EVALUATION OF ALTERNATIVE RISK SPECIFICATIONS IN FARM PROGRAMMING MODELS

The use of alternative probability density functions to specify risk in farm programming models is explored and compared to a traditional specification using historical data. A method is described that compares risk efficient crop mixes using stochastic dominance techniques to examine impacts of different risk specifications on farm plans. Results indicate that a traditional method using historical farm data is as efficient for risk averse producers as two other methods of incorporating risk in farm programming models when evaluated using second degree stochastic dominance. Stochastic dominance with respect to a function further discriminates among the distributions, indicating that a density function based on the historic forecasting accuracy of the futures market results in a more risk-efficient crop mix for highly risk averse producers. Results also illustrate the need to validate alternative risk specifications perceived as improvements to traditional methods.Risk and Uncertainty,

MEASURING HISTORICAL RISK IN QUARTERLY MILK PRICES

Various methods have been used to estimate risk indices with historical data. An industry perception of increasing milk price risk over time provides a standard for evaluating several techniques used to measure historical risk. Risk measures from a regression model and an ARIMA model were consistent with the perception of increasing risk.Risk and Uncertainty,

A COMPARISON OF NOMINAL AND REAL HISTORICAL RISK MEASURES

Previous studies of historical risk have used either nominal or real data to calculate risk measures for agricultural prices and income. However, the effects of using nominal and real data have not been evaluated. This study utilizes theoretical variance approximation relationships to examine variances from detrended real and nominal time series. The relationships between variances are derived for quarterly U.S. farm milk prices for 1960-72, 1973-80, and 1981-90. Contrary to common intuitive arguments, results indicate that variances of real time series can be larger than variances of nominal series. While definitive conclusions are not possible, several reasons for using nominal data in risk analysis are given

EVALUATION OF ALTERNATIVE RISK SPECIFICATIONS IN FARM PROGRAMMING MODELS

The use of alternative probability density functions to specify risk in farm programming models is explored and compared to a traditional specification using historical data. A method is described that compares risk efficient crop mixes using stochastic dominance techniques to examine impacts of different risk specifications on farm plans. Results indicate that a traditional method using historical farm data is as efficient for risk averse producers as two other methods of incorporating risk in farm programming models when evaluated using second degree stochastic dominance. Stochastic dominance with respect to a function further discriminates among the distributions, indicating that a density function based on the historic forecasting accuracy of the futures market results in a more risk-efficient crop mix for highly risk averse producers. Results also illustrate the need to validate alternative risk specifications perceived as improvements to traditional methods

A COMPARISON OF NOMINAL AND REAL HISTORICAL RISK MEASURES

Previous studies of historical risk have used either nominal or real data to calculate risk measures for agricultural prices and income. However, the effects of using nominal and real data have not been evaluated. This study utilizes theoretical variance approximation relationships to examine variances from detrended real and nominal time series. The relationships between variances are derived for quarterly U.S. farm milk prices for 1960-72, 1973-80, and 1981-90. Contrary to common intuitive arguments, results indicate that variances of real time series can be larger than variances of nominal series. While definitive conclusions are not possible, several reasons for using nominal data in risk analysis are given.Detrending, Indices, Nominal data, Risk measurement, Risk and Uncertainty,

MEASURING HISTORICAL RISK IN QUARTERLY MILK PRICES

Various methods have been used to estimate risk indices with historical data. An industry perception of increasing milk price risk over time provides a standard for evaluating several techniques used to measure historical risk. Risk measures from a regression model and an ARIMA model were consistent with the perception of increasing risk