IDEAS home Printed from https://ideas.repec.org/a/ris/isecst/0176.html
   My bibliography  Save this article

Co-Movement and Volatility Transmission between Islamic and Conventional Equity Index in Bangladesh

Author

Listed:
  • HASAN, MD ABU

    (Bangladesh Civil Service (General Education), Ministry of Education, Bangladesh)

Abstract

Though the issues of co-movement and volatility transmission between Islamic and conventional stock indices have been extensively studied worldwide, this is the first study in reference to Bangladesh to the best of our knowledge. The broad objective of this paper is to investigate whether Islamic stock index provides more diversification benefits than the conventional index from the perspective of cointegration and volatility spillover employing ARDL bounds testing cointegration procedure and GARCH family models. This study uses daily conventional (DS30) and Islamic (DSES) indices from the Dhaka Stock Exchange over the period from 20 January 2014 to 25 June 2018. Typically longer series of data are used in stock market research; however, this study is constrained to take only four and a half years of daily data as Islamic stock index in Bangladesh launched only just in January 2014. The results from ARDL bounds testing and error correction modeling show that both the markets are interlinked in the short-run and long-run. Since two markets move together in the long and short-run, one can predict its future price using any of the index prices. Univariate GARCH(1,1) model finds evidence of volatility clustering in both index returns which have a tendency to last a long time. The results of the EGARCH(1,1) model reveal that both markets are more sensitive to the bad news than with good news. Employing a bivariate GARCH-BEKK model, we find the existence of significant volatility transmission from conventional to Islamic stock market in Bangladesh. Results of GARCH-CCC framework show the evidence of strong direct interconnections between the markets. Finally, we test the presence of time-varying correlation between markets applying the GARCH-DCC model, and the results reveal that correlations are not only conditional but also significantly time-varying. The result also shows that the correlation process is mean reverting. Therefore, we conclude that conventional and Islamic stock markets in Bangladesh do not offer any diversification benefits to investors having both indices in their portfolios. Hence, faith-based investors and portfolio managers should add in other categories of assets in their portfolios to mitigate risk.

Suggested Citation

  • Hasan, Md Abu, 2019. "Co-Movement and Volatility Transmission between Islamic and Conventional Equity Index in Bangladesh," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 26, pages 43-71.
  • Handle: RePEc:ris:isecst:0176
    as

    Download full text from publisher

    File URL: http://www.irti.org/English/Research/Documents/IES/244.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Abbas, Qaisar & Khan, Sabeen & Shah, Syed Zulfiqar Ali, 2013. "Volatility transmission in regional Asian stock markets," Emerging Markets Review, Elsevier, vol. 16(C), pages 66-77.
    4. Dewandaru, Ginanjar & Rizvi, Syed Aun R. & Masih, Rumi & Masih, Mansur & Alhabshi, Syed Othman, 2014. "Stock market co-movements: Islamic versus conventional equity indices with multi-timescales analysis," Economic Systems, Elsevier, vol. 38(4), pages 553-571.
    5. repec:cii:cepiei:2014-q1-137-5 is not listed on IDEAS
    6. Richard D. F. Harris & Anirut Pisedtasalasai, 2006. "Return and Volatility Spillovers Between Large and Small Stocks in the UK," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1556-1571, November.
    7. Buerhan Saiti & Obiyathulla I. Bacha & Mansur Masih, 2014. "The diversification benefits from Islamic investment during the financial turmoil: The case for the US-based equity investors," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 14(4), pages 196-211, December.
    8. Majdoub, Jihed & Mansour, Walid & Jouini, Jamel, 2016. "Market integration between conventional and Islamic stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 436-457.
    9. Fathi Abid & Pui Lam Leung & Mourad Mroua & Wing Keung Wong, 2014. "International Diversification Versus Domestic Diversification: Mean-Variance Portfolio Optimization and Stochastic Dominance Approaches," JRFM, MDPI, vol. 7(2), pages 1-22, May.
    10. Bekaert, Geert & Harvey, Campbell R., 1997. "Emerging equity market volatility," Journal of Financial Economics, Elsevier, vol. 43(1), pages 29-77, January.
    11. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    12. Ahmet Sensoy, 2016. "Systematic Risk in Conventional and Islamic Equity Markets," International Review of Finance, International Review of Finance Ltd., vol. 16(3), pages 457-466, September.
    13. Larry R. Gorman & Bjorn N. Jorgensen, 2002. "Domestic versus International Portfolio Selection: A Statistical Examination of the Home Bias," Multinational Finance Journal, Multinational Finance Journal, vol. 6(3-4), pages 131-166, September.
    14. French, Kenneth R & Poterba, James M, 1991. "Investor Diversification and International Equity Markets," American Economic Review, American Economic Association, vol. 81(2), pages 222-226, May.
    15. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," The Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    16. Tesar, Linda L. & Werner, Ingrid M., 1995. "Home bias and high turnover," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 467-492, August.
    17. Miron, Dumitru & Tudor, Cristiana, 2010. "Asymmetric Conditional Volatility Models: Empirical Estimation and Comparison of Forecasting Accuracy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), September.
    18. Fredj Jawadi & Nabila Jawadi & Waël Louhichi, 2014. "Conventional and Islamic stock price performance: An empirical investigation," International Economics, CEPII research center, issue 137, pages 73-87.
    19. 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.
    20. Rizvi , Syed Aun R & Arshad , Shaista, 2014. "An Empirical Study of Islamic Equity as a Better Alternative during Crisis Using Multivariate GARCH DCC," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 22, pages 159-184.
    21. Hamao, Yasushi & Masulis, Ronald W & Ng, Victor, 1990. "Correlations in Price Changes and Volatility across International Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 281-307.
    22. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    23. Richard D. F. Harris & Anirut Pisedtasalasai, 2006. "Return and Volatility Spillovers Between Large and Small Stocks in the UK," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1556-1571, November.
    24. Y. Angela Liu & Ming-Shiun Pan, 1997. "Mean and Volatility Spillover Effects in the U.S. and Pacific–Basin Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 1(1), pages 47-62, March.
    25. Hassan Mohammadi & Yuting Tan, 2015. "Return and Volatility Spillovers across Equity Markets in Mainland China, Hong Kong and the United States," Econometrics, MDPI, vol. 3(2), pages 1-18, April.
    26. Slim Mseddi & Noureddine Benlagha, 2017. "An Analysis of Spillovers Between Islamic and Conventional Stock Bank Returns: Evidence from the GCC Countries," Multinational Finance Journal, Multinational Finance Journal, vol. 21(2), pages 91-132, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sherif, Mohamed, 2020. "The impact of Coronavirus (COVID-19) outbreak on faith-based investments: An original analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).

