.org/10.1016/j.econmod.2014.10.018

Apergis, N., & Fahmy, H. (2024). Geopolitical risk and energy price crash risk. Energy Economics, 140, 107975. https://doi.org/10.1016/j.eneco.2024.107975

Arouri, M., Estay, C., Rault, C., & Roubaud, D. (2016). Economic policy uncertainty and stock markets: Long-run evidence from the US. Finance Research Letters, 18, 136–141. https://doi.org/10.1016/j.frl.2016.04.011

Aydin, M., Pata, U. K., & Inal, V. (2022). Economic policy uncertainty and stock prices in BRIC countries: evidence from asymmetric frequency domain causality approach. Applied Economic Analysis, 30(89), 114–129. https://doi.org/10.1108/AEA-12-2020-0172

Aye, G. C. (2018). Causality between economic policy uncertainty and real housing returns in emerging economies: A cross-sample validation approach. Cogent Economics & Finance, 6(1), 1473708. https://doi.org/10.1080/23322039.2018.1473708

Bagh, T., Waheed, A., Khan, M. A., & Naseer, M. M. (2023). Effect of economic policy uncertainty on China’s stock price index: a comprehensive analysis using wavelet coherence approach. Sage Open, 13(4). https://doi.org/10.1177/21582440231210368

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qiw024

Balcilar, M., Bekiros, S., & Gupta, R. (2017). The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method. Empirical Economics, 53(3), 879-889. https://doi.org/10.1007/s00181-016-1150-0

Balcilar, M., & Ozdemir, Z. A. (2013). The export-output growth nexus in Japan: A bootstrap rolling window approach. Empirical Economics, 44, 639–660. https://doi.org/10.1007/s00181-012-0562-8

Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance, 1, 1053-1128. https://doi.org/10.1016/S1574-0102(03)01027-6

Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. https://doi.org/10.1093/jjfinec/nby001

Bekaert, G., & Hoerova, M. (2016). What do asset prices have to say about risk appetite and uncertainty?. Journal of Banking & Finance, 67, 103–118. https://doi.org/10.1016/j.jbankfin.2015.06.015

Bekiros, S., Gupta, R., & Majumdar, A. (2016). Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis. Finance Research Letters, 18, 291-296. https://doi.org/10.1016/j.frl.2016.01.012

Brogaard, J., & Detzel, A. (2015). The asset-pricing implications of government economic policy uncertainty. Management Science, 61(1), 3–18. https://doi.org/10.1287/mnsc.2014.2044

Cheng, Y., Hui, Y., Liu, S., & Wong, W. K. (2022). Could significant regression be treated as insignificant: An anomaly in statistics?. Communications in Statistics: Case Studies, Data Analysis and Applications, 8(1), 133-151. https://doi.org/10.1080/23737484.2021.1986171

Cheng, Y., Hui, Y., McAleer, M., & Wong, W. K. (2021). Spurious relationships for nearly non-stationary series. Journal of Risk and Financial Management, 14(8), 366. https://doi.org/10.3390/jrfm14080366

Davidson, R., & MacKinnon, J. G. (2004). Econometric theory and methods (Vol. 5). Oxford University Press.

Dew-Becker, I., & Giglio, S. (2016). Asset pricing in the frequency domain: theory and empirics. The Review of Financial Studies, 29(8), 2029–2068. https://doi.org/10.1093/rfs/hhw027

Dumayiri, M., Alagidede, I. P., & Sare, Y. A. (2024). Frequency-domain approach to the causal nexus between domestic and international economic policy uncertainties and equity returns of G20 countries. Cogent Economics & Finance, 12(1), 2383083. https://doi.org/10.1080/23322039.2024.2383083

Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. Chapman and Hall/CRC.

Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55, 251–276. https://doi.org/10.2307/1913236

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417. https://www.jstor.org/stable/2325486

Ghosh, S., & Adebayo, T. S. (2024). The effect of world policy uncertainty and geopolitical risk factors on export-led growth for Japan: novel insights by wavelet local multiple correlation methods. Quality & Quantity, 58(4), 3605–3633. https://doi.org/10.1007/s11135-023-01814-5

Hall, P. (2013). The bootstrap and Edgeworth expansion. Springer Science & Business Media.

