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Hedging downside risk in agricultural commodities: A novel nonparametric kernel method

Author

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  • Jiang, Qi
  • Fan, Yawen

Abstract

Using a nonparametric kernel method, this paper develops a weighted conditional value-at-risk hedge model to hedge downside risks in agricultural commodities. The model exhibits convexity, ensuring the acquisition of its global optimal solution. Simulations show that the nonparametric kernel method enhances the accuracy of the weighted conditional value-at-risk and hedge ratio determination, outperforming traditional estimation methods. Using major agricultural commodities, empirical evidence shows the superiority of the proposed model in reducing downside risks, compared to the minimum variance, minimum value-at-risk, and minimum conditional value-at-risk hedge models.

Suggested Citation

  • Jiang, Qi & Fan, Yawen, 2024. "Hedging downside risk in agricultural commodities: A novel nonparametric kernel method," Finance Research Letters, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:finlet:v:70:y:2024:i:c:s1544612324013692
    DOI: 10.1016/j.frl.2024.106340
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    More about this item

    Keywords

    Downside risk; Weighted conditional value-at-risk; Nonparametric kernel method; Agricultural commodity;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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