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An Analysis of the Global Oil Market Using SVARMA Models

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  • Mala Raghavan

Abstract

The paper analyses the importance of supply versus demand shocks on the global oil market from 1974 to 2017, using a parsimonious structural vector autoregressive moving average (SVARMA) model. The superior out-of-sample forecasting performance of the reduced form VARMA compared to VAR alternatives advocates the suitability of this framework. We specifically account for the changes in the oil market over three distinctive sub-periods - pre moderation, great moderation and post moderation periods, to provide a means of identifying the changing nature of shock transmission mechanism across times. The findings shed some light on the effects of supply versus demand related oil shocks under different economic environment. Oil supply shocks explain large fraction of the movements in the global oil market in the pre and post moderation periods, i.e. during the slower economic growth periods. The importance of global activity shock on oil price movements is obvious during the 2003-2008 boom period. The oil specific shock has an interesting transmission path on the global economic activity, where the global activity responded positively and negatively during the global economic expansion and contraction respectively, emphasising the precautionary nature of the shock.

Suggested Citation

  • Mala Raghavan, 2019. "An Analysis of the Global Oil Market Using SVARMA Models," CAMA Working Papers 2019-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2019-25
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    File URL: https://crawford.anu.edu.au/sites/default/files/2025-01/25_2019_raghavan_0.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. Al Jabri, Salwa & Raghavan, Mala & Vespignani, Joaquin, 2022. "Oil prices and fiscal policy in an oil-exporter country: Empirical evidence from Oman," Energy Economics, Elsevier, vol. 111(C).
    3. Basel Maraqa & Murad Bein, 2020. "Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil," Sustainability, MDPI, vol. 12(9), pages 1-14, May.

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    Keywords

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    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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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