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Asymmetric effects of market uncertainties on agricultural commodities

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  • Bossman, Ahmed
  • Gubareva, Mariya
  • Teplova, Tamara

Abstract

The interplay between energy and agricultural commodities and the responses of agricultural commodities to global uncertainty factors have been receiving increasing attention due to the pivotal contributions of commodity markets to the world economy. This study investigates the asymmetric effects of oil, market volatility and sentiment, economic policy uncertainty, and geopolitical risk on agricultural commodities in a nonparametric quantile-on-quantile regression framework. The sample used for empirical analysis spans January 03, 2013, to October 21, 2022, and includes ten agricultural commodities (canola, cocoa, coffee, corn, feeder cattle, lean hogs, live cattle, orange juice, sugar, and wheat), crude oil, crude oil implied volatility (OVX), market sentiment (VIX), economic policy uncertainty of the US (USEPU), and geopolitical risk (GPR). The empirical findings suggest that crude oil fails to serve as either a hedge or safe-haven for agricultural commodities. Meanwhile, OVX and VIX serve as potential hedges against agricultural commodity shocks. At elevated levels of USEPU, agricultural commodities reveal only weak hedging capacities against the USEPU-driven downside risks and mostly act just as diversifiers of economic policy uncertainty effects. However, they could hedge against the crystallisations of the downside geopolitical risks. These findings are important for effective regulation policies, risk management, and portfolio decisions across bullish, bearish, and normal market conditions.

Suggested Citation

  • Bossman, Ahmed & Gubareva, Mariya & Teplova, Tamara, 2023. "Asymmetric effects of market uncertainties on agricultural commodities," Energy Economics, Elsevier, vol. 127(PB).
    • Handle: RePEc:eee:eneeco:v:127:y:2023:i:pb:s0140988323005789
    DOI: 10.1016/j.eneco.2023.107080
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    More about this item

    Keywords

    Agricultural commodities; Oil; Investor sentiment; Geopolitical risk; Economic policy uncertainty; Causality-in-quantiles; Quantile regression model; Quantile-on-quantile regression; Hedge; Diversification; Safe-haven;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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