Measurement of price risk in revenue insurance: Implications of distributional assumptions

A variety of crop revenue insurance programs have recently been introduced. A critical component of revenue insurance contracts is quantifying the ris k associated with stochastic prices. Forward-looking, market-based measures of price risk which are often available in the form of options premia are preferable. Because such measures are not available for every crop, some cu rrent revenue insurance programs alternatively utilize historical price dat a to construct measures of price risk. This study evaluates the distributio nal implications of alternative methods for estimating price risk and deriv ing insurance premium rates. A variety of specification tests are employed to evaluate distributional assumptions. Conditional heteroskedasticity mode ls are used to determine the extent to which price distributions may be cha racterized by nonconstant variances. In addition, these models are used to identify variables which may be used for conditioning distributions for rat ing purposes. Discrete mixtures of normals provide flexible parametric spec ifications capable of recognizing the skewness and kurtosis present in comm odity prices.