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.


Issue Date:
2008
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/101971
Total Pages:
41
JEL Codes:
E17; G19; N52
Note:
This paper is from the Centre for Financial Markets (CFM) Working Paper series at University College Dublin.
Series Statement:
Working Paper

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 Record created 2017-04-01, last modified 2017-08-26

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