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Oil Returns and Volatility: The Role of Mergers and Acquisitions

Author

Listed:
  • Martijn Bos

    (Tilburg School of Economics and Management (TISEM), Tilburg University, Tilburg, the Netherlands)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, USA)

  • Rangan Gupta

    (University of Pretoria, Pretoria, South Africa and IPAG Business School, Paris, France)

  • Aviral Kumar Tiwari

    (Center for Energy and Sustainable Development (CESD), Montpellier Business School, Montpellier, France)

Abstract

This paper provides a novel perspective to the oil-stock market nexus by examining the predictive ability of mergers and acquisitions (M&A) over West Texas Intermediate (WTI) oil returns and volatility using a nonparametric quantile-based methodology. Our findings suggest that M&A activity carries significant predictive power over oil return and volatility, while predictability displays remarkably distinct patterns across various quantiles representing normal, bull and bear market states. We also observe that M&A activity by oil firms, i.e. both the acquiring and target firms considered active in the oil and gas (O&G) industry, generally carries greater predictive power over both oil returns and volatility compared to M&A activity by non-oil acquirers, i.e. acquirers that have entered the O&G industry by buying an oil company. Our findings imply that M&A activity in the O&G industry carries valuable fundamental information regarding future expectations on oil price dynamics and should be taken into account in forecasting exercises.

Suggested Citation

  • Martijn Bos & Riza Demirer & Rangan Gupta & Aviral Kumar Tiwari, 2017. "Oil Returns and Volatility: The Role of Mergers and Acquisitions," Working Papers 201775, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201775
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    2. Qingbang Mu & Wanxiao Zhang & Wenxiu Hu, 2023. "Enterprise Transformation and Innovation: A Study of Performance Compensation from the Perspective of Information Asymmetry," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    3. Balcilar, Mehmet & Gupta, Rangan & Wang, Shixuan & Wohar, Mark E., 2020. "Oil price uncertainty and movements in the US government bond risk premia," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M., 2020. "The (in)efficiency of NYMEX energy futures: A multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    5. Şevkat Özgür & Franz Wirl, 2020. "Cross-Border Mergers and Acquisitions in the Oil and Gas Industry: An Overview," Energies, MDPI, vol. 13(21), pages 1-25, October.
    6. Guo, Yue & Yang, Yu & Wang, Chang, 2021. "Global energy networks: Geographies of mergers and acquisitions of worldwide oil companies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).

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    More about this item

    Keywords

    Oil Returns and Volatility; Mergers and Acquisitions; Oil and Gas Industry; Nonparametric Quantile Causality;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices

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