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Abstract

This paper presents a copula-based methodology for modeling joint yield distributions. Copulas have been used extensively in financial literature, but have not been widely used in agricultural economics and particularly risk management. The copula approach provides a powerful and flexible method to model multivariate distributions and thus go beyond joint normality, regressibility, and mean-variance criterion. Accurate estimation of joint distributions may help to improve the results in the area of risk management and insurance obtained under more limiting assumptions.

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