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.

中文翻译：

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农产品中对冲下行风险：一种新颖的非参数核方法


本文使用非参数核方法开发了一个加权条件风险值对冲模型来对冲农产品的下行风险。该模型表现出凸性，确保获得其全局最优解。模拟表明，非参数核方法提高了加权条件风险值和对冲比率确定的准确性，优于传统的估计方法。使用主要农产品，实证证据表明，与最小方差、最小风险值和最小条件风险值对冲模型相比，所提出的模型在降低下行风险方面具有优势。