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Robust estimation of conditional risk measures using machine learning algorithm for commodity futures prices in the presence of outliers

Author

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  • Byers, J.W.
  • Popova, I.
  • Simkins, B.J.

Abstract

In this study, we address three key goals. First, we investigate the existence of outliers in commodity futures price data. Second, using an innovative and robust unsupervised machine learning outlier identification algorithm (UMLA), we investigate intervention models to filter the outlier effects. Third, using the specified UMLA models, we assess the impact of outliers on risk metrics of commodities and the improvement of the inference capabilities of these models. Our results show the importance of investigating and controlling for potential outlier effects because of the impact on risk metrics. We illustrate how risk metrics based on raw data can lead to higher than expected actual losses. Our research demonstrates that it is crucial to include intervention parameters to address outlier impacts in order to obtain robust and coherent risk metrics from which more informed decisions are made in regards to risk and credit management, governance, and compliance activities.

Suggested Citation

  • Byers, J.W. & Popova, I. & Simkins, B.J., 2021. "Robust estimation of conditional risk measures using machine learning algorithm for commodity futures prices in the presence of outliers," Journal of Commodity Markets, Elsevier, vol. 24(C).
    • Handle: RePEc:eee:jocoma:v:24:y:2021:i:c:s2405851321000088
    DOI: 10.1016/j.jcomm.2021.100174
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    References listed on IDEAS

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