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Analyzing the time-frequency lead–lag relationship between oil and agricultural commodities

Author

Listed:
  • Aviral Kumar Tiwari

    (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Rabeh Khalfaoui

    (ICN Business School)

  • Sakiru Adebola Solarin
  • Muhammad Shahbaz

    (MRM - Montpellier Research in Management - UPVM - Université Paul-Valéry - Montpellier 3 - UPVD - Université de Perpignan Via Domitia - Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School - UM - Université de Montpellier)

Abstract

We analyze the time-frequency co-movement of and lead–lag relationship between price indices of oil and 21 agricultural commodities and attempt to identify the leader and follower among the considered price indices for the 1980M1–2017M5 period. The empirical analysis is conducted using four wavelet tools: wavelet coherency, phase-difference, multiple correlation and multiple cross-correlation. The first two tools are used to identify the time-frequency co-movement of and lead–lag relationship between price indices of oil and 21 agricultural commodities, and the third and fourth tools are used to identify the leader and follower among all series of price indices across different scales. Our results on wavelet coherency show a high degree of co-movement at a long-run horizon for the entire period between the price indices of oil and coal, cotton, fishmeal, maize, rice, rubber and wheat. Furthermore, the connection between these commodity markets and the oil market strengthened after 2000, indicating the importance of financial crisis phenomena and geopolitical turbulence. Additional findings show that short-run investors should invest in the beef and swine (pork) markets, as they have very little correlation with the oil markets. The results of multiple correlation and multiple cross-correlation analysis show that the coffee price was leader or follower across all time scales, except wavelet scale 16, where barely was a leader or a follower.
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Suggested Citation

  • Aviral Kumar Tiwari & Rabeh Khalfaoui & Sakiru Adebola Solarin & Muhammad Shahbaz, 2018. "Analyzing the time-frequency lead–lag relationship between oil and agricultural commodities," Post-Print hal-03797590, HAL.
    • Handle: RePEc:hal:journl:hal-03797590
    DOI: 10.1016/j.eneco.2018.10.037
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    3. Albulescu, Claudiu Tiberiu & Tiwari, Aviral Kumar & Ji, Qiang, 2020. "Copula-based local dependence among energy, agriculture and metal commodities markets," Energy, Elsevier, vol. 202(C).
    4. Cao, Yan & Cheng, Sheng, 2021. "Impact of COVID-19 outbreak on multi-scale asymmetric spillovers between food and oil prices," Resources Policy, Elsevier, vol. 74(C).
    5. Claudiu Albulescu & Aviral Tiwari & Qiang Ji, 2020. "Copula-based local dependence between energy, agriculture and metal commodity markets," Papers 2003.04007, arXiv.org.
    6. Hung, Ngo Thai, 2021. "Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Resources Policy, Elsevier, vol. 73(C).
    7. Wang, Kai-Hua & Kan, Jia-Min & Qiu, Lianhong & Xu, Shulin, 2023. "Climate policy uncertainty, oil price and agricultural commodity: From quantile and time perspective," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 256-272.
    8. Ngo Thai Hung, 2020. "Identifying the Dynamic Connectedness between Propane and Oil Prices: Evidence from Wavelet Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 315-326.
    9. Chowdhury, Mohammad Ashraful Ferdous & Meo, Muhammad Saeed & Uddin, Ajim & Haque, Md. Mahmudul, 2021. "Asymmetric effect of energy price on commodity price: New evidence from NARDL and time frequency wavelet approaches," Energy, Elsevier, vol. 231(C).
    10. Jiang, Zhuhua & Yoon, Seong-Min, 2020. "Dynamic co-movement between oil and stock markets in oil-importing and oil-exporting countries: Two types of wavelet analysis," Energy Economics, Elsevier, vol. 90(C).
    11. Cheng, Sheng & Cao, Yan, 2019. "On the relation between global food and crude oil prices: An empirical investigation in a nonlinear framework," Energy Economics, Elsevier, vol. 81(C), pages 422-432.
    12. Ding Chen & Umar Muhammad Gummi & Shan-Bing Lu & Asiya Mu'azu, 2020. "Modelling the impact of oil price fluctuations on food price in high and low-income oil exporting countries," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(10), pages 458-468.
    13. Mokni, Khaled & Ben-Salha, Ousama, 2020. "Asymmetric causality in quantiles analysis of the oil-food ‏ ‏nexus since the 1960s," Resources Policy, Elsevier, vol. 69(C).
    14. 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).
    15. Mensi, Walid & Rehman, Mobeen Ur & Maitra, Debasish & Al-Yahyaee, Khamis Hamed & Vo, Xuan Vinh, 2021. "Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain," Resources Policy, Elsevier, vol. 72(C).
    16. Pradhan, Rudra P. & Hall, John H. & du Toit, Elda, 2021. "The lead–lag relationship between spot and futures prices: Empirical evidence from the Indian commodity market," Resources Policy, Elsevier, vol. 70(C).
    17. Chun, Dohyun & Cho, Hoon & Kim, Jihun, 2022. "The relationship between carbon-intensive fuel and renewable energy stock prices under the emissions trading system," Energy Economics, Elsevier, vol. 114(C).
    18. Elsayed, Ahmed H. & Nasreen, Samia & Tiwari, Aviral Kumar, 2020. "Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies," Energy Economics, Elsevier, vol. 90(C).

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    More about this item

    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
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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