Integration, Contagion and Turmoils; Evidence from Emerging markets

NEIFAR, MALIKA and HarzAllah, AMIRA (2025): Integration, Contagion and Turmoils; Evidence from Emerging markets.

Abstract

Purpose – Based on weekly data from 2012 to 2024, this paper aims to evaluate empirically ‎the integration and contagion properties of some emerging stock markets from North Africa ‎including Morocco, Tunisia and Egypt, and to deepen the understanding of the linkage ‎between them during stable and turmoil periods (Covid 19, Ukrainian war and Gazza war).‎

Design/methodology/approach – Besides traditional Granger causality (GC) test (Granger, ‎‎1969), the (Shi, Hurn, & Phillips, 2020)’ time-varying (TV) GC test, the (Song & Taamouti, ‎‎2020)’ quantiles GC test, and the (Breitung-Candelon, 2006)’ frequency domain (FD) GC ‎tests are used for the contagion (diversification) check between market volatility (returns). ‎Then, the returns DCC- GARCH specifications are used for the integration investigations. ‎Then, based on the returns DCC dynamic regressions, the contagion analysis between ‎considered markets that are related to the unexpected events is done.‎

Findings – As the results from the standard GC, all considered tests reveal that in mean, ‎Tunisian returns R_T and Egyptian R_E are predictable by Moroccan R_M. Only Tunisian ‎and Egyptian return can play then the role of diversifier. Results from these causality tests ‎detect some contagion in variance between markets, which was denied from dynamic DDC ‎regression regressions in returns. From dynamic DCC-GARCH model, our empirical results ‎show a weak integration between returns. ‎

Originality/value – Via the dynamic DCC ARCH and the DCC quantile regression, the time ‎varying GC, the quantile GC, and the spectral GC tests, this paper provides a deeper ‎understanding of North African marginal stock market behavior and linkage.‎

• Item Type: MPRA Paper
• Language: English
• Keywords: MASI, TUNINDEX, and EGX30 indexes; Time varying (TV), quantile (Q) and frequency ‎domain (FD) GC tests; Dynamic conditional correlation (DCC)- GARCH model; DCC ‎quantile regression; Contagion, hedges/diversifiers, Safe Havens properties
• Subjects: G - Financial Economics > G1 - General Financial Markets; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

Alexander, C. (2009). Practical Financial Econometrics. John Wiley & Sons, Ltd.‎

Anyikwa, I., & Le Roux, P. (2020). Integration of African Stock Markets with the Developed ‎Stock ‎Markets: An Analysis of Co-Movements, Volatility and Contagion. International Economic ‎‎Journal. doi:10.1080/10168737.2020.1755715‎

Arora, V., & Shi, S. (2016). Energy consumption and economic growth in the United States. Applied ‎‎Economics, 48 (39), 3763–3773‎.‎

Ashena, M., Khezri, H. L., & ‎Shahpari, G. (2024). Investigation into the dynamic relationships ‎between global economic uncertainty and price ‎volatilities of commodities, raw materials, ‎and energy. Applied Economic Analysis, 32(94), ‎23-40 ‎. doi:10.1108/AEA-06-2023-0207‎

Baum, C. F., Otero, J., & Hurn, S. (2021 ). Testing for time-varying Granger causality. https://www.stata.com/symposiums/economics21/slides/Econ21_Baum.pdf. Stata ‎Economics Virtual Symposium.‎

Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate ‎GARCH models: A survey. Journal of ‎Applied Econometrics, 21, 79–109‎.‎

Bello, J. G., & Newaz, M. (2022). Financial contagion effects of major crises in African stock markets. ‎International Review of Financial Analysis, 82. ‎doi:https://doi.org/10.1016/j.irfa.2022.102128‎

Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance. ‎Journal of Econometrics, ‎‎52, 5–59‎.‎

Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH Modelling in Finance: A Review of the Theory ‎and Empirical Evidence. Journal of Econometrics, 52(5), 5-59.‎

Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988). A capital asset ‎pricing model with time-‎varying covariances. Journal of Political Economy ‎‎, 96, 116 –131‎.‎

Bollerslev, T., Engle, R., & Nelson, D. B. (1994). ARCH Models. Handbook of Econometrics, 4,: in R.F. ‎Engle and D. McFadden eds.‎ Boyer, B. H., Kumagai, T., & Yuan, K. (2006 ). How Do Crises Spread? Evidence ‎from Accessible and ‎Inaccessible Stock Indices. The Journal of Finance, 61(2), 957–1003.‎

