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Estimation of value at risk for copper

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  • Gkillas, Konstantinos
  • Konstantatos, Christoforos
  • Papathanasiou, Spyros
  • Wohar, Mark

Abstract

We analyze various types of models for Value at Risk (VaR) forecasts for daily copper returns. The period of the analysis is from January 4, 2000 to January 14, 2021 including 5290 daily closing prices. The models considered are GARCH-type models, the Generalized Autoregressive Score model, the Dynamic Quantile Regression model, and the Conditional Autoregressive Value at Risk model specifications. The best model is selected using the Model Confidence Set approach. This approach provides a superior set of models by testing the null hypothesis of equal predictive ability. The findings suggest that the EGARCH model outperforms the rest of the models for the copper commodity under investigation.

Suggested Citation

  • Gkillas, Konstantinos & Konstantatos, Christoforos & Papathanasiou, Spyros & Wohar, Mark, 2023. "Estimation of value at risk for copper," Journal of Commodity Markets, Elsevier, vol. 32(C).
    • Handle: RePEc:eee:jocoma:v:32:y:2023:i:c:s2405851323000417
    DOI: 10.1016/j.jcomm.2023.100351
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    More about this item

    Keywords

    Commodities market; Copper; VaR forecasts; GARCH-Type models; CAViaR; DQR; JEL; Classification: C46; C58; G15; F31;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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