Journal of Risk Model Validation 9(2), 49–78 Research Paper Commodity value-at-risk modeling: comparing RiskMetrics, historic simulation and quantile regression Marie Steen, 1 Sjur Westgaard 2 and Ole Gjølberg 1 1 School of Economics and Business, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway; emails: marie.steen@nmbu.no, ole.gjolberg@nmbu.no 2 Department of Industrial Economics and Technology Management, Norwegian University of Science andTechnology, Alfred Getz vei 3, 7491 Trondheim, Norway; email: sjur.westgaard@iot.ntnu.no (Received October 21, 2014; revised February 13, 2015; accepted February 13, 2015) 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. Keywords: quantile regression; value-at-risk; commodity prices; risk management; volatility; return distribution. Corresponding author: M. Steen Print ISSN 1753-9579 j Online ISSN 1753-9587 Copyright © 2015 Incisive Risk Information (IP) Limited 49