文章基本信息

标题：The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US
本地全文：下载
作者：Jittima Singvejsakul ; Chukiat Chaiboonsri ; Songsak Sriboonchitta 等
期刊名称：Economies
印刷版ISSN：2227-7099
出版年度：2021
卷号：9
期号：1
页码：30
DOI：10.3390/economies9010030
语种：English
出版社：MDPI, Open Access Journal
摘要：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.