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Which Commodity Sectors Effectively Hedge Emerging Eastern European Stock Markets? Evidence from MGARCH Models

Author

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  • Amel Melki

    (CODECI Laboratory, Department of Quantitative Methods, Faculty of Economics and Management (FSEG) of Sfax, University of Sfax, Sfax 3018, Tunisia)

  • Ahmed Ghorbel

    (CODECI Laboratory, Department of Quantitative Methods, Faculty of Economics and Management (FSEG) of Sfax, University of Sfax, Sfax 3018, Tunisia)

Abstract

This study aims at examining whether hedging emerging Eastern Europe stock markets with commodities sectors can help in reducing market risks and whether it has the same effectiveness among different sectors. As an attempt to achieve this goal, we opt for three types of MGARCH model. These are DCC, ADCC and GO-GARCH, which are used with each bivariate series to model dynamic conditional correlations, optimal hedge ratios and hedging effectiveness. Rolling window analysis is used for out-of-sample one-step-ahead forecasts from December 1994 to June 2022. The results have shown that the commodities sectors of industrial metals and energy represent the optimal hedging instruments for emerging Eastern Europe stock markets as they have the highest hedging effectiveness. Additionally, our empirical results have proved that hedge ratios estimated by the DCC and ADCC models are very similar, which is not the case for GO-GARCH, and that hedging effectiveness is preferably estimated by the ADCC model.

Suggested Citation

  • Amel Melki & Ahmed Ghorbel, 2023. "Which Commodity Sectors Effectively Hedge Emerging Eastern European Stock Markets? Evidence from MGARCH Models," Commodities, MDPI, vol. 2(3), pages 1-19, August.
  • Handle: RePEc:gam:jcommo:v:2:y:2023:i:3:p:16-279:d:1209981
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    References listed on IDEAS

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    1. Sang Hoon Kang & Ron McIver & Seong-Min Yoon, 2016. "Modeling Time-Varying Correlations in Volatility Between BRICS and Commodity Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(7), pages 1698-1723, July.
    2. Weide, R. van der, 2002. "Generalized Orthogonal GARCH. A Multivariate GARCH model," CeNDEF Working Papers 02-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. Mohamed Yousfi & Abderrazak Dhaoui & Houssam Bouzgarrou, 2021. "Risk Spillover during the COVID-19 Global Pandemic and Portfolio Management," JRFM, MDPI, vol. 14(5), pages 1-29, May.
    4. repec:dau:papers:123456789/14980 is not listed on IDEAS
    5. 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.
    6. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    7. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(4), pages 535-551, December.
    8. de Boyrie Maria E. & Pavlova Ivelina, 2018. "Equities and Commodities Comovements: Evidence from Emerging Markets," Global Economy Journal, De Gruyter, vol. 18(3), pages 1-14, September.
    9. Bessler, Wolfgang & Wolff, Dominik, 2015. "Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 1-20.
    10. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    11. Mourad Mroua & Hejer Bouattour, 2023. "Connectedness among various financial markets classes under Covid-19 pandemic and 2022 Russo-Ukrainian war: evidence from TVP-VAR approach," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 15(2), pages 140-163, February.
    12. Wajdi Hamma & Ahmed Ghorbel & Anis Jarboui, 2021. "Hedging Islamic and conventional stock markets with other financial assets: comparison between competing DCC models on hedging effectiveness," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 179-199, May.
    13. Roy van der Weide, 2002. "GO-GARCH: a multivariate generalized orthogonal GARCH model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 549-564.
    14. Fatma Khalifa & Abderrazak Dhaoui & Mohamed Sahbi Nakhli & Saad Bourouis & Saloua Benammou, 2023. "Do oil prices predict the dynamics of equity market? Fresh evidence from DCC, ADCC and Go-GARCH models," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 45(1), pages 66-85.
    15. Lu, Ran & Xu, Wen & Zeng, Hongjun & Zhou, Xiangjing, 2023. "Volatility connectedness among the Indian equity and major commodity markets under the COVID-19 scenario," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1465-1481.
    16. Simon A. Broda & Marc S. Paolella, 2009. "CHICAGO: A Fast and Accurate Method for Portfolio Risk Calculation," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 412-436, Fall.
    17. Ahmad, Wasim & Sadorsky, Perry & Sharma, Amit, 2018. "Optimal hedge ratios for clean energy equities," Economic Modelling, Elsevier, vol. 72(C), pages 278-295.
    18. Maria E. de Boyrie & Ivelina Pavlova, 2018. "Equities and Commodities Comovements: Evidence from Emerging Markets," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., vol. 18(3), pages 1-14, September.
    19. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    20. Belousova, Julia & Dorfleitner, Gregor, 2012. "On the diversification benefits of commodities from the perspective of euro investors," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2455-2472.
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