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Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets

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  • Okhrin, Yarema
  • Uddin, Gazi Salah
  • Yahya, Muhammad

Abstract

In light of the COVID-19 outbreak and the recent Russian war in Ukraine, this paper explores the asymmetric and nonlinear interconnectedness between financial and commodity markets using high-frequency intraday data. We employ cross-quantilograms (CQ), paired vine-based copulas, and copula vine-based regression analysis to examine the heterogeneous and asymmetrical connectedness among various assets. Our study presents several key findings: (1) connectedness among assets increases sharply during the COVID-19 pandemic and intensifies with the Russia–Ukraine war; (2) stronger tail dependence is observed in the lower tail, indicating asymmetric connectedness among the assets; (3) the S&P 500 and natural gas have a predictive influence on the crude oil market; and (4) increased uncertainty and volatility in global markets due to these events impact the interconnectedness of the assets in our study, particularly the dependence between crude oil and the other assets in the sample. These results have important implications for governmental agencies, policymakers, investors, and portfolio managers, emphasizing the need for a non-linear framework to capture heterogeneous and asymmetric connectedness dynamics under extreme market conditions.

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  • Okhrin, Yarema & Uddin, Gazi Salah & Yahya, Muhammad, 2023. "Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets," Energy Economics, Elsevier, vol. 125(C).
    • Handle: RePEc:eee:eneeco:v:125:y:2023:i:c:s0140988323003511
    DOI: 10.1016/j.eneco.2023.106853
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    4. Priya, Pragati & Pal, Debdatta, 2024. "Does crude oil price volatility respond asymmetrically to financial shocks?," Resources Policy, Elsevier, vol. 92(C).

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    More about this item

    Keywords

    Crude oil; Vine copula; COVID-19; Russia–Ukraine; Complex dependence;
    All these keywords.

    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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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