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US partisan conflict uncertainty and oil prices

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  • Apergis, Nicholas
  • Hayat, Tasawar
  • Saeed, Tareq

Abstract

This empirical study significantly contributes in building emerging literature by investigating the impact of US partisan conflict uncertainty on international oil prices. It models oil prices through non-linear Quantile Autoregressive Distributed Lag (QARDL) methods in order to consider potential (non-linear) asymmetric effects of partisan political uncertainty on oil prices. The empirical results clearly document the asymmetric (non-linear) impact of partisan conflict uncertainty on international oil prices, which has been in contrast to the linear case. The findings also expose that the transmission mechanism of partisan political uncertainty to oil prices is validated through the economic growth channel. The empirical findings contribute to existing research by assisting investors in the oil industry with risk identification, analysis, and mitigation. The results can assist in discovering the links between US political risk and oil markets, determining an important element of political risk factors facing investors who want to participate in the oil industry.

Suggested Citation

  • Apergis, Nicholas & Hayat, Tasawar & Saeed, Tareq, 2021. "US partisan conflict uncertainty and oil prices," Energy Policy, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:enepol:v:150:y:2021:i:c:s0301421520308296
    DOI: 10.1016/j.enpol.2020.112118
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    Cited by:

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    2. Valérie Mignon & Jamel Saadaoui, 2022. "Asymmetries in the oil market: Accounting for the growing role of China through quantile regressions," Working Papers of BETA 2022-36, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    3. Beyer, Deborah B. & Fan, Zaifeng S., 2023. "The calming effects of conflict: The impact of partisan conflict on market volatility," International Review of Financial Analysis, Elsevier, vol. 85(C).
    4. Jiang, Qisheng & Cheng, Sheng, 2021. "How the fiscal and monetary policy uncertainty of China respond to global oil price volatility: A multi-regime-on-scale approach," Resources Policy, Elsevier, vol. 72(C).
    5. Qadan, Mahmoud & Idilbi, Yasmeen, 2022. "Presidential honeymoons, political cycles and the commodity market," Resources Policy, Elsevier, vol. 77(C).
    6. Rufei Zhang & Haizhen Zhang & Qingzhu Fan & Wang Gao & Xue Luo & Shixiong Yang, 2022. "Partisan Conflict, National Security Policy Uncertainty and Tourism," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    7. Narasingha Das & Partha Gangopadhyay, 2023. "Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    8. Sheng Cheng & Wei Liu & Qisheng Jiang & Yan Cao, 2023. "Multi–Scale Risk Connectedness Between Economic Policy Uncertainty of China and Global Oil Prices in Time–Frequency Domains," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1593-1616, April.

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    More about this item

    Keywords

    US partisan Conflict; Oil prices; Quantile ARDL;
    All these keywords.

    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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