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Managing exposure to volatile oil prices: Evidence from U.S. sectoral and industry-level data

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  • Selmi, Refk
  • Bouoiyour, Jamal
  • Miftah, Amal
  • Wohar, Mark E.

Abstract

This study compares the hedging abilities of sectoral and industry-level U.S. stock market for an investor who wants to protect a portfolio from oil price volatility dynamics. To this end, we employ a relatively new econometric model which is able to capture asymmetric volatility clustering, volatility persistence and regime shifts exhibited in the underlying data. We extend the volatility spillovers investigation by estimating the optimal weights for a minimum risk portfolio and computing the hedge ratio, in an attempt to evaluate optimal portfolio’ allocations and the hedging strategies’ effectiveness. Our findings provide crucial information to agents with respect to the sectors and industries of the stock market in which they need to invest during high volatility regimes in an effort to minimise risk and maximise returns. The protecting capabilities of U.S. stock sectors and industries against oil price volatility seem to be sensitive to a myriad of factors, including the various types of news (oil price changes due to normal or abnormal innovations), and the different volatility regimes (low, normal or high). Overall, this research implication can be relevant for portfolio managers and individual investors who are willing to invest in U.S. stocks.

Suggested Citation

  • Selmi, Refk & Bouoiyour, Jamal & Miftah, Amal & Wohar, Mark E., 2021. "Managing exposure to volatile oil prices: Evidence from U.S. sectoral and industry-level data," Resources Policy, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721001574
    DOI: 10.1016/j.resourpol.2021.102143
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    More about this item

    Keywords

    Oil price volatility; Sectoral analysis; Industry-level analysis; Hedges; The United States;
    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
    • G1 - Financial Economics - - General Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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