DYNAMIC INTERCONNECTIONS AND CONTAGION EFFECTS AMONG GLOBAL STOCK MARKETS: A VECM ANALYSIS
DOI:
https://doi.org/10.2478/eoik-2024-0039Abstract
This paper investigates the nature of the associations and the poten-
tial existence of both short-run and long-run relationships between
the stock market indices of Morocco, France, Germany, the United
Kingdom, China, and the United States from January 2014 to January
2024. The purpose of analyzing dynamic interconnections and conta-
gion effects is to determine how the stock markets of these countries
influence and relate to each other. The study employs a time series
Vector Error Correction Model (VECM) approach, incorporating
stationarity, cointegration, and Granger causality tests. Additionally,
the Impulse Response Function (IRF) is used to analyze the response
of variables to shocks. The bivariate Granger causality test reveals
significant causal influences: from France, Germany, and the USA
to Morocco; from the USA to the DAX and France; and from the
UK to Germany. After establishing the Granger causal relationships,
long-run and short-run relationships are further examined. Using the
Johansen multivariate cointegration approach, the study suggests a
long-term equilibrium among the six stock market indices over time.
The short-run adjustments are analyzed using the VECM, which re-
veals that adjustments in the CAC 40, DAX, and MASI tend to correct
deviations from equilibrium, indicating a tendency to move towards
equilibrium. For the FTSE 100, S&P 500, and SSEC, the VECM cap-
tures the speed and direction of adjustments as these indices respond
to short-term disruptions and work towards restoring equilibrium. The
findings underscore the importance of closely connected global stock
markets, which means that international regulators must coordinate
their efforts to reduce the risks of contagion. Policymakers should
prioritize improving financial stability through integrated frameworks
considering short-term disruptions and long-term equilibrium trends.
References
Babaei, H., Hübner, G., Muller, A., 2023. The effects of uncertainty on the dynamics of stock market in-
terdependence: Evidence from the time-varying cointegration of the G7 stock markets. Journal
of International Money and Finance, 139, 102961.
https://doi.org/10.1016/j.jimonfin.2023.102961
Baker, S.R., Bloom, N., Davis, S.J., Kost, K., Sammon, M., Viratyosin, T., 2020. The unprecedented
stock market reaction to COVID-19. The review of asset pricing studies 10, 742–758.
https://doi.org/10.1093/rapstu/raaa008
Bashir, U., Zebende, G.F., Yu, Y., Hussain, M., Ali, A., Abbas, G., 2019. Differential market reactions
to pre and post Brexit referendum. Physica A: Statistical Mechanics and its Applications, 515,
–158. https://doi.org/10.1016/j.physa.2018.09.182
Cevik, E.I., Terzioglu, H.C., Kilic, Y., Bugan, M.F., Dibooglu, S., 2024. Interconnectedness and System-
ic Risk: Evidence from Global Stock Markets. Research in International Business and Finance,
https://doi.org/10.1016/j.ribaf.2024.102282
Cheikh, N.B., Zaied, Y.B., Saidi, S., Sellami, M., 2022. Global pandemic crisis and risk contagion in
GCC stock markets. Journal of Economic Behavior & Organization, 202, 746–761.
https://doi.org/10.1016/j.jebo.2022.08.036
David, S.A., Inácio Jr, C.M., Machado, J.A.T., 2021. The recovery of global stock markets indices after
impacts due to pandemics. Research in International Business and Finance, 55, 101335.
https://doi.org/10.1016/j.ribaf.2020.101335
Dickey, D.A., Fuller, W.A., 1979. Distribution of the Estimators for Autoregressive Time Series with a
Unit Root. Journal of the American Statistical Association, 74, 427–431.
https://doi.org/10.2307/2286348
Gil-Alana, L.A., Infante, J., Martín-Valmayor, M.A., 2023. Persistence and long run co-movements
across stock market prices. The Quarterly Review of Economics and Finance, 89, 347–357.
https://doi.org/10.1016/j.qref.2022.10.001
Herranz, E., 2017. Unit root tests. WIREs Computational Stats 9, e1396.
https://doi.org/10.1002/wics.1396
Huang, B.-N., Yang, C.-W., Hu, J.W.-S., 2000. Causality and cointegration of stock markets among the
United States, Japan and the South China Growth Triangle. International Review of Financial
Analysis, 9, 281–297. https://doi.org/10.1016/S1057-5219(00)00031-4
Kenourgios, D., Dadinakis, E., Tsakalos, I., 2020. Brexit referendum and European stock markets: a
sector analysis. Managerial Finance 46, 913–933. http://dx.doi.org/10.1108/MF-07-2019-0366
Khan, A.M., Khan, U., 2021. The stimulus of export and import performance on economic growth in
Oman. Montenegrin Journal of Economics, 17, 71–86.
http://dx.doi.org/10.14254/1800-5845/2021.17-3.6
Kılcı, E., 2019. Analysis of the relationship between economic freedom index and stock market indices;
evidence from Turkey. MALİYE FİNANS YAZILARI. https://doi.org/10.33203/mfy.491636
Kwiatkowski, D., Phillips, P.C., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationarity
against the alternative of a unit root: How sure are we that economic time series have a unit root?
Journal of econometrics, 54, 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
Liu, M., Choo, W.-C., Lee, C.-C., 2020. The Response of the Stock Market to the Announcement of
Global Pandemic. Emerging Markets Finance and Trade, 56, 3562–3577.
https://doi.org/10.1080/1540496X.2020.1850441
Maysami, R.C., Koh, T.S., 2000. A vector error correction model of the Singapore stock market. Inter-
national Review of Economics & Finance, 9, 79–96.
https://doi.org/10.1016/S1059-0560(99)00042-8
Mallieswari, R., Palanisamy, V., Senthilnathan, A. T., Gurumurthy, S., Selvakumar, J. J., & Pachiyappan,
S. (2024). A Stochastic Method for Optimizing Portfolios Using a Combined Monte Carlo and
Markowitz Model: Approach on Python. ECONOMICS. https://doi.org/10.2478/eoik-2024-0014
Mohti, W., Dionísio, A., Vieira, I., Ferreira, P., 2019. Regional and global integration of Asian stock
markets. Research in International Business and Finance, 50, 357–368.
https://doi.org/10.1016/j.ribaf.2019.06.003
Naceur, S.B., Ghazouani, S., 2007. Stock markets, banks, and economic growth: Empirical evidence
from the MENA region. Research in International Business and Finance, 21, 297–315.
https://doi.org/10.1016/j.ribaf.2006.05.002
Phillips, P.C., Perron, P., 1988. Testing for a unit root in time series regression. Biometrika, 75, 335–346.
https://doi.org/10.2307/2336182
Poh, C.W., Tan, R., 1997. Performance of Johansen’s Cointegration Test. In: East Asian Economic Is-
sues. WORLD SCIENTIFIC, pp. 402–414. https://doi.org/10.1142/9789812819376_0029
Shahrier, N.A., 2022. Contagion effects in ASEAN-5 exchange rates during the Covid-19 pandemic. The
North American Journal of Economics and Finance, 62, 101707.
https://doi.org/10.1016/j.najef.2022.101707
Sugiarto, T., Madu, L., & Subagyo, A. (2018). International Application Model Short-Long Term Be-
tween GDP and Consumption: Case Study Indonesia. Economics, 6(1), 81-90.
https://doi.org/10.2478/eoik-2018-0004
Stoupos, N., Kiohos, A., 2022. Euro area stock markets integration: Empirical evidence after the end of
debt crisis. Finance Research Letters, 46, 102423. https://doi.org/10.1016/j.frl.2021.102423
Tang, Y., Xiong, J.J., Luo, Y., Zhang, Y.-C., 2019. How Do the Global Stock Markets Influence One An-
other? Evidence from Finance Big Data and Granger Causality Directed Network. International
Journal of Electronic Commerce, 23, 85–109. http://dx.doi.org/10.1080/10864415.2018.1512283
Yarovaya, L., Lau, M.C.K., 2016. Stock market comovements around the Global Financial Crisis: Ev-
idence from the UK, BRICS and MIST markets. Research in International Business and Fi-
nance, 37, 605–619. https://doi.org/10.1016/j.ribaf.2016.01.023
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