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Modelling Oil Price Volatility with the Beta-Skew-t-EGARCH Framework

Author

Listed:
  • Afees A. Salisu

    (Department of Economics, Federal University of Agriculture Abeokuta, Nigeria)

Abstract

This paper employs the Beta-Skew-t-EGARCH framework proposed by Harvey and Succarat (2014) to model oil price volatility. It utilizes two prominent oil proxies and also accounts for structural break to gauge the robustness of results. In all, it finds that the approach seems more suitable than the standard symmetric and asymmetric GARCH models if the oil price return exhibits fat tails, leverage and skewness.

Suggested Citation

  • Afees A. Salisu, 2016. "Modelling Oil Price Volatility with the Beta-Skew-t-EGARCH Framework," Economics Bulletin, AccessEcon, vol. 36(3), pages 1315-1324.
    • Handle: RePEc:ebl:ecbull:eb-15-00762
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2016/Volume36/EB-16-V36-I3-P130.pdf
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    References listed on IDEAS

    as
    1. Salisu, Afees A. & Fasanya, Ismail O., 2013. "Modelling oil price volatility with structural breaks," Energy Policy, Elsevier, vol. 52(C), pages 554-562.
    2. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    3. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, November.
    4. Michael McAleer & Les Oxley, 2002. "The Econometrics of Financial Time Series," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 237-243, July.
    5. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    6. repec:bla:jecsur:v:16:y:2002:i:3:p:245-69 is not listed on IDEAS
    7. W. K. Li & Shiqing Ling & Michael McAleer, 2002. "Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 245-269, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Mohamed Chikhi & Claude Diebolt & Tapas Mishra, 2019. "Measuring Success: Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers 11-19, Association Française de Cliométrie (AFC).
    2. François Benhmad & Mohamed Chikhi, 2025. "The Asymmetric Effect of COVID-19 Pandemic on the US Market Risk Premium: Evidence from AEGAS-M Model," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1691-1713, August.
    3. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers of BETA 2019-43, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model," Working Papers 07-19, Association Française de Cliométrie (AFC).
    5. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    6. Bala A. Dahiru & Pam W. Jim & Kalu N. Nwonyuku, 2017. "Equity markets volatility dynamics in developed and newly emerging economies: EGARCH-with-skewed-t density approach," Economics Bulletin, AccessEcon, vol. 37(4), pages 2394-2412.

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    Keywords

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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