Risk quantification for commodity ETFs: Backtesting value-at-risk and expected shortfall |
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| Institution: | 1. University of Salamanca (IME), Campus Miguel de Unamuno (Edif. F.E.S.), 37007 Salamanca, Spain;2. Universidad de los Andes, School of Management, Bogotá, Colombia;1. Universitá Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy;2. Universitá degli Studi di Milano Bicocca, Piazza dell’Ateneo Nuovo 1, Milano 20126, Italy;1. Department of Econometrics and Business Statistics, Monash Business School, Monash University, Wellington Road, Clayton Campus, VIC 3800, Australia;2. Hull University Business School, University of Hull, Cottingham Rd, Hull HU6 7RX, United Kingdom;3. Trinity Business School, The University of Dublin, Luce Hall, Pearse St, Dublin 2 D02 H308, Ireland;1. Dept. Fundamentos del Análisis Económico, Universidad de Alicante, Campus San Vicente del Raspeig, Alicante 03080, Spain;2. Westminster Business School, University of Westminster, 35 Marylebone Road, London NW1 5LS, UK;1. Lancaster University, UK;2. University of Kent, UK |
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| Abstract: | This paper calibrates risk assessment of alternative methods for modeling commodity ETFs. We implement recently 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 assumptions 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 distributions and semi-nonparametric techniques to mitigate regulation concerns about global financial stability of commodity business. |
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