Hansen, E. (1992). Test for parameter instability in regressions with I(1) processes. Journal of Business and Economic Statistics, 10, 321–336.

Hashmi, S. M., Chang, B. H., & Rong, L. (2021). Asymmetric effect of COVID-19 pandemic on E7 stock indices: Evidence from quantile-on-quantile regression approach. Research in International Business and Finance, 58, 101485. https://doi.org/10.1016/j.ribaf.2021.101485

Hassan, K., Hoque, A., & Gasbarro, D. (2019). Separating BRIC using Islamic stocks and crude oil: Dynamic conditional correlation and volatility spillover analysis. Energy Economics, 80, 950–969. https://doi.org/10.1016/j.eneco.2019.02.016

Hui, E. C., Chan, K. K., & Yiu, C. Y. (2017). A test for nonlinearity in financial time series. Journal of Financial Econometrics, 15(1), 1–25. https://doi.org/10.1093/jjfinec/nbw002

Khan, M. A., Ahmed, M., Popp, J., & Olah, J. (2020). US policy uncertainty and stock market nexus revisited through dynamic ARDL simulation and threshold modelling. Mathematics, 8(11), 2073. https://doi.org/10.3390/math8112073

Khan, N., Zada, H., Siddiqui, O., & Ullah, E. (2025). Sectoral Response to Economic Policy Uncertainty in Japan: An Empirical Evidence from the Cross-Quantilogram Approach. Computational Economics, 1–36. https://doi.org/10.1007/s10614-025-10867-7

Kido, Y. (2018). The transmission of US economic policy uncertainty shocks to Asian and global financial markets. The North American Journal of Economics and Finance, 46, 222–231. https://doi.org/10.1016/j.najef.2018.04.008

Kundu, S., & Paul, A. (2022). Effect of economic policy uncertainty on stock market return and volatility under heterogeneous market characteristics. International Review of Economics & Finance, 80, 597–612. https://doi.org/10.1016/j.iref.2022.02.047

Li, R., Li, S., Yuan, D., & Yu, K. (2020). Does economic policy uncertainty in the US influence stock markets in China and India? Time-frequency evidence. Applied Economics, 52(39), 4300–4316. https://doi.org/10.1080/00036846.2020.1734182

Marín-Rodríguez, N. J., González-Ruiz, J. D., & Botero, S. (2025). Dynamic Linkages Between Economic Policy Uncertainty and External Variables in Latin America: Wavelet Analysis. Economies, 13(2), 22. https://doi.org/10.3390/economies13020022

Mensi, W., Hkiri, B., Al-Yahyaee, K. H., & Kang, S. H. (2018). Analyzing time-frequency co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach. International Review of Economics & Finance, 54, 74–102. https://doi.org/10.1016/j.iref.2017.07.032

Mensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H., & Vo, X. V. (2021). Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resources Policy, 72, 102062. https://doi.org/10.1016/j.resourpol.2021.102062

Menzly, L., Santos, T., & Veronesi, P. (2004). Understanding predictability. Journal of Political Economy, 112(1), 1–47. https://doi.org/10.1086/379934

Minlah, M. K., & Zhang, X. (2021). Testing for the existence of the Environmental Kuznets Curve (EKC) for CO2 emissions in Ghana: evidence from the bootstrap rolling window Granger causality test. Environmental Science and Pollution Research, 28, 2119–2131. https://doi.org/10.1007/s11356-020-10600-x

Mishra, S., Sinha, A., Sharif, A., & Suki, N. M. (2020). Dynamic linkages between tourism, transportation, growth and carbon emission in the USA: evidence from partial and multiple wavelet coherence. Current Issues in Tourism, 23(21), 2733-2755. https://doi.org/10.1080/13683500.2019.1667965

Nakhli, M. S., Dhaoui, A., & Chevallier, J. (2022). Bootstrap rolling-window Granger causality dynamics between momentum and sentiment: Implications for investors. Annals of Finance, 18(2), 267–283. https://doi.org/10.1007/s10436-021-00399-z

Nakhli, M. S., Mokni, K., & Youssef, M. (2025). Time-varying causality between investor sentiment and oil price: Does uncertainty matter?. International Journal of Finance & Economics, 30(1), 369–381. https://doi.org/10.1002/ijfe.2922

Pastor, L., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. https://doi.org/10.1016/j.jfineco.2013.08.007

