Commodity value-at-risk modeling: comparing RiskMetrics, historic simulation and quantile regression

Abstract

Commodities constitute a nonhomogeneous asset class. Return distributions differ widely across different commodities, both in terms of tail fatness and skewness. These are features that we need to take into account when modeling risk. In this paper, we outline the return characteristics of nineteen different commodity futures during the period 1992–2013.We then evaluate the performance of two standard risk modeling approaches, ie, RiskMetrics and historical simulation, against a quantile regression (QR) approach. Our findings strongly support the conclusion that QR outperforms these standard approaches in predicting value-at-risk for most commodities.