The purpose of this study is to evaluate the risks faced by fed cattle producers. With the development of livestock insurance programs as part of the Agricultural Risk Protection Act of 2000, a thorough investigation into the probabilistic measures of individual risk factors is needed. This research jointly models cattle production yield risk factors, using a multivariate dynamic regression model. A multivariate framework is necessary to characterize yield risk in terms of four yield factors (dry matter feed conversion, averaged daily gain, mortality, and veterinary costs), which are highly correlated. Additionally, a conditional Tobit model is used to handle censored yield variables (e.g., mortality). The proposed econometric model estimates parameters that influence the mean and variance of each production yield factor, as well as the covariance between variables. Following the model fitting using a maximum likelihood approach, simulation methods allow for profits, revenue, and gross margins to be evaluated given different assumptions concerning volatility among other shocks. The profit function is composed of random draws, based on conditioning variables, as well as parameter estimates. Shocks to variability, yield factors, or prices allow for a visual representation of the vulnerability of cattle feeder profits to these shocks.