Ebook Extreme Measures of Agricultural Financial Risk
The inherent variability in agricultural production (weather, pests, animal illness and so forth) alongside demand variations (food scares, fads, etc.) make for a marketing environment for farmers that is characterised by significant levels of risk (Moschini and Hennessy, 2001, Chern and Ricketsen (2003) and Carter and Smith (2007)). A natural question then arises how do you measure the magnitude of risk being faced by agents? and the last decade and a half have witnessed an explosion of research on different measures of financial risk, and especially on one particular measure, the Value-at-Risk (VaR). This ‘VaR revolution’ began when JP Morgan published its famous RiskMetrics model on the web in October 1994. VaR models were first used by financial institutions for their own risk management purposes, but have since been adopted by many non-financial corporates as well.
Amongst their many uses, VaR models can be used to determine capital and reserving requirements, establish position limits and assess hedging strategies. They can also be used to manage cashflow, liquidity and credit risks as well as the market risks for which they were first developed. Estimation methods have improved considerably over the years, and the properties and especially the limitations of the VaR itself have become better understood. Various new measures of financial risk have also been proposed and these include, most notably, the coherent risk measures proposed by Artzner et alia (1999). These risk measures have the highly desirable property of sub-additivity, which the VaR lacks. Thus, not only have VaR estimation methods improved over time, but there have also been improvements in the financial risk measures themselves, of which the VaR is but one.
The relevance of these developments to agricultural financial risks is selfevident. Yet, ironically, to date they have had only a limited impact on the agricultural economics and finance literature. Some indication of the current state of the art in agricultural financial risk measurement can be obtained from Table 1. This lists the main points of 8 different studies on this subject. Most of these studies use multivariate parametric approaches to estimate VaR, and these are typically based on the assumption that underlying risks factors are multivariate normally distributed. Some studies also use historical simulation methods to estimate the VaR. One study (Zhang et al. (2007)) uses Monte Carlo methods, and two (Siaplay et al. (2005) and Odening and Hinrichs (2003)) include results based on Extreme-Value Theory (EVT). It is also noteworthy that all but one of these studies focuses exclusively on the VaR risk measure. To our knowledge, there are no studies so far of coherent risk measures applied to agricultural risk problems.
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