    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. Usman M. Umer, Metin Coskun, Kasim Kiraci, 2018. "Time-varying Return and Volatility Spillover among EAGLEs Stock Markets: A Multivariate GARCH Analysis," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 3(1), pages 23-42, March.
    2. Muhammad Anas & Ghulam Mujtaba & Sadaf Nayyar & Saira Ashfaq, 2020. "Time-Frequency Based Dynamics of Decoupling or Integration between Islamic and Conventional Equity Markets," JRFM, MDPI, vol. 13(7), pages 1-27, July.
    3. Muhammad Niaz Khan & Suzanne G. M. Fifield & Nongnuch Tantisantiwong & David M. Power, 2022. "Changes in co-movement and risk transmission between South Asian stock markets amidst the development of regional co-operation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(1), pages 87-117, March.
    4. Helena Chuliá & Hipòlit Torró, 2008. "The economic value of volatility transmission between the stock and bond markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(11), pages 1066-1094, November.
    5. Ewing, Bradley T. & Malik, Farooq & Ozfidan, Ozkan, 2002. "Volatility transmission in the oil and natural gas markets," Energy Economics, Elsevier, vol. 24(6), pages 525-538, November.
    6. Fowowe, Babajide & Shuaibu, Mohammed, 2016. "Dynamic spillovers between Nigerian, South African and international equity markets," International Economics, Elsevier, vol. 148(C), pages 59-80.
    7. Martin Hoesli & Kustrim Reka, 2013. "Volatility Spillovers, Comovements and Contagion in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 1-35, July.
    8. Ghaemi Asl, Mahdi & Rashidi, Muhammad Mahdi & Tavakkoli, Hamid Raza & Rezgui, Hichem, 2024. "Does Islamic investing modify portfolio performance? Time-varying optimization strategies for conventional and Shariah energy-ESG-utilities portfolio," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 37-57.
    9. Koulakiotis, Athanasios & Babalos, Vassilios & Papasyriopoulos, Nicholas, 2016. "Financial crisis, liquidity and dynamic linkages between large and small stocks: Evidence from the Athens Stock Exchange," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 46-62.
    10. Ziadat, Salem Adel & Herbst, Patrick & McMillan, David G., 2020. "Inter- and intra-regional stock market relations for the GCC bloc," Research in International Business and Finance, Elsevier, vol. 54(C).
    11. Milunovich, George & Thorp, Susan, 2006. "Valuing volatility spillovers," Global Finance Journal, Elsevier, vol. 17(1), pages 1-22, September.
    12. Ahmed El Ghini & Youssef Saidi, 2015. "Financial market contagion during the global financial crisis: evidence from the Moroccan stock market," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 4(1), pages 78-95.
    13. Mensi, Walid & Hammoudeh, Shawkat & Al-Jarrah, Idries Mohammad Wanas & Sensoy, Ahmet & Kang, Sang Hoon, 2017. "Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications," Energy Economics, Elsevier, vol. 67(C), pages 454-475.
    14. Tom A. FEARNLEY, 2002. "Estimation of an International Capital Asset Pricing Model with Stocks and Government Bonds," FAME Research Paper Series rp95, International Center for Financial Asset Management and Engineering.
    15. Haddad, Hedi Ben & Mezghani, Imed & Al Dohaiman, Mohammed, 2020. "Common shocks, common transmission mechanisms and time-varying connectedness among Dow Jones Islamic stock market indices and global risk factors," Economic Systems, Elsevier, vol. 44(2).
    16. Delle Foglie, Andrea & Panetta, Ida Claudia, 2020. "Islamic stock market versus conventional: Are islamic investing a ‘Safe Haven’ for investors? A systematic literature review," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
    17. Elsayed, Ahmed H. & Ahmed, Habib & Husam Helmi, Mohamad, 2023. "Determinants of financial stability and risk transmission in dual financial system: Evidence from the COVID pandemic," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    18. Alotaibi, Abdullah R. & Mishra, Anil V., 2015. "Global and regional volatility spillovers to GCC stock markets," Economic Modelling, Elsevier, vol. 45(C), pages 38-49.
    19. Ahmed, Abdullahi D. & Huo, Rui, 2019. "Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence, information transmission and market co-movement," Economic Modelling, Elsevier, vol. 79(C), pages 28-46.
    20. Jin, Xiaoye, 2015. "Volatility transmission and volatility impulse response functions among the Greater China stock markets," Journal of Asian Economics, Elsevier, vol. 39(C), pages 43-58.

    More about this item

    Keywords

    Islamic and Conventional Equity Market; Cointegration; Volatility Spillover; GARCH-BEKK Model; GARCH-DCC Model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • 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:ris:isecst:0176. 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: IRTI Staff or the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/irisbsa.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.