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Volatility Transmissions between Oil Prices and Emerging Market Sectors: Implications for Portfolio Management and Hedging Strategies

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  • Sercan Demiralay

    (Department of International Finance, Faculty of Commercial Sciences, Yeditepe University, 34755, Istanbul, Turkey)

  • Hatice Gaye Gencer

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Yeditepe University, 34755, Istanbul, Turkey)

Abstract

This paper investigates the mechanisms of return and volatility transmissions between oil prices and five emerging market sector returns. For the empirical method, we utilize a recent and novel technique: Vector Autoregressive-Asymmetric GARCH (VAR-AGARCH) model. We find some significant cross shock and volatility linkages between oil prices and the sectors. However, our results manifest that the sector indices are not affected equally or simultaneously by movements in oil prices. Additionally, we compute the optimal holding weights and hedge ratios for the two-asset portfolio consisting of oil and each sector index. Our empirical findings have potential implications for investors and portfolio managers.

Suggested Citation

  • Sercan Demiralay & Hatice Gaye Gencer, 2014. "Volatility Transmissions between Oil Prices and Emerging Market Sectors: Implications for Portfolio Management and Hedging Strategies," International Journal of Energy Economics and Policy, Econjournals, vol. 4(3), pages 442-447.
    • Handle: RePEc:eco:journ2:2014-03-13
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    Cited by:

    1. Belhassine, Olfa, 2020. "Volatility spillovers and hedging effectiveness between the oil market and Eurozone sectors: A tale of two crises," Research in International Business and Finance, Elsevier, vol. 53(C).
    2. Tanattrin Bunnag, 2015. "Hedging Petroleum Futures with Multivariate GARCH Models," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 105-120.
    3. Gao, Zhenbin & Zhang, Jie, 2023. "The fluctuation correlation between investor sentiment and stock index using VMD-LSTM: Evidence from China stock market," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    4. Mohamed Osman, 2015. "Dynamic Asymmetries in the Electric Consumption of the GCC Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 461-467.
    5. Emenike Kalu O., 2017. "The Interrelationship between Crude Oil Price Volatility and Money Market Rate Volatility in a Developing, Oil-Producing Economy," Eastern European Business and Economics Journal, Eastern European Business and Economics Studies Centre, vol. 3(1), pages 28-47.

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    More about this item

    Keywords

    Emerging sector indices; oil prices; volatility transmission; optimal weights; hedge ratios;
    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
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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