Patel, R. (2025). Analyzing the energy markets and financial markets linkage: A bibliometric analysis and future research agenda. Review of Financial Economics, 43(1), 23–61. https://doi.org/10.1002/rfe.1216

Pesaran, M. H., & Timmermann, A. (2005). Small sample properties of forecasts from autoregressive models under structural breaks. Journal of Econometrics, 129(1-2), 183-217. https://doi.org/10.1016/j.jeconom.2004.09.007

Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241–252. https://doi.org/10.1016/j.eneco.2016.10.015

Rua, A., & Nunes, L. C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16(4), 632–639. https://doi.org/10.1016/j.jempfin.2009.02.002

Rubbaniy, G., Khalid, A. A., Tessema, A., & Baqrain, A. (2023). Do stock market fear and economic policy uncertainty co-move with COVID-19 fear? Evidence from the US and UK. Studies in Economics and Finance, 40(1), 192–212. https://doi.org/10.1108/SEF-10-2021-0408

Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70, 101496. https://doi.org/10.1016/j.irfa.2020.101496

Soni, R. K., Nandan, T., & Chatnani, N. N. (2023). Dynamic association of economic policy uncertainty with oil, stock and gold: a wavelet-based approach. Journal of Economic Studies, 50(7), 1501–1525. https://doi.org/10.1108/JES-05-2022-0267

Su, C. W., Wang, X. Q., Tao, R., & Oana-Ramona, L. (2019). Do oil prices drive agricultural commodity prices? Further evidence in a global bio-energy context. Energy, 172, 691–701. https://doi.org/10.1016/j.energy.2019.02.028

Su, Y., Cherian, J., Sial, M. S., Badulescu, A., Thu, P. A., Badulescu, D., & Samad, S. (2021). Does tourism affect economic growth of China? A panel granger causality approach. Sustainability, 13(3), 1349. https://doi.org/10.3390/su13031349

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8

Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34(1), 241–247. https://doi.org/10.1016/j.eneco.2011.10.007

Wilks, S. S. (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9(1), 60–62.

Wong, W. K., Cheng, Y., & Yue, M. (2024). Could regression of stationary series be spurious?. Asia-Pacific Journal of Operational Research, 2440017. https://doi.org/10.1142/S0217595924400177

Wong, W. K., & Pham, M. T. (2025). How to model a simple stationary series with a non-stationary series. The International Journal of Finance, 37(1), 1-19.

Wong, W. K., & Yue, M. (2024). Could regressing a stationary series on a non-stationary series obtain meaningful outcomes?. Annals of Financial Economics, 19(03), 2450011. https://doi.org/10.1142/S2010495224500118

World Bank. (2019). World Development Report 2019: The Changing Nature of Work. Washington, DC: World Bank. https://doi:10.1596/978-1-4648-1328-3

Wu, T. P., & Wu, H. C. (2020). A multiple and partial wavelet analysis of the economic policy uncertainty and tourism nexus in BRIC. Current Issues in Tourism, 23(7), 906–916. https://doi.org/10.1080/13683500.2019.1566302

Wu, T. P., Wu, H. C., Liu, S. B., Wu, C. F., & Wu, Y. Y. (2022). Causality between global economic policy uncertainty and tourism in fragile five countries: A three-dimensional wavelet approach. Tourism Recreation Research, 47(5–6), 608–622. https://doi.org/10.1080/02508281.2020.1870072

Xu, Y., Wang, J., Chen, Z., & Liang, C. (2021). Economic policy uncertainty and stock market returns: New evidence. The North American journal of economics and finance, 58, 101525. https://doi.org/10.1016/j.najef.2021.101525

Zeileis, A., Leisch, F., Kleiber, C., & Hornik, K. (2005). Monitoring structural change in dynamic econometric models. Journal of Applied Econometrics, 20(1), 99–121. https://doi.org/10.1002/jac.776

Published

2026-02-03

How to Cite

Ben-Ammar, H., & El Abed, R. (2026). Economic Policy Uncertainty and Stock Market Co-Movements in BRIC Countries: Evidence from Wavelet Coherence and Rolling Bootstrap Granger Causality. Advances in Decision Sciences, 30(1), 103-135. https://doi.org/10.47654/v30y2026i1p103-135