Breitung, J., & Candelon, B. (2006). Testing for short-and long-run causality: A frequency-domain ‎approach. J Econ, 132(2), 363–378. ‎ Brock, W. A., Dechert, D., Scheinkman, H., & LeBaron, B. (1996). A Test for Independence Based on ‎the Correlation Dimension. Econometric Reviews, 15, 197--235.‎

Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge University.‎

Çevik, E. ˙., Atukeren, E. .., & Korkmaz, T. (2018). Oil Prices and Global Stock Markets: A Time-‎Varying Causality-In-Mean and Causality-in-Variance Analysis. Energies, 11, 2848. ‎doi:10.3390/en11102848‎

Davidson, R., & MacKinnon, J. G. (1993). Estimation and Inference in ‎Econometrics. New York: ‎Oxford University Press.‎

Enders, W. (2004). Applied Econometric Time Series. 2nd ed. New York: Wiley‎.‎

Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of ‎United Kingdom Inflation. Econometrica, 50, 987–1007.‎

Engle, R. F. ( 2009). Anticipating Correlations: A New Paradigm for Risk ‎Management. Princeton, NJ: ‎Princeton University Press‎.‎

Engle, R. F. (2002). Dynamic conditional correlation: A simple class of ‎multivariate generalized ‎autoregressive conditional heteroskedasticity ‎models. Journal of Business & Economic ‎Statistics, 20, 339–350‎.‎

Engle, R., & Ng, V. (1993). Measuring and testing the impact of news on volatility. Journal of ‎Finance, 48(5), 1749-1778.‎

Engle, R., & Ng, V. (1993). Measuring and Testing the Impact of News on Volatility. Journal of ‎Finance, 48, 1749–1778.‎

Fama, E. (1990 ). Stock returns, Expected Returns, and Real Activity. Journal of Finance, 1189-1108.‎

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral ‎methods. Econometrica, 37, 424–438. doi:10.2307/1912791‎

Greene, W. H. (2018). Econometric Analysis. 8th ed. New York: Pearson‎.‎

Hamilton, J. D. (1994). Time Series Analysis. Princeton, New Jersy: Princeton university press.‎

Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman ‎Filter. Cambridge: ‎Cambridge University Press.‎

Nelson, D. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica ‎‎, 59, 347-370., 59, 347-370.‎

Nguyen, T. N., Phan, T. K., & Nguyen, T. L. (2022). FINANCIAL CONTAGION DURING GLOBAL ‎‎FINANCIAL CRISIS AND COVID–19 PANDEMIC: THE EVIDENCE FROM DCC–GARCH MODEL. ‎Cogent Economics & ‎Finance, 10(1), 2051824. doi:10.1080/23322039.2022.2051824‎

Ozcelebi, O., & Tevfik Izgi, M. (2023). Assessing the impacts of economic policy uncertainty of the ‎US on the exchange rates and stock returns of Korea, Mexico, Poland and Russia. Eastern ‎European Economics, 61 (1), 1-22. doi:10.1080/00128775.2022.2107937‎

Pagan, A. R., & Schwert, G. W. (1990). Alternative Models for Conditional Stock Volatilities. Journal ‎of Econometrics, 45, 267-90.‎

Shi, S., Hurn, S., & Phillips, P. C. (2020). Causal Change Detection in Possibly Integrated Systems: ‎Revisiting the Money–Income Relationship. Journal of Financial Econometrics, 18, 158–180‎. ‎doi:doi: 10.1093/jjfinec/nbz004‎

Shi, S., Phillips, P., & Hurn, S. (2018). Change detection and the causal impact of the yield ‎curve. ‎Journal of Time Series Analysis, 39, 966-987‎.‎

Silvennoinen, A., & Terasvirta, T. (2009). Multivariate GARCH models. In ‎Handbook of Financial Time ‎Series, ed‎.‎

Song, X., & Taamouti, A. (2020). Measuring Granger Causality in Quantiles. Journal of ‎Business & ‎Economic Statistics, 1–48. doi:doi:10.1080/07350015.2020.1739531 ‎

Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics‎. Updated 3rd ed. ‎Hoboken, NJ: ‎Pearson.‎

Tastan, H. (2015). Testing for spectral Granger causality. The Stata Journal, 15(4), 1157–1166.‎

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

Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. 6th ed. ‎Boston: ‎Cengage‎.‎

Yilanci, V., & Kilci, E. (2021). The role of economic policy uncertainty and geopolitical risk in predicting ‎prices of precious ‎metals: evidence from a time-varying bootstrap causality test. Resources ‎Policy, 72, 102039. doi:‎‎10.1016/j.res