Abstract

Value-at-Risk, known as VaR, gives a prediction of potential portfolio losses, with a certain level of confidence, that may be encountered over a specified time period due to adverse price movements in the portfolio's assets. For example, a VaR of 1 million dollars at the 95% level of confidence implies that overall portfolio losses should not exceed 1 million dollars more than 5% of the time over a given holding period. This research examines the effectiveness of VaR measures, developed using alternative estimation techniques, in predicting large losses in the cattle feeding margin. Results show that several estimation techniques, both parametric and non-parametric, provide well-calibrated estimates of VaR such that violations (losses exceeding the VaR estimate) are commensurate with the desired level of confidence. In particular, estimates developed using JP Morgan's Risk Metrics methodology appear robust for instruments that have linear payoff structures such as cash commodity prices.

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