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Oil commodity returns and macroeconomic factors: A time-varying approach

Author

Listed:
  • Christophe Schalck
  • Régis Chenavaz

    (LTCI - Laboratoire Traitement et Communication de l'Information - Télécom ParisTech - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique, GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper analyses the dynamic influence of macroeconomic factors on oil commodity returns (crude oil and heating oil) shown in monthly data over the period of 1990-2013. Using a time-varying parameter model via the Kalman filter, we find that macroeconomic factors are relevant for explaining oil commodity returns. We find that multilateral exchange rates have a negative effect on commodity returns. We confirm the existence of a strong linkage between energy and non-energy commodities. More importantly, we find shifts in global demand and SP500 effects that are not identified through the constant parameter model. These variables have had a progressively positive effect on oil commodity returns, especially since 2008.

Suggested Citation

  • Christophe Schalck & Régis Chenavaz, 2015. "Oil commodity returns and macroeconomic factors: A time-varying approach," Post-Print hal-01457334, HAL.
    • Handle: RePEc:hal:journl:hal-01457334
    DOI: 10.1016/j.ribaf.2014.05.002
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    Cited by:

    1. M. Thenmozhi & Shipra Maurya, 2020. "Crude Oil Volatility Transmission Across Food Commodity Markets: A Multivariate BEKK-GARCH Approach," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 20(2), pages 131-164, August.
    2. Huchet, Nicolas & Fam, Papa Gueye, 2016. "The role of speculation in international futures markets on commodity prices," Research in International Business and Finance, Elsevier, vol. 37(C), pages 49-65.
    3. Seiler, Volker, 2021. "China-to-FOB price transmission in the rare earth elements market and the end of Chinese export restrictions," Energy Economics, Elsevier, vol. 102(C).
    4. Mohammad Sharik Essa & Evangelos Giouvris, 2020. "Oil Price, Oil Price Implied Volatility (OVX) and Illiquidity Premiums in the US: (A)symmetry and the Impact of Macroeconomic Factors," JRFM, MDPI, vol. 13(4), pages 1-40, April.
    5. Yu, Xing & Li, Yanyan & Zhao, Qian, 2024. "Research on optimization strategy of futures hedging dependent on market state," Applied Energy, Elsevier, vol. 373(C).
    6. Brice V. Dupoyet & Corey A. Shank, 2018. "Oil prices implied volatility or direction: Which matters more to financial markets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 275-295, August.
    7. Aboura, Sofiane & Chevallier, Julien, 2017. "Oil vs. gasoline: The dark side of volatility and taxation," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 976-989.
    8. Han, Xu & Laing, Elaine & Lucey, Brian M. & Vigne, Samuel, 2023. "Corporate commodity exposure: A multi-country longitudinal study," Journal of Commodity Markets, Elsevier, vol. 30(C).

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    Keywords

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    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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