000030830 001__ 30830
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000030830 037__ $$a1835-2016-149076
000030830 037__ $$a1835-2016-150473
000030830 041__ $$aen
000030830 260__ $$c2000-07
000030830 269__ $$a2000-07
000030830 300__ $$a20
000030830 336__ $$aJournal Article
000030830 446__ $$aEnglish
000030830 520__ $$aA variety of crop revenue insurance programs have recently been introduced. A critical component of revenue insurance contracts is quantifying the risk associated with stochastic prices. Forward-looking, market-based measures of price risk which are often available in form of options premia are preferable. Because such measures are not available for every crop, some current revenue insurance programs alternatively utilize historical price data to construct measures of price risk. This study evaluates the distributional implications of alternative methods for estimating price risk and deriving insurance premium rates. A variety of specification tests are employed to evaluate distributional assumptions. Conditional heteroskedasticity models are used to determine the extent to which price distributions may be characterized by nonconstant variances. In addition, these models are used to identify variables which may be used for conditioning distributions for rating purposes. Discrete mixtures of normals provide flexible parametric specifications capable of recognizing the skewness and kurtosis present in commodity prices
000030830 650__ $$aRisk and Uncertainty
000030830 700__ $$aGoodwin, Barry K.
000030830 700__ $$aRoberts, Matthew C.
000030830 700__ $$aCoble, Keith H.
000030830 773__ $$dJuly 2000$$j 25$$k 1$$o214$$q195$$tJournal of Agricultural and Resource Economics
000030830 8564_ $$s1433540$$uhttps://ageconsearch.umn.edu/record/30830/files/25010195.pdf
000030830 887__ $$ahttp://purl.umn.edu/30830
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  Previous issue date: 2000-07
000030830 982__ $$gJournal of Agricultural and Resource Economics>Volume 25, Number 01, July 2000
000030830 980__ $$a1835