IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/123775.html
   My bibliography  Save this paper

Integration, Contagion and Turmoils; Evidence from Emerging markets

Author

Listed:
  • NEIFAR, MALIKA
  • HarzAllah, AMIRA

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.‎

Suggested Citation

  • NEIFAR, MALIKA & HarzAllah, AMIRA, 2025. "Integration, Contagion and Turmoils; Evidence from Emerging markets," MPRA Paper 123775, University Library of Munich, Germany, revised 25 Feb 2025.
    • Handle: RePEc:pra:mprapa:123775
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/123775/1/MPRA_paper_123775.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brooks,Chris, 2008. "RATS Handbook to Accompany Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9780521896955, January.
    2. Oguzhan Ozcelebi & Mehmet Tevfik Izgi, 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, Taylor & Francis Journals, vol. 61(1), pages 1-22, January.
    3. Izunna Anyikwa & Pierre Le Roux, 2020. "Integration of African Stock Markets with the Developed Stock Markets: An Analysis of Co-Movements, Volatility and Contagion," International Economic Journal, Taylor & Francis Journals, vol. 34(2), pages 279-296, April.
    4. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    5. Vipin Arora & Shuping Shi, 2016. "Energy consumption and economic growth in the United States," Applied Economics, Taylor & Francis Journals, vol. 48(39), pages 3763-3773, August.
    6. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    7. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    8. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
    10. Fama, Eugene F, 1990. "Stock Returns, Expected Returns, and Real Activity," Journal of Finance, American Finance Association, vol. 45(4), pages 1089-1108, September.
    11. Shuping Shi & Stan Hurn & Peter C B Phillips, 2020. "Causal Change Detection in Possibly Integrated Systems: Revisiting the Money–Income Relationship [Energy Consumption and Economic Growth in the United States]," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 18(1), pages 158-180.
    12. Yilanci, Veli & Kilci, Esra N., 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, Elsevier, vol. 72(C).
    13. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    14. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    15. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    16. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    17. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    18. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    19. Emrah İ. Çevik & Erdal Atukeren & Turhan Korkmaz, 2018. "Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis," Energies, MDPI, vol. 11(10), pages 1-22, October.
    20. Hüseyin Tastan, 2015. "Testing for spectral Granger causality," Stata Journal, StataCorp LLC, vol. 15(4), pages 1157-1166, December.
    21. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    22. Bello, Jaliyyah & Guo, Jiaqi & Newaz, Mohammad Khaleq, 2022. "Financial contagion effects of major crises in African stock markets," International Review of Financial Analysis, Elsevier, vol. 82(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, July.
    2. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2008. "Dynamic Stock Market Interactions between the Canadian, Mexican, and the United States Markets: The NAFTA Experience," Working papers 2008-49, University of Connecticut, Department of Economics.
    3. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    4. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    5. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    6. Hasanov, Akram Shavkatovich & Do, Hung Xuan & Shaiban, Mohammed Sharaf, 2016. "Fossil fuel price uncertainty and feedstock edible oil prices: Evidence from MGARCH-M and VIRF analysis," Energy Economics, Elsevier, vol. 57(C), pages 16-27.
    7. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    9. Neifar, Malika, 2020. "Multivariate GARCH Approaches: case of major sectorial Tunisian stock markets," MPRA Paper 99658, University Library of Munich, Germany.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    11. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Multivariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 669, Stockholm School of Economics, revised 18 Jan 2008.
    12. Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. Estadística y Econometría. DS 3664, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Neifar, Malika, 2020. "Stock Market Volatility Analysis: A Case Study of TUNindex," MPRA Paper 99140, University Library of Munich, Germany.
    14. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
    15. Chulwoo Han & Frank C. Park & Jangkoo Kang, 2017. "A geometric treatment of time-varying volatilities," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 1121-1141, November.
    16. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    17. de Goeij, Peter & Marquering, Wessel, 2009. "Stock and bond market interactions with level and asymmetry dynamics: An out-of-sample application," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 318-329, March.
    18. Hicham Ayad & Ousama Ben-Salha & Miloud Ouafi, 2023. "Do oil prices predict the exchange rate in Algeria? Time, frequency, and time‐varying Granger causality analysis," Economic Change and Restructuring, Springer, vol. 56(5), pages 3545-3566, October.
    19. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    20. Shi Chen & Cathy Yi-Hsuan Chen & Wolfgang Karl Hardle, 2020. "A first econometric analysis of the CRIX family," Papers 2009.12129, arXiv.org.

    More about this item

    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;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:123775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.