Estimation of value at risk for copper

Konstantinos Gkillas, Christoforos Konstantatos, Spyros Papathanasiou and Mark Wohar

Journal of Commodity Markets, 2023, vol. 32, issue C

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.

Keywords: Commodities market; Copper; VaR forecasts; GARCH-Type models; CAViaR; DQR; JEL; Classification: C46; C58; G15; F31 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jocoma:v:32:y:2023:i:c:s2405851323000417

DOI: 10.1016/j.jcomm.2023.100351

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