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Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach

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  • Kumar, Satish
  • Tiwari, Aviral
  • Raheem, Ibrahim
  • Hille, Erik

Abstract

We examine the energy-food nexus using the dependence-switching copula model. Specifically, we look at the dependence for four distinct market states, such as, increasing oil–increasing commodity, declining oil–declining commodity, increasing oil–declining commodity, as well as declining oil–increasing commodity markets. Our results support the argument that the crash of oil markets and agricultural commodities happen at the same time, especially during crisis period. However, the same is not true during times of normal economic conditions, implying that investors cannot make excess profits in both agricultural and oil markets at once. Furthermore, our analysis suggests that the return chasing effect dominates for all commodities on maximum occasions. The CoVaR and ∆CoVaR results indicate important risk spillover from oil to agricultural markets, especially around the financial crisis.

Suggested Citation

  • Kumar, Satish & Tiwari, Aviral & Raheem, Ibrahim & Hille, Erik, 2021. "Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach," MPRA Paper 106684, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:106684
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    Cited by:

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    2. Ben Ameur, Hachmi & Ftiti, Zied & Louhichi, Waël, 2021. "Intraday spillover between commodity markets," Resources Policy, Elsevier, vol. 74(C).
    3. Ling, Aifan & Li, Jinlong & Zhang, Yugui, 2023. "Can firms with higher ESG ratings bear higher bank systemic tail risk spillover?—Evidence from Chinese A-share market," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    4. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    5. Li, Songsong & Zhang, Weiqian & Zhang, Wei, 2023. "Dynamic time-frequency connectedness and risk spillover between geopolitical risks and natural resources," Resources Policy, Elsevier, vol. 82(C).
    6. Naeem, Muhammad Abubakr & Hasan, Mudassar & Arif, Muhammad & Suleman, Muhammad Tahir & Kang, Sang Hoon, 2022. "Oil and gold as a hedge and safe-haven for metals and agricultural commodities with portfolio implications," Energy Economics, Elsevier, vol. 105(C).
    7. Wei-Xing Zhou & Yun-Shi Dai & Kiet Tuan Duong & Peng-Fei Dai, 2023. "The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots," Papers 2310.16850, arXiv.org.
    8. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    9. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    10. Bossman, Ahmed & Gubareva, Mariya & Teplova, Tamara, 2023. "Asymmetric effects of market uncertainties on agricultural commodities," Energy Economics, Elsevier, vol. 127(PB).
    11. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    12. Hung, Ngo Thai, 2021. "Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Resources Policy, Elsevier, vol. 73(C).
    13. Aye, Goodness C. & Odhiambo, Nicholas M., 2021. "Oil prices and agricultural growth in South Africa: A threshold analysis," Resources Policy, Elsevier, vol. 73(C).
    14. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2022. "Spillover effects between commodity and stock markets: A SDSES approach," Resources Policy, Elsevier, vol. 79(C).
    15. Cui, Jinxin & Maghyereh, Aktham, 2023. "Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," International Review of Financial Analysis, Elsevier, vol. 86(C).
    16. Liya Hau & Huiming Zhu & Muhammad Shahbaz & Ke Huang, 2023. "Quantile Dependence between Crude Oil and China’s Biofuel Feedstock Commodity Market," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    17. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2023. "Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets," Papers 2303.11030, arXiv.org.

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    More about this item

    Keywords

    Agricultural commodities; Oil; CoVaR; Dependence-switching copula; Tail dependence.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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