Risk quantification for commodity ETFs: Backtesting value-at-risk and expected shortfall

This paper calibrates risk assessment of alternative methods for modeling commodity ETFs. We implement re- cently proposed backtesting techniques for both value -at -risk (VaR) and expected shortfall (ES) under parametric and semi-nonparametric techniques. Our results indicate that skewed -t and Gram-Charlier distributional as- sumptions present the best relative performance for individual Commodity ETFs for those confidence levels recommended by Basel Accords. In view of these results, we recommend the application of leptokurtic dis- tributions and semi-nonparametric techniques to mitigate regulation concerns about global financial stability of commodity business.