The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US

Jittima Singvejsakul, Chukiat Chaiboonsri and Songsak Sriboonchitta
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Jittima Singvejsakul: Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand
Chukiat Chaiboonsri: Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand
Songsak Sriboonchitta: Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand

Economies, 2021, vol. 9, issue 1, 1-10

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.

Keywords: agricultural commodity future prices; extreme value; NON-stationary Extreme Value Analysis (NEVA); Newton-optimal method (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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