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Analyzing Extreme Comovements in Agricultural and Energy Commodity Markets Using a Regular Vine Copula Method

Author

Listed:
  • Kunlapath Sukcharoen

    (West Texas A&M University, United States,)

  • David Leatham

    (Texas A&M University, United States)

Abstract

Using a regular vine copula approach, this paper analyzes the dependence structure and tail dependence patterns among daily prices of three agricultural commodities (corn, soybean, and wheat) and two energy commodities (ethanol and crude oil) from June 2006 to June 2016. Our findings suggest that the prices of corn and crude oil are linked through the ethanol market, which are consistent with the results from previous studies. We also find that crude oil and agricultural commodity prices are statistically dependent during the extreme market downturns but independent during the extreme market upturns. In addition, the results from our sub-sample analysis show that both the upper and lower tail dependence between crude oil and other commodity markets become weaker in the recent years when the ethanol market became more mature.

Suggested Citation

  • Kunlapath Sukcharoen & David Leatham, 2018. "Analyzing Extreme Comovements in Agricultural and Energy Commodity Markets Using a Regular Vine Copula Method," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 193-201.
    • Handle: RePEc:eco:journ2:2018-05-25
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    References listed on IDEAS

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    Cited by:

    1. Krzysztof Drachal & Michał Pawłowski, 2024. "Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression," IJFS, MDPI, vol. 12(2), pages 1-56, March.
    2. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

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    More about this item

    Keywords

    Agricultural Markets; Energy Markets; Price Dependence; Tail Dependence; Vine Copulas;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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