  Risk is an inherent feature of agricultural production and marketing and
accurate measurement of it helps inform more efficient use of resources. This
paper examines three tail quantile-based risk measures applied to the
estimation of extreme agricultural financial risk for corn and soybean
production in the US: Value at Risk (VaR), Expected Shortfall (ES) and Spectral
Risk Measures (SRMs). We use Extreme Value Theory (EVT) to model the tail
returns and present results for these three different risk measures using
agricultural futures market data. We compare the estimated risk measures in
terms of their size and precision, and find that they are all considerably
higher than normal estimates; they are also quite uncertain, and become more
uncertain as the risks involved become more extreme.
