Extreme Measures of Agricultural Financial Risk
AbstractRisk 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.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1103.5962.
Date of creation: Mar 2011
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Web page: http://arxiv.org/
Other versions of this item:
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- G19 - Financial Economics - - General Financial Markets - - - Other
- N52 - Economic History - - Agriculture, Natural Resources, Environment and Extractive Industries - - - U.S.; Canada: 1913-
This paper has been announced in the following NEP Reports:
- NEP-AGR-2011-04-09 (Agricultural Economics)
- NEP-ALL-2011-04-09 (All new papers)
- NEP-RMG-2011-04-09 (Risk Management)
- NEP-UPT-2011-04-09 (Utility Models & Prospect Theory)
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