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dc.contributor.authorJittima Singvejsakulen_US
dc.contributor.authorChukiat Chaiboonsrien_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.description.abstractBayesian 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.en_US
dc.subjectEconomics, Econometrics and Financeen_US
dc.subjectSocial Sciencesen_US
dc.titleThe optimization of Bayesian extreme value: Empirical evidence for the agricultural commodities in the USen_US
article.volume9en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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