Extreme Measures of Agricultural Financial Risk

TL;DR

This paper applies extreme value theory to quantify and compare tail risk measures like VaR, ES, and SRMs for US corn and soybean futures, revealing high and uncertain risks in agricultural markets.

Contribution

It introduces a comparative analysis of three tail risk measures for agricultural commodities using EVT, highlighting their differences in size and uncertainty.

Findings

All risk measures are significantly higher than normal estimates.

Risk estimates become more uncertain at more extreme levels.

Spectral Risk Measures show higher sensitivity to tail risks.

Abstract

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.