Extreme measures of agricultural financial risk

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.