By: Zhu, Ying; Goodwin, Barry K.; Ghosh, Sujit K.
The objective of this study is to evaluate the risk associated with major agricultural commodity yields in the United States. We are particularly concerned with the nonstationary nature of the yield distribution, which arises primarily as a result of technological progress and changing environmental conditions over time. In contrast to common two-stage methods, we propose an alternative parametric model that allows the moments of yield distributions to change with time. Several model selection techniques suggest the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of-sample predictive power, and the prediction accuracy of insurance premium rates.
By: Woodard, Joshua D.; Sherrick, Bruce J.; Schnitkey, Gary D.
This study examines the actuarial implications of the loss cost ratio (LCR) ratemaking methodology employed by the Risk Management Agency as a component of base rates for U.S. crop insurance programs, and identifies specific conditions required for the LCR methodology to result in unbiased rates when liabilities trend. Specifically, constant relative yield risk resulting in growing absolute variance through time and other restrictive requirements are required for the LCR to result in unbiased rates. These requirements are tested against a large farm-level data set for Illinois corn. Our findings indicate that the conditions required for appropriate use of the LCR methodology are violated for this high premium volume market, resulting in large implied rate biases. The process does not correct itself through time with the addition of longer rating periods as sometimes claimed. A simple correction function is suggested and demonstrated.