Bayesian extreme value analysis was used to forecast the optimal point in agricultural commodity futures prices in the United States for cocoa, coffee, corn, soybeans and wheat. Data were collected daily between 2000 and 2020. The estimation of extreme value can be empirically interpreted as representing crises or unusual time series trends, while the extreme optimal point is useful for investors and agriculturists to make decisions and better understand agricultural commodities future prices warning levels. Results from the Non-stationary Extreme Value Analysis (NEVA) software package using Bayesian inference and the Newton-optimal methods provided optimal interval values. These indicated extreme maximum points of future prices to inform investors and agriculturists to sell the contract and product before the commodity prices dropped to the next local minimum values. Thus, agriculturists can use this information as an advanced warming of alarming points of agricultural commodity prices to predict the efficient quantity of their agricultural product to sell, with better ways to manage this risk.

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贝叶斯极值的优化：美国农业商品的经验证据

贝叶斯极值分析被用来预测美国可可，咖啡，玉米，大豆和小麦的农产品期货价格的最优点。在2000年至2020年之间每天收集数据。极端价值的估计可以凭经验解释为代表危机或不寻常的时间序列趋势，而极端最佳点则对投资者和农业学家做出决策以及更好地理解农产品未来价格警告水平很有用。使用贝叶斯推断和牛顿最优方法的非平稳极值分析（NEVA）软件包的结果提供了最佳区间值。这些指示了期货价格的极端最高点，以便在商品价格跌至下一个当地最低价格之前，通知投资者和农业学家出售合约和产品。因此，农业学家可以使用此信息作为农产品价格预警点的高级变暖，以预测他们出售农产品的有效量，并以更好的方式来管理这种风险。