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Modelling crude oil-petroleum products’ price nexus using dynamic conditional correlation GARCH models

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  • Yaya, OlaOluwa
  • Ogbonna, Ahamuefula

Abstract

Modelling volatility in returns has continued to gain popularity with the evolution of the GARCH-type models under different frameworks. This study therefore examined the different variants of the multivariate GARCH model with focus on those that incorporated asymmetry and constant or dynamic conditional correlations. These variants were used in modelling the crude oil-petroleum products’ (gasoline, heating oil, kerosene, propane and diesel) price nexuses. Comparatively, the DCC-VAR-AMGARCH model fitted the return series more appropriately in four out of the five investigated nexuses, while the DCC-AMGARCH variant fitted the return series in just one nexus. With the exception of propane own market spillover, the overall volatility persistence of spillovers from own market and other markets for the nexuses of crude oil and the other four petroleum products (gasoline, heating oil, diesel and kerosene) were mean reverting. The study also adopted two hedging strategies, for each of the five crude oil-petroleum product nexuses, to ascertain plausible portfolio investment options. The empirical evidence on the different portfolio investments that were herein provided are especially useful for stakeholders/investors desiring to channel their resources into less risky investment portfolios.

Suggested Citation

  • Yaya, OlaOluwa & Ogbonna, Ahamuefula, 2018. "Modelling crude oil-petroleum products’ price nexus using dynamic conditional correlation GARCH models," MPRA Paper 91227, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:91227
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    References listed on IDEAS

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    1. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    2. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," "Marco Fanno" Working Papers 0124, Dipartimento di Scienze Economiche "Marco Fanno".
    6. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.
    7. Walid Mensi & Shawkat Hammoude & Seong-Min Yoon, 2014. "Structural Breaks, Dynamic Correlations, Volatility Transmission, and Hedging Strategies for International Petroleum Prices and U.S. Dollar Exchange Rate," Working Papers 884, Economic Research Forum, revised Dec 2014.
    8. 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.
    9. 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.
    10. Matteo Manera, Marcella Nicolini, and Ilaria Vignati, 2013. "Financial Speculation in Energy and Agriculture Futures Markets: A Multivariate GARCH Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    11. 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.
    12. Severin Borenstein & A. Colin Cameron & Richard Gilbert, 1997. "Do Gasoline Prices Respond Asymmetrically to Crude Oil Price Changes?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(1), pages 305-339.
    13. Michael McAleer & Suhejla Hoti & Felix Chan, 2009. "Structure and Asymptotic Theory for Multivariate Asymmetric Conditional Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 28(5), pages 422-440.
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    More about this item

    Keywords

    Asymmetry; Hedging Strategy; Multivariate; Portfolio Management; West Texas Intermediate;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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