Abstract
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.
Highlights
This paper focused on the estimation of the local maximum which, using the Nonstationary Extreme Value Analysis (NEVA) software package with Bayesian inference and the Newton-optimal methods, provided optimal interval values
Several methods were employed to determine the forecast of Bayesian extreme value optimization in agricultural commodity future prices
The results from the Non-stationary Extreme Value Analysis (NEVA) were plugged into the random variable method to obtain a finite random set, before estimation using the Newton-optimal processing method to determine the optimal extreme point
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The largest crops grown in the United States are corn and soybeans, with wheat, coffee and cocoa as the second rank of production. Extreme price changes have become increasingly interesting in financial markets for many agricultural commodities. Futures prices for cocoa, coffee, corn, soybeans and wheat, as a broad range of agricultural commodities, are employed to statistically investigate the optimal point in future prices to try to understand what could trigger the global crisis and when. Estimation of the optimal local maximum point is useful for investors and agriculturists to plan their investments and initiate product sales before the agricultural commodity future prices drop to the local minimum point. The study of early warning points would be useful in a global financial crisis and commodity prices, especially in the agriculture sector.
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