A Real-Time Framework for Forecasting Metal Prices
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| Format: | Preprint |
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2025
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| _version_ | 1866917154113191936 |
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| author | Bastianin, Andrea Rossini, Luca Tonni, Lorenzo |
| author_facet | Bastianin, Andrea Rossini, Luca Tonni, Lorenzo |
| contents | This paper develops a real-time forecasting framework for the monthly real prices of four key industrial metals -- aluminum, copper, nickel, and zinc -- whose demand is rising due to their widespread use in manufacturing and low-carbon technologies. To replicate the information set available to forecasters in real time, we construct a new dataset combining daily financial variables with first-release macroeconomic indicators and use nowcasting techniques to address publication lags. Within this real-time environment, we evaluate the predictive accuracy of a broad set of univariate, multivariate, and factor-augmented models, comparing their performance with two industry benchmarks: survey expectations and futures-spot spread models. Results show that although short-run metal price movements remain difficult to predict, medium-term horizons display substantial forecastability. Indicators of manufacturing activity tied to primary metals -- such as new orders and capacity utilization -- significantly improve forecasting accuracy for aluminum and copper, with more moderate gains for zinc and limited improvements for nickel. Futures and survey forecasts generally underperform the real-time econometric models. These findings highlight the value of incorporating timely macroeconomic information into forecasting frameworks for industrial metal markets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_16521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Real-Time Framework for Forecasting Metal Prices Bastianin, Andrea Rossini, Luca Tonni, Lorenzo Econometrics This paper develops a real-time forecasting framework for the monthly real prices of four key industrial metals -- aluminum, copper, nickel, and zinc -- whose demand is rising due to their widespread use in manufacturing and low-carbon technologies. To replicate the information set available to forecasters in real time, we construct a new dataset combining daily financial variables with first-release macroeconomic indicators and use nowcasting techniques to address publication lags. Within this real-time environment, we evaluate the predictive accuracy of a broad set of univariate, multivariate, and factor-augmented models, comparing their performance with two industry benchmarks: survey expectations and futures-spot spread models. Results show that although short-run metal price movements remain difficult to predict, medium-term horizons display substantial forecastability. Indicators of manufacturing activity tied to primary metals -- such as new orders and capacity utilization -- significantly improve forecasting accuracy for aluminum and copper, with more moderate gains for zinc and limited improvements for nickel. Futures and survey forecasts generally underperform the real-time econometric models. These findings highlight the value of incorporating timely macroeconomic information into forecasting frameworks for industrial metal markets. |
| title | A Real-Time Framework for Forecasting Metal Prices |
| topic | Econometrics |
| url | https://arxiv.org/abs/2512.16521 |