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

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  • Del Brio, Esther B.
  • Mora-Valencia, Andrés
  • Perote, Javier

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.

Suggested Citation

  • Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2020. "Risk quantification for commodity ETFs: Backtesting value-at-risk and expected shortfall," International Review of Financial Analysis, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:finana:v:70:y:2020:i:c:s1057521917301801
    DOI: 10.1016/j.irfa.2017.11.007
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    More about this item

    Keywords

    Value-at-risk; Expected shortfall; Backtesting; Gram-Charlier expansion;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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