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.

我们分析了各种类型的模型，以预测每日铜收益的风险价值 (VaR)。分析期间为2000年1月4日至2021年1月14日，包括5290个每日收盘价。考虑的模型是 GARCH 类型模型、广义自回归评分模型、动态分位数回归模型和条件自回归风险价值模型规范。使用模型置信集方法选择最佳模型。该方法通过测试同等预测能力的原假设提供了一组优越的模型。研究结果表明，对于所调查的铜商品，EGARCH 模型的表现优于其他模型。