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Do structural shocks in the crude oil market affect biofuel prices?

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  • Aktham I. Maghyereh
  • Osama D. Sweidan

Abstract

Literature shows that the crude oil market has a vital link with subcomponents products, i.e., gasoline and diesel, and alternative energy markets, i.e., natural gas and biofuels. The biofuel industry is a promising solution for many problems such as global warming and high oil prices. Therefore, our paper explores the effect of structural crude oil market shocks on the real price of biofuel. We use structural vector autoregression (SVAR) model as proposed by Kilian (2009) over the period from March 1995 to June 2019. Our main finding reveals that long-run fluctuations in the real prices of biofuel are attributed mainly to global demand shocks and oil-market specific shocks, whereas oil supply shocks have an insignificant role. Our results also indicate that the three shocks combined can justify 68 percent of the variation in the real prices of biofuel, whereas the aggregate demand shocks are the most significant contributor because it justifies 35 percent, and oil-specific demand shocks explain 28 percent. The policy implication of our paper is that the stability of the biofuel market comes from the demand side, not the supply side. Thus, energy demand management is a critical tool to stabilize the biofuel market.

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  • Aktham I. Maghyereh & Osama D. Sweidan, 2020. "Do structural shocks in the crude oil market affect biofuel prices?," International Economics, CEPII research center, issue 164, pages 183-193.
    • Handle: RePEc:cii:cepiie:2020-q4-164-11
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    More about this item

    Keywords

    Oil market shocks; Biofuels; SVAR models;
    All these keywords.

    JEL classification:

    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • 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

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