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Component structure for nonstationary time series: Application to benchmark oil prices


  • Bhar, Ramaprasad
  • Hammoudeh, Shawkat
  • Thompson, Mark A.


The oil market is characterized by several hundreds of different grades of crude extracted from various locations on the planet, but prices of those grades are structured with reference to only a handful of benchmark varieties. In this context, the ability to predict near term benchmark oil prices takes on special importance. In this paper, we explore an approach to model the benchmark oil price behaviors using a structure of permanent and transitory components. This initial attempt seems very encouraging at least with respect to one-week ahead forecast and deserves further investigation. In contrast to the equities, the weekly oil permanent components do not seem to be explainable by fundamental factors. However, the returns of the short-run, transitory oil components or cycles, which differ in terms of their degrees of persistence, are mostly affected by contagion spillovers and not by the fundamentals. Their volatilities vary slightly in terms of their sensitivity to major geopolitical events. The overall findings underscore the importance of benefiting more from spillover-catching strategies over diversification ones in the short-run.

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  • Bhar, Ramaprasad & Hammoudeh, Shawkat & Thompson, Mark A., 2008. "Component structure for nonstationary time series: Application to benchmark oil prices," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 971-983, December.
  • Handle: RePEc:eee:finana:v:17:y:2008:i:5:p:971-983

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    References listed on IDEAS

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    Cited by:

    1. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
    2. Hammoudeh, Shawkat & Araújo Santos, Paulo & Al-Hassan, Abdullah, 2013. "Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 318-334.
    3. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Wang, Lijun, 2017. "Evolution of world crude oil market integration and diversification: A wavelet-based complex network perspective," Applied Energy, Elsevier, vol. 185(P2), pages 1788-1798.
    4. Shawkat M.Hammoudeh & Yuan Yuan & Michael McAleer, 2010. "Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies," Working Papers in Economics 10/33, University of Canterbury, Department of Economics and Finance.
    5. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.
    6. Shawkat M. Hammoudeh & Yuan Yuan & Michael McAleer, 2009. "Exchange Rate and Industrial Commodity Volatility Transmissions and Hedging Strategies," CARF F-Series CARF-F-172, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    7. Jia, Xiaoliang & An, Haizhong & Fang, Wei & Sun, Xiaoqi & Huang, Xuan, 2015. "How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective," Energy Economics, Elsevier, vol. 49(C), pages 588-598.
    8. Dayanandan, Ajit & Donker, Han, 2011. "Oil prices and accounting profits of oil and gas companies," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 252-257.
    9. Kisswani, Khalid M. & Nusair, Salah A., 2013. "Non-linearities in the dynamics of oil prices," Energy Economics, Elsevier, vol. 36(C), pages 341-353.
    10. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Gao, Xiangyun, 2016. "Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 331-